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Post-adoption beliefs and continuance intention of smart device use among people with physical disabilities
Disability and Health Journal xxx (xxxx) xxx
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Original Article
Post-adoption beliefs and continuance intention of smart device use among people with physical disabilities Jaehee Cho a, *, H. Erin Lee b a b
School of Communication, Sogang University, #811, Bldg. Matthew, Heukseok-ro 84, Mapo-gu, Seoul, South Korea Media Communication Division, Hankuk University of Foreign Studies, South Korea
a r t i c l e i n f o
a b s t r a c t
Article history: Received 29 July 2019 Received in revised form 8 November 2019 Accepted 26 November 2019
Background: There has been a continuous increase of smart device use among people with physical disabilities in Korea. In spite of previous research on those people’s motives to adopt smart devices, little investigation has been conducted to scrutinize post-adoption behaviors of using smart devices among people with physical disabilities. Objective: /Hypothesis: Based on the post-adoption model, this study examined the relationship between post-adoption beliefs regarding smart devices and behavioral intentions toward continued use of smartphones among people with physical disabilities. Moreover, this study investigated the moderating effects of self-efficacy on the relationships among the main study variables. Methods: Both online and paper-pencil surveys were conducted, resulting in a total of 108 questionnaires collected from people with physical disabilities. Results: A path analysis showed that, with the exception of perceived ease of use, all variables (confirmation, perceived usefulness, and satisfaction of smart device use) had significant effects on continuance intention to use smart devices. Another main finding of this study was the significant moderating effects of general self-efficacy on the relationships among the three variables of confirmation, perceived usefulness, and continuance intention of smart devices. The relationships among the three variables were significantly stronger among people with lower levels of general self-efficacy. Conclusions: This study’s main findings will aid the thorough comprehension of the mechanisms that lead people with physical disabilities to continue to use smart devices. © 2019 Elsevier Inc. All rights reserved.
Keywords: People with physical disabilities Post-adoption model (PAM) Expectation confirmation theory (ECT) Technology acceptance model (TAM) Smart devices Continuance intention
Introduction In this hyper-connected society, smart devices such as the smartphone and tablet PC have become some of the most important and influential technologies in people’s everyday lives. According to a nationwide study conducted in Korea during 2018 Ministry of Science and ICT (MSIT) & Korea Internet & Security Agency (KISA), 91.4% of individuals age 6 and older personally owned a smart device (including smartphones, tablet PCs, and wearable devices).1 Though the number is smaller, among those with disabilities between the ages of 7 and 69, ownership of smart devices increased to 76.1% in 2018, from 72.4% in 2017.1 Previous research has investigated the major motives for using smart devices among people with physical disabilities,2 and many
* Corresponding author. E-mail addresses: [email protected] (J. Cho), [email protected] (H.E. Lee).
others have observed the usefulness of smart devices for people with physical disabilities, particularly in terms of facilitating daily, routine activities.3e5 Supported by mobile smart devices, people with impairments in movement and mobility are able to more easily communicate with others, find useful information regarding their disabilities (e.g., assistive devices), learn useful skills needed to seek out and secure jobs, and make reservations for various services (e.g., call taxi services), and much more. As Lee and Cho found,6 the use of smart devices help people with movement and mobility disabilities to lead more independent lives. Focusing on such usefulness of smart devices for improving the lives of people with physical disabilities, scholars and practitioners have taken much effort to develop technologies that support smart device functions specialized for people with disabilities.3,7e9 Eye movement detection functions are an example of such technologies. Such functions are critical for people who have limitations in hand movements. Engineers have also been developing specific apps for people with physical disabilities (e.g., apps that provide
https://doi.org/10.1016/j.dhjo.2019.100878 1936-6574/© 2019 Elsevier Inc. All rights reserved.
Please cite this article as: Cho J, Lee HE, Post-adoption beliefs and continuance intention of smart device use among people with physical disabilities, Disability and Health Journal, https://doi.org/10.1016/j.dhjo.2019.100878
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information regarding convenient facilities for the disabled).7,8 In spite of the theoretical and practical implications of those studies, there is a lack of research concerned with post-adoption behaviors related to smart device use among people with physical disabilities. However, without adequate comprehension of post-adoption behaviors, it becomes difficult for practitioners to identify practical and effective methods to improve the original technology. Furthermore, individuals’ perceptions about a specific medium continue to change based on their experiences. Channel expansion theory explains such continuous changes in perception (i.e., media richness) of a particular medium, placing major emphasis on experiences of media use.10e12 This implies the necessity to more thoroughly analyze people’s post-adoption behaviors, especially in order to further develop strategies of leading people to implement and maintain use of a technology. Therefore, this present study was aimed at examining the postadoption beliefs in using smart devices among people with physical disabilities, particularly those with impairments in movement and mobility. In more detail, based on the post-adoption model (PAM)dwhich relies on expectation-confirmation theory (ECT) and technology acceptance model (TAM)dthis study examined how post-adoption beliefs in smart device use influences the behavioral intention to continue using smart devices among people with physical disabilities. Moreover, considering the close relationship between self-efficacy and technology uses, this study investigated the moderating effect of self-efficacy on the relationships among the main study variables. The following section will discuss the main study variables and present the study’s hypotheses and research questions. Theoretical framework ECT & PAM of technology use Researchers have taken much effort to investigate the micromechanisms involved in individuals’ adoption and use of new technologies.13e15 To explain the psychological factors that lead people to adopt and use new technologies, previous research has depended on various theoretical approaches, including selfdetermination theory,16e18 diffusion of innovations,19,20 extended versions of technology acceptance models,21,22 uses and gratifications,6,23,24 and so on. Based on these theoretical foundations, previous research has intensively explored the mechanisms predicting people’s behavioral intentions to adopt and actually use new technologies. Despite the theoretical and practical implications of previous studies, as Thong et al.25 and Bhattacherjee26 also argue, those studies were not able to provide evaluation of people’s behaviors ‘after’ first adopting and using new technologies. Nevertheless, without proper analyses of these ‘post-adoption’ behaviors, it becomes extremely difficult to predict whether people will choose to continue or discontinue use of a particular technology. Particularly, for the purpose of improving the functions and services supported by a technology, developers must take significant efforts to evaluate post-adoption behaviors. Therefore, evaluation research has become increasingly important and common in various disciplines, such as the medical sciences, studies of public campaigns, etc. In regards to technology development, research on post-adoption behaviors is also necessary for increasing the efficiency of an existing technology and developing new technologies that are extensions of the original. Therefore, focusing on the significance of post-adoption analysis, Bhattacherjee26 proposed a post-adoption model that explains continued use of information systems, mainly based on expectation-confirmation theory (ECT). First, the core argument of ECT is that people’s confirmation of
their expectations for a target product is a crucial factor in determining their satisfaction with it and in ultimately predicting intentions to continue using it or buying related-products in the future.26 Therefore, ECT has been widely applied to research in industrial psychology, marketing, engineering, and so on. Particularly, ECT has often been used to understand people’s evaluations and behaviors after the initial purchase of new products.26e28 ECT is composed of three main components, which are ‘confirmation’ of initial expectations, ‘satisfaction’ with the original product, and ‘continuance’ of using the product. When the discrepancy is minimized between original expectations about a purchased product and experienced performances of the product, confirmation can be obtained. For example, a person’s initial expectation regarding a wearable smart band can be confirmed when his/her heartbeat is accurately measured. Such ‘perceptual confirmation’ of the initial expectation is directly connected to users’ affective outcome, that is ‘satisfaction’, with the purchased product. ECT further assumes that there is a relationship between the affective outcome (satisfaction) and behavioral intention (continuance), proposing a brief model composed of the three fundamental elements: confirmation/satisfaction/continuance. Considering the sequential associations among, perception, emotion, and behavior, ECT is an appropriate approach to investigating people’s post-adoption behaviors. Next, although ECT is a well-developed theoretical framework for studying post-adoption behaviors, its simplicity leads to its limitation in specifically explaining the relationship between confirmation and satisfaction.25 In other words, because a user’s satisfaction with a product can also be significantly related to his/ her perceptions about the product, previous research has attempted to identify the specific perceptual dimensions involved. Particularly, in regards to technology use, Thong et al.25 integrated ECT and TAM, based on the power of the two perceptual elements of TAMdperceived usefulness (PU) and perceived ease of use (PEOU)din predicting people’s behavioral intentions to adopt/use a technology. That is, according to the original TAM, when people perceive higher levels of usefulness in a technology and lower levels of difficulty in using it, the more likely they are to use the technology.15 Since the original TAM was proposed, research conducted in many different academic areas has provided plentiful evidence supporting the significance of the strong effects of PU and PEOU on the determination of people’s behavioral intentions regarding technology adoption/use.29 Focusing on previous findings, Thong et al.25 combined the two major components of TAM into ECT, proposing an expanded version of ECT. In this way, an expanded version of PAM is composed of the five major components of confirmation, perceived usefulness, perceived ease of use, satisfaction, and continuance use intention, and assumes positive relationships among all five factors.23,26 As elaborated above, ECT proposes positive associations among confirmation, satisfaction, and continuance intention, and existing studies have supported these associations.26 In regards to use of smart devices, it can be hypothesized that users’ confirmation of initial expectations will be positively associated with their satisfaction with smart devices, which in the end will increase the behavioral intentions to continue use. Therefore, the following two hypotheses were established and tested in the context of smart device use among people with disabilities: H1. Confirmation will be positively associated with satisfaction. H2. Satisfaction will be positively associated with continuance intention. Next, according to PAM, confirmation of the initial expectations regarding a technology are directly connected to its PU26 and
