Keywords

1 Introduction

Today, smartphones become increasingly important in peoples’ daily life [7]. However, not everyone are using and adopting smartphones. For example, many older adults are still using basic mobile phones in China. The digital divide remains when it comes to new technologies [10]. The digital divide refers to the gap between those who do and those who do not have access to new forms of information technology [20]. Middle aged adults and older adults face challenges when they are using smartphones. A gap seems to exist in the adoption and use of smartphones by middle aged adults and older adults. We would like to examine the use and adoption of smartphones with both middle aged adults and older adults in China.

The objective of this research is to examine the adoption and usage of smartphones with both middle aged adults and older adults in China. This research aims to make contributions to studies on ageing and the adoption of smartphones. We investigated how various factors impact middle aged adults’ and older adults’ intention to use smartphones in China by a research model based on previous technology diffusion and acceptance theories (e.g., [18, 22]). Since most previous research (e.g., [15, 24]) on the adoption of smartphones tended to focus on people below the age of 35 (e.g., students, young people), we wanted to examine the user group with people over the age of 35 in this study. We defined older adults as people over the age of 45 in this study and middle aged adults as people between the age of 35 and 45.

The remainder of this paper is organized as follows: Sect. 2 discusses the theoretical background of this study. The research model and hypotheses are presented in Sect. 3. The research method and results are described in Sect. 4. This is followed by a discussion of the findings in Sect. 5. Section 6 concludes this research.

2 Background

2.1 Digital Divide

Scholarly research on the digital divide has a long history back to the 1990s. There is well documented research (e.g., [3]) on digital divide connecting to Internet and computer penetration through the lens of technology diffusion theory (e.g., TAM [4], IDT [18], UTAUT [22]) in the past two decades. Digital divide is generally referred to as the ‘uneven diffusion’ or ‘gap’ or ‘disparities’ between different socio-economic levels or across countries or between developed and developing countries in terms of ‘access’ and ‘use’ in ICTs [12]. Research on digital divide often starts by looking at users’ access to new technologies. Along with the popularity of computers and digital technologies, the digital divide in terms of physical access seems to be reduced in most developed countries. Van Dijk [20] found a shifted research attention on digital divide from physical access to skills and usage.

The uneven spread of the mobile applications on smartphones has contributed to the popularity of the concept of the ‘digital divide’ associated with smartphones. It highlights the emerging social gap between those individuals who use mobile applications on smartphones and those who do not. It is believed that a significant component of the digital divide is age. For example, some older adults may feel no need for smartphones because they are not aware of the benefits of smartphones. To reduce digital inequalities, we must understand the reasons for different age groups’ resistance to the use of smartphones. Investigating this digital inequality is of help to understand the diffusion of smartphones with populations of different age.

2.2 Research on the Adoption of Smartphones

Research has been carried out in studying various aspects related to the adoption of smartphones [6, 8]. In [5], Gao et al. investigated the role of lifestyles on the adoption of smartphones. The findings indicated that users with different lifestyles had different preferences related to different services on smartphones. Based on a study on the performance of mobile applications, Huang et al. [11] indicated that smartphones could become a suitable substitute of the traditional computer. But, the performance of the applications on smartphones is poorly understood.

Although significant effort has been done to explore the adoption of smartphones, the samples used in previous research on the adoption of smartphones were relatively young. An examination of the current literature reveals that few studies have addressed the use and adoption of smartphones by middle aged adults and older adults. In [9], Gao et al. studied the adoption of smartphones among older adults in China. The results indicated that perceived enjoyment was the most important determinant for older adults to adopt smartphones. Pheeraphuttharangkoon et al. [16] investigated the adoption and use of smartphones with older adults in the UK. However, the sample size with people over 40 years old in their study is quite small.

This research was a continuing effort from our previous research on the adoption of smartphones with older adults in China [9]. The digital divide along the age dimension has become a major concern in China. Two different age groups were defined in this study. We examined how the role of age played in the explanation of variability in the intention to use smartphones.

