Linear ordering of selected gerontechnologies using selected MCGDM methods

    Katarzyna Halicka   Affiliation
    ; Dariusz Kacprzak   Affiliation


For over last 20 years, significant changes have been observed in the age structure of the world’s population. The percentage of working-age population is steadily decreasing all over the world, and a relative number of retired people is increasing. It confirms that our society is ageing. Moreover, according to the United Nations population forecast the situation will get worse. The increasing number of seniors is also connected with the need to provide them with institutional support in the form of care. One of the key elements of helping older adults may be gerontechnology – an interdisciplinary field of research that uses technology to implement the aspirations and abilities of seniors.

On the basis of a meticulous literature review, 9 groups of gerontechnology have been identified that have been rated with respect to 30 criteria. In the period December 2019 – January 2020 a representative sample of 1.152 Poles aged over 40 (acting as decision makers) took part in the research consisting of completing the prepared questionnaire. Based on selected Multiple Criteria Group Decision Making methods, linear ordering of gerontechnologies was prepared and the most preferred by respondents participating in the study was indicated.

Keyword : ageing population, gerontechnology selection, decision maker, Multiple Criteria Group Decision Making, SAW, TOPSIS

How to Cite
Halicka, K., & Kacprzak , D. (2021). Linear ordering of selected gerontechnologies using selected MCGDM methods. Technological and Economic Development of Economy, 27(4), 921-947.
Published in Issue
Jun 18, 2021
Abstract Views
PDF Downloads
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.


AARP International. (2020). Retrieved April 1, 2020, from

Ahmadi, S., & Amin, S. H. (2019). An integrated chance-constrained stochastic model for a mobile phone closed-loop supply chain network with provider selection. Journal of Cleaner Production, 226, 988–1003.

Alikhani, R., Torabi, S. A., & Altay, N. (2019). Strategic provider selection under sustainability and risk criteria. International Journal of Production Economics, 208, 69–82.

Alvarez, E. A., Garrido, M., Ponce, D. P., Pizarro, G., Córdova, A. A., Vera, F., Ruiz, R., Fernández, R., Velásquez, J. D., Tobar, E., & Salech, F. (2020). A software to prevent delirium in hospitalised older adults: Development and feasibility assessment. Age and Ageing, 49(2), 239–245.

Arthanat, S., Wilcox, J., & Macuch, M. (2019). Profiles and predictors of smart home technology adoption by older adults. OTJR Occupation, Participation and Health, 39(4), 247–256.

Asimo. (2020). Retrieved April 1, 2020, from

Behzadian, M., Otaghsara, S. K., Yazdan, M., & Ignatius, J. (2012). A state-of the art survey of TOPSIS applications. Expert Systems with Applications, 39(17), 13051–13069.

Bhattacharyya, S., Konar, A., & Tibarewala, D. (2014). A differential evolution based energy trajectory planner for artificial limb control using motor imagery EEG signal. Biomedical Signal Processing and Control, 11, 107–113.

Bouma, H. (1992). Gerontechnology: Making technology relevant for the elderly. In H. Bouma & J. A. M. Graafmans (Eds.), Studies in health technology and informatics: Vol. 3. Gerontechnology (pp. 1–5). IOS Press.

Bronswijk, J. E. M. H., Bouma, H., Fozard, J. L., Kearns, W. D., Davison, G. C., & Tuan, P.-Ch. (2009). Defining gerontechnology for R&D purposes. Gerontechnology, 8(1), 3–10.

Charness, N., Dunlop, M., Munteanu, C., Nicol, E., Oulasvirta, A., Ren, X., Sarcar, S., & Silpasuwanchai, C. (2016, May). Rethinking mobile interfaces for older adults. In CHI EA’16: Proceedings of the 2016 CHI Conference Extended Abstracts on Human Factors in Computing Systems (pp. 1131–1134).

Chen, C. T. (2000). Extensions of the TOPSIS for group decision-making under fuzzy environment. Fuzzy Sets and Systems, 114(1), 1–9.

Chen, Y.-S., Hsu, Y.-L., Wu, C.-C., Chen, Y.-W., & Wang, J.-A. (2011). Development of the “care delivery frame” for senior users. In B. Abdulrazak, S. Giroux, B. Bouchard, H. Pigot, & M. Mokhtari (Eds.), Lecture notes in computer science: Vol. 6719. Toward useful services for elderly and people with disabilities (pp. 176–183). Springer.

