Mitigating Issues With/of/for True Personalization

Abstract

A common but false perception persists about the level and type of personalization in the offerings of contemporary software, information systems, and services, known as Personalization Myopia: this involves a tendency for researchers to think that there are many more personalized services than there genuinely are, for the general audience to think that they are offered personalized services when they really are not, and for practitioners to have a mistaken idea of what makes a service personalized. And yet in an era, which mashes up large amounts of data, business analytics, deep learning, and persuasive systems, true personalization is a most promising approach for innovating and developing new types of systems and services—including support for behavior change. The potential of true personalization is elaborated in this article, especially with regards to persuasive software features and the oft-neglected fact that users change over time.

A Novel Method for Setting Meaningful Thresholds for RMS-Energy Oscillation Detectors

“…Future studies can use machine learning algorithms (see (Turkington et al, 2018)) to identify and predict usage patterns and persuasive features associated with the effectiveness of an intervention (Van Gemert-Pijnen et al, 2014). These insights can be used to tailor the interventions for user groups or personalize them to meet an individual user's need (Oinas-Kukkonen et al, 2022).…”

A panoramic view of personalization based on individual differences in persuasive and behavior change interventions

“…Recent research has been focusing on how personalization can be effective. For example, Oinas-Kukkonen et al (2022) argue that because a user's behavior can change over time, the grounds for personalization may change without the system detecting the change, which results in an "outdated" view of the user. Therefore, the more personalized the intervention, the more users relate to it and find it helpful, which will, in turn, increase the likelihood of adoption of the intervention.…”

“…This section presents our results regarding the models, scales, and theories adopted in the considered papers. These scales and General health Goldberg, 1999;Cialdini, 2001;Fogg and Fogg, 2003;Fogg, 2009;Oinas-Kukkonen and Harjumaa, 2009;Brynjarsdóttir et al, 2012;Deterding, 2012;Gardner et al, 2012;Orji et al, 2014Orji et al, , 2019aMatthews et al, 2016;Altmeyer et al, 2018;Hofstede and Milosevic, 2018;Martin and Kwaku, 2019;Mazeas et al, 2022;Oinas-Kukkonen et al, 2022 Physical activity Costa, 1987, 1989;Bandura, 2002;Andrew et al, 2007;Midden et al, 2008;O'Keefe, 2013;Orji, 2016;Orji et al, 2017bOrji et al, , 2019bModic et al, 2018;Mora et al, 2019;Alslaity and Tran, 2020a Healthy eating Fogg, 2002Fogg, , 2003Goldberg et al, 2006;Knowles et al, 2014;Martin and Kwaku, 2019;Klock et al, 2020;Feroz et al, 2021 Mental health Bassili, 1996;Booth and Grant, 2009;Barratt, theories are used for different purposes, including identifying individuals' differences and users modeling, systems design, and evaluation. Figure 7 depicts the most common theories and models used in the reviewed papers.…”

“…Mobile application Costa, 1987, 1989;Bandura, 2002;Fogg and Fogg, 2003;Midden et al, 2008;Booth and Grant, 2009;Fogg, 2009;Brynjarsdóttir et al, 2012;Deterding, 2012;Gardner et al, 2012;Matthews et al, 2016;Orji, 2016;Adaji, 2017;Orji et al, 2017bOrji et al, , 2019bAltmeyer et al, 2018;Hofstede and Milosevic, 2018;Modic et al, 2018;Mora et al, 2019;Aldenaini et al, 2020;Tran, 2020a, 2021;Feroz et al, 2021;Mazeas et al, 2022;Oinas-Kukkonen et al, 2022Game Goldberg, 1999O'Keefe, 2013;Heath, 2014;Busch et al, 2015;Anagnostopoulou et al, 2018;Orji and Moffatt, 2018;Orji et al, 2018b;Martin and Kwaku, 2019;Alslaity and Tran, 2020b;Klock et al, 2020;Oyebode et al, 2021b;Arden-Close et al, 2022;Ghorbani and Semiyari, 2022 Web application Goldberg et al, 2006;Forget et al, 2008;Oinas-Kukkonen and Harjumaa, 2008;Gardner et al, 2012;Nacke et al, 2014;…”

Sensing the care: advancing unobtrusive sensing solutions to support informal caregivers of older adults with cognitive impairment

“…Features, such as reminders and suggestions, were found to be particularly useful in emergency situations whereas features such as reduction, trustworthiness, and social learning were identified as ingeneral design considerations for an interaction platform. Overall, careful consideration of the intended usage (pros and cons) of PSD features should be given before their implementation [302].…”