Please cite this article as: Cho J, Lee HE, Post-adoption beliefs and continuance intention of smart device use among people with physical disabilities, Disability and Health Journal, https://doi.org/10.1016/j.dhjo.2019.100878
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PEOU.25 First, when a user’s expectations are confirmed, it becomes more likely for him/her to perceive higher levels of usefulness in the particular technology. This is primarily because expectation confirmation signifies that the technology is functioning in proper ways, achieving the originally-set goals. Particularly in the context of technology use, technological performance in expected ways is directly related to the usefulness of that technology. This is because according to previous research on TAM, perceived usefulness refers to the extent with which a person can use a technology in adequate ways for achieving task-related goals.15 Moreover, when a technology functions properly by meeting a user’s expectation, s/he may no longer need to take efforts to learn the appropriate way to use the technology, implying an increase in perceived ease of use. Therefore, based on these arguments, the following two hypotheses were established in relation to use of smart devices among people with disabilities: H3. Confirmation will be positively associated with perceived usefulness. H4. Confirmation will be positively associated with perceived ease of use. It has been well known, especially within the literature of TAM, that PU and PEOU are, in general, positively associated with users’ attitudes toward a technology.29 Considering these findings, it is also logical to assume that when individuals perceive a technology as being useful for accomplishing a given goal, they are likely to experience satisfaction with it. For example, when a patient with post-traumatic stress disorder experiences improvement in mental conditions by using VR-based treatment systems, s/he will become satisfied with those systems.30,31 Furthermore, the existing literature continues to emphasize the crucial roles of PU and PEOU in predicting behavioral intentions to adopt and use a technology.32 Therefore, this present study also proposed the following hypotheses regarding smart device uses and people with physical disabilities: H5. Perceived usefulness will be positively associated with satisfaction. H6. Perceived ease of use will be positively associated with satisfaction. H7. Perceived usefulness will be positively associated with continuance intention.
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H8. Perceived ease of use will be positively associated with continuance intention. As such, this study proposed eight hypotheses corresponding to the relationships among five major study variables. Fig. 1 presents the final PAM-based model. Expansion of PAM: moderating effects of self-efficacy In order to more fully understand technology use, it is necessary to consider diverse personality-oriented variables that can directly and indirectly impact the relationships among major variables involved in technology use.33e36 For example, there have been studies that extensively analyzed how the Big-5 personality traits (e.g., extroversion, agreeableness) influence technology adoption and use.37,38 An example of such findings are that, agreeableness, which is conceptualized as the extent to which a person is open to and accepts others’ opinions, is significantly associated with people’s use of smartphones.36 Similarly, innovativeness as a personality dimension is also positively related to the adoption of new technologies.39 In regards to such personality-oriented factors, one factor that has garnered much attention from scholars is self-efficacy.40e44 According to Bandura,45 self-efficacy refers to the level of an individual’s perception regarding his/her abilities to conduct certain activities. This general definition has been widely applied to numerous academic areas. While organizational scientists are interested in efficacy regarding the accomplishment of organizational and personal goals, connecting self-efficacy with goal-setting theories,46e48 researchers in rehabilitation studies have focused on patients’ beliefs in their abilities to handle life exigencies.49,50 Likewise, research on technology adoption and use has intensively explored people’s beliefs in their personal abilities to use particular technologies.41,51,52 Numerous studies have observed direct and positive effects of technology-oriented self-efficacy on people’s attitudes toward a technology.41,44,51 For instance, Buchanan et al.51 found that Internet-use efficacy is positively associated with behaviors related to usage of learning technologies. Particularly in terms of technology adoption and use, specification in the conceptualization of self-efficacy is helpful for investigating the direct effects of self-efficacy. Nevertheless, it is still meaningful to explore the roles of general self-efficacy in indirectly affecting the relationships among various factors associated with technology use. Particularly, general self-
Fig. 1. Main study model. Notes: CNF¼Confirmation, PU¼Perceived Usefulness, PEOU¼Perceived Ease of Use, SAT¼Satisfaction, CI¼Continuance Intention.
Please cite this article as: Cho J, Lee HE, Post-adoption beliefs and continuance intention of smart device use among people with physical disabilities, Disability and Health Journal, https://doi.org/10.1016/j.dhjo.2019.100878
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as a smartphone or tablet PC, at the time of data collection. The sample consisted of a larger number of male participants (64.9%), and the average age of the sample was 43.7 years (SD ¼ 11.7). More than half of the participants (57.5%) had some college education or higher degrees. In terms of employment status, only 46.9% of participants were currently employed. However, only half of those employed participants were employed full-time. The median monthly income was $1000~$2,000, indicating a relatively lower level of monthly income when compared to people without disabilities.
efficacy is strongly connected with many aspects in the lives of people with disabilities.53,54 As disabilities can limit people’s abilities to handle everyday life activities, such individuals often suffer from a lack of general self-efficacy. For example, people with impairments in movement find it difficult to leave their homes, eventually losing many opportunities to advance their education or careers, leading to lowered self-efficacy. This relatively lower level of self-efficacy affects the daily lives of this population in various ways. In terms of technology adoption and use, because people with disabilities have experienced lower levels of general selfefficacy for long amounts of time, other personality traits (e.g., openness, innovativeness), which generally tend to positively affect people’s acceptance of technology, are likely to have weaker effects. Therefore, through the following research question, this study investigated the moderating effects of general self-efficacy on the relationships among the main study variables regarding smart device use.
Instruments Instruments developed by previous studies were used to measure the main five study variables. All five variables were assessed with composite measures using a five-point Likert scale (1 ¼ Strongly disagree to 5 ¼ Strongly agree). All measures reached acceptable reliability scores; all Chronbach’s alpha scores were higher than 0.70. The correlations among the main study variables are given in Table 1.
RQ1. How does general self-efficacy moderate the relationships among the five study variables? Methods
Confirmation
Survey procedure and participants
To measure the PAM variable of confirmation, three items from the original scale proposed by Bhattacherjee26 were reworded corresponding to the study’s topic of smart devices. The following three items were used (M ¼ 3.72, SD ¼ 0.69, a ¼ 0.90): a) My experience of using smart devices is better than my initial expectation; b) The level of service provided by smart devices was higher than my initial expectation; and c) Smart devices provide more services than I initially expected.
In order to test the hypotheses, data was collected through an online and paper-and-pencil survey from participants recruited through purposive and convenient sampling methods. First, an invitation for the online survey was posted to a popular Koreabased online cafe targeted toward people with disabilities in movement and mobility. The online consent form specifically stated that the survey was intended for people with impairments in physical movement and mobility, and at the beginning of the online survey, people were asked to identify their physical disabilities from the following choices: movement and mobility disabilities associated with spinal cord injury, myelopathy, muscular dystrophy, brain disorder or injury, and/or other. Only those who were able to identify as having movement or mobility related disabilities as listed above were allowed to complete the questionnaire online. Next, the researchers enlisted the help of a Korean association for physical disability support for further data collection. This specific association is a non-profit organization, with five regional chapters across the nation, and primarily focuses on providing medical information for those with movement and mobility impairments, and organizing events for disability awareness and forums for policy-making initiatives. Paper-and-pencil questionnaires were collected from on-site visitors to the Seoul office, and the association distributed email invitations to its members. In total, 108 adults with movement and mobility disabilities participated in the survey. More than 40% of the participants had disabilities associated with spinal cord injury or myelopathy. All participants were currently using one or more smart devices, such
Satisfaction User satisfaction with smart devices was measured using three PAM items developed by Bhattacherjee.26 The original items were reworded to reflect the study’s focus on smart devices (M ¼ 3.71, SD ¼ 0.71, a ¼ 0.91): a) I am satisfied with the performance of smart devices; b) I have good experiences using smart devices; and c) My decision to use smart devices was wise. Perceived usefulness Perceived usefulness was measured by rewording the original TAM items developed by Davis, Bagozzi, and Warshaw.55 The following three items were used to measure the perceived usefulness of smart device use (M ¼ 3.94, SD ¼ 0.63, a ¼ 0.95): a) Smart devices are useful for my everyday life; b) Smart devices are advantageous to me; and c) Smart devices are valuable to me. Perceived ease of use The perceived ease of use smartphones was measured with
Table 1 Correlations among the main study variables.
