Predicting the acceptance and use of information and communication technology by older adults: An empirical examination of the revised UTAUT2

Accepted Manuscript Predicting the acceptance and use of information and communication technology by older adults: An empirical examination of the revised UTAUT2

Isabel Maria Macedo PII:

S0747-5632(17)30384-9

DOI:

10.1016/j.chb.2017.06.013

Reference:

CHB 5023

To appear in:

Computers in Human Behavior

Received Date:

30 August 2016

Revised Date:

26 March 2017

Accepted Date:

10 June 2017

Please cite this article as: Isabel Maria Macedo, Predicting the acceptance and use of information and communication technology by older adults: An empirical examination of the revised UTAUT2, Computers in Human Behavior (2017), doi: 10.1016/j.chb.2017.06.013

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ACCEPTED MANUSCRIPT Predicting the acceptance and use of information and communication technology by older adults: An empirical examination of the revised UTAUT2

Introduction Over recent decades, the growing impact of information and communication technology (ICT) on society together with the unprecedented growth of the ageing population have posed challenges to governments, academics and to all those with responsibility for promoting a more inclusive society. ICTs can play a significant role in preparing for the economic and social shifts associated with an ageing population. Since the progress of a modern society depends heavily on its citizens’ capacity to embrace such technologies, the acceptance and usage of these technologies by individuals in general, and by older adults in particular, is critical for the economy and for society. Ensuring that every citizen has a basic level of “universal access” to information technologies has become a key challenge for governments and policy makers who have been actively committed to promoting a society within which everybody is able to contribute to, and benefit from the digital economy (Verdegem & Verhoest, 2009; Weaver, Zorn, & Richardson, 2010). However, despite recognized benefits of ICT for older people, this group still evidences lower levels of computer and internet use compared with younger people. Recent data indicate a 59% Internet use in the USA (Smith, 2014) for people aged 65 and older, and in Europe a 46% rate of by regular users for the age group 55-74 (Seybert, & Reinecke, 2014; Eurostat). Reflecting these trends, current research points to a digital divide between people who have regular access to technologies and those who do not (Niehaves & Plattfaut, 2014). Although the use of computers and the Internet among older adults is increasing, research shows that these adults are still less likely than all other age groups to use the internet (Barnard, Bradley, Hodgson & Lloyd, 2013; Czaja & Lee, 2007; Magsamen-Conrad, Upadhyayab, Joa & Dowd, 2015). Consequently, the study of ICT acceptance and use by older adults has become an increasingly relevant field of study (Wagner, Hassanein & Head, 2015). This study addresses the acceptance of technology among older adults. Attitudes toward technology and its use are the most commonly studied elements of research regarding the relationship between aging and technology adoption (Magsamen-Conrad et al., 2015). While the research to date provides important insights into ICT use among older adults, there are inconsistencies and areas that lack clarity in the literature and in-depth knowledge is still needed (Morris, Goodman & Brading, 2007; Ramón-Jerónimo, Peral-Peral & Arenas-Gaitán, 2013; Lian & Yen, 2014; Taiwo & Downe, 2013). This is in line with a growing body of 1

ACCEPTED MANUSCRIPT research reinforcing the need to further examine computer technology acceptance and usage among older adults from the users’ perspective. (Gonzalez et al., 2012; Kania-Lundholm & Torres, 2015; Lee & Coughlin, 2014; Magsamen-Conrada et al., 2015; Verdegem & de Marez, 2013). Moreover, a critical review from extant research addressing the adoption and use of ICT among older adults reveals a varying use of analysis techniques that have, to a larger extent, been confined to first generation statistical tools. These constraints may explain some contradictory findings reported in a number of studies. This view is in line with other authors (Vehovar, Sicherl, Hüsing, Dolnicar, 2006: 281) who claim that bivariate analysis may not reveal the true relationships and that the use of advanced statistical measures may dramatically change the research findings. In order to gain a holistic knowledge of this phenomenon, studies ought to integrate in their analysis the various simultaneous relationships among the drivers of technology acceptance and usage. In recent decades, several theoretical models have been proposed to predict and explain the acceptance and usage of technology, with particular reference to the technology acceptance model (TAM) (Davis, 1989) and to the unified theory of acceptance and use of technology (UTAUT) (Venkatesh, Morris, Davis & Davis, 2003). So far, few studies have employed these frameworks to understand ICT usage among older adults (Lian & Yen, 2014; MagsamenConrad et al., 2015; Nagle & Schmidt, 2012). Additionally, the suitability of existing models that have been used in this field have not been substantively researched. Besides, these technology acceptance theories were conceived/ operationalised to be applied to workplace/organizational contexts, and it is most likely that applying these frameworks to predict consumers’ behavioural intentions and actual use toward ICT may require further adaptation. Recently, the UTAUT framework (2003) has been revised in order to more accurately predict ICT usage within the consumer context, resulting in the extended UTAUT2 (Venkatesh, Thong, & Xu, 2012). To the best of our knowledge, no empirical examination has yet substantiated the relevance of this theory to understand ICT acceptance and use among older adults. The present study intends to fill this gap by empirically testing the suitability of the revised version of UTAUT (Venkatesh et al., 2012) to be applied to older adults. The UTAUT2 was chosen because of its comprehensiveness and high explanatory power as compared to other technology acceptance models (Verdegem & de Marez, 2011; Pan & Marsh, 2010). Besides, UTAUT2 in addition to being holistic and integrative has a focus on the consumer setting which was deemed suitable for the present study – to explain older adults’ behavioral intentions and 2

ACCEPTED MANUSCRIPT use regarding ICTs. Yet, such claims need to be further legitimated. Moreover, a recent research stream supports the view that due to rapid changing technology trends and greater exposure of older adults to technology, we may be dealing with a “second order divide” (KaniaLundholm & Torres, 2015) or a “grey divide”, which needs to be more thoroughly researched. The present study builds on this emerging argument to test the relevance of UTAUT2 to address older adults’ technology acceptance. In particular, the main goal of this article is to enhance our knowledge concerning the predictive role of UTAUT2 to assess ICT intention behaviour and actual use among older adults. The study further examines the relevant relationships among UTAUT constructs to foster a thorough understanding regarding ICT use among this group. The empirical study was conducted in a Southern European country, which contributes to enlarge the geographical scope of existing research on the topic. The remainder of the article is organised as follows. The next section presents a review of the mainstream literature on ICT usage among older adults. In addition, an overview of prior theory and research pertaining to technology acceptance frameworks leading to the development of hypotheses is presented. In the subsequent sections the methodology is described and the results are presented. The paper ends with a discussion and conclusion section that summarises the study’s findings, identifies contributions and suggests some potential future research directions.

Conceptual Framework 2.1 ICT use and adoption by older adults Population ageing is considered as one of the most significant social transformations of the twenty-first century, with implications for nearly all sectors of society (United Nations, 2015). According to data from World Population Prospects (United Nations, 2015), the number of older persons – those aged 60 years or over – has increased substantially in most countries, and that growth is projected to accelerate in the coming decades. By 2050, it is anticipated that the global population of older persons will more than double in size compared to numbers in 2015, while the number of people aged 80 or over is expected to have more than tripled since 2015. Similar trends are occurring worldwide. In the euro zone, recent data projected for the 20132060 period estimates that by 2060 the proportion of people aged 65 and over will rise from 18% to 28% of the population while those aged 80 and over will rise from 5% to 12% (European Commission, 2014). This population shift will undoubtedly not only create 3