3 Research Model and Hypotheses

A research model that identifies important factors that impact users’ intention to use smartphones was developed in this research. The proposed research model (see Fig. 1) is an extension of UTAUT [22], with a consideration of observability and compatibility from IDT [18], and perceived enjoyment [19, 21] and price value [23] from other technology diffusion theories. We have developed the following eight research hypotheses (labeled in Fig. 1) based on the research model.

Fig. 1.
figure 1

Research model

Full size image

Hypotheses Developed from UTAUT.

Four key factors from UTAUT, Social Influence, Facilitating Conditions, Performance Expectancy and Effort Expectancy, were included in our research model. Social Influence is the extent to which users perceive that important others (e.g., family and friends) believe they should use a particular technology. Previous research also indicated that social influence is important for the adoption of smartphones [25]. Facilitating Conditions refer to users’ perceptions of the resources and support available to perform a behavior. Users need to have digital skills to use smartphones. Performance Expectancy is defined as the degree to which using a technology will provide benefits to consumers in performing certain activities. Smartphones are able to provide potential benefits (e.g., always connected, healthcare information) for users. Once users have recognized these benefits, they are likely to use and adopt smartphones. Effort Expectancy is the degree of ease associated with users’ use of technology. Learning a new technology often takes time and effort. If using smartphones is considered as an easy and straightforward process, users are likely to adopt smartphones. Thus, we proposed the following four hypotheses.

H1::

Social Influence (SI) has a positive influence on users’ intention to use smartphones

H2::

Facilitating Conditions (FC) has a positive influence on users’ intention to use smartphones

H3::

Performance Expectancy (PE) has a positive influence on users’ intention to use smartphones

H4::

Effort Expectancy (EE) has a positive influence on users’ intention to use smartphones

Hypotheses Developed from IDT.

Rogers [18] indicated that innovation that are perceived by individuals as having greater relative advantage, compatibility, trialability, observability, and less complexity will be adopted more rapidly than other innovation. To further understand older adults’ intention to use smartphones, two factors from IDT were included into our research model. As for the case of smartphones, Observability can be defined as the degree to which smartphones are visible to potential users. Compatibility can be seen as users’ belief in the consistency of using smartphones with the way they live and work. Previous research also demonstrated that the importance of Observability and Compatibility to the adoption of new technologies (e.g., e-banking [13]). Therefore, the following two hypotheses were proposed.

H5::

Observability (OBS) has a positive influence on users’ intention to use smartphones

H6::

Compatibility (COM) has a positive influence on users’ intention to use smartphones

Perceived Enjoyment and Price Value.

Perceived Enjoyment is defined as the extent to which the activity of using a specific system is perceived to be enjoyable in its own right, aside from any performance consequences resulting from system use [19, 21]. Users can have fun when they are playing games, and playing music on smartphones.

Price value is another significant factor affects users’ adoption of a new technology. Price value can be defined as consumers’ cognitive tradeoff between the perceived benefit of the applications and the monetary cost for using them [23]. It is believed that users are likely to adopt smartphones when the benefits of using smartphones are perceived to be greater than the monetary cost of smartphones. Hence, we proposed the following hypotheses.

H7::

Perceived Enjoyment (PEJ) has a positive influence on users’ intention to use smartphones

H8::

Price Value (PV) has a positive influence on users’ intention to use smartphones

4 An Empirical Study with the Research Model

To understand middle aged adults’ and older adults’ use and adoption of smartphones in China, the proposed research model and hypotheses were empirically tested using the structural equation modeling approach.

4.1 Instrument Development

The validated instrument measures from previous research [4, 18, 21–23] were used as the foundation to create the instrument for this study. In order to ensure that the instrument better fit this empirical study, some minor words changes were made to ensure easy interpretation and comprehension of the questions. For instance, wording was modified to fit the context of use of smartphones in China. A questionnaire was developed first in English and then translated into Chinese. Back-translation was conducted by bilingual third party to improve the translation accuracy. The final measurement questionnaire consisted of 24 itemsFootnote 1. A seven point Likert scale was used to examine participants’ responses to all items in this part.

