Applying UTAUT2 to Explain the Use of Physical Activity Logger Applications Among Young Elderly

This is a self-archived version of an original article. This version may differ from the original in pagination and typographic details. Author(s): Title: Year: Version: Copyright: Rights: Rights url: Please cite the original version: CC BY 4.0 https://creativecommons.org/licenses/by/4.0/ Applying UTAUT2 to Explain the Use of Physical Activity Logger Applications Among Young Elderly © Authors 2020 Published version Makkonen, Markus; Kari, Tuomas; Frank, Lauri Makkonen, M., Kari, T., & Frank, L. (2020). Applying UTAUT2 to Explain the Use of Physical Activity Logger Applications Among Young Elderly. In A. Pucihar, M. Kljajic Borstnar, R. Bons, H. Cripps, A. Sheombar, & D. Vidmar (Eds.), 33rd Bled eConference : Enabling technology for a sustainable society (pp. 567-582). University of Maribor. https://doi.org/10.18690/978-961-286362-3.38 2020 APPLYING UTAUT2 TO EXPLAIN THE USE OF PHYSICAL ACTIVITY LOGGER APPLICATIONS AMONG YOUNG ELDERLY Ključne besede: physical activity logger applications, young elderly, UTAUT2, digital wellness technologies, partial least squares. M ARKUS M AKKONEN 1,2, T UOMAS K ARI 1,2 & L AURI F RANK 2 1 Institute for Advanced Management Systems Research, Turku, Finland, e-mail: markus.v.makkone[email protected], [email protected] 2 University of Jyvaskyla, Jyvaskyla, Finland, e-mail: [email protected], tuomas.t.[email protected], lauri.fra[email protected]i Abstract Digital wellness technologies have been proposed as a promising way to promote the levels of physical activity and to solve the prevalent problem of physical inactivity among elderly people. In this study, we propose and test a research model for explaining the acceptance and use of these technologies in the case of the young elderly segment (people aged 60–75 years) and physical activity logger applications. The proposed model is theoretically founded on UTAUT2, and it is empirically tested by using the data collected from 115 Finnish young elderly users of a physical activity logger application and analysed with partial least squares based structural equation modelling (PLS-SEM). We find habit to act as the strongest antecedent of use intention, followed by performance expectancy and hedonic motivation with approximately equally strong effects. In contrast, the effects of effort expectancy and social influence on use intention were found as statistically not significant. 568 33RD BLED ECONFERENCE ENABLING TECHNOLOGY FOR A SUSTAINABLE SOCIETY 1 Introduction In the recent years, failing to meet the recommendations for adequate amounts of physical activity has become a prevalent problem among elderly people (Sun, Norman & While, 2013). Therefore, new and innovative ways to promote the levels of physical activity in this segment are urgently needed. One potential way to achieve this are different types of digital wellness technologies, such as smartphone and smartwatch applications, which have been found very promising in terms of promoting the levels of physical activity not only among young but also among elderly people (e.g., Changizi & Kaveh, 2017; Muellmann et al., 2018; Elavsky, Knapova, Klocek & Smahel, 2019; Stockwell et al., 2019; Yerrakalva, Yerrakalva, Hajna & Griffin, 2019), although more high-quality studies especially on their longterm effects are still called for. In addition to elderly people in general, their potential has also been highlighted in the more specific segment of young elderly, which consists of people aged approximately 60–75 years (e.g., Carlsson & Walden, 2015– 2019; Carlsson & Carlsson, 2016; Walden & Sell, 2017; Allmér, 2018). However, despite their promising status as a solution for the aforementioned inactivity problem, there is a severe lack of prior studies on the antecedents of the acceptance and use of digital wellness technologies among elderly people, which can be considered a serious shortcoming in both theoretical and practical terms. The objective of the present study is to address this gap in prior research by proposing and testing a research model for explaining the phenomenon in the case of the young elderly segment and one common type of digital wellness technology: physical activity logger applications. By physical activity logger applications, we refer to mobile applications that enable users to log and keep track of their physical activities as well as view different types of reports about them. The data about the physical activities may be entered to the application manually by the users or it may be measured automatically by the application itself or by other applications or devices, from which it is then transferred to the application in question. The application may also act as an aggregator that extracts the data from multiple different sources. The theoretical foundation of the proposed model is based on UTAUT2 by Venkatesh, Thong, and Xu (2012). The model is empirically tested by using the data collected from 115 Finnish young elderly users of a physical activity logger application and analysed with partial least squares based structural equation modelling (PLS-SEM). Markus Makkonen, Tuomas Kari and Lauri Frank: Applying UTAUT2 to Explain the Use of Physical Activity Logger Applications Among Young Elderly 569 After this introductory section, we describe in more detail the research setting and the research model of the study in Sections 2 and 3. This is followed by a description of the research methodology and reporting of the research results in Sections 4 and 5. The results will be discussed in more detail in Section 6. Finally, we will conclude the paper with a brief discussion about the limitations of the study and potential paths of future research in Section 7. 