A Proposal: Human Factors Related to the User Acceptance Behavior in Adapting to New Technologies or New User Experience
Author
Hima Bindu Sadashiv Reddy , Roopesh Reddy Sadashiva Reddy,Ratnaditya Jonnalagadda
Abstract
We
are aware of the tremendous growth of new mobile phones released in the market
every year. The increase in the users’ needs with respect to the new devices is
not to be ignored as well. Each product has so many brands with n-number of
models and versions.The way the users choose to buy these products is
perplexing. For example,decision made to buy a new mobile or change to new brand
or even continuing the same brand is not an easy task. The user acceptance for
any new behavior is very complex. This paper explores all the possible human
factors connected to either pleasant or unpleasant user experiences. Various researchstudies
discovered that loyalty of the users for a particular brand plays an important
role for the success of the product. Long-term usage of the product by a user
would have a negative impact, as the user is not willing to switch to other
brands and this has a positive impact on the companies. User memory,
expectations and experiences are closely knit to understand the user acceptance
for choosing a product. A positive emotion such as pleasant user experience is
significant because users recommend the products to others based on these
emotional experiences. Age plays an important role with respect to user
experience. Old age users were not enthusiastic in going for a change as they
preferred to continue with the same technology. Cultural aspects of users are
interesting to know in understanding the product purchases. There was biased
information regarding user’s visual attractiveness and long-term usage memory. Certain
studies explored UX curve and user burden scale that was used to analyze user
experiences. It was interesting to know sensory characteristics formed a base
for both pleasant and unpleasant user experiences. This research will help
companies of mobile devices in identifying the various factors (both positive
and negative) related to user experiences and behavior. Future research will be
conducted in the areas of short-term user experience and in areas where
illiteracy prevail. This study will help the companies to improve their product
better based on customer satisfaction
Keywords
Human Computer Interaction, User Experience, User Withdrawal, Long-Term User Experience, Short-term User Experience
DOI : https://doi.org/10.55248/gengpi.2022.3.8.1
Full Text:
Download Paper PDF
References
Suh, H., Shahriaree, N., Hekler, E. B., & Kientz, J.
A. (2016, May). Developing and validating the user burden scale: A tool for
assessing user burden in computing systems. In Proceedings of the 2016 CHI
Conference on Human Factors in Computing Systems (pp. 3988-3999). ACM.
Kujala, S., & Miron-Shatz, T. (2015, September). The
evolving role of expectations in long-term user experience. In Proceedings of
the 19th International Academic Mindtrek Conference (pp. 167-174). ACM.
Kujala, S., & Miron-Shatz, T. (2013, April). Emotions,
experiences and usability in real-life mobile phone use. In Proceedings of the
SIGCHI Conference on Human Factors in Computing Systems (pp. 1061-1070). ACM.
Park, J., Han, S. H., Kim, H. K., Oh, S., & Moon, H.
(2013). Modeling user experience: A case study on a mobile device.
International Journal of Industrial Ergonomics, 43(2), 187-196.
Karapanos, E. (2013). User experience over time. In
Modeling Users' Experiences with Interactive Systems (pp. 57-83). Springer
Berlin Heidelberg.
Tuch, A. N., Trusell, R., & Hornbæk, K. (2013,
April). Analyzing users' narratives to understand experience with interactive
products. In Proceedings of the SIGCHI Conference on human factors in computing
systems (pp. 2079-2088). ACM.
Nicolas, O., Carlos, J., & Aurisicchio, M. (2011).
The scenario of user experience. In DS 68-7: Proceedings of the 18th
International Conference on Engineering Design (ICED 11), Impacting Society
through Engineering Design, Vol. 7: Human Behaviour in Design,
Lyngby/Copenhagen, Denmark, 15.-19.08.
Kujala, S., Roto, V., Väänänen-Vainio-Mattila, K.,
Karapanos, E., & Sinnelä, A. (2011). UX Curve: A method for evaluating
long-term user experience. Interacting with Computers, 23(5), 473-483.
