Abstract
This study examines two traditional technology acceptance models and their applications in Aging Service Technology (AST). Technology Acceptance Model (TAM) and United Theory of Acceptance and Use of Technology (UTAUT) have been widely used in diverse technology adoption research. However, these models excessively depend on self-reported subjective measures, attitudes and perceptions of users, and their solutions lack practical contributions. In addition, both models lack unique contextual barriers needed for understanding the AST adoption process. To complement their limitations, a new approach that includes not only additional contextual constructs but also objective measures such as behavioral data or physiological measures is suggested. The new approach will contribute to the development of practical solutions to achieve Aging in Place.
Keywords
1 Introduction
In the US, the number of the 65 and older population is 46 million in 2015 and will increase to 74 million in 2030 [1]. Most aging adults prefer to live independently for as long as they possibly can. However, physical and cognitive impairment, chronic diseases, and less social interactions challenge them to pursue independent living or aging in place. Aging Service Technologies (ASTs) such as sensor-based networks, fall and wandering detection technologies, and diverse electronic health applications, may relieve some of these challenges. These technologies mainly provide safety, security, independence and enjoyment in aging adults’ living [2]. While their potential usefulness has been well-recognized, the adoption rates has not met the expectation [3]. Current AST does not fully consider important design and usability aspects such as motivation to use, demographic diversity, and specific technology contexts. Additionally, though aging adults cherish independence, privacy, and social interactions, current technology mostly focuses on safety and physical assistance.
The adoption of AST is crucial to achieve aging in place, which is defined as “the ability to live in one’s own home and community safely, independently, and comfortably, regardless of age, income, or ability level” [4]. Early evidence indicates the advantages of an aging in place program, and people who lived in this program have improved cognition, lower rates of depression, and decreased activities of daily living (ADL) assistance [5]. As the number of aging adults who prefer to stay at their home increases, the technologies that help them remain in home are thus more needed. To maximize the benefits of aging in place, successful adoption of ASTs is critical. This requires the understanding of aging adults’ patterns of technology usage in pre- and post-adoption to achieve better aging adults-technology interactions.
This study reviews existing approaches in technology adoption or acceptance research and discusses their applicability in aging adults’ adoption of AST. Recently, a growing number of studies have been conducted in aging adults’ technology adoption, but the methodologies used in the studies have not reflected enough aging adults’ actual adoption behaviors and their usage patterns. In addition, the unique features of the technologies have not fully accommodated aging adults’ attitudes and perception, and also the measures are not much differentiated from those of information technology adoption for general population. This study scrutinizes the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT) which dominantly used in AST adoption research and addresses their limitations. As a conclusion, a new approach that may lighten the drawbacks of two approaches is suggested.
2 Existing Approaches in Aging Service Technology Adoption Research
2.1 Technology Acceptance Model (TAM)
The technology acceptance model (TAM) is the most cited information systems theory to explain and predict the process of users’ system adoption attitudes. It is developed based on Theory of Reasoned Action (TRA) and Theory of Planned Behavior (TPB). Based on the investigation of the adoption process of basic information technology systems, TAM maintains that both perceived ease of use (PEOU) and perceived usefulness (PU) distinctively correspond to the adoption of self-reported system [6]. PU is defined as “the degree to which a person believes that using a particular system would enhance job performance” [6]. PEOU is described as “the degree to which a person believes that using a particular system would be free from effort” [6]. Due to its simplicity and applicability, TAM is the most prevailing theoretical framework that has been used to explain the information system adoption. The major constructs of TAM are shown in Fig. 1.
