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User Preference Analysis of a Sustainable Workstation Design for Online Classes: A Conjoint Analysis Approach

Author

Listed:
  • Ma. Janice J. Gumasing

    (School of Industrial Engineering and Engineering Management, Mapúa University, 658 Muralla St., Intramuros, Manila 1002, Philippines)

  • Ardvin Kester S. Ong

    (School of Industrial Engineering and Engineering Management, Mapúa University, 658 Muralla St., Intramuros, Manila 1002, Philippines)

  • Maria Angelica D. Bare

    (School of Industrial Engineering and Engineering Management, Mapúa University, 658 Muralla St., Intramuros, Manila 1002, Philippines)

Abstract

The impact of the COVID-19 pandemic has caused the sudden shift of the interactions between students and teachers from the four corners of the classroom to fully online learning through the workstations. By using a conjoint analysis approach, this study aimed to evaluate the preference of undergraduate students from the Philippines on the workstation design attributes during the pandemic. Seven attributes were examined through orthogonal design: the type of gadget, keyboard, mouse, earpiece, desk, kind of chair, and light device. Through a purposive sampling approach, a total of 315 undergraduate students from the Philippines were gathered. Through an online survey with two holdouts, the respondents voluntarily responded to 51 stimuli produced by IBM SPSS using a 7-point Likert scale. Type of gadget (37.925%) was found to be the most significant attribute preferred by the students, followed by the type of mouse (28.345%), kind of chair (14.840%), type of keyboard (7.548%), earpiece (7.177%), light device (2.109%), and desk (2.056%). It is worth noting that the preferred workstation design combination is the personal computer, mechanical keyboard, wired mouse, headset, height-adjustable table, high-back office chair with neck support, and floor lamp. This study is the first to use a conjoint technique to examine undergraduate students’ preferences for workstation design attributes during the COVID-19 pandemic. Finally, by applying the attributes and design developed in this study, the conjoint method can be used and expanded to evaluate the workstation design attributes of other courses and even employees worldwide.

Suggested Citation

  • Ma. Janice J. Gumasing & Ardvin Kester S. Ong & Maria Angelica D. Bare, 2022. "User Preference Analysis of a Sustainable Workstation Design for Online Classes: A Conjoint Analysis Approach," Sustainability, MDPI, vol. 14(19), pages 1-23, September.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:19:p:12346-:d:927882
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    References listed on IDEAS

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    1. S. Ong, Ardvin Kester & Prasetyo, Yogi Tri & Chuenyindee, Thanatorn & Young, Michael Nayat & Doma, Bonifacio T. & Caballes, Dennis G. & Centeno, Raffy S. & Morfe, Anthony S. & Bautista, Christine S., 2022. "Preference analysis on the online learning attributes among senior high school students during the COVID-19 pandemic: A conjoint analysis approach," Evaluation and Program Planning, Elsevier, vol. 92(C).
    2. Emil Kozlowski, 2022. "Noise Parameters of Headsets Designed for Communication Platforms," IJERPH, MDPI, vol. 19(6), pages 1-17, March.
    3. Yogi Tri Prasetyo & Ardvin Kester S. Ong & Giero Krissianne Frances Concepcion & Francheska Mikaela B. Navata & Raphael Andrei V. Robles & Isaiash Jeremy T. Tomagos & Michael Nayat Young & John Franci, 2021. "Determining Factors Affecting Acceptance of E-Learning Platforms during the COVID-19 Pandemic: Integrating Extended Technology Acceptance Model and DeLone & McLean IS Success Model," Sustainability, MDPI, vol. 13(15), pages 1-16, July.
    4. Éva Csepregi & Zsuzsanna Gyurcsik & Ilona Veres-Balajti & Attila Csaba Nagy & Zoltán Szekanecz & Sándor Szántó, 2022. "Effects of Classical Breathing Exercises on Posture, Spinal and Chest Mobility among Female University Students Compared to Currently Popular Training Programs," IJERPH, MDPI, vol. 19(6), pages 1-22, March.
    5. Sethuraman, Raj & Kerin, Roger A. & Cron, William L., 2005. "A field study comparing online and offline data collection methods for identifying product attribute preferences using conjoint analysis," Journal of Business Research, Elsevier, vol. 58(5), pages 602-610, May.
    6. Ardvin Kester S. Ong & Yogi Tri Prasetyo & Michael Nayat Young & John Francis T. Diaz & Thanatorn Chuenyindee & Poonyawat Kusonwattana & Nattakit Yuduang & Reny Nadlifatin & Anak Agung Ngurah Perwira , 2021. "Students’ Preference Analysis on Online Learning Attributes in Industrial Engineering Education during the COVID-19 Pandemic: A Conjoint Analysis Approach for Sustainable Industrial Engineers," Sustainability, MDPI, vol. 13(15), pages 1-20, July.
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