ACCEPTED MANUSCRIPT challenges to public services, but also opportunities for businesses (Lee & Coughlin, 2014; Niehaves & Plattfaut, 2014; Gassmann & Keupp, 2009). ICTs, such as the computer, Internet and mobile communications provide great potential for addressing many societal challenges, including those related with an ageing population. As eGovernment and eCommerce become more important, the arguments regarding the benefits associated with older adult’s use of ICT take on increasing importance. However, successful realization of ICTs is largely dependent on the acceptance and actual use of technology. It is, therefore, crucial that everyone has accessibility to ICTs in order to access public and commercial services. Such concerns have led governments around the world to implement policy initiatives in order to address the divide and/or digital exclusion (Cruz-Jesus, Oliveira & Bacao, 2012; Kania-Lundholm & Torres, 2015; Selwyn, 2004) and to promote a more inclusive information society (Hakkarainen, 2012; Verdegem & Marez, 2013; Weaver et al., 2010). Previous research has identified a wide range of benefits of ICT use for older adults, which that include, but are not restricted to benefits related to social and self-understanding (e.g. increased access to current affairs and health information), interaction benefits (e.g. increased connectivity and social support) and task-oriented goals (e.g., ICT-assisted work, travel, shopping and financial management) (Selwyn, 2004). Furthermore, the use of the internet may also offer educational and employment opportunities for older adults (Czaja & Lee, 2007). Such advantages have gained an increasing popularity, particularly in the context of the potential for services targeting the enhancement of social participation, social inclusion which ultimately may impinge on the quality of life of older people (Barnard et al., 2013; Hakkarainen, 2012; Hill, Betts & Gardner, 2015; Mitzner et al., 2010; Peacock & Kunemund, 2007). Thus, a significant number of studies share a consensus view of computer and internet engagement as beneficial by providing older people with “overwhelmingly positive experiences and outcomes” (Weaver et al., 2010:700). Other studies on the adoption and use of technology by older adults have focused on characterising the nature and scope of the digital divide among generations (Marston et al., 2016; Magsamen-Conrada et al., 2015). However, according to some authors, the debate on the digital divide does not effectively capture the complexities of the digital world (KaniaLundholm & Torres, 2015; Selwyn, 2004). This is because within the digital divide debate, research on ICT usage tends to concentrate on those that are digitally excluded. Reflecting this perspective, research has been conducted in a variety of fields to provide knowledge on the reasons for non-use, and on the barriers to computer use by older adults. These factors include 4

ACCEPTED MANUSCRIPT fear of technology, or computer anxiety (Dyck & Smither 1995, Ellis & Allaire 1999), lack of knowledge (Peacock & Kunemund, 2007), absence of perceived benefits (Melenhorst, Rogers & Bouwhuis, 2006), cost (Carpenter & Buday, 2007; Saunders, 2004; White & Weatherall, 2000) and lack of interest or motivation, (Carpenter & Buday, 2007; Morris et al., 2007; Selwyn, Gorard, Furlong & Madden, 2003; Peacock and Kunemund, 2007). While prior research has provided an overall picture of the key factors affecting the patterns and performance of the actual use of technologies, the dual focus on comparing younger versus older segments has mitigated the reality regarding this heterogeneous group and to some extent has hampered in-depth knowledge within the older group itself. Increasingly, older adults are viewed as a group that is heterogeneous and diverse in its interests, education, health or socioeconomic level (Lee & Coughlin, 2014; González et al., 2012). This heterogeneity poses challenges to the study of ICT acceptance and usage. Hence, further research is needed to provide more comprehensive approaches to understand the determinants for technology acceptance (Verdegem & de Marez, 2011). In the rapidly evolving ICT environment, this knowledge is crucial to assist ICT managers, policy makers and researchers to conceive appropriate strategies to face ICT related challenges (Verdegem & de Marez, 2011). Since the acceptance of a technology is greatly determined by the user’s attitude and behaviour, it is then crucial to reach some consensus on a comprehensive model of the factors influencing technology acceptance (Lee & Coughlin, 2014). Technology adoption has been widely researched among the general population. However, the topic has been less popular for consumers and users among the older population (Lee & Couglin, 2014). Despite the popularity of influential technology acceptance models, such as TAM and UTAUT, a review of existing studies on this topic addressing older adults (Table 1) reveals that few studies have adopted such theoretical frameworks. What is perhaps notable is the reduced number of studies using UTAUT (2003), with particular reference to the fact that UTAUT2 (2012) was not applied to any of the studies that comprise this review. Table 1 Review of relevant empirical studies on ICT use and acceptance among older adults. Focus

Study

Nonusers

Weaver et al. (2010)

Technology Methodological Acceptance approach Theory n.a.

Qualitative/ Focus Groups

Sample Size/ Nº of cases 83

Resp. rate (%)

Analysis

Location

n.a.

Narrative thematic analysis

New Zealand

5

ACCEPTED MANUSCRIPT

Users

Users and nonusers

Hakkarainen (2012)

n.a.

Qualitative

126

n.a.

Grounded Theory

Finland

Selwyn (2004)

n.a.

Qualitative/ Interviews

35

n.a.

Comparison technique

UK

Eastman & Iyer (2005)

n.a.

Quantitative/ Survey

171

13.2%

Multivariate Analysis

USA

Porter & Donthu (2006)

TAM

Quantitative/ Survey

539

n.a.

Confirmatory Analysis (SEM)

USA

Niemelä-Nyrhinen (2007)

n.a.

Quantitative/ Survey

620

41.3%

Multivariate Analysis

Finland

Reisenwitz et al. (2007)

n.a.

Quantitative/ Survey

374

n.a.

Multivariate Analysis

USA

Mitzner et al. (2010)

n.a.

Qualitative/ Focus groups

113

n.a.

Qualitative Analysis

USA

González et al. (2012)

n.a.

Quantitative/ Survey

240

n.a.

Descriptive Analysis

Spain

Braun (2013)

TAM

Quantitative/ Survey

124

n.a.

Regression

USA

Rámon-Jerónimo et al., 2013

TAM

Quantitative/ Survey

492

n.a.

Confirmatory Analysis (SEM)

Spain

Hill et al. (2015)

n.a.

Qualitative/ Focus Groups

G1=10 G2= 7

n.a.

Interpretative Analysis

UK

Ihm & Hsieh (2015)

n.a.

Quantitative/ Survey

1780

7%

Confirmatory Analysis (SEM)

USA

Kania-Lundholm & Torres (2015)

n.a.

Qualitative/ Focus Groups

30

n.a.

Positioning analysis

Sweden

Niehaves & Plattfaut (2014) Zheng (2014)

UTAUT MATH n.a.

150

n.a.

PLS

Germany

339

n.a.

UTAUT (2003)

899

n.a.

Van Dijk’s model n.a.

Quantitative/ Panel data Quantitative/ Survey

1.224

Regression Analysis Factor Analysis Multivariate Analysis Confirmatory Analysis (SEM) Descriptive Analysis

USA

MagsamenConrad et al. (2015) 1 van Deursen & van Dijk (2015) 1 Vosner et al. (2016)

Quantitative/ Survey Quantitative/ Survey Quantitative/ Survey

Gilly & Zeithaml (1985)

n.a.

Quantitative/ Survey

S1 = 2500 S2 = 2500

21.8%

Descriptive Analysis

USA

Selwyn et al. (2003)

n.a.

Mixed approach/ Survey/ Interviews

75%

Selwyn et al. (2005) Melenhorst et al. (2006) Carpenter & Buday (2007)

n.a.

Descriptive Analysis Descriptive Analysis

n.a. n.a

Mixed approach Survey/ Interviews Qualitative/ Focus groups Quantitative/ Interviews

54

352 100 1001 100 S1: 48 S2: 20 324

47% n.a.

n.a. n.a. n.a.

USA Netherlands Slovenia

UK

Descriptive Analysis/

UK S1: USA S2: Netherlands USA

6

ACCEPTED MANUSCRIPT Regression Morris et al. (2007)

n.a.

Peacock and Kunemund (2007)

n.a.

Pan & JordanMarsh (2010) Nayak et al. (2010) Chang et al. (2015)

TAM

Lian & Yen (2014) 1 Vroman et al. (2015)

UTAUT (2003) n.a.