4.2 Samples

The data for this study was collected through self-administered questionnaires in seven provinces in China. The survey was distributed as paper-based questionnaires to individuals from Dec 1st 2014 to Dec 30th, 2014. 359 completed questionnaires were collected, among which 341 of them were valid questionnaires (i.e., valid respondent rate 95 %). Among the participants, 186 of the participants were male, and 155 were female. Moreover, 85.3 % of participants had full-time jobs, 10.3 % of participants had part-time jobs, 4.4 % of participants had retired. In terms of age, 195 participants were from the age group of middle aged adults, while 146 participants were from the age group of older adults.

Further, the top three most used featured on smartphones for the age group of older adults were: making phone calls, text messaging and instant messaging services (e.g., QQ, Wechat). With regard to the age group of middle aged adults, instant messaging services, making phone calls, and websites browsing were rated as most frequently used features on smartphones. Moreover, using social media services is more popular in the age group of middle aged adults than the age group of older adults. Further, both the age groups have limited use of Emailing and mobile games on smartphones.

4.3 Measurement Model

The quality of the measurement model is determined by (1). Content validity, (2). Construct reliability and (3). Discriminant validity [1]. To ensure the content validity of our constructs, a pretest with 5 Chinese researchers in E-business was carried out. And we found that the questionnaire was well understood by all the researchers.

To further test the reliability and validity of each construct in the research model, the Internal Consistency of Reliability (ICR) of each construct was tested with Cronbach’s Alpha coefficient. As a result, for the age group of middle aged adults, the Cronbach’s Alpha values range from 0.71 to 0.93. With regard to the age group of older adults, the Cronbach’s Alpha values range from 0.82 to 0.94. A score of 0.7 is marked as an acceptable reliability coefficient for Cronbach’s Alpha [17]. All the constructs in the research model for both the age groups were above 0.70. Consequently, the scales were deemed acceptable to continue.

Convergent validity was assessed through composite reliability (CR) and the average variance extracted (AVE). Bagozzi and Yi [2] proposed the following three measurement criteria: factor loadings for all items should exceed 0.5, the CR should exceed 0.7, and the AVE of each construct should exceed 0.5. As shown in Tables 1 and 2, all constructs were in acceptable ranges for the age group of middle aged adults and the age group of older adults respectively.

Table 1. Factor loadings, composite reliability, and AVE for each construct (for the age group of middle aged adults).
Full size table
Table 2. Factor loadings, composite reliability, and AVE for each construct (for the age group of older adults).
Full size table

The measurements of discriminant validity for both the age groups were presented in Tables 3 and 4. According to the results, the variances extracted by the constructs were more than the squared correlations among variables. The fact revealed that constructs were empirically distinct for both the age groups. As good results for convergent validity and discriminant validity were achieved, the test result of the measurement model was good.

Table 3. Discriminant validity (for the age group of middle aged adults)
Full size table
Table 4. Discriminant validity (for the age group of older adults)
Full size table

4.4 Structural Model and Hypotheses Testing

The structural model was tested using SmartPLS. Table 5 presents the path coefficients, which are standardized regression coefficients. For the age group of middle aged adults, observability, compatibility and perceived enjoyment (H5, H6, H7) were found to have a statistically significant effect on users’ intention to use smart phones, while the other hypotheses were not supported. For the age group of older adults, five (H1, H3, H5, H6, H7) of the proposed eight hypotheses were supported.

Table 5. Test of hypotheses based on path coefficient for both the age groups
Full size table

The R2 (R square) denotes to coefficient of determination. It provides a measure of how well future outcomes are likely to be predicted by the model, the amount of variability of a given construct. In our analysis, the R2 coefficient of determination is a statistical measure of how well the regression coefficients approximate the real data point. According to the result, for the middle aged adults, 58 % of the variance of behavior intention can be explained by the research model. With respect to the older adults, the research model manages to explain 78 % of the variable in the values of intention to use. The results revealed that the predicative strength of the research model for both the age groups was quite strong. Focusing on the two different age groups, the research model had a stronger predicative strength for the age group of older adults than the age group of middle aged adults.