2 Research Setting This study was conducted as part of a broader research program that uses digital wellness technology to study and promote the physical activity of young elderly in Finland. The multiyear and nationwide program is conducted in close co-operation with Finnish pensioners’ associations, which are responsible for recruiting volunteer participants to the program amongst their members. The interaction between the researchers and the participants takes place mainly in group meetings of about 20– 50 participants, in which typically one or two researchers present the participants information about the program and collect data through surveys. The first three of these group meetings take place during the first few weeks of participation. This is followed by multiple successive self-monitoring periods of about four months, during which the participants are asked to use a physical activity logger application to collect data about their actual physical activity. At the end of each self-monitoring period, there is another group meeting. The application, like participating in the program itself, is totally free for the participants. However, all the participants are required to own a smartphone on which the application can be installed. The application is available for both Google’s Android and Apple’s iOS operating systems, and it is developed by the research program itself on top of the Wellmo (2020) platform. In the group meetings, the participants are trained to use the application and instructed to conduct the logging manually by entering the type, intensity, time, and duration of their physical activities. The application also has the ability to extract the data about the physical activities automatically from other applications, such as Google Fit and Apple Health. However, in the group meetings, the participants are not trained or instructed to take this feature into use, which is why few are likely to use it. Based on the logged data, the application shows the users different types of reports about their physical activities. 570 33RD BLED ECONFERENCE ENABLING TECHNOLOGY FOR A SUSTAINABLE SOCIETY 3 Research Model The research model of the study is based on UTAUT2 by Venkatesh et al. (2012), which is an extension of the unified theory of acceptance and use of technology (UTAUT) by Venkatesh, Morris, Davis, and Davis (2003) from organisational to consumer contexts. UTAUT2 has been applied to explain technology acceptance and use in numerous information systems (IS) contexts, including also the context of mobile health and fitness applications and devices (e.g., Yuan, Ma, Kanthawala & Peng, 2015; Beh, Ganesan, Iranmanesh & Foroughi, 2019; Dhiman, Arora, Dogra & Gupta, 2019; Duarte & Pinho, 2019; Talukder, Chiong, Bao & Malik, 2019) and the context of elderly users (e.g., Macedo, 2017). However, no prior studies that we are aware of have combined these two contexts by studying, for example, the acceptance and use physical activity logger applications among young elderly, as it is done in the present study. In UTAUT2, the behavioural intention to use a particular technology is hypothesised to be positively affected by seven antecedent constructs (Venkatesh et al., 2012): performance expectancy (i.e., the degree to which using a technology will provide benefits to consumers in performing certain activities), effort expectancy (i.e., the degree of ease associated with consumers’ use of technology), social influence (i.e., the extent to which consumers perceive that important others believe they should use a particular technology), facilitating conditions (i.e., consumers’ perceptions of the resources and support available to perform a behaviour), hedonic motivation (i.e., the fun or pleasure derived from using a technology), price value (i.e., the consumers’ cognitive trade-off between the perceived benefits of the technology and the monetary cost for using it), and habit (i.e., the extent to which people tend to perform behaviours automatically because of learning). In addition, UTAUT2 also introduces three moderators for the effects of these seven antecedent constructs on use intention: age, gender, and experience. However, because of the limited number of participants in our research program at the time of conducting the present study, these moderators are omitted in our research model. In addition, we also omit two of the seven antecedent constructs: facilitating conditions and price value. These were considered irrelevant in the current research setting because all the participants had identical resource requirements for participating in the program (i.e., owning a smartphone) and were given identical training and support for installing and using the physical activity logger application. In addition, as already mentioned above, the application Markus Makkonen, Tuomas Kari and Lauri Frank: Applying UTAUT2 to Explain the Use of Physical Activity Logger Applications Among Young Elderly 571 was totally free