TAM n.a.

Quantitative/ Mixed Study 1/ Survey Study 2/Interviews

n.a. 353 120 n.a.

Quantitative Panel data/ Eurobarometer Quantitative/ Survey Quantitative/ Survey Quantitative/ Survey

374

n.a.

592

59.2%

567

n.a.

Quantitative/Survey

Older: 574 Young: 246 198

Quantitative/ Survey

40% 80% n.a.

Descriptive Analysis Descriptive Analysis Descriptive Analysis Multivariate Analysis Multivariate Analysis Multiple Linear Regression Descriptive Analysis/ Regression PLS Descriptive Analysis

Scotland Europe zone

China UK USA Taiwan USA

Note: 1- the sample consisted of younger and older adults; (n.a.) – Not applicable

In addition to the reduced number of studies that have used a specific technology acceptance framework to understand older adults' adoption and usage of technology, the limited use of more robust statistical methods, such as SEM and PLS is also apparent. The use of these statistical tools are crucial to enable further validation of causal relationships between the factors and the predictive variables (Zheng, 2015). It also seems worth noting that very few studies have focused on non-users. 2.2 Theoretical approaches concerning the adoption and use of technology Over recent decades, technology acceptance has been studied from established interdisciplinary fields as diverse as sociology, social psychology, information systems and innovation management, by numerous authors over the last decades and has been applied in a wide variety of sectors. In 1989, Davis developed the technology acceptance model (TAM; Davis, 1989; Davis, Bagozzi & Warshaw, 1989) to predict the potential users’ behavioural intention to use a new technological innovation. TAM has its roots in the theory of reasoned action (TRA – Fishbein & Ajzen, 1975) and its extension, the theory of planned behaviour (TPB), which includes a third determinant of behavioural intention, perceived behavioural control. In essence, TAM theorizes that the use of a technology is influenced by two major beliefs: perceived ease of use (PEU) and perceived usefulness (PU). Later studies carried out by 7

ACCEPTED MANUSCRIPT Venkatesh et al. (2003) have extended technology determinants to performance expectancy, effort expectancy, social influence and facilitating conditions (see detailed review on Venkatesh et al, 2003) within the proposed unified theory of acceptance and use of technology (UTAUT). The UTAUT (Venkatesh et al., 2003) is based on the theory of planned behaviour (TPB) (Ajzen, 1985; 1991), which states that a specific behaviour, such as use of technology, is preceded by behavioural intention. In addition, intention to behave is determined by attitude, norm and the perception of control over the behaviour. Hence, the authors propose four attitudinal dimensions (performance expectancy, effort expectancy, social influence and facilitating conditions) as the determinants of behavioural intention (i.e. intention to use the technology) which lead to actual use of the technology. UTAUT provides a framework that not only explains the acceptance of ICTs but also elucidates on the actual use of such technologies. Due to its capacity to integrate the different models of technology acceptance, UTAUT offers an important contribution to the understanding of technology acceptance and use (Venkatesh et al., 2003). However, this model was constructed in a working context, it investigated the determinants that affect employees’ acceptance and use of ICT. The UTAUT lays emphasis on the ‘utilitarian value’ (extrinsic motivation), such as performance expectancy and effort expectancy. In other words, in many cases enhancing working efficiency and performance were the main reasons for adopting the technology. More recently, Venkatesh et al. (2012), realising the need for adaptation to be context specific, i.e. working vs. non- working environment, modified the model to reflect the characteristics of consumers and developed the UTAUT2. The revised framework adjusts the emphasis of ‘utilitarian value’ (extrinsic motivation) of the previous UTAUT to a consumer setting, and integrates three additional dimensions, including price value, hedonic motivation and habit. It is worth noting that in UTAUT2, an important distinction is made between behavioural intention to use a technology and its actual use, with behavioural intention being considered as the nearest proxy for use behaviour (Verdegem & de Marez, 2011). In the present study, UTAUT2 (Venkatesh et al., 2012) will be used to simultaneously examine multiple relationships

among key attitudinal constructs such as “performance

expectancy”, “effort expectancy”, “social influence”, “facilitating conditions”, “Hedonic Motivation”, “Price value”, “Habit”, “Behavioural Intention” and “Use behaviour”. 2.3 Model and Hypothesis development Based on the stated goals of our research, Fig. 1 presents the proposed model for the study. 8

ACCEPTED MANUSCRIPT H 1 1

H2

H 2 Performance Expectancy H1

Effort Expectancy

Social Influence

Facilitating Conditions Hedonic Motivation

Price Value

Habit

H2 H3 H4 2 H5 32 H6 43 2H7 54 32 H8 65 43 H9 2 76 54 32

Behavioural Intention

Use Behaviour

H10 876 543 2

Age

Gender

Education

Experience

Control variables

Fig. 1 Research Model.

In UTAUT2, ‘Performance expectancy’ is one of the three constructs added to the previous UTAUT developed in 2003. In essence, this construct reflects the utilitarian value associated with behavioural intention to use or adopt technology and it aims to capture users’ perceptions of the extent to which using a particular technology may help them achieve an intended goal. For instance, in the case of older adults, this could be applied to infer the extent to which the participants perceive the potential value of using Skype to keep in touch with their relatives abroad (Barnard et al, 2013). Although, to the best of our knowledge, the revised UTAUT2 has not yet been applied in recent empirical studies, we derive from studies which used perceived usefulness (a root construct of performance expectancy). An extensive literature review supports a positive impact of perceived usefulness on the intention to use technology (see Taiwo & Downe, 2013). In the context of studies on older adults, Braun (2013) has similarly found that perceived usefulness was a relevant factor to predict intentions to use social networking websites. Therefore, the following hypothesis is proposed: 9

ACCEPTED MANUSCRIPT H1. Performance expectancy has a positive impact on behavioural intention. The second determinant of usage intention, ‘Effort Expectancy’ is defined as “the degree of ease associated with the use of the system” (Venkatesh et al., 2003: p. 450). It was originally conceptualised as “perceived ease of use” within TAM and aimed to assess the individual’s perception of the effort involved in learning and using a technology. In the context of the present study, effort expectancy reflects the extent to which older citizens feel comfortable and find ICTs easy to use. In other words, it proposes to gauge the participants’ perception of the level of effort required to use the internet. Porter and Donthu (2006) found that perceived ease of use (equivalent to effort expectancy) has a strong effect on Internet usage. Similarly, in a study of e-commerce activities among older adults, McCloskey (2006) found that ease of use positively affects user behaviour. More recently, other studies support a direct and positive effect of ease of use on the actual use of the Internet (Braun, 2013; Ramón-Jerónimo et al., 2013). Therefore, the following hypothesis is proposed: H2. Effort expectancy has a positive impact on the behavioural intention to use ICTs. Social influence, represented as a subjective norm in TAM, TRA and TPB, is defined as “the degree to which an individual perceives that the important others believe he or she should use the new system” (Venkatesh, et al., 2003: 451). It relates to the older adults’ perceptions of the importance attributed to using a computer by those who are significant in their lives, like close relatives and friends or simply people whose opinions are valued. Although social influence is undoubtedly a complex construct (Venkatesh, et al , 2003) and in spite of the fact that some studies do not clearly evidence a significant effect on intention (Magsamen-Conrad et al., 2015), the study relies on the contribution of Oh and Yoon (2014) who found a strong path between social influence and intention. Accordingly, their study evidenced that users subject to a high level of social influence (e.g. opinions of important persons or immediate social environments) were likely to use the Internet. Contributions of a qualitative research, undertaken by Selwyn (2004), addressing older adults´ use of information and communications technology also revealed that family and friends play an important role in the adoption of technology. Therefore, the following hypothesis is proposed:

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ACCEPTED MANUSCRIPT H3. Social influence has a positive impact on the behavioural intention to use computers and the Internet Facilitating conditions refer to “the degree to which an individual believes that a technical infrastructure exists to support use of the system” Venkatesh et al, 2003: 453). In other words, this relates to the environmental barriers or availability of resources that older adults may perceive in relation to Internet use. In the earlier UTAUT version, facilitating conditions were theorised as a driver of use behaviour, meaning that the more the users perceive the availability of resources, knowledge and support, the more it is that they will actually use a new technology. In UTAUT2 (Venkatesh, et al, 2012), facilitating conditions are also hypothesised as influencing behavioural intentions. While Taiwo and Downe (2013) reported a small effect of facilitating conditions on tablet use intentions, Magsamen-Conrad et al. (2015) found empirical support to corroborate the influence of facilitating conditions on technology adoption. This finding was obtained in a study examining the predictive power of UTAUT (2003) in the context of tablet devices across multiple generations. Therefore, the following hypotheses are proposed: H4. Facilitating conditions have a positive impact on the behavioural intention to use the computer and the Internet. H5. Facilitating conditions have a positive impact on use behaviour. Hedonic motivation is one of the three key constructs added to UTAUT2 (Venkatesh et al., 2012) as a result of its extension to the consumer context. Drawing on prior research on both consumer behaviour and information systems, the authors recognised the importance of hedonic motivation (e.g. enjoyment) in explaining the reasons why people use ICTs (Brown & Venkatesh 2005; Childers, Carr, Peck & Carson, 2001). Hedonic motivation is defined as the fun or pleasure derived from using a technology. According to the authors, integrating hedonic motivation into UTAUT will complement the theory’s focus on intentionality as the overarching mechanism and key driver of behaviour (Venkatesh et al., 2012:158). In their study, testing UTAUT2 in the context of consumer use of mobile Internet technology, hedonic motivation was found to be a critical determinant of behavioural intention. Since most studies

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ACCEPTED MANUSCRIPT have applied the earlier version of UTAUT (2003), further studies are needed to consolidate this claim. Hence, the following hypothesis is proposed: H6. Hedonic motivation has a positive impact on the behavioural intention to use computers and the Internet As a result of extending the UTAUT framework to the consumer context, price value was integrated as a way to address the cost issue of technology use. Drawing from marketing literature, in which monetary cost/price has long been conceptualised as an influential factor of perceived value of products or services, Venkatesh et al. (2012) reflect a similar assumption in the UTAUT2 framework and conceptualise price value as a predictor of the intention of consumers to use a technology. Along these lines, price value is defined as a consumer’s cognitive trade-off between the perceived benefits of the applications and the monetary cost of using them. Therefore, the following hypothesis is proposed: H7. Price/value has a positive impact on the behavioural intention to use ICTs. Habit is another construct added to UTAUT2. While in the previous UTAUT (2003), habit was conceptualised as being associated with user experience and only exerted a moderating role, in UTAUT2 (2012) habit is modelled as having both a direct effect on use and an indirect effect through behavioural intention. According to the authors (Venkatesh et al., 2012: 158), “integrating habit into UTAUT will complement the theory’s focus on intentionality as the overarching mechanism and key driver of behaviour”. Drawing on two main theoretical perspectives of habit (see Quellette and Wood, 1998; Limayen et al., 2007), the authors clarify the distinctiveness of habit and experience as a base to define the conceptual domain of habit in the updated UTAUT2. Inspired by both the TPB-based view of habit (i.e. as stored intentions) and the automatic view of habit (i.e. as a direct link between stimulus and behaviour), habit is defined as a perceptual construct reflecting the results of prior experiences. Therefore, the following hypotheses are proposed: H8. Habit has a positive impact on the behavioural intention to use ICTs. H9. Habit has a positive impact on use behaviour. 12

ACCEPTED MANUSCRIPT The main goal of UTAUT2 is to predict technology acceptance and use. Reflecting the influence of relevant theories of technology acceptance, use behaviour is conceptualised as a dependent variable determined by behaviour intention, facilitating conditions and habit. In this framework the relation between intention and use behaviour is crucial to predict the actual use of technology. Because the extended UTAUT2 has only recently been proposed, the study relies on research, which applied the earlier UTAUT version (2003). According to the founders of this theory, the effect of behavioural intention on technology use was substantially greater in UTAUT2 when compared to the results obtained in the previous 2003 version. Thus, the following hypothesis is proposed: H10. Behavioural Intention has a positive impact on use behaviour.

3. Methodology In order to empirically test the proposed research model (Figure 1), the study employed a survey design using self-administered questionnaires. The study was part of a larger project, the aim of which was to explore and collect data on key features regarding the use and adoption of digital technologies and active ageing activities from a convenience sample of older adults in a large city in Portugal. 3.1. Participants A total of 278 older adults between 55 and 94 years of age participated in the study. The mean age was 67 years. As expected for this aged group, the majority of the participants were retired (81.3%). The sample was relatively balanced in terms of gender (43.2% were females and 56.8% were males). Participants varied with respect to education level as 43.9% of the participants reported having attended between 7 to 12 years of school, followed by those who had only completed 4 to 6 years of school (31.7%). Only 17.3% of the participants reported having a college degree. Table 2 shows the complete demographic profile of the sample group. With respect to computer/Internet use (Table 3), the majority reported using it every day (66.2%) and the most common activity performed was information-seeking (89.2%), followed by sending/receiving e-mails (76.2%).

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ACCEPTED MANUSCRIPT Table 2 Demographic profile of respondents (N = 278). Demographic Profile Gender Female Male Age (years) 55 – 65 65 – 69 70 – 74 75 – 79 > 80 Marital status Single Married/Civil union Divorced/Separated Widowed Education level Elementary school 5 – 6 years 7 – 9 years 10 – 12 years Bachelor College degree Post-graduate degree/PhD Professional status Active Retired Physically disabled Without occupation Unemployed

Number

Percentage

120 158

43.2 56.8

98 92 52 26 10

35.3 33.1 18.7 9.4 3.6

20 194 20 44

7.2 69.8 7.2 15.8

70 18 58 64 14 42 6

25.2 6.5 20.9 23.0 5.0 15.1 2.2

33 226 6 6 3

12.0 81.3 2.2 2.2 1.1

Number

Percentage

Table 3 Computer/Internet usage characteristics. Characteristics Time of Internet usage Less than 1 year 1 to 2 years More than two years Frequency of Internet use Every day or almost every day At least once a week At least once a month Less than once a month Internet Activities performed Sending/ receiving emails Seeking information Booking (hotels; services) Banking operations Seeking information on health News and general information Shopping online Financial operations Facebook Chat rooms

60 34 184

21.6 12.2 66.2

184 80 10 4

66.2 28.8 3.6 1.4

212 248 48 56 166 48 22 4 164 38

76.3 89.2 17.3 20.1 59.7 17.3 7.9 1.4 59.0 13.8

14

ACCEPTED MANUSCRIPT 3.2 Procedure Given the public interest in the topic under research, senior universities and local senior centres were invited to collaborate in the study by providing access to the older adults enrolled in various activities/courses promoted by these organizations. During these contacts, the researcher explained the main objectives of the project and sought institutional permission to administer the questionnaire. In a second stage, the questionnaire was distributed by the researcher during activities/classes whose instructors agreed to participate in the study. It is worth mention that the researcher was personally involved in the administration of the survey by presenting the objectives of the study while explaining the questionnaire and encouraging honest and truthful answers. The participants were also informed that by filling out the questionnaire they were providing their informed consent. In addition, the respondents participated on a voluntary basis and were assured that their answers were anonymous, confidential and would be used for research purposes only. It took the participants about 30 minutes, on average, to fill in the questionnaire. Overall, 510 participants completed the questionnaire, of which 32 were rejected due to incomplete responses. For the purpose of the present study, the responses from participants who did not engage in computer and Internet activities were not considered in this study. Hence, the final sample consisted of 278 valid responses. Since the majority of the items in the questionnaire were originally formulated in English, back translation was then employed to prepare the survey questionnaire for the Portuguese respondents. The accuracy of the translation was verified by using back translation (Brislin, 1970). As a result, minor adjustments were made to the Portuguese version of the questionnaire to ensure that the meaning of all items had been preserved during the translation process. Additionally, the questionnaire was carefully designed to be applied to this group. For instance, in order to facilitate the reading and filling out of the questionnaire, a larger size font was used. On the front page of the survey, the university name and logo appeared followed by a brief written introduction to the project and to its goals. Before the questionnaire was finalized, three academics familiar with the topic under research reviewed the questionnaire to assure its content validity. In order to eliminate possible ambiguities and, following established recommendations (Hunt, Sparkman, & Wilcox, 1982), the survey instrument was pre-tested using ten older adults enrolled in a local senior center, who were not included in the final survey. 15