2.2 Unified Theory of Acceptance and Use of Technology (UTAUT)
The United Theory of Acceptance and Use of Technology (UTAUT) was suggested to overcome the simplicity issue of TAM. UTAUT model is developed to explain the intention and use of new technology and to achieve a unified view of user acceptance by comprehensive examinations of various models including the Theory of Reasoned Action, the Innovation Diffusion Theory, Theory of Planned Behavior, Technology Acceptance Model, the model of PC Utilization, and Social Cognitive Theory [7]. It is empirically developed to understand user intentions to adopt information systems by four constructs: “effort expectancy, performance expectancy, facilitating conditions, and social influence [8]. These constructs identify dynamic relationships among organizational context, user experience, and demographic characteristics in the implementations of information systems [7]. In addition, UTAUT smoothly combines its constructs with two constructs of TAM by incorporating PU into “performance expectancy”, and PEOU into “effort expectancy”, “social norms”, and “facilitating conditions” [9] (Fig. 2).
3 Challenges and Opportunities in Aging Service Technology Adoption
3.1 Limitation of TAM and UTAUT in Aging Research
One of the limitations of TAM and UTAUT is that some constructs are not clearly defined, which results in the ambiguous relationship among them [10]. This issue makes it difficult to put the studies using TAM and UTAUT into practice. Instead of providing practical recommendations to improve the adoption, most studies just identify other factors that might influence adoption process [7]. In addition, most TAM-based studies adhere the same constructs without consideration of adoption environments or unique contexts. For an example, in healthcare system adoption, Van Schaik et al. [11] evaluated the effect of a portable system that assessed posture on clinicians. Since the study uses the same questionnaire as the TAM study, the results are simply repeated without any distinctive aspects of healthcare systems. UTAUT is also criticized by many aging researchers because its conceptual framework is based on unrealistic or oversimplified individual and contextual assumptions about how older persons decide to accept new technologically oriented products.
Though several studies about older adults’ technology acceptance using TAM or UTAUT have been conducted [3, 12], few have investigated contextual usability issues and changes in acceptance patterns over extended periods of time. Originally, PU is indicated as the improvement or gains in task performance through the use of information system. However, task performance of AST is well beyond the simple use of the technology and includes various criteria such as physical functioning, attitude to life and satisfaction, gerontechnology self-efficacy, self-reported health conditions, cognitive ability, and social relationships [3]. Another issue is that, since PU depends more on individual performance, it is difficult to transfer PU to explain the collaboration among aging adults, family members, or care givers. Additionally, task performance improvement from process enhancement or workflow adjustment is difficult to be categorized as PU [13]. Similarly, the definitions of PEOU are vague and broad as well. Original TAM study simply defines PEOU as the lack of effort. This definition may fail to recognize the usability issues and user perspective problems of AST systems.
A better understanding of the barriers and facilitators that older adults face when using new AST could promote use and support to build successful deployment strategies. Examining attitudes and intentions about AST over time may be a useful method to elicit barriers and facilitators to acceptance because aging adults will have more time with the technology and may have the opportunity to use it in a variety of contexts.
3.2 Adoption Barriers of Aging Service Technology
AST is expected to improve the quality of living for aging adults and promote aging in place. However, such technology also brings forth new issues or concerns of aging adults, caregivers, and healthcare providers. There are multiple barriers to fully incorporate AST. These barriers include the technology complexity, lack of standards in data and design, privacy concerns, trust in technology, and risk of malfunctions. To overcome these barriers, performance incentives by payers and government, certification and standardization of data, removal of legal barriers, and security of medical data need to be considered [14]. A monitoring system may be a useful tool to assist aging adults maintaining independence in their living, but there are concerns such as social isolation, privacy, and information security [15].
4 New Approach in Aging Service Technology Adoption
Technology adoption of aging adults is different from those of other population. Aging adults’ attitudes and perceptions on new technology are built and maintained by utilities, trustworthiness, powerfulness, and relevance with their past experience [16]. However, subjective measures, attitudes and perceptions, are not enough to analyze and envision the whole adoption process. Research based on self-reported measures may show different results with that employing direct objective measurement such as actual usage [17]. Though it cannot be excluded that self-reported subjective measurements of adoption or usage are biased [17], a new adoption model is needed to include more objective measures such as behavioral data or physiological measures. The patterns of behavioral data can educate and motivate individuals toward building better usage and better habits. The gap between collecting self-reported attitudes and perception and changing adoption behaviors or usages may be substantial, and just increasing in popularity of a new AST is not enough to bridge the gap. For example, health-related behaviors such as eating well and exercising regularly could lead to meaningful improvements in actual adoption of new health-related devices. New technologies need to create consistent new habits to use, to sustain external motivations or to turn them into internal motivations. This requirement of the pattern analysis of behavioral data and physiological measures will be an essential aspect to understand and enhance the AST adoption and usage.