1 2 3 4 5 6
Confirmation Satisfaction Perceived Usefulness Perceived Ease of Use Continuance Intention General Self-Efficacy
1
2
3
4
5
.826*** .653*** .464*** .647*** .381***
.716*** .588*** .686*** .388***
.554*** .729*** .335**
.530*** .206
. 448***
**p < .01, ***p < .001.
Please cite this article as: Cho J, Lee HE, Post-adoption beliefs and continuance intention of smart device use among people with physical disabilities, Disability and Health Journal, https://doi.org/10.1016/j.dhjo.2019.100878
J. Cho, H.E. Lee / Disability and Health Journal xxx (xxxx) xxx
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three revised items from Davis et al.‘s55 original TAM measure (M ¼ 3.63, SD ¼ 0.87, a ¼ 0.97): a) To me, it is easy to learn the instructions for using smart devices; b) To me, it is easy to understand the use of smart devices; and c) Smart devices are easy to use.
education or higher degree. Five (30%) of the participants held fulltime jobs, while two were employed part-time; the remaining participants were unemployed. The median monthly income was $1000~$2000.
Continuance intention
Results
Both PAM and TAM focused on the behavioral intentions to either adopt or continue use of a specific technology. In order to measure these intentions, three items proposed by Davis et al.55 were revised for this research focusing on smart devices (M ¼ 4.02, SD ¼ 0.74, a ¼ 0.96): a) I want to keep using smart devices; b) I plan to keep using smart devices; and c) I predict that I will keep using smart devices.
Results from survey: path model
General self-efficacy Schwarzer and Jerusalem’s56 original scale composed of twelve items was used to measure general self-efficacy (M ¼ 3.58, SD ¼ 0.70, a ¼ 0.96). Some examples of these items are: a) It is easy for me to stick to my aims and accomplish my goals; b) I can always manage to solve difficult problems if I try hard enough; c) I am confident that I could deal efficiently with unexpected events; d) I can usually handle whatever comes my way; and e) I can solve most problems if I invest the necessary effort. Focus group procedure and participants Qualitative data was also collected through focus group interviews in order to understand the experiences of the participants contextually and in more detail. The physical disability association mentioned above helped recruit the participants. Three focus groups were conducted, with five individuals in each group leading to a total of 15 participants. Group 1 consisted of participants with spinal cord injury or myelopathy; Group 2, participants with muscular dystrophy; and Group 3, participants with brain disorder/ injury. Each interview session lasted approximately 75 min, and all groups met for one session each. The majority of participants was male (n ¼ 11) with an average age of 44.3 years. More than half (n ¼ 9) had some college
A path analysis was conducted in order to test the proposed hypotheses. In order to check the model fit of the proposed model, three model fit indices were reviewed: standardized root mean squared residuals (SRMR, acceptable when lower than 0.08), comparative fit index (CFI, acceptable when higher than 0.90), and infinite fit index (IFI, acceptable when higher than 0.90). The path analysis results indicated acceptable model fit (c2(df ¼ 2) ¼ 15.74, CFI ¼ 0.95, IFI ¼ 0.95, RMSEA ¼ 0.07). H1 and H2 predicted the associations among the three main components of PAMdconfirmation, satisfaction, and continuance intentiondin regards to smart device use among people with physical disabilities. As Fig. 2 shows, confirmation strongly predicted participants’ satisfaction with using smart devices (b ¼ 0.60, p < .001), fully supporting H1. There was also a direct effect of satisfaction on continuance intention, and thus H2 was also supported (b ¼ 0.33, p ¼ .003). Next, H3 and H4 predicted relationships between confirmation and the two components of TAM: perceived usefulness (PU) and perceived ease of use (PEOU) of smart devices. Results from the path analysis supported both hypotheses, indicating that confirmation from smart device use was significantly and positively associated with both PU (b ¼ 0.62, p < .001) and PEOU (b ¼ 0.61, p < .001). While H5 and H6 focused on the relationships among PU, PEOU, and satisfaction in regards to smart device use, H7 and H8 hypothesized positive associations between the two components of TAM and continuance intention. First, both PU (b ¼ 0.28, p < .001) and PEOU (b ¼ 0.14, p ¼ .004) positively predicted satisfaction with using smart devices, confirming H5 and H6. Next, in regards to the effects of those two variables on continuance intention regarding smart device use, while PU was strongly and positively associated
Fig. 2. Results from the path analysis. Notes: CNF¼Confirmation, PU¼Perceived Usefulness, PEOU¼Perceived Ease of Use, SAT¼Satisfaction, CI¼Continuance Intention; Dotted line ¼ Insignificant path; *p < .001, **p < .01.
Please cite this article as: Cho J, Lee HE, Post-adoption beliefs and continuance intention of smart device use among people with physical disabilities, Disability and Health Journal, https://doi.org/10.1016/j.dhjo.2019.100878
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with continuous usage intentions (b ¼ 0.48, p < .001), PEOU was not significantly associated with participants’ intentions to continue use (b ¼ 0.10, p ¼ .15). In sum, while H7 was confirmed, H8 was rejected. According to the results from the path analysis, 43.7% of the variance of PU was explained through the inclusion of confirmation into the regression model (R2 ¼ 0.427). About 24% of the variance of PEOU was explained through confirmation (R2 ¼ 0.242). The inclusion of the three variables, PU, PEOU, and confirmation, into the regression model accounted for 76.4% of the variance of satisfaction (R2 ¼ 0.764). Lastly, 58.2% of the variance of continuance intention was explained by the three variables of PU, PEOU, and satisfaction. Moderating effects of general self-efficacy In order to examine the research question regarding the moderating effects of general self-efficacy (GSE) on the relationships among the main study variables, the sample was first divided into two groups based on a mean-based split for GSE. Then, a multigroup path analysis was conducted in order to compare how the relationships among the five study variables differed between the low and high GSE groups. Fisher’s Z-score for each pair of two regression coefficients was calculated and reviewed. As Table 2 shows, differences in two pathsdconfirmation to PU, PU to continuance intentiondwere observed to be statistically significant. In more detail, confirmation’s effect on PU was stronger in the lower GSE group (b ¼ 0.74) than it was in the higher GSE group (b ¼ 0.44). Further, PU’s effect on continuance intention was also stronger in the lower GSE group (b ¼ 0.76) than in the higher GSE group (b ¼ 0.31). Results from focus group interviews In order to more deeply understand how people with physical disabilities evaluate and think about smart devices, this study conducted a series of focus group interviews (FGIs). In the interviews, discussions revolved around how smart devices affected and changed the participants’ everyday lives and what meanings they assigned to those technologies. From qualitative data collected through the FGIs, the following main themes regarding smart devices were observed: 1) heavy dependence, 2) advances in task completion, and 3) ‘invaluable life-changer’. First, most FGI participants mentioned that they were heavily reliant on smart devices on a daily base. This was mainly because of two reasons. One was the convenience of smart devices, a quality strengthened by the mobility of the devices. Compared to other computer-based technologies, people with physical disabilities can more easily access and use smart devices for various purposes, spending a great amount of time on them to complete everyday life tasks. The other
reason was that, because people with physical disabilities have less entertainment or pass-time options, smart devices become their primary source of enjoyment (e.g., playing games, web surfing, online community participation). Second, participants often emphasized that they were now able to complete an increasing number of tasksdtasks that they were not able to previously conduct or finish. For example, unlike the past, participants mentioned that they were now able to 1) directly contact family members and friends without the help of their caregivers, thanks to voice-recognition services; 2) easily make reservations for various services, such as taxi call-services; 3) create official documents; 4) read e-books in more convenient ways; 5) use banking services; and so on. Here, it is important to consider that such an increase in the type of tasks that can be completed by oneself is directly and positively associated with an increase in general self-efficacy among people with physical disabilities. That is, as these individuals gain the ability to complete a greater number and diverse types of tasks, both simple (e.g., reading books) and relatively more complicated (e.g., online shopping, online banking), they were able to experience increased levels of efficacy. Third, as the two findings above indicate, participants were likely to rely on smart devices mainly due to ‘necessity’ as well as ‘limited alternatives’. As participants became more and more dependent on smart devices, the technologies had largely penetrated their everyday lives, taking on a crucial role. Therefore, when participants were asked to describe the personal meanings they prescribed to smart devices, the majority commonly addressed the significance of the devices. For example, they described their experiences in expressions such as, “life saver, critical part of my body, something indispensable, window to the world outside,” and “close friend.” In this way, FGI participants commonly and strongly emphasized the integral roles played by smart devices mostly in terms of the dramatic changes brought about in their ways of life and how smart devices now acted as the primary source of assistance. Particularly, it was observed that the participants’ active uses of smart devices led to heightened levels of self-efficacy. Therefore, these findings support the importance of analyzing the moderating effects of general self-efficacy on the relationships among the main variables of this study. Discussion This study had two major research objectives: 1) to identify the main factors that predict continuance intentions regarding use of smart devices among people with physical disabilities, and 2) to test the moderating effects of general self-efficacy on the relationships among post-adoption and technology acceptance variables. The results of a path analysis showed that most of the
Table 2 Differences between low and high general self-efficacy groups. IV
Confirmation Confirmation Confirmation Satisfaction PU PEOU PU PEOU
DV
Satisfaction PU PEOU CI Satisfaction Satisfaction CI CI
Low Self-Efficacy
High Self-Efficacy
Low Self-Efficacy
High Self-Efficacy
b
b
SE
SE
0.703 0.740 0.664 0.151 0.151 0.162 0.756 0.076
0.493 0.435 0.479 0.296 0.296 0.152 0.307 0.081
0.109 0.104 0.163 0.102 0.102 0.065 0.147 0.103
0.119 0.135 0.217 0.116 0.116 0.072 0.15 0.092
Z-Score
1.30 1.79* 0.68 0.94 0.94 0.10 2.14** 0.04
Notes: PU¼Perceived Usefulness, PEOU¼Perceived Ease of Use, CI¼Continuance Intention *p < .05, **p < .01.