Kujala, S., Roto, V., Väänänen-Vainio-Mattila, K., &
Sinnelä, A. (2011, June). Identifying hedonic factors in long-term user
experience. In Proceedings of the 2011 Conference on Designing Pleasurable
Products and Interfaces (p. 17). ACM.
Novick, D. G., Santaella, B., Cervantes, A., &
Andrade, C. (2012, October). Short-term methodology for long-term usability. In
Proceedings of the 30th ACM international conference on Design of communication
(pp. 205-212). ACM.
Fenko, A., Schifferstein, H. N., & Hekkert, P.
(2010). Shifts in sensory dominance between various stages of user–product
interactions. Applied ergonomics, 41(1), 34-40.
Fenko, A., Schifferstein, H. N., & Hekkert, P.
(2009). Which senses dominate at different stages of product experience?
Karapanos, E., Zimmerman, J., Forlizzi, J., &
Martens, J. B. (2010). Measuring the dynamics of remembered experience over
time. Interacting with Computers, 22(5), 328-335.
Roto, V., Rantavuo, H., & Väänänen-Vainio-Mattila, K.
(2009, October). Evaluating user experience of early product concepts. In Proc.
DPPI (Vol. 9, pp. 199-208).
Kacen, J. J., & Lee, J. A. (2002). The influence of
culture on consumer impulsive buying behavior. Journal of consumer psychology,
12(2), 163-176.
Sayago, S., Sloan, D., & Blat, J. (2011). Everyday
use of computer-mediated communication tools and its evolution over time: An
ethnographical study with older people. Interacting with Computers, 23(5),
543–554.
Rodriguez, K. M., Reddy, R. S., Barreiros, A. Q., &
Zehtab, M. (2012, June). Optimizing Program Operations: Creating a Web-Based Application
to Assign and Monitor Patient Outcomes, Educator Productivity and Service
Reimbursement. In DIABETES (Vol. 61, pp. A631-A631). 1701 N BEAUREGARD ST,
ALEXANDRIA, VA 22311-1717 USA: AMER DIABETES ASSOC.
Kwon, D., Reddy,
R., & Reis, I. M. (2021). ABCMETAapp: R shiny application for
simulation-based estimation of mean and standard deviation for meta-analysis
via approximate Bayesian computation. Research synthesis methods, 12(6),
842–848. https://doi.org/10.1002/jrsm.1505
Reddy, H. B. S.,
Reddy, R. R. S., Jonnalagadda, R., Singh, P., & Gogineni, A. (2022).
Usability Evaluation of an Unpopular Restaurant Recommender Web Application
Zomato. Asian Journal of Research in Computer Science, 13(4), 12-33.
Reddy, H. B. S.,
Reddy, R. R. S., Jonnalagadda, R., Singh, P., & Gogineni, A. (2022b).
Analysis of the Unexplored Security Issues Common to All Types of NoSQL
Databases. Asian Journal of Research in Computer Science, 14(1), 1-12.
Singh, P.,
Williams, K., Jonnalagadda, R., Gogineni, A., &; Reddy, R. R. (2022).
International students: What’s missing and what matters. Open Journal of Social
Sciences, 10(02),
Jonnalagadda, R.,
Singh, P., Gogineni, A., Reddy, R. R., & Reddy, H. B. (2022). Developing,
implementing and evaluating training for online graduate teaching assistants
based on Addie Model. Asian Journal of Education and Social Studies, 1-10.
Sarmiento, J. M.,
Gogineni, A., Bernstein, J. N., Lee, C., Lineen, E. B., Pust, G. D., &
Byers, P. M. (2020).Alcohol/illicit substance use in fatal motorcycle crashes.
Journal of surgical research, 256, 243-250.
Brown, M. E.,
Rizzuto, T., & Singh, P. (2019). Strategic compatibility, collaboration and
collective impact for community change. Leadership & Organization
Development Journal.
Sprague-Jones, J.,
Singh, P., Rousseau, M., Counts, J., & Firman, C. (2020). The Protective
Factors Survey: Establishing validity and reliability of a self-report measure
of protective factors against child maltreatment. Children and Youth Services
Review, 111, 104868
Share your valuable work from Social Media Buttons