ACCEPTED MANUSCRIPT With regard to minimizing the common method bias, the following steps were taken: 1) respondents were assured anonymity; 2) attention was paid to avoid statements relating to the dependent variable not being located close to the independent variables of the questionnaire (Podsakoff, MacKenzie, Lee, & Podsakoff, 2003). Ex post, Harman’s single-factor test was computed based on principal component analysis (PCA) (Podsakoff et al., 2003) and revealed seven components with eigenvalues greater than 1.0. This result suggests no evidence of common method bias. 3.4 Measures All theoretical constructs were operationalised using previously validated multi-item scales. The scales for the UTAUT2 constructs (i.e., performance expectancy, effort expectancy, social influence, facilitating conditions, and behavioural intention) were adapted from Venkatesh et al. (2012) with slight modifications. It is important to notice that when rephrasing the items, particular care was taken to ensure that the final questions would be as close to the original form as possible. Individuals were asked to indicate their agreement or disagreement with several statements using a five-point Likert-type scale ranging from strongly disagree (1) to strongly agree (5). Table 4 shows the items regarding UTAUT2. As can be observed, reliability of each measurement was all above 0.70, which indicates internal consistency (Nunnally, 1978). Questions relating to Internet usage included the frequency of computer/ Internet use based on the Likert-type scale response. Additional questions included sources of support, type of computer/Internet activities performed and Internet experience. Sociodemographic questions such as marital status, education, and income were single item measures, based on categorical survey responses. Age was computed by subtracting the reported year of birth from the survey year.

4. Analysis and Results Data was analyzed using both the statistical software Statistical Package for Social Sciences (IBM SPSS v.22) and the Partial Least Squares (PLS) with SmartPLS 3.2.1 (Ringle, Wende & Becker 2015). PLS is an appropriate technique because the study has a predictive research goal and a relatively complex model (Roldán and Sánchez-Franco 2012; Sarstedt, Ringle & Hair, 2014). The PLS model is analyzed and interpreted in two stages: 1) the assessment of the reliability and validity of the measurement model; and 2) the assessment of the structural model. These two stages certify that the constructs measured are valid and reliable 16

ACCEPTED MANUSCRIPT before attempting to draw conclusions concerning the relationships among constructs (Barclay, Higgins & Thompson, 1995). 4.1. Measurement Model The measurement model for the reflective constructs is examined in terms of individual composite reliability, construct validity of the measurement scales and discriminant validity. (Table 4). Individual composite reliability represents the shared variance among a set of observed variables measuring an underlying construct (Fornell & Larcker, 1981). For each reflective construct, all composite reliability values exceeded 0.7 and average variance extracted (AVE) was above 0.5 (Bagozzi & Yi, 1988), thereby establishing sufficient convergent validity. The internal consistency reliability measures of the Cronbach's Alpha are also above the recommended level of 0.7 for all constructs (Nunally, 1978). Table 4 Measurement Model. Construct / Dimension /Item

Loadsa t-value

Effort Expectancy (EE)

Learning how to use computers /Internet is easy for me [EE1]

0.83

31.5

My interaction with computers /Internet is clear and understandable [EE2]

0.85

42.7

I find computers/ Internet easy to use [EE3]

0.91

57.2

It is easy for me to become skillful at using computers/ Internet [EE4]

0.91

69.4

Performance Expectancy (PE)

I find computers/Internet useful in my daily life. [PE1]

0.87

50.1

Using computers/Internet helps me accomplish things more quickly. [PE2]

0.88

49.8

Using computers/Internet increases my productivity [PE3]

0.88

66.3

Social Influence (SI)

People who are important to me think that I should use computers/Internet [SI1]

0.86

41.0

People who influence my behavior think that I should use computers/ Internet [SI2] 0.90

50.9

People whose opinions that I value prefer that I use computers/Internet [SI6]

12.1

0.71

Facilitating Conditions (FC)

I have the resources necessary to use computers/Internet [FC1]

0.75

18.0

I have the knowledge necessary to use computers/Internet [FC2]

0.89

68.6

Computers/ Internet is compatible with other technologies I use [FC3]

0.68

11.1

Hedonic Motivation (HM)

Using computers/ Internet is fun [HM1]

0.83

19.5

Using mobile computers/ Internet is enjoyable [HM2]

0.90

77.3

Using mobile computers/ Internet is very entertaining [HM3]

0.83

30.4

Price Value (PV)

CAb 0.90

CRc 0.93

AVEd 0.70

0.85

0.91

0.77

0.77

0.86

0.68

0.70

0.82

0.70

0.82

0.89

0.74

0.80

0.88

0.71

17

ACCEPTED MANUSCRIPT Internet is reasonably priced [PV1]

0.84

19.2

Internet is a good value for the money [PV2]

0.89

36.2

At the current price, Internet provides a good value[PV3]

0.81

20.2

Habit (HT) The use of computers / Internet has become a habit for me [HT1]

0.88

63.9

I am addicted to using computers/ Internet [HT2]

0.76

21.4

I must use computers/ Internet. [HT3]

0.67

14.6

Behavior Intention (BI) I intend to continue using computers/ Internet in the future [BI1]

0.85

36.7

I will always try to use computers/ Internet in my daily life [BI2]

0.88

51.3

I plan to continue to use computers/Internet frequently [BI3]

0.90

67.1

0.70

0.81

0.70

0.85

0.90

0.77

Note: aLoadings; bCronbach alpha (CA); cComposite reliability (CR); dAverage variance extracted (AVE)

Discriminant validity indicates the extent to which a given construct differs from other constructs. In this study, discriminant validity was assessed by using two approaches: the Fornell-Larker criterion and the HTMT.90 criterion. Concerning the first criterion, the square root of the Average variance extracted (AVE) was computed for each construct. For adequate discriminant validity, the diagonal elements should be significantly greater than the offdiagonal elements in the corresponding rows and columns (Roldán & Sánchez-Franco 2012). As illustrated in Table 5, all reflective constructs satisfy this condition.

Table 5 Discriminant Validity - Fornell-Larcker Criterion. BI

EE

FC

HM

HT

PE

PV

SI

BI EE FC HM HT PE PV SI USE

0,878 0,535 0,564 0,428 0,532 0,643 0,332 0,408 0,496

0,878 0,619 0,293 0,491 0,489 0,356 0,143 0,487

0,779 0,288 0,483 0,411 0,308 0,202 0,413

0,862 0,347 0,387 0,282 0,284 0,311

0,78 0,539 0,224 0,295 0,513

0,882 0,245 0,848 0,422 0,19 0,504 0,285

0,829 0,227

Mean

3.642

3.066

3.388

3.947 2.623

3.676 3.239

3.544

S.D

0.792

0.940

0.805

0.582 0.825

0.857 0.812

0.718

Note: BI= Behavioral intention; EE=Effort expectancy; FC=Facilitating conditions; HM=Hedonic motivation; HT=Habit; PE=Performance expectancy; PV=Price value; SI=Social influence; USE=Use behavior; Diagonal elements (bold) are the square root of variance shared between the constructs and their measures (AVE). Off-diagonal elements are the correlations among constructs.