Please cite this article as: Cho J, Lee HE, Post-adoption beliefs and continuance intention of smart device use among people with physical disabilities, Disability and Health Journal, https://doi.org/10.1016/j.dhjo.2019.100878
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hypotheses were supported, except for the effect of perceived ease of use on continuance intentions for using smart devices. Considering the high correlation score between perceived ease of use and continuance (r ¼ 0.53), the effect of perceived ease of use on continuance intention would be suppressed by the effects of the two other factors on continuance intention. In other words, because the perceived usefulness and satisfaction of using smart devices largely predicted continuance intention, the effect of perceived ease of use on continuance intention could have been weakened. Findings from the FGIs also support this interpretation. Smartphones were often considered a “life saver” for our participants with physical disabilities (i.e., smartphone use during emergency situations such as falling down in the bathroom), signifying that they experienced very high levels of smartphones’ perceived usefulness. Therefore, to the participants, although the perceived ease of use would be a significant factor for continuing to use smartphones, its direct effect on continuous usage may be significantly smaller than that of perceived usefulness. In other words, whether smart device use is considered to be easy or not, people with physical disabilities will tend to use it, mainly because of its usefulness. Indeed, most FGI participants strongly addressed the usefulness of smartphones in terms of various aspects. Next, another main finding of this study was the significant moderating effects of general self-efficacy on relationships among three variables, confirmation, perceived usefulness, and continuance intention of smart devices. The relationships among the three variables were significantly stronger among people with lower levels of general self-efficacy. These findings are quite interesting because according to previous research, general self-efficacy is often positively associated with people’s use of technologies. Such results would be explained through the extent to which people with physical disabilities were dependent on smart devices. According to FGI participants, smart devices play key roles in overcoming one’s limitations in physical mobility. For example, through mobile banking apps, people who are unable to use a PC or laptop due to serious physical disabilities, can now access and use banking services on their own. This is a huge advantage for such individuals as they can gain or recover autonomy over the management and protection of their financial assets. Here, it must be considered that general self-efficacy is often significantly and negatively associated with the extent of one’s disability. As many people with serious disabilities experience difficulty in completing routine, daily activities (e.g., brushing teeth, changing clothes, texting), their general self-efficacy tends to be low. Although this study did not measure the extent of disabilities of the participants, it is highly plausible that people with lower general self-efficacy may have more serious disabilities, experiencing greater physical limitations. Therefore, the diverse functions of smart devices, which allow these individuals to accomplish what was previously impossible, may have more strongly influenced participants with lower general self-efficacy, compared to those with higher general self-efficacy. This study holds the following implications. First, in spite of the many previous studies that have investigated smart device use among people with physical disabilities, there lack intensive examination of the mechanisms involved in continuance intentions regarding smart device use. Therefore, the findings of this present study provide researchers with opportunities to understand the fundamental motivators that lead people with physical disabilities to continue using smart devices and to comprehend how the use of smart technologies is associated with this population’s self-efficacy and everyday lives. Therefore, this study aids in developing novel and advanced models of explaining the unique use of smart devices among people with disabilities. Next, it is theoretically meaningful that unlike previous research, which had found positive effects of self-efficacy on
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technology use, this present study observed negative moderating effects of general self-efficacy on the relationships between the perceived usefulness of smart devices and the two variables of confirmation and continuance intention. As elaborated above, this is most likely due to the greater needs of technological support in individuals with lower general self-efficacy, which leads to increases in the perceived usefulness of smart devices. Such findings address the necessity to consider personal contexts in order to more thoroughly analyze the effects of self-efficacy in relation to use of new technologies. Finally, the findings provide general goals for smart-device developers, app developers, and health practitioners. These entities should consider further developing and refining technologies and services that can increase the usefulness and ease of use of smart devices as well as appsdspecifically from the perspective of people with disabilities. Current smart technologies and services are primarily targeted toward people without disabilities. However, as the findings show, more focused effort is required to reflect the wants and needs of those with disabilities so as to encourage their continued use of smart devices and enhance their experiences with the devices. Limitations and future directions This study’s limitations need be addressed and considered for future research. First, the study findings need to be interpreted with caution, considering the limitations of the sample. Specifically, the sample was relatively small and lacked representativeness in terms of certain qualities, such as gender, age, place of residence, and type of disability. Considering the relatively older and urban characteristics of the participants for this studydmostly due to the composition of the organizations used for sampling (i.e., online disability cafe and disability association)dfuture research will benefit by collecting data from younger individuals and/or those who live in rural areas, as different patterns of use are expected. Particularly, comparisons in regards to residential areas will be valuable for investigating how digital gaps among those populations may be exacerbated by the conditions of residence. Further, exclusion of those who are not members of the particular online cafe or support organization we relied on rendered it difficult to observe the digital experiences of those who lack in overall informational resources and social support networks. Finally, different types and degrees of physical impairments lead to differential use, and thus sophisticated categorization and inclusion of disabilities is needed. In order to improve sample representativeness, future research must take efforts to collect larger data sets from diverse groups of people with physical disabilities through multiple sampling sites. Next, people with disabilities have been considered media minorities with relatively lower levels of digital media literacy, mainly due to educational and economic inequalities. According to previous studies,57e59 media literacy is one of the most critical factors that determine individuals’ uses of digital technologies, especially smart devices. Therefore, researchers need to examine how media literacy levels might impact specific dimensions (i.e., accessibility, content creation skills, critical interpretation, etc.) of media literacy among people with physical disabilities. It will be helpful for developing practical strategies for media education targeted to such media minorities, aiding to overcome digital gaps as well as health gaps.60e62 Thirdly, it is also recommended for interested researchers to place more attention on the specific features and functions of smart devices. For the purpose of supporting people with physical disabilities, previous research has been devoted to the development and advancement of various assistive technologies (e.g., voice
Please cite this article as: Cho J, Lee HE, Post-adoption beliefs and continuance intention of smart device use among people with physical disabilities, Disability and Health Journal, https://doi.org/10.1016/j.dhjo.2019.100878
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recognition, eye movement recognition, etc.).3,4 Moreover, practitioners have also continuously taken great effort to developing numerous disability-aiding services that are provided through mobile apps (e.g., apps providing information regarding facilities for people with disabilities). Understanding of this topic regarding physical disabilities and smart devices will be further enriched with more focus given to the particular uses of assistive technologies and services and the effects such uses have on individuals in physical, mental, economic, and social aspects. Declaration of competing interest
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This manuscript has no conflict of interest and official funding. 30.