5 Conclusion
This study provides a new approach that examines the predictors and influencing factors of aging adults’ adoption of AST. Drawing from TAM and UTAUT, we examined limitations and challenges of the previous adoption research of AST and suggested a new approach including behavioral data and additional constructs such as perceived privacy risk and perceived benefit. Based on this new approach, we will collect the advantages and disadvantages of the activity tracker use in their daily activity and evaluate attitude and perception changes on new technology adoption. The outcomes of this study will be a foundation to further integrated interface design for aging adults’ wearable device use.
References
Ortman, J.M., Velkoff, V.A., Hogan, H.: An aging nation: the older population in the United States. United States Census Bureau, Economics and Statistics Administration, US Department of Commerce (2014)
Kim, K., Gollamudi, S.S., Steinhubl, S.: Digital technology to enable aging in place. Exp. Gerontol. 88, 25–31 (2017)
Chen, K., Chan, A.H.S.: Gerontechnology acceptance by elderly Hong Kong Chinese: a senior technology acceptance model (STAM). Ergonomics 57, 635–652 (2014)
American Planning Association and the National Association of County and City Health: Healthy places terminology. https://www.cdc.gov/healthyplaces/terminology.htm
Chen, Y.-M., Berkowitz, B.: Older adults’ home-and community-based care service use and residential transitions: a longitudinal study. BMC Geriatr. 12, 44 (2012)
Davis, F.D.: Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q. 13, 319–340 (1989)
Venkatesh, V., Morris, M.G., Davis, G.B., Davis, F.D.: User acceptance of information technology: toward a unified view. MIS Q. 27, 425–478 (2003)
Venkatesh, V., Davis, F.D.: A theoretical extension of the technology acceptance model: four longitudinal field studies. Manag. Sci. 46, 186–204 (2000)
Holden, R.J., Karsh, B.T.: The technology acceptance model: its past and its future in health care. J. Biomed. Inform. 43, 159–172 (2010)
Karsh, B.T.: Beyond usability: designing effective technology implementation systems to promote patient safety. Qual. Saf. Health Care 13, 388–394 (2004)
Van Schaik, P., Bettany-Saltikov, J.A., Warren, J.G.: Clinical acceptance of a low-cost portable system for postural assessment. Behav. Inf. Technol. 21, 47–57 (2002)
Chen, K., Chan, A.H.: Use or non-use of gerontechnology—a qualitative study. Int. J. Environ. Res. Public Health 10, 4645–4666 (2013)
Holden, R.J., Karsh, B.T.: A review of medical error reporting system design considerations and a proposed cross-level systems research framework. Hum. Factors: J. Hum. Factors Ergon. Soc. 49, 257–276 (2007)
Anderson, M., Perrin, A.: Technology use among seniors. Pew Research Center, Internet and Technology Washington, D.C. (2017)
Draper, H., Sorell, T.: Telecare, remote monitoring and care. Bioethics 27, 365–372 (2013)
Golant, S.M.: A theoretical model to explain the smart technology adoption behaviors of elder consumers (Elderadopt). J. Aging Stud. 42, 56–73 (2017)
Akter, S., D’Ambra, J., Ray, P.: Service quality of mHealth platforms: development and validation of a hierarchical model using PLS. Electron. Mark. 20, 209–227 (2010)
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Lee, B.C., Xie, J. (2018). How Do Aging Adults Adopt and Use a New Technology? New Approach to Understand Aging Service Technology Adoption. In: Stephanidis, C. (eds) HCI International 2018 – Posters' Extended Abstracts. HCI 2018. Communications in Computer and Information Science, vol 851. Springer, Cham. https://doi.org/10.1007/978-3-319-92279-9_22
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