Chodakowska, E., & Nazarko, J. (2017). Environmental DEA method for assessing productivity of European countries. Technological and Economic Development of Economy, 23(4), 589–607.

Choi, H., Park, J. O., Ko, S. Y., & Park, S. (2014). Deflection analysis of a robotic bed on the applied loads and its postures for a heavy-ion therapeutic system. In H. Ibrahim, S. Iqbal, S. Teoh, & M. Mustaffa (Eds.), Lecture notes in electrical engineering: Vol. 398. 9th International Conference on Robotic, Vision, Signal Processing and Power Applications (pp. 343–350). Springer.

Dhillon, J. S., Wünsche, B., & Lutteroth, C. (2016). Designing and evaluating a patient-centred health management system for seniors. Journal of Telemedicine and Telecare, 22(2), 96–104.

Dilara, A., Hernandez, A., & Astell, A. (2018). Design recommendations for a self-care app to be used by people with cognitive challenges. Gerontechnology, 17(Suppl.), 79.

Dinh, A., & Brown, J. A. (2019). Examining communication technology usage among older adults with aphasia within the context of Socioemotional Selectivity Theory. Gerontechnology, 18(4), 223–230.

Ejdys, J., & Halicka, K. (2018). Sustainable adaptation of new technology – The case of humanoids used for the care of older adults. Sustainability, 10(10), 3770.

Ettore, E., Wyckaert, E., David, R., Robert, P., Guérin, O., & Prate, F. (2016). Robotique et amélioration de la qualité des soins en gériatrie [Robotics and improvement of the quality of geriatric care]. Soins Gerontologie, 21(121), 15–17.

Galambos, C., Rantz, M., Craver, A., Bongiorno, M., Pelts, M., Holik, A. J., & Jun, J. S. (2019). Living with intelligent sensors: Older adult and family member perceptions. CIN – Computers Informatics Nursing, 37(12), 615–627.

Gobeil, J., Pigot, H., Laliberté, C., Dépelteau, A., Laverdière, O., David-Grégoire, M., Laprise, N., Beauchamp, I., Couture, M., Adelise, Y., & Bier, N. (2019). Facilitating day-to-day life management of older people with Alzheimer’s disease: A revelatory single-case study on the acceptability of the AMELIS interactive calendar. Gerontechnology, 18(4), 243–257.

Gomi, T., & Griffith, A. (1998). Developing intelligent wheelchairs for the handicapped. In V. Mittal, H. Yanco, J. Aronis, & R. Simpson (Eds.), Lecture notes in computer science: Vol. 1458. Assistive technology and artificial intelligence (pp. 150–178). Springer.

Graafmans, J. A. M., Taipale, V., & Charness, N. (1998). Gerontechnology: A sustainable investment in the future (Studies in Health technology and informatics: Vol. 48). IOS Press.

Halicka, K. (2019). Gerontechnology – the assessment of one selected technology improving the quality of life of older adults. Engineering Management in Production and Services, 11(2), 43–51.

Halicka, K. (2020). Technology selection using the TOPSIS method. Foresight and STI Governance, 14(1), 85–96.

Hsu, Y.-L., Hsu, P.-E., Tu, C.-H., Lu, J.-H., & Wei, C.-Y. (2010). Platform design for the intelligent robotic wheelchair. In Sustainable Mobility Revolution: 25th World Battery, Hybrid and Fuel Cell Electric Vehicle Symposium and Exhibition.

Hwang, C. L., & Yoon, K. (1981). Multiple attribute decision making: Methods and applications. Springer.

Jansson, T., & Kupiainen, T. (2017). Aged people’s experiences of gerontechnology used at home. A narrative literature review. Helsinki Metropolia University of Applied Sciences.

Jenko, M., Guna, J., Kos, A., Pustišek, M., & Bešter, J. (2007). Zasnova večpredstavnega konvergenčnega uporabniškega vmesnika kot del koncepta pametnega doma za potrebe starejših [Designing a multimedia convergence user interface as a part of the concept of the smart home for the target group of the elderly]. Elektrotehniski Vestnik/Electrotechnical Review, 74(3), 125–130.

Kacprzak, D. (2019). A doubly extended TOPSIS method for group decision making based on ordered fuzzy numbers. Expert Systems with Applications, 116, 243–254.

Kacprzak, D. (2020). An extended TOPSIS method based on ordered fuzzy numbers for group decision making. Artificial Intelligence Review, 53(3), 2099–2129.