Acknowledgement 31.
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Consumer adoption of mobile banking in Jordan: examining the role of usefulness, ease of use, perceived risk and self-efficacy. J Enterp Inf Manag. 2006;29:118e139. Hsu M-H, Chiu C-M. Internet self-efficacy and electronic service acceptance. Decis Support Syst. 2004;38(3):369e381. Hsu MK, Wang SW, Chiu KK. Computer attitude, statistics anxiety and selfefficacy on statistical software adoption behavior: an empirical study of online MBA learners. Comput Hum Behav. 2009;25(2):412e420. Lai M-L. Technology readiness, internet self-efficacy and computing experience of professional accounting students. Campus-Wide Inf Syst. 2008;25:18e29. Yang K. The effects of technology self-efficacy and innovativeness on consumer mobile data service adoption between American and Korean consumers. J Int Consum Mark. 2010;22(2):117e127. Bandura A. Self-efficacy: toward a unified theory of behavior change. Psychol Rev. 1977;94:191e215. Phillips JM, Gully SM. Role of goal orientation, ability, need for achievement, and locus of control in the self-efficacy and goal-setting process. J Appl Psychol. 1997;82(5):792e802. Schunk DH. Goal setting and self-efficacy during self-regulated learning. Educ Psychol. 1990;25:71e86. Zimmerman BJ, Bandura A, Martinez-Pons M. Self-motivation for academic attainment: the role of self-efficacy beliefs and personal goal setting. Am Educ Res J. 1992;29(3):663e676. Hampton NZ. Sources of academic self-efficacy scale: an assessment tool for rehabilitation counselors. Rehabil Couns Bull. 1998;41(4):260e277. Scholz U, Sniehotta FF, Schwarzer R. Predicting physical exercise in cardiac rehabilitation: the role of phase-specific self-efficacy beliefs. Exercise Psychol. 2005;27(2):135e151. Buchanan T, Sainter P, Saunders G. Factors affecting faculty use of learning technologies: implications for models of technology adoption. J Comput High Educ. 2013;25(1):1e11. Ellen PS, Bearden WO, Sharma S. 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Original Article
Post-adoption beliefs and continuance intention of smart device use among people with physical disabilities Jaehee Cho a, *, H. Erin Lee b a b
School of Communication, Sogang University, #811, Bldg. Matthew, Heukseok-ro 84, Mapo-gu, Seoul, South Korea Media Communication Division, Hankuk University of Foreign Studies, South Korea
a r t i c l e i n f o
a b s t r a c t
Article history: Received 29 July 2019 Received in revised form 8 November 2019 Accepted 26 November 2019
Background: There has been a continuous increase of smart device use among people with physical disabilities in Korea. In spite of previous research on those people’s motives to adopt smart devices, little investigation has been conducted to scrutinize post-adoption behaviors of using smart devices among people with physical disabilities. Objective: /Hypothesis: Based on the post-adoption model, this study examined the relationship between post-adoption beliefs regarding smart devices and behavioral intentions toward continued use of smartphones among people with physical disabilities. Moreover, this study investigated the moderating effects of self-efficacy on the relationships among the main study variables. Methods: Both online and paper-pencil surveys were conducted, resulting in a total of 108 questionnaires collected from people with physical disabilities. Results: A path analysis showed that, with the exception of perceived ease of use, all variables (confirmation, perceived usefulness, and satisfaction of smart device use) had significant effects on continuance intention to use smart devices. Another main finding of this study was the significant moderating effects of general self-efficacy on the relationships among the three variables of confirmation, perceived usefulness, and continuance intention of smart devices. The relationships among the three variables were significantly stronger among people with lower levels of general self-efficacy. Conclusions: This study’s main findings will aid the thorough comprehension of the mechanisms that lead people with physical disabilities to continue to use smart devices. © 2019 Elsevier Inc. All rights reserved.
Keywords: People with physical disabilities Post-adoption model (PAM) Expectation confirmation theory (ECT) Technology acceptance model (TAM) Smart devices Continuance intention
Introduction In this hyper-connected society, smart devices such as the smartphone and tablet PC have become some of the most important and influential technologies in people’s everyday lives. According to a nationwide study conducted in Korea during 2018 Ministry of Science and ICT (MSIT) & Korea Internet & Security Agency (KISA), 91.4% of individuals age 6 and older personally owned a smart device (including smartphones, tablet PCs, and wearable devices).1 Though the number is smaller, among those with disabilities between the ages of 7 and 69, ownership of smart devices increased to 76.1% in 2018, from 72.4% in 2017.1 Previous research has investigated the major motives for using smart devices among people with physical disabilities,2 and many
* Corresponding author. E-mail addresses: [email protected] (J. Cho), [email protected] (H.E. Lee).
others have observed the usefulness of smart devices for people with physical disabilities, particularly in terms of facilitating daily, routine activities.3e5 Supported by mobile smart devices, people with impairments in movement and mobility are able to more easily communicate with others, find useful information regarding their disabilities (e.g., assistive devices), learn useful skills needed to seek out and secure jobs, and make reservations for various services (e.g., call taxi services), and much more. As Lee and Cho found,6 the use of smart devices help people with movement and mobility disabilities to lead more independent lives. Focusing on such usefulness of smart devices for improving the lives of people with physical disabilities, scholars and practitioners have taken much effort to develop technologies that support smart device functions specialized for people with disabilities.3,7e9 Eye movement detection functions are an example of such technologies. Such functions are critical for people who have limitations in hand movements. Engineers have also been developing specific apps for people with physical disabilities (e.g., apps that provide
https://doi.org/10.1016/j.dhjo.2019.100878 1936-6574/© 2019 Elsevier Inc. All rights reserved.
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information regarding convenient facilities for the disabled).7,8 In spite of the theoretical and practical implications of those studies, there is a lack of research concerned with post-adoption behaviors related to smart device use among people with physical disabilities. However, without adequate comprehension of post-adoption behaviors, it becomes difficult for practitioners to identify practical and effective methods to improve the original technology. Furthermore, individuals’ perceptions about a specific medium continue to change based on their experiences. Channel expansion theory explains such continuous changes in perception (i.e., media richness) of a particular medium, placing major emphasis on experiences of media use.10e12 This implies the necessity to more thoroughly analyze people’s post-adoption behaviors, especially in order to further develop strategies of leading people to implement and maintain use of a technology. Therefore, this present study was aimed at examining the postadoption beliefs in using smart devices among people with physical disabilities, particularly those with impairments in movement and mobility. In more detail, based on the post-adoption model (PAM)dwhich relies on expectation-confirmation theory (ECT) and technology acceptance model (TAM)dthis study examined how post-adoption beliefs in smart device use influences the behavioral intention to continue using smart devices among people with physical disabilities. Moreover, considering the close relationship between self-efficacy and technology uses, this study investigated the moderating effect of self-efficacy on the relationships among the main study variables. The following section will discuss the main study variables and present the study’s hypotheses and research questions. Theoretical framework ECT & PAM of technology use Researchers have taken much effort to investigate the micromechanisms involved in individuals’ adoption and use of new technologies.13e15 To explain the psychological factors that lead people to adopt and use new technologies, previous research has depended on various theoretical approaches, including selfdetermination theory,16e18 diffusion of innovations,19,20 extended versions of technology acceptance models,21,22 uses and gratifications,6,23,24 and so on. Based on these theoretical foundations, previous research has intensively explored the mechanisms predicting people’s behavioral intentions to adopt and actually use new technologies. Despite the theoretical and practical implications of previous studies, as Thong et al.25 and Bhattacherjee26 also argue, those studies were not able to provide evaluation of people’s behaviors ‘after’ first adopting and using new technologies. Nevertheless, without proper analyses of these ‘post-adoption’ behaviors, it becomes extremely difficult to predict whether people will choose to continue or discontinue use of a particular technology. Particularly, for the purpose of improving the functions and services supported by a technology, developers must take significant efforts to evaluate post-adoption behaviors. Therefore, evaluation research has become increasingly important and common in various disciplines, such as the medical sciences, studies of public campaigns, etc. In regards to technology development, research on post-adoption behaviors is also necessary for increasing the efficiency of an existing technology and developing new technologies that are extensions of the original. Therefore, focusing on the significance of post-adoption analysis, Bhattacherjee26 proposed a post-adoption model that explains continued use of information systems, mainly based on expectation-confirmation theory (ECT). First, the core argument of ECT is that people’s confirmation of