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With respect to the second criterion, the study applies the heterotrait–monotrait ratio (HTMT) (Henseler, Ringle and Sarstedt, 2015). In this analysis (Table 6), the obtained values were compared to a predefined threshold, whose value is 0.85 (the most conservative) (Henseler, Ringle & Sarstedt, 2015). Since the values for the reflective variables are lower than the most conservative criterion, there is evidence that the internal validity of the measurement model appears adequate. Table 6 Discriminant Validity - Heterotrait-Monotrait Ratio (HTMT).

BI EE FC HM HT PE PV SI USE

BI

EE

FC

HM

HT

PE

PV

SI

0,605 0,738 0,508 0,67 0,75 0,395 0,499 0,538

0,743 0,342 0,61 0,548 0,417 0,161 0,51

0,381 0,683 0,522 0,419 0,303 0,479

0,414 0,457 0,341 0,342 0,34

0,685 0,258 0,404 0,595

0,285 0,505 0,541

0,244 0,311

0,257

Note: BI= Behavioral intention; EE=Effort expectancy; FC=Facilitating conditions; HM=Hedonic motivation; HT=Habit; PE=Performance expectancy; PV=Price value; SI=Social influence; USE=Use behavior;

Convergent validity indicates the extent to which a set of items represents one and the same underlying construct. Average variance extracted (AVE) can be used as a criterion of convergent validity (Fornell & Larcker 1981). To assess convergent validity, AVE should be greater than 0.5, meaning that at least 50% of the variance is explained by different reflective items. Convergent validity is also demonstrated by the large and significant standardized loadings of each item on its intended construct. Overall, the examination of the psychometric properties of the scale shows unidimensionality and conceptual consistency. 4.2. The Structural Model The study assesses the structural model on the basis of the algebraic sign, magnitude and significance of the structural path, the R2 values and the Q2 (redundancy) test for assessing the predictive relevance (Roldán & Sánchez-Franco 2012). Additionally, both the bootstrapping procedure with 5000 resamples (Hair, Ringle, & Arstedt, 2011) and the percentile bootstrap

19

ACCEPTED MANUSCRIPT 95% confidence interval (Chin, 2010) show the statistical significance of the path coefficients (Table 5 and Fig. 2). H 1 1

H2

H 2 Performance Expectancy 0.325***

Effort Expectancy

0.107* 0.135

Social Influence

**

0.239** *

Facilitating Conditions Hedonic Motivation

Behavioural Intention (R2 = 57%)

Use Behaviour (R2 = 38%)

0.290***

0.016ns

0.113** -0.008ns -0.047ns **

0.062ns Age

0.133*

0.155*

**

Gender

Experience

Education

0.100** Control variables

Price Value 0.267*** Habit

Note. ns: not significant; based on t(4999) one-tailed test; [*p<0.05 (t=1.645); **p<0.01(t=2.327); ***p<0.001 (t=3.092)]. Fig. 2 Final Research Model.

The bootstrapping confidence interval (B.C.I.) of standardized regression coefficients is reported in Table 7. Results indicate that eight out of the ten hypotheses in the model were supported. Performance expectancy (H1), effort expectancy (H2), social influence (H3), facilitating conditions (H4), hedonic motivation (H6) and habit (H8) were significantly related to the behavioural intention to use ICTs. By contrast, only the influence of price on behavioural intention (H7) revealed to be not significant. The same result was found for the impact of facilitating conditions on use behaviour (H5). This study went further and included several control variables, namely experience with Internet use, level of education, gender and age. Of these variables, the level of education and experience in using the Internet have a significant 20

ACCEPTED MANUSCRIPT impact on use behaviour. On the contrary, gender and age have no significant impact on use behaviour. Table 7 Effects on endogenous variables (direct effects). Direct effects on endogenous variables

Direct Effect

t-value (bootstrap)

H1: P_Expectancy→B_Intention

0.325

5.409***

Percentile 95% Confidence Intervals [0.219;0.413]

H2: E_ Expectancy→B_Intention

0.107

1.898*

[0.019;0.206]

Supported

H3: S_Influence→B_Intention

0.135

2.688**

[0.05;0.219]

Supported

H4: F_Conditions→ B_Intention

0.239

3.331***

[0.115;0.356]

Supported

H5: F_Conditions→ Use Behaviour

0.016

0.241ns

[-0.087;0.130]

Not supported

H6: H_Motivation → B_Intention

0.113

2.314*

[0.035;0.198]

Supported

H7: Price Value → B_Intention

0.062

1.328ns

[-0.021;0.132]

Not supported

H8: Habit → B_Intention

0.100

1.973*

[0.015;0.176]

Supported

H9: Habit → Use Behaviour

0.267

4.795***

[0.170;0.352]

Supported

0.290

4.975***

[0.189;0.385]

Supported

Education→ Use Behaviour

0.155

2.258

[0.051;0.276]

Experience→ Use Behaviour

0.133

2.037

[0.02;0.236]

Gender→ Use Behaviour

-0.04

0.820

[-0.141;0.041]

Age→ Use Behaviour

-0.08

0.185

[-0.085;0.067]

H10: B_Intention → Use Behaviour

Hypothesis validation Supported

Control variables

Note. ns: not significant; based on t(4999) one-tailed test; [*p<0.05 (t=1.645); **p<0.01(t=2.327); ***p<0.001 (t=3.092)].

As previously mentioned, to assess the quality of the model, the coefficient of determination (R2), which represents the amount of explained variance of each endogenous latent variable was computed (Hair, Hult, Ringle, & Sarstedt, 2014). The proportion of the total variance of each endogenous construct explained by the model is 38% for use behaviour and 57% for behavioural intention. The high R2 value substantiates the model’s predictive validity (Hair et al. 2014). In addition, results confirm the indirect effects of performance expectancy, effort expectancy, social influence, hedonic motivation, price value and habit on the actual use through behaviour intention. The only exception is price value whose indirect effect on use behaviour was not significant (Table 8).

21

ACCEPTED MANUSCRIPT

Table 8 Indirect effects on endogenous variables. Indirect effects

Original Sample

E. Expectancy → Use Behaviour

0.031

Sample Mean 0.03

Standard Deviation 0.018

F._Conditions→ Use Behaviour

0.069

0.07

H. Motivation → Use Behaviour

0.033

Habit → Use Behaviour

T Statistics P Values 1.691

0.046*

0.023

3.036

0.001**

0.031

0.015

2.156

0.016*

0.028

0.027

0.015

1.866

0.031*

P. Expectancy → Use Behaviour

0.094

0.095

0.029

3.288

0.001***

Price Value → Use Behaviour

0.018

0.019

0.014

1.255

0.105ns

Social Influence → Use Behaviour

0.039

0.039

0.018

2.163

0.015*

Note. ns: not significant; based on t(4999) one-tailed test; [*p<0.05 (t=1.645); **p<0.01(t=2.327); ***p<0.001 (t=3.092)].

The predictive power of the model was also examined by computing the cross-validated redundancy index (Q2) for the endogenous variable. A Q2 greater than 0 implies that the model has predictive relevance (Chin 1998). The results confirm that the structural model has satisfactory predictive relevance for the use behaviour construct (Table 9). Table 9 Effect size of the endogenous variables. Latent Variables Effort Expectancy

CV-C Q2 0.61

CV-R Q2 -

Performance Expectancy

0.53

-

Social Influence

0.38

-

Facilitating Conditions

0.23

-

Hedonic Motivation

0.47

-

Price value

0.42

-

Habit

0.23

-

Behavioural Intention

0.51

0.43

-

0.37

Use Behaviour

Note: CV-C (Cross-validated communality); CV-R (Cross-validated redundancy)

Currently, the only approximate model fit criterion implemented for PLS path modelling is the standardized root mean square residual (SRMR). This criterion represents the root of the square discrepancy between the observed correlations matrix and the model-implied, i.e., the Eucleadian distance between two matrices (Henseler, Hubona and Ray, 2016). Assuming a cut22

ACCEPTED MANUSCRIPT off value of 0.08, as proposed by Hu & Bentler (1999), the model presented in this study shows an acceptable fit (SRMR=0.068).