5 Discussion

In this research, we studied the adoption of smartphones between middle aged adults and older adults in China. From an academic perspective, this research contributed to the literature on the adoption of smartphones with middle aged adults and older adults in China by building upon previous technology diffusion theories. From a practical perspective, it offered some insights for smartphones providers and mobile services providers to promote the use of smartphones to different age groups in China.

The findings suggested that perceived enjoyment, observability, and compatibility proved to be important determinants for the adoption of smartphones with both middle aged adults and older adults. The most important determinant for both the age groups’ intention to use smartphones was perceived enjoyment. If using smartphones is fun, both middle aged adults and older adults are more likely to accept smartphones. It highlighted the role that hedonic aspects play in the adoption of smartphones by both the age groups.

The findings also identified age-related differences in the use and adoption of smartphones. The effects of both performance expectancy and social influence on users’ intention to use smartphones were significant for older adults, but insignificant for middle aged adults. Most middle aged adults tended to use features like voice phone calls, instant messaging services on their smartphones. They might use these features with basic mobile phones before. However, the advanced features (e.g., Emailing, document processing) on smartphones were less used by middle aged adults. They might be able to use email services on smartphones but chose not to because they did not want to change their habits. Consequently, they might not perceive the potential benefits provided by smartphones. For them, the costs of changing their habits may limit the adoption of smartphones. Therefore, the presence of the performance expectancy did not motivate older adults to use smartphones. In contrast, some older adults might not use basic mobile phones before. They found using basic features on their smartphones useful for their work and life. Thus, Performance expectancy had a significant positive impact on older adults’ intention to use smartphones. Furthermore, middle aged adults appeared to have higher levels of self-recognition of new technologies than older adults. As a result, middle aged adults tended to decide for themselves whether to adopt smartphones without being influenced by those around them. Therefore, social influence did not have a significant positive impact on middle aged adults’ intention to use smartphones.

There was no significant positive impact of facilitating conditions on the intention to use smartphones with both the age groups. One possible reason was that facilitating conditions might be considered as a limiting factor when the needed facilitating conditions are not perceived by them. Therefore, the presence of the facilitating conditions did not motivate them to use smartphones. Effort expectancy did not have a strong positive influence on both the age groups’ intention to use smartphones. It seemed that they did not use smartphones just because it was easy to use, but rather because they found it fun to use. Another interesting finding was that price value had no significant positive impact on the intention to use smartphones with both the age groups. Since Chinese’ economy is growing fast and smartphones has become inexpensive in China, most participants in this study can afford smartphones. It appeared that price value of smartphones became unimportant when it came to the adoption of smartphones with middle aged adults and older adults in China.

However, we were also aware of some limitations. Firstly, we only tested the research model and research hypotheses with samples from seven provinces in China. This sample might not be fully representative of the entire middle aged adults and older adults in China. Secondly, all the data were collected using self-reported scales in the research. This may lead to some caution because common method variance may account for some of the results that has been cited as one of the stronger criticisms of tests of theories with TAM and TAM-extended research [14]. However, our data analysis with convergent and discriminant validity does not support the presence of a strong common methods factor.

6 Conclusion and Future Research

This research was designed to study the differences in adoption of smartphones between middle aged adults and older adults in China. Since China is experiencing an increase in the average age of their population, the understanding on how middle aged adults and older adults use and adopt smartphones is important to increase their quality of life. The key contributions of this study are threefold. First, this study investigated middle aged adults’ and older adults’ adoption of smartphones by extending UTAUT with a consideration of observability and compatibility from IDT, and perceived enjoyment and price value. Second, the findings indicated that the effects of perceived enjoyment, compatibility, and observability on users’ intention to use smartphones were significant, but no age differences between middle aged adults and older adults were found to exist. Third, the effects of performance expectancy and social influence on users’ intention to use smartphones were moderated by age, such that it was significant for older adults but insignificant for middle aged adults. The results demonstrated that there was a difference between the two different age groups in China.

Continuing with this stream of research, we plan to further examine the applicability of the research model with other group of users in China (e.g., people below 35 years old). Future research is also needed to carry out a comparative study with middle aged adults and older adults in other countries.