their expectations for a target product is a crucial factor in determining their satisfaction with it and in ultimately predicting intentions to continue using it or buying related-products in the future.26 Therefore, ECT has been widely applied to research in industrial psychology, marketing, engineering, and so on. Particularly, ECT has often been used to understand people’s evaluations and behaviors after the initial purchase of new products.26e28 ECT is composed of three main components, which are ‘confirmation’ of initial expectations, ‘satisfaction’ with the original product, and ‘continuance’ of using the product. When the discrepancy is minimized between original expectations about a purchased product and experienced performances of the product, confirmation can be obtained. For example, a person’s initial expectation regarding a wearable smart band can be confirmed when his/her heartbeat is accurately measured. Such ‘perceptual confirmation’ of the initial expectation is directly connected to users’ affective outcome, that is ‘satisfaction’, with the purchased product. ECT further assumes that there is a relationship between the affective outcome (satisfaction) and behavioral intention (continuance), proposing a brief model composed of the three fundamental elements: confirmation/satisfaction/continuance. Considering the sequential associations among, perception, emotion, and behavior, ECT is an appropriate approach to investigating people’s post-adoption behaviors. Next, although ECT is a well-developed theoretical framework for studying post-adoption behaviors, its simplicity leads to its limitation in specifically explaining the relationship between confirmation and satisfaction.25 In other words, because a user’s satisfaction with a product can also be significantly related to his/ her perceptions about the product, previous research has attempted to identify the specific perceptual dimensions involved. Particularly, in regards to technology use, Thong et al.25 integrated ECT and TAM, based on the power of the two perceptual elements of TAMdperceived usefulness (PU) and perceived ease of use (PEOU)din predicting people’s behavioral intentions to adopt/use a technology. That is, according to the original TAM, when people perceive higher levels of usefulness in a technology and lower levels of difficulty in using it, the more likely they are to use the technology.15 Since the original TAM was proposed, research conducted in many different academic areas has provided plentiful evidence supporting the significance of the strong effects of PU and PEOU on the determination of people’s behavioral intentions regarding technology adoption/use.29 Focusing on previous findings, Thong et al.25 combined the two major components of TAM into ECT, proposing an expanded version of ECT. In this way, an expanded version of PAM is composed of the five major components of confirmation, perceived usefulness, perceived ease of use, satisfaction, and continuance use intention, and assumes positive relationships among all five factors.23,26 As elaborated above, ECT proposes positive associations among confirmation, satisfaction, and continuance intention, and existing studies have supported these associations.26 In regards to use of smart devices, it can be hypothesized that users’ confirmation of initial expectations will be positively associated with their satisfaction with smart devices, which in the end will increase the behavioral intentions to continue use. Therefore, the following two hypotheses were established and tested in the context of smart device use among people with disabilities: H1. Confirmation will be positively associated with satisfaction. H2. Satisfaction will be positively associated with continuance intention. Next, according to PAM, confirmation of the initial expectations regarding a technology are directly connected to its PU26 and
Please cite this article as: Cho J, Lee HE, Post-adoption beliefs and continuance intention of smart device use among people with physical disabilities, Disability and Health Journal, https://doi.org/10.1016/j.dhjo.2019.100878
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PEOU.25 First, when a user’s expectations are confirmed, it becomes more likely for him/her to perceive higher levels of usefulness in the particular technology. This is primarily because expectation confirmation signifies that the technology is functioning in proper ways, achieving the originally-set goals. Particularly in the context of technology use, technological performance in expected ways is directly related to the usefulness of that technology. This is because according to previous research on TAM, perceived usefulness refers to the extent with which a person can use a technology in adequate ways for achieving task-related goals.15 Moreover, when a technology functions properly by meeting a user’s expectation, s/he may no longer need to take efforts to learn the appropriate way to use the technology, implying an increase in perceived ease of use. Therefore, based on these arguments, the following two hypotheses were established in relation to use of smart devices among people with disabilities: H3. Confirmation will be positively associated with perceived usefulness. H4. Confirmation will be positively associated with perceived ease of use. It has been well known, especially within the literature of TAM, that PU and PEOU are, in general, positively associated with users’ attitudes toward a technology.29 Considering these findings, it is also logical to assume that when individuals perceive a technology as being useful for accomplishing a given goal, they are likely to experience satisfaction with it. For example, when a patient with post-traumatic stress disorder experiences improvement in mental conditions by using VR-based treatment systems, s/he will become satisfied with those systems.30,31 Furthermore, the existing literature continues to emphasize the crucial roles of PU and PEOU in predicting behavioral intentions to adopt and use a technology.32 Therefore, this present study also proposed the following hypotheses regarding smart device uses and people with physical disabilities: H5. Perceived usefulness will be positively associated with satisfaction. H6. Perceived ease of use will be positively associated with satisfaction. H7. Perceived usefulness will be positively associated with continuance intention.
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H8. Perceived ease of use will be positively associated with continuance intention. As such, this study proposed eight hypotheses corresponding to the relationships among five major study variables. Fig. 1 presents the final PAM-based model. Expansion of PAM: moderating effects of self-efficacy In order to more fully understand technology use, it is necessary to consider diverse personality-oriented variables that can directly and indirectly impact the relationships among major variables involved in technology use.33e36 For example, there have been studies that extensively analyzed how the Big-5 personality traits (e.g., extroversion, agreeableness) influence technology adoption and use.37,38 An example of such findings are that, agreeableness, which is conceptualized as the extent to which a person is open to and accepts others’ opinions, is significantly associated with people’s use of smartphones.36 Similarly, innovativeness as a personality dimension is also positively related to the adoption of new technologies.39 In regards to such personality-oriented factors, one factor that has garnered much attention from scholars is self-efficacy.40e44 According to Bandura,45 self-efficacy refers to the level of an individual’s perception regarding his/her abilities to conduct certain activities. This general definition has been widely applied to numerous academic areas. While organizational scientists are interested in efficacy regarding the accomplishment of organizational and personal goals, connecting self-efficacy with goal-setting theories,46e48 researchers in rehabilitation studies have focused on patients’ beliefs in their abilities to handle life exigencies.49,50 Likewise, research on technology adoption and use has intensively explored people’s beliefs in their personal abilities to use particular technologies.41,51,52 Numerous studies have observed direct and positive effects of technology-oriented self-efficacy on people’s attitudes toward a technology.41,44,51 For instance, Buchanan et al.51 found that Internet-use efficacy is positively associated with behaviors related to usage of learning technologies. Particularly in terms of technology adoption and use, specification in the conceptualization of self-efficacy is helpful for investigating the direct effects of self-efficacy. Nevertheless, it is still meaningful to explore the roles of general self-efficacy in indirectly affecting the relationships among various factors associated with technology use. Particularly, general self-
Fig. 1. Main study model. Notes: CNF¼Confirmation, PU¼Perceived Usefulness, PEOU¼Perceived Ease of Use, SAT¼Satisfaction, CI¼Continuance Intention.
Please cite this article as: Cho J, Lee HE, Post-adoption beliefs and continuance intention of smart device use among people with physical disabilities, Disability and Health Journal, https://doi.org/10.1016/j.dhjo.2019.100878
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as a smartphone or tablet PC, at the time of data collection. The sample consisted of a larger number of male participants (64.9%), and the average age of the sample was 43.7 years (SD ¼ 11.7). More than half of the participants (57.5%) had some college education or higher degrees. In terms of employment status, only 46.9% of participants were currently employed. However, only half of those employed participants were employed full-time. The median monthly income was $1000~$2,000, indicating a relatively lower level of monthly income when compared to people without disabilities.
efficacy is strongly connected with many aspects in the lives of people with disabilities.53,54 As disabilities can limit people’s abilities to handle everyday life activities, such individuals often suffer from a lack of general self-efficacy. For example, people with impairments in movement find it difficult to leave their homes, eventually losing many opportunities to advance their education or careers, leading to lowered self-efficacy. This relatively lower level of self-efficacy affects the daily lives of this population in various ways. In terms of technology adoption and use, because people with disabilities have experienced lower levels of general selfefficacy for long amounts of time, other personality traits (e.g., openness, innovativeness), which generally tend to positively affect people’s acceptance of technology, are likely to have weaker effects. Therefore, through the following research question, this study investigated the moderating effects of general self-efficacy on the relationships among the main study variables regarding smart device use.
Instruments Instruments developed by previous studies were used to measure the main five study variables. All five variables were assessed with composite measures using a five-point Likert scale (1 ¼ Strongly disagree to 5 ¼ Strongly agree). All measures reached acceptable reliability scores; all Chronbach’s alpha scores were higher than 0.70. The correlations among the main study variables are given in Table 1.