Karaca Şalgamcıoğlu, B. (2020). Future older adults and mobile applications for health. In A. Woodcock, L. Moody, D. McDonagh, A. Jain, & L. C. Jain (Eds.), Intelligent systems reference library: Vol. 167. Design of assistive technology for ageing populations (pp. 275–292). Springer.

Kaufman, D., Gayowsky, T., Sauvé, L., Renaud, L., & Duplàa, E. (2018). Older adults’ perceived benefits of digital gameplay: Associations with demographics and game use patterns. Gerontechnology, 17(1), 56–67.

Kaplinski, O., Peldschus, F., Nazarko, J., Kaklauskas, A., & Baušys, R. (2019). MCDM, operational research and sustainable development in the trans-border Lithuanian–German–Polish co‐operation. Engineering Management in Production and Services, 11(2), 7–18.

Kazerooni, H. (2005, August). Exoskeletons for human power augmentation. In 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems (pp. 3459–3464).

Keršulienė, V., & Turskis, Z. (2014). An integrated multi-criteria group decision making process: Selection of the chief accountant. Procedia – Social and Behavioral Sciences, 110, 897–904.

Lebron, J., Escalante, K., Coppola, J., Dr., & Gaur, C. (2015). Activity tracker technologies for older adults: Successful adoption via intergenerational telehealth. In Long Island Systems, Applications and Technology Conference, (pp. 1–6, 7160200). IEEE.

Lee, J. S., Liang, S., Park, S., & Yan, C. (2015). Grandpa!: A communication tool connecting grandparents and grandchildren living apart. In MobileHCI 2015 – Proceedings of the 17th International Conference on Human-Computer Interaction with Mobile Devices and Services Adjunct (pp. 674–679).

Lipphardt, A.-M., Held, P., Leen-Thomele, E., & Hain, L. (2018). ICT enhanced learning for older adults: Influencing factors on satisfaction and the role of learning motivation. Gerontechnology, 17(Suppl.), 60.

Liu, S., Chan, F. T. S., & Ran, W. (2016). Decision making for the selection of cloud vendor: An improved approach under group decision-making with integrated weights and objective/subjective attributes. Expert Systems with Applications, 55, 37–47.

Mahoney, D. F. (2004). Linking home care and the workplace through innovative wireless technology: The worker interactive networking (WIN) project. Home Health Care Management and Practice, 16(5), 417–428.

Marcelino, I., Laza, R., Domingues, P., Gómez-Meire, S., & Pereira, A. (2015). eServices – service platform for pervasive elderly care. In A. Mohamed, P. Novais, A. Pereira, G. Villarrubia González, & A. Fernández-Caballero (Eds.), Advances in intelligent systems and computing: Vol. 376. Ambient intelligence – Software and applications (pp. 203–211). Springer.

Mc Carthy, S., Sayers, H., & Mc Kevitt, P. (2007). Investigating the usability of PDAs with ageing users. People and Computers XXI HCI. But Not as We Know It. In Proceedings of HCI: The 21st British HCI Group Annual Conference (Vol. 2, pp. 1–4). ScienceOpen.

McWhorter, R. R., Delello, J. A., Gipson, Ch. S., Mastel-Smith, B., & Caruso, K. (2020). Do loneliness and social connectedness improve in older adults through mobile technology? In J. A. Delello & R. R. McWhorter, Disruptive and emerging technology trends across education and the workplace (pp. 221–242). IGI Global.

Millán-Calenti, J. & Maseda, A. (2011). Elderly people, disability, dependence and new technologies. In J. Pereira (Ed.), Handbook of research on personal autonomy technologies and disability informatics (pp. 36–55). IGI Global.

Muravev, D., & Mijic, N. (2020). A novel integrated provider selection multicriteria model: The BWMMABAC model. Decision Making: Applications in Management and Engineering, 3(1), 60–78.

Namanee, C., & Tuaycharoen, N. (2019). Task lighting for Thai older adults: Study of the visual performance of lighting effect characteristics. Gerontechnology, 18(4), 215–222.

Nazarko, J., Ejdys, J., Halicka, K., Magruk, A., Nazarko, Ł., & Skorek, A. (2017). Application of enhanced SWOT analysis in the future-oriented public management of technology. Procedia Engineering, 182, 482–490.