5. Discussion and Conclusions The main goal of this study was to evaluate the predictive relevance of UTAUT2 (Venkatesh et al., 2012) in order to understand older adults’ acceptance and use of ICT. The findings of this study have provided evidence in support of the validity of UTAUT2 as a relevant theoretical base to effectively explain behavioural intentions and ICT use among this population group. 5.1 Examining the predictive power of behaviour intention in UTAUT2 In the present study, intention behaviour appears to be highly influential in determining the actual use of a technology. Specifically, empirical support was found for eight of the study’s ten research hypotheses. While the results showed that performance expectancy (H1), effort expectancy (H2), social influence (H3), facilitating conditions (H4), hedonic motivation (H5) and habit (H6) were significant predictors of older adults’ intentions to use ICT, some differences exist regarding the weight of the coefficient paths of each variable in predicting behavioural intention. Performance expectancy had the greatest impact on individuals’ intention to use ICT (H1), implying that older adults’ decisions regarding the use of ICT are highly influenced by the perceived advantages associated with its use in their daily life. This result is consistent with the original UTAUT (Venkatesh et al., 2003) and with previous studies evidencing that amongst the four major constructs of UTAUT, performance expectancy has the highest coefficient path weight (see Taiwo & Downe; 2013). In the specific context of older adults’ studies, similar results were found supporting the relevance of performance expectancy to predict users’ intentions to use ICT (Lian & Yen, 2014; Braun, 2013). The second most important predictor of behavioural intention was facilitating conditions (H4), suggesting that for older adults, the intention to use ICT is reinforced when they feel familiar with the technology and are equipped with the resources and the knowledge required for its use. Contrasting with the previous UTAUT (2003) facilitating conditions are theorised in UTAUT2 (2012) as having both a direct effect on use and an indirect effect through behavioural intention. While the direct effect of facilitating conditions on use behaviour was present in UTAUT, the indirect effect has only recently been incorporated into UTAUT2, and therefore it is not possible to compare this result with previous UTAUT studies. However, Pan 23

ACCEPTED MANUSCRIPT et al. (2010) have proposed an expanded TAM model to examine older adults’ decisions to adopt the Internet, in which facilitating conditions were conceived as a factor impacting on acceptance and usage. Interestingly and consistent with the findings of the present study, facilitating conditions were found to have a significant effect on Internet use intention. Moreover, drawing from TPB, it is possible to establish some conceptual parallels between the TPB construct of perceived behavioural control and facilitating conditions considered in UTAUT2 as a driver of intention. As originally theorised, intention is influenced by perceived behavioural control, which shares aspects of facilitating conditions (Nagle & Schmidt, 2012). This implies that a sense of control over aspects related to technology usage is important to trigger behavioural intention. This inferred explanation may be useful as a recommendation for fostering ICT acceptance among older adults. However, future studies need to substantiate this claim. Consistent with previous UTAUT assumptions, the study shows that effort expectancy (H2) and social influence (H3), although presenting smaller coefficient weights, have a significant effect on intention. As Czaja (2006) demonstrated, older adults are usually receptive to the adoption of ICTs as long as they perceive them as useful and feel that they are easy to use. Similar results are found in previous research (Oh &Yoon, 2014; Pan & Jordan-Marsh, 2010; Porter & Donthu, 2006), which lends support to earlier claims (Davis, 1989) that effort expectancy plays a crucial role in technology acceptance. In a similar way, social influence was demonstrated to have a significant effect on ICT behavioural intention, suggesting that older adults are not indifferent to other peoples’ thoughts and opinions regarding this matter. This also implies that any strategic approach to encourage ICT adoption among older adults needs to take into consideration a wider context of multiple actors that may serve as a vehicle to positively influence this population segment. Thus, word-of-mouth communication approaches are likely to be a successful marketing strategy to target older adults within ICT campaigns (Oh & Yoon, 2014). With regard to the constructs added to UTAUT2, the hypothesised path coefficients testing the influence of hedonic motivation (H6) and habit (H8) on intention to use a technology also demonstrated significance. The first result suggests that in a context of consumer technology usage, particularly focused on older adults, hedonic motivation (conceptualised as perceived enjoyment) is an important determinant of behavioural intention. Considering that most participants in the study were retired from work (representing 81.3%) it seems plausible to expect that ICT use would heavily involve activities or simply the performance of tasks that 24

ACCEPTED MANUSCRIPT are somehow related to leisure time. For instance, some of the activities reported by respondents included being in contact with friends or relatives via email, searching for general information, Facebook, and, to a lesser extent, making hotel reservations. White and Weatherall (2000) have examined this issue and concluded that the technology’s potential, perceived by older adults, was closely linked with their interests and hobbies. The second hypothesis that habit influences behavioural intention (H8) was also supported. Nevertheless, further research is needed to validate these findings in other settings and involving other types of users. Contrary to previous findings (Venkatesh et al., 2012), the results of this study did not support the inclusion of price value (H7) as a predictor of behavioural intention. This seems to be a surprising result particularly when considering the financial problems affecting families as a result of the current economic crisis. Besides, previous studies addressing technology adoption among older adults have identified cost as a problematic issue with implications on both the adoption and use of ICT (Carpenter & Buday, 2007; Lee, Chen, & Hewitt, 2011; Saunders, 2004; White & Weatherall, 2000). From a marketing perspective, based on a study on the factors impacting the engagement of older consumers with higher forms of ICT, other authors have concluded, that price seemingly has little or no influence on encouraging engagement (Hough & Kobylanski, 2009). However, another interpretation of this theory could suggest that cost is perceived as a barrier only by those adults who are not ICT users (Chang, McAllister & McCaslin, 2015). In the present study, all participants were, to varying degrees, engaged in computer and Internet activities. Accordingly, the lack of influence of price value on behavioural intention to adopt ICT might also be understood within the wider context of the current globalisation of an information society, in which financial concerns seem to play a minor role, probably due to the decreasing costs of computers and the Internet today (Peacock & Kunemund, 2007). 5.2 Examining the predictive power of ICT usage in UTAUT2 As conceptualised in UTAUT2, technology use is determined by behavioural intention, facilitating conditions and habit. The positive effect of intention behaviour on use (H10), demonstrated in this study, is consistent with previous UTAUT research. However, the variance explained in both behavioural intention (57%) and technology use (38%) is smaller when compared to the results obtained in UTAUT2 (Venkatesh et al., 2012). Additionally, the results also showed that while facilitating conditions had an effect on intention behaviour, when testing the direct effect of facilitating conditions on actual use (H5) no significant effect was 25