RQ1. How does general self-efficacy moderate the relationships among the five study variables? Methods
Confirmation
Survey procedure and participants
To measure the PAM variable of confirmation, three items from the original scale proposed by Bhattacherjee26 were reworded corresponding to the study’s topic of smart devices. The following three items were used (M ¼ 3.72, SD ¼ 0.69, a ¼ 0.90): a) My experience of using smart devices is better than my initial expectation; b) The level of service provided by smart devices was higher than my initial expectation; and c) Smart devices provide more services than I initially expected.
In order to test the hypotheses, data was collected through an online and paper-and-pencil survey from participants recruited through purposive and convenient sampling methods. First, an invitation for the online survey was posted to a popular Koreabased online cafe targeted toward people with disabilities in movement and mobility. The online consent form specifically stated that the survey was intended for people with impairments in physical movement and mobility, and at the beginning of the online survey, people were asked to identify their physical disabilities from the following choices: movement and mobility disabilities associated with spinal cord injury, myelopathy, muscular dystrophy, brain disorder or injury, and/or other. Only those who were able to identify as having movement or mobility related disabilities as listed above were allowed to complete the questionnaire online. Next, the researchers enlisted the help of a Korean association for physical disability support for further data collection. This specific association is a non-profit organization, with five regional chapters across the nation, and primarily focuses on providing medical information for those with movement and mobility impairments, and organizing events for disability awareness and forums for policy-making initiatives. Paper-and-pencil questionnaires were collected from on-site visitors to the Seoul office, and the association distributed email invitations to its members. In total, 108 adults with movement and mobility disabilities participated in the survey. More than 40% of the participants had disabilities associated with spinal cord injury or myelopathy. All participants were currently using one or more smart devices, such
Satisfaction User satisfaction with smart devices was measured using three PAM items developed by Bhattacherjee.26 The original items were reworded to reflect the study’s focus on smart devices (M ¼ 3.71, SD ¼ 0.71, a ¼ 0.91): a) I am satisfied with the performance of smart devices; b) I have good experiences using smart devices; and c) My decision to use smart devices was wise. Perceived usefulness Perceived usefulness was measured by rewording the original TAM items developed by Davis, Bagozzi, and Warshaw.55 The following three items were used to measure the perceived usefulness of smart device use (M ¼ 3.94, SD ¼ 0.63, a ¼ 0.95): a) Smart devices are useful for my everyday life; b) Smart devices are advantageous to me; and c) Smart devices are valuable to me. Perceived ease of use The perceived ease of use smartphones was measured with
Table 1 Correlations among the main study variables.
1 2 3 4 5 6
Confirmation Satisfaction Perceived Usefulness Perceived Ease of Use Continuance Intention General Self-Efficacy
1
2
3
4
5
.826*** .653*** .464*** .647*** .381***
.716*** .588*** .686*** .388***
.554*** .729*** .335**
.530*** .206
. 448***
**p < .01, ***p < .001.
Please cite this article as: Cho J, Lee HE, Post-adoption beliefs and continuance intention of smart device use among people with physical disabilities, Disability and Health Journal, https://doi.org/10.1016/j.dhjo.2019.100878
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three revised items from Davis et al.‘s55 original TAM measure (M ¼ 3.63, SD ¼ 0.87, a ¼ 0.97): a) To me, it is easy to learn the instructions for using smart devices; b) To me, it is easy to understand the use of smart devices; and c) Smart devices are easy to use.
education or higher degree. Five (30%) of the participants held fulltime jobs, while two were employed part-time; the remaining participants were unemployed. The median monthly income was $1000~$2000.
Continuance intention
Results
Both PAM and TAM focused on the behavioral intentions to either adopt or continue use of a specific technology. In order to measure these intentions, three items proposed by Davis et al.55 were revised for this research focusing on smart devices (M ¼ 4.02, SD ¼ 0.74, a ¼ 0.96): a) I want to keep using smart devices; b) I plan to keep using smart devices; and c) I predict that I will keep using smart devices.
Results from survey: path model
General self-efficacy Schwarzer and Jerusalem’s56 original scale composed of twelve items was used to measure general self-efficacy (M ¼ 3.58, SD ¼ 0.70, a ¼ 0.96). Some examples of these items are: a) It is easy for me to stick to my aims and accomplish my goals; b) I can always manage to solve difficult problems if I try hard enough; c) I am confident that I could deal efficiently with unexpected events; d) I can usually handle whatever comes my way; and e) I can solve most problems if I invest the necessary effort. Focus group procedure and participants Qualitative data was also collected through focus group interviews in order to understand the experiences of the participants contextually and in more detail. The physical disability association mentioned above helped recruit the participants. Three focus groups were conducted, with five individuals in each group leading to a total of 15 participants. Group 1 consisted of participants with spinal cord injury or myelopathy; Group 2, participants with muscular dystrophy; and Group 3, participants with brain disorder/ injury. Each interview session lasted approximately 75 min, and all groups met for one session each. The majority of participants was male (n ¼ 11) with an average age of 44.3 years. More than half (n ¼ 9) had some college
A path analysis was conducted in order to test the proposed hypotheses. In order to check the model fit of the proposed model, three model fit indices were reviewed: standardized root mean squared residuals (SRMR, acceptable when lower than 0.08), comparative fit index (CFI, acceptable when higher than 0.90), and infinite fit index (IFI, acceptable when higher than 0.90). The path analysis results indicated acceptable model fit (c2(df ¼ 2) ¼ 15.74, CFI ¼ 0.95, IFI ¼ 0.95, RMSEA ¼ 0.07). H1 and H2 predicted the associations among the three main components of PAMdconfirmation, satisfaction, and continuance intentiondin regards to smart device use among people with physical disabilities. As Fig. 2 shows, confirmation strongly predicted participants’ satisfaction with using smart devices (b ¼ 0.60, p < .001), fully supporting H1. There was also a direct effect of satisfaction on continuance intention, and thus H2 was also supported (b ¼ 0.33, p ¼ .003). Next, H3 and H4 predicted relationships between confirmation and the two components of TAM: perceived usefulness (PU) and perceived ease of use (PEOU) of smart devices. Results from the path analysis supported both hypotheses, indicating that confirmation from smart device use was significantly and positively associated with both PU (b ¼ 0.62, p < .001) and PEOU (b ¼ 0.61, p < .001). While H5 and H6 focused on the relationships among PU, PEOU, and satisfaction in regards to smart device use, H7 and H8 hypothesized positive associations between the two components of TAM and continuance intention. First, both PU (b ¼ 0.28, p < .001) and PEOU (b ¼ 0.14, p ¼ .004) positively predicted satisfaction with using smart devices, confirming H5 and H6. Next, in regards to the effects of those two variables on continuance intention regarding smart device use, while PU was strongly and positively associated
Fig. 2. Results from the path analysis. Notes: CNF¼Confirmation, PU¼Perceived Usefulness, PEOU¼Perceived Ease of Use, SAT¼Satisfaction, CI¼Continuance Intention; Dotted line ¼ Insignificant path; *p < .001, **p < .01.