Nazarko, L. (2016, May). Responsible research and innovation – A new paradigm of technology management [Conference presentation]. 9th International Scientific Conference “Business and Management 2016”, Vilnius Gediminas Technical University.

Nazarko, L. (2017). Future-oriented technology assessment. Procedia Engineering, 182, 504–509.

Opricovic, S., & Tzeng, G. H. (2004). Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS. European Journal of Operational Research, 156(2), 445–455.

Petrovic, I., & Kankaras, M. (2020). A hybridized IT2FS-DEMATEL-AHP-TOPSIS multi-criteria decision making approach: Case study of selection and evaluation of criteria for determination of air traffic control radar position. Decision Making: Applications in Management and Engineering, 3(1), 146–164.

Piezzo, Ch., & Suzuki, K. (2017). Feasibility study of a socially assistive humanoid robot for guiding elderly individuals during walking. Future Internet, 9(3), 30.

Rahmawati, N., & Jiang, B. C. (2019). Develop a bedroom design guideline for progressive ageing residence: A case study of Indonesian older adults. Gerontechnology, 18(3), 180–192.

Ross, D. B., Eleno-Orama, M., & Vultaggio Salah, E. (2018). The aging and technological society: Learning our way through the decades. In V. C. Bryan, A. T. Musgrove, & J. R. Powers (Eds.), Handbook of research on human development in the digital age (pp. 205–234). IGI Global.

Roszkowska, E., & Kacprzak, D. (2016). The fuzzy SAW and fuzzy TOPSIS procedures based on ordered fuzzy numbers. Information Sciences, 369, 564–584.

Sayago, S., Rosales, A., Righi, V., Ferreira, S. M., Coleman, G. W., & Blat, J. (2016). On the conceptualization, design, and evaluation of appealing, meaningful, and playable digital games for older people. Games and Culture, 11(1–2), 53–80.

Sale, P. (2018). Gerontechnology. domotics and robotics. In S. Masiero & U. Carraro (Eds.), Practical issues in geriatrics. Rehabilitation medicine for elderly patients (pp. 161–169). Springer.

Shibata, T., & Wada, K. (2011). Robot therapy: A new approach for mental healthcare of the elderly – a mini-review. Gerontology, 57, 378–386.

Shih, H. S., Shyur, H. J., & Lee, E. S. (2007). An extension of TOPSIS for group decision making. Mathematical and Computer Modelling, 45(7–8), 801–813.

United Nations. (2017). World population prospects: The 2017 revision. Retrieved April 1, 2020, from

Wang, T. C., & Chang, T. H. (2007). Application of TOPSIS in evaluating initial training aircraft under a fuzzy environment. Expert Systems with Applications, 33(4), 870–880.

Woolrych, R., Sixsmith, J., Makita, M., Fisher, J., & Lawthom, R. (2018). Exploring the potential of smart cities in the design of age-friendly urban environments. Gerontechnology, 17(Suppl.), 68.

Yan, T., Cempini, M., Oddo, C., & Vitiello, N. (2015). Review of assistive strategies in powered lowerlimb orthoses and exoskeletons. Robotics and Autonomous Systems, 64, 120–136.

Ye, F., & Li, Y. N. (2009). Group multi-attribute decision model to partner selection in the formation of virtual enterprise under incomplete information. Expert Systems with Applications, 36(5), 9350–9357.

Yue, Z. (2011). An extended TOPSIS for determining weights of decision makers with interval numbers. Knowledge-Based Systems, 24(1), 146–153.

Zavadskas, E. K., Govindan, K., Antucheviciene, J., & Turskis, Z. (2016). Hybrid multiple criteria decisionmaking methods: A review of applications for sustainability issues. Economic Research-Ekonomska Istraživanja, 29(1), 857–887.

Zavadskas, E. K., & Turskis, Z. (2011). Multiple criteria decision making (MCDM) methods in economics: An overview. Technological and Economic Development of Economy, 17(2), 397–427.

Zavadskas, E. K., Turskis, Z., & Antucheviciene, J. (2015a). Selecting a contractor by using a novel method for multiple attribute analysis: Weighted Aggregated Sum Product Assessment with Grey Values (WASPAS-G). Studies in Informatics and Control, 24(2), 141–150.

Zavadskas, E. K., Turskis, Z., & Bagocius, V. (2015b). Multi-criteria selection of a deep-water port in the Eastern Baltic Sea. Applied Soft Computing, 26, 180–192.