ACCEPTED MANUSCRIPT found for this relationship. Likewise, Pan et al. (2010) also found no significant effect for facilitating conditions on actual use of Internet adoption. These results are consistent with those of Taiwo and Downe (2013) who, in a meta-analytic review of studies using UTAUT, found a small effect size for facilitating conditions on use behaviour, therefore leading the authors to consider this relation to be inconclusive. In the present study, a possible explanation is that facilitating conditions might be crucial to induce individuals’ intentions to adopt technology. However once they are involved in ICT activities, their role in actual use decreases and they are therefore perceived as not important. Further research is necessary to clarify the influence of facilitating conditions on actual use of ICT. This study went further and included several control variables, namely experience with Internet use, level of education, age and gender. Of these variables, gender and age do not seem to have any impact on use behaviour. On the other hand, experience in using the Internet and the level of education have a significant impact on use behaviour. With respect to experience, extant research has found a causal relationship between experience and technology adoption suggesting that experience is important to build confidence in older adults with regard to technology usage (Lee & Coughlin, 2015). In addition, it has been suggested that experience makes the Internet easier to use and helps users to feel more comfortable while making more effective use of the Internet (Black & Dutton, 2012). Regarding the effect of education on ICT use, the results of this study are aligned with other studies suggesting that people with higher education are more likely to have the intention to use the Internet (Niehaves & Plattfaut, 2014). Empirical evidence supporting a positive correlation between educational level and Internet access can also be found in digital divide research (Cruz-Jesus, Vicente, Bacao & Oliveira, 2016; Peacock & Künemund, 2007). 5.3 Contributions This research’s positioning contrasts with studies undertaken within the technological determinist paradigm within which the understanding of ICT use has been mostly explained by demographic variables (Verdegem & De Marez, 2011). The literature section presented an overview of relevant studies addressing ICT use among older adults. The review reported in those studies reveals that earlier research has mainly focused on examining the potential role of demographic variables while characterising and explaining patterns of ICT use among this group. Other studies have focused on identifying specific attitudes/determinants of technology adoption, but they lack the comprehensiveness of recognised theoretical models. Apart from 26

ACCEPTED MANUSCRIPT attitude toward computers, few relationships have been examined in multiple studies and therefore do not allow further validation of results (Wagner et al., 2010). There is a sparsity of studies that have employed comprehensive frameworks, such as UTAUT to more thoroughly explain and predict technology adoption among older adults. This study takes the holistic perspective from UTATU2 testing and examining the framework applied to older adults in a southern European context. Three relevant contributions emerge from the present research. First, the study adds to existing knowledge in the field of technology acceptance, by examining the predictive value of the updated UTAUT2 (Venkatesh et al., 2012). On the basis of the partial least squares structural equation modelling (PLS-SEM) undertaken in this study, the findings support most construct relationships within UTAUT2. The previous UTAUT version (Venkatesh et. al, 2003) was developed as a framework to examine technology acceptance and use in an employee context while UTAUT2 expands its applicability to the consumer user setting. According to the authors (Venkatesh et al., 2012) UTAUT2 incorporates not only the main relationships from UTAUT, but also new constructs and relationships that are relevant to predict technology acceptance and use in a consumer context. Despite this meritorious work, Venkatesh et al. (2012) recognise some limitations that may affect the generalizability of their findings and encourage further research to test UTAUT2 in different countries among different age groups and with different technologies. This study is a response to such appeal. The UTAUT2 framework was tested on a sample of older adults from a Southern European country, Portugal. In sum, the present study also contributes to strengthen the importance of studying ICT acceptance from a wider perspective, based on “attitudinal adoption determinants,” only possible when using comprehensive frameworks such as UTAUT2. Second, in addition to evaluating the predictive relevance of the UTAUT2 in older adults, the study’s findings acknowledge the key attitudinal predictors of ICT intention and usage. In sum, most attitudinal predictors, some of them directly (i.e. behavioural intention and habit) and others indirectly (i.e. performance expectancy, effort expectancy, social influence; facilitating conditions and hedonic motivation) were found to be relevant for this population group. It is Interesting to note that performance expectancy had the greatest impact on individuals’ intention to use ICT, followed by facilitating conditions. On the basis of this, it is anticipated that older adults will use ICT when it is helpful to them, either in their daily tasks or in activities related with their hobbies and individual interests. However, for this to happen, future ICT working systems and services must be conceived to meet such functional 27

ACCEPTED MANUSCRIPT requirements. This is an important point because, in the private and public domain, a wide range of services are increasingly offered to this population group through ICT. This knowledge could be used to assist ICT managers and public institutions in finding solutions to effectively respond to the needs of this population group. Although the model was supported with regard to most of its theorised relationships, two constructs of UTAUT2 were not supported. This applies particularly to the direct effect of facilitating conditions on use behaviour and to the effect of price value on behavioural intention to use ICT. Third, the majority of studies addressing ICT use among older adults have been mainly focused on Anglo-Saxon countries. With few exceptions (Rámon-Jerónimo et al., 2013; González et al. 2012), less attention has been paid to other regional contexts, such as Southern Europe. The present study is based on empirical evidence obtained through a sample of Portuguese older adults. Thus, this study contributes to expand the geographical scope of existing research on this relevant topic. 5.4. Limitations and Future Research Directions Despite these contributions, some limitations should be acknowledged. The main goal of this study was to evaluate UTAUT2 as a general framework. Recent studies (Niehaves & Plattfaut, 2014) have proposed theoretical models that combine the comprehensiveness of existing technology acceptance models with socio demographic variables and other related psychological constructs to more accurately assess ICT intention behaviour and use among older adults. Future research may combine a more comprehensive approach by including other relationhips and constructs to bridge technology acceptance theories, such as UTAUT2, with relevant socio-demographic variables. For instance, education and experience might provide additional insights into a better understanding of ICT acceptance and usage among such a highly heterogeneous population group as the older adults. In a similar vein, exploring the impact of an individual’s learning orientation (considered as a predisposition to learn new things) may also be fruitful. Indeed, in the process of administrating the survey, the interaction with the study’s participants shed some light on the motivations and personal views of different individuals regarding the use of ICT. For instance, from this interaction it was possible to observe that some participants had a noticeable learning orientation attitude evidenced by their eagerness to learn new things and asking questions about ICT use. In contrast, other participants simply evidenced a less enthusiastic posture. Although such valuable feedback was not directly used in the study, since it was beyond of the scope of the present study, it would 28

ACCEPTED MANUSCRIPT certainly appear to be an interesting issue for future studies. Additionally, the surprising result of price seeming to rank low in importance in older adults’ decision to become ICT users could be further investigated. Further empirical evidence might prove fruitful to consolidate current hypotheses. Secondly, ICT was limited in this study to computer use and internet services. Although, access to the Internet is available from various sources, computers remain the primary mean of Internet access (Nayak et al., 2010) thus justifying the choice of Internet and computer use to evaluate UTAUT2. In a similar vein, other authors maintain that the Internet can be seen as a basic technology that has to be mastered prior to using other ICT derived services (Niehaves & Plattfaut, 2014) and therefore can be seen as a proxy to assess the use of ICT of individuals for general purposes. However, given the pervasiveness of ICT in our daily lives it would be a valuable endeavour for future research to examine older adults’ ICT usage regarding specific activities/functions in which people engage. Thirdly, it is worth noting the geographical limitation of the study and the use of a nonrandom sample. Although the aforementioned shortcomings do not compromise the results of the present study, the generalization of the results should be treated with caution. Finally, it is expected that the present study will stimulate future studies to elucidate potential conceptual adjustments in UTAUT2, in order to improve its predictive value regarding ICT intention and usage among older adults.

Declaration of Conflicting Interests The author declared no potential conflicts of interests with respect to the authorship and/or publication of this article.

Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. References Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179-211. 29

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ACCEPTED MANUSCRIPT

Title. Predicting the acceptance and use of information and communication technology by older adults: An empirical examination of the revised UTAUT2

Author name and affiliation. Isabel Maria Macedo School of Economics and Management University of Minho Campus de Gualtar 4710-057 Braga Portugal ++351253601926 Email: [email protected]

ACCEPTED MANUSCRIPT Predicting the acceptance and use of information and communication technology by older adults: An empirical examination of the revised UTAUT2

Highlights

The study examines the predictive relevance of UTAUT2 to explain older adults’ intention behaviour and usage of ICT.

Findings provide evidence in support of the validity of UTAU2 to be applied to older adults.

Intention behaviour appears to be highly influential in determining the actual use of ICT.

Performance expectancy had the greatest impact on older adults’ intention to use ICT.