for all the participants. Finally, our research model also concentrates on explaining only use intention and not actual use behaviour. The research model, with the omitted constructs and effects presented as dashed, is illustrated in Figure 1. Figure 1: Research model (the dashed constructs and effects are omitted in this study) 4 Methodology The data for the study was collected from the participants of our aforementioned research program with pen-and-paper questionnaires in the group meetings that were arranged in autumn 2019 after the first four-month self-monitoring period. Because Finland has two official languages, the participants had the option to response to the questionnaire in either Finnish or Swedish. In the questionnaire, each construct of the research model was measured reflectively by three indicators, which were all adapted from the study by Venkatesh et al. (2012). The wordings of 572 33RD BLED ECONFERENCE ENABLING TECHNOLOGY FOR A SUSTAINABLE SOCIETY these indicators in English are reported in Table 1. The measurement scale of the indicators was a seven-point Likert scale ranging from one (strongly disagree) to seven (strongly agree). In addition, the participants also had the option not to respond to a particular item, which resulted in a missing value. Table 1: Indicator wordings (PE = performance expectancy, EE = effort expectancy, SI = social influence, HM = hedonic motivation, HT = habit, BI = behavioural intention) Indicator Wording PE1 I find the app useful in achieving my daily exercise goals. PE2 Using the app helps me achieve my exercise goals more quickly. PE3 Using the app increases my efficiency in achieving my exercise goals. EE1 Learning how to use the app to achieve my exercise goals is easy for me. EE2 I find using the app to achieve my exercise goals easy. EE3 It is easy for me to become skilful at using the app to achieve my exercise goals. SI1 People who are important to me think that I should use the app to achieve my exercise goals. SI2 People who influence my behaviour think that I should use the app to achieve my exercise goals. SI3 People whose opinions I value prefer that I use the app to achieve my exercise goals. HM1 Using the app to achieve my exercise goals is fun. HM2 Using the app to achieve my exercise goals is enjoyable. HM3 Using the app to achieve my exercise goals is entertaining. HT1 The use of the app to achieve my exercise goals has become a habit for me. HT2 I am addicted to using the app to achieve my exercise goals. HT3 I must use the app to achieve my exercise goals. BI1 I intend to continue using the app to achieve my exercise goals. BI2 I will always try to use the app to achieve my exercise goals. BI3 I plan to use the app regularly to achieve my exercise goals. Due to the relatively small sample size, the collected data was analysed with variancebased structural equation modelling (VB-SEM), more specifically partial least squares (PLS), by using the SmartPLS version 3.2.9 software by Ringle, Wende, and Becker (2015). When running the analyses and reporting the results, we followed the guidelines given by Hair, Hollingsworth, Randolph, and Chong (2017) for IS research. For example, in the model estimation, we used mode A as the indicator weighting mode of the constructs, path weighting as the weighting scheme, and +1 as the initial weights, while the statistical significance of the model estimates was tested by using bootstrapping with 2,500 subsamples and individual sign changes. As the threshold for statistical significance, we used p < 0.05. The potential missing values were handled by using mean replacement. Markus Makkonen, Tuomas Kari and Lauri Frank: Applying UTAUT2 to Explain the Use of Physical Activity Logger Applications Among Young Elderly 573 5 Results We received valid responses from a total of 115 participants. The descriptive statistics of this sample in terms of the gender, age, and response language of the participants as well as their subjective assessment of own level of physical activity are reported in Table 2. As can be seen, nearly two-thirds of the respondents were women, and nearly nine out of ten assessed their level of physical activity as either moderate or higher. The age of the respondents ranged from 49 to 80 years, with a mean of 69.3 years and a standard deviation 5.0 years. A vast majority of the respondents belonged to the young elderly segment, but there were also a few respondents who were slightly younger or older than our target segment consisting of people aged approximately 60–75 years. However, we decided not to drop these respondents from the study due to our relatively small sample size. Table 2: Sample statistics (N = 115) N % Gender Man 43 37.4 Woman 72 62.6 Age Under 60 years 3 2.6 60–64 years 11 9.6 65–69 years 44 38.3 70–74 years 39 33.9 75 years or over 18 15.7 Language Finnsh 69 60.0 Swedish 46 40.0 Level of physical activity Very high 1 0.9 High 18 15.7 Moderate 84 73.0 Low 4 3.5 Very low 8 7.0 Totally passive 0 0.0 574 33RD BLED ECONFERENCE ENABLING TECHNOLOGY FOR A SUSTAINABLE SOCIETY 5.1 Estimation Results Estimation results of the model in terms of the size and statistical significance of the standardised path coefficients as well as the proportion of explained variance in the behavioural intention construct are reported in Figure 2. 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