Please cite this article as: Cho J, Lee HE, Post-adoption beliefs and continuance intention of smart device use among people with physical disabilities, Disability and Health Journal, https://doi.org/10.1016/j.dhjo.2019.100878
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with continuous usage intentions (b ¼ 0.48, p < .001), PEOU was not significantly associated with participants’ intentions to continue use (b ¼ 0.10, p ¼ .15). In sum, while H7 was confirmed, H8 was rejected. According to the results from the path analysis, 43.7% of the variance of PU was explained through the inclusion of confirmation into the regression model (R2 ¼ 0.427). About 24% of the variance of PEOU was explained through confirmation (R2 ¼ 0.242). The inclusion of the three variables, PU, PEOU, and confirmation, into the regression model accounted for 76.4% of the variance of satisfaction (R2 ¼ 0.764). Lastly, 58.2% of the variance of continuance intention was explained by the three variables of PU, PEOU, and satisfaction. Moderating effects of general self-efficacy In order to examine the research question regarding the moderating effects of general self-efficacy (GSE) on the relationships among the main study variables, the sample was first divided into two groups based on a mean-based split for GSE. Then, a multigroup path analysis was conducted in order to compare how the relationships among the five study variables differed between the low and high GSE groups. Fisher’s Z-score for each pair of two regression coefficients was calculated and reviewed. As Table 2 shows, differences in two pathsdconfirmation to PU, PU to continuance intentiondwere observed to be statistically significant. In more detail, confirmation’s effect on PU was stronger in the lower GSE group (b ¼ 0.74) than it was in the higher GSE group (b ¼ 0.44). Further, PU’s effect on continuance intention was also stronger in the lower GSE group (b ¼ 0.76) than in the higher GSE group (b ¼ 0.31). Results from focus group interviews In order to more deeply understand how people with physical disabilities evaluate and think about smart devices, this study conducted a series of focus group interviews (FGIs). In the interviews, discussions revolved around how smart devices affected and changed the participants’ everyday lives and what meanings they assigned to those technologies. From qualitative data collected through the FGIs, the following main themes regarding smart devices were observed: 1) heavy dependence, 2) advances in task completion, and 3) ‘invaluable life-changer’. First, most FGI participants mentioned that they were heavily reliant on smart devices on a daily base. This was mainly because of two reasons. One was the convenience of smart devices, a quality strengthened by the mobility of the devices. Compared to other computer-based technologies, people with physical disabilities can more easily access and use smart devices for various purposes, spending a great amount of time on them to complete everyday life tasks. The other
reason was that, because people with physical disabilities have less entertainment or pass-time options, smart devices become their primary source of enjoyment (e.g., playing games, web surfing, online community participation). Second, participants often emphasized that they were now able to complete an increasing number of tasksdtasks that they were not able to previously conduct or finish. For example, unlike the past, participants mentioned that they were now able to 1) directly contact family members and friends without the help of their caregivers, thanks to voice-recognition services; 2) easily make reservations for various services, such as taxi call-services; 3) create official documents; 4) read e-books in more convenient ways; 5) use banking services; and so on. Here, it is important to consider that such an increase in the type of tasks that can be completed by oneself is directly and positively associated with an increase in general self-efficacy among people with physical disabilities. That is, as these individuals gain the ability to complete a greater number and diverse types of tasks, both simple (e.g., reading books) and relatively more complicated (e.g., online shopping, online banking), they were able to experience increased levels of efficacy. Third, as the two findings above indicate, participants were likely to rely on smart devices mainly due to ‘necessity’ as well as ‘limited alternatives’. As participants became more and more dependent on smart devices, the technologies had largely penetrated their everyday lives, taking on a crucial role. Therefore, when participants were asked to describe the personal meanings they prescribed to smart devices, the majority commonly addressed the significance of the devices. For example, they described their experiences in expressions such as, “life saver, critical part of my body, something indispensable, window to the world outside,” and “close friend.” In this way, FGI participants commonly and strongly emphasized the integral roles played by smart devices mostly in terms of the dramatic changes brought about in their ways of life and how smart devices now acted as the primary source of assistance. Particularly, it was observed that the participants’ active uses of smart devices led to heightened levels of self-efficacy. Therefore, these findings support the importance of analyzing the moderating effects of general self-efficacy on the relationships among the main variables of this study. Discussion This study had two major research objectives: 1) to identify the main factors that predict continuance intentions regarding use of smart devices among people with physical disabilities, and 2) to test the moderating effects of general self-efficacy on the relationships among post-adoption and technology acceptance variables. The results of a path analysis showed that most of the
Table 2 Differences between low and high general self-efficacy groups. IV
Confirmation Confirmation Confirmation Satisfaction PU PEOU PU PEOU
DV
Satisfaction PU PEOU CI Satisfaction Satisfaction CI CI
Low Self-Efficacy
High Self-Efficacy
Low Self-Efficacy
High Self-Efficacy
b
b
SE
SE
0.703 0.740 0.664 0.151 0.151 0.162 0.756 0.076
0.493 0.435 0.479 0.296 0.296 0.152 0.307 0.081
0.109 0.104 0.163 0.102 0.102 0.065 0.147 0.103
0.119 0.135 0.217 0.116 0.116 0.072 0.15 0.092
Z-Score
1.30 1.79* 0.68 0.94 0.94 0.10 2.14** 0.04
Notes: PU¼Perceived Usefulness, PEOU¼Perceived Ease of Use, CI¼Continuance Intention *p < .05, **p < .01.
Please cite this article as: Cho J, Lee HE, Post-adoption beliefs and continuance intention of smart device use among people with physical disabilities, Disability and Health Journal, https://doi.org/10.1016/j.dhjo.2019.100878
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hypotheses were supported, except for the effect of perceived ease of use on continuance intentions for using smart devices. Considering the high correlation score between perceived ease of use and continuance (r ¼ 0.53), the effect of perceived ease of use on continuance intention would be suppressed by the effects of the two other factors on continuance intention. In other words, because the perceived usefulness and satisfaction of using smart devices largely predicted continuance intention, the effect of perceived ease of use on continuance intention could have been weakened. Findings from the FGIs also support this interpretation. Smartphones were often considered a “life saver” for our participants with physical disabilities (i.e., smartphone use during emergency situations such as falling down in the bathroom), signifying that they experienced very high levels of smartphones’ perceived usefulness. Therefore, to the participants, although the perceived ease of use would be a significant factor for continuing to use smartphones, its direct effect on continuous usage may be significantly smaller than that of perceived usefulness. In other words, whether smart device use is considered to be easy or not, people with physical disabilities will tend to use it, mainly because of its usefulness. Indeed, most FGI participants strongly addressed the usefulness of smartphones in terms of various aspects. Next, another main finding of this study was the significant moderating effects of general self-efficacy on relationships among three variables, confirmation, perceived usefulness, and continuance intention of smart devices. The relationships among the three variables were significantly stronger among people with lower levels of general self-efficacy. These findings are quite interesting because according to previous research, general self-efficacy is often positively associated with people’s use of technologies. Such results would be explained through the extent to which people with physical disabilities were dependent on smart devices. According to FGI participants, smart devices play key roles in overcoming one’s limitations in physical mobility. For example, through mobile banking apps, people who are unable to use a PC or laptop due to serious physical disabilities, can now access and use banking services on their own. This is a huge advantage for such individuals as they can gain or recover autonomy over the management and protection of their financial assets. Here, it must be considered that general self-efficacy is often significantly and negatively associated with the extent of one’s disability. As many people with serious disabilities experience difficulty in completing routine, daily activities (e.g., brushing teeth, changing clothes, texting), their general self-efficacy tends to be low. Although this study did not measure the extent of disabilities of the participants, it is highly plausible that people with lower general self-efficacy may have more serious disabilities, experiencing greater physical limitations. Therefore, the diverse functions of smart devices, which allow these individuals to accomplish what was previously impossible, may have more strongly influenced participants with lower general self-efficacy, compared to those with higher general self-efficacy. This study holds the following implications. First, in spite of the many previous studies that have investigated smart device use among people with physical disabilities, there lack intensive examination of the mechanisms involved in continuance intentions regarding smart device use. Therefore, the findings of this present study provide researchers with opportunities to understand the fundamental motivators that lead people with physical disabilities to continue using smart devices and to comprehend how the use of smart technologies is associated with this population’s self-efficacy and everyday lives. Therefore, this study aids in developing novel and advanced models of explaining the unique use of smart devices among people with disabilities. Next, it is theoretically meaningful that unlike previous research, which had found positive effects of self-efficacy on
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technology use, this present study observed negative moderating effects of general self-efficacy on the relationships between the perceived usefulness of smart devices and the two variables of confirmation and continuance intention. As elaborated above, this is most likely due to the greater needs of technological support in individuals with lower general self-efficacy, which leads to increases in the perceived usefulness of smart devices. Such findings address the necessity to consider personal contexts in order to more thoroughly analyze the effects of self-efficacy in relation to use of new technologies. Finally, the findings provide general goals for smart-device developers, app developers, and health practitioners. These entities should consider further developing and refining technologies and services that can increase the usefulness and ease of use of smart devices as well as appsdspecifically from the perspective of people with disabilities. Current smart technologies and services are primarily targeted toward people without disabilities. However, as the findings show, more focused effort is required to reflect the wants and needs of those with disabilities so as to encourage their continued use of smart devices and enhance their experiences with the devices. Limitations and future directions This study’s limitations need be addressed and considered for future research. First, the study findings need to be interpreted with caution, considering the limitations of the sample. Specifically, the sample was relatively small and lacked representativeness in terms of certain qualities, such as gender, age, place of residence, and type of disability. Considering the relatively older and urban characteristics of the participants for this studydmostly due to the composition of the organizations used for sampling (i.e., online disability cafe and disability association)dfuture research will benefit by collecting data from younger individuals and/or those who live in rural areas, as different patterns of use are expected. Particularly, comparisons in regards to residential areas will be valuable for investigating how digital gaps among those populations may be exacerbated by the conditions of residence. Further, exclusion of those who are not members of the particular online cafe or support organization we relied on rendered it difficult to observe the digital experiences of those who lack in overall informational resources and social support networks. Finally, different types and degrees of physical impairments lead to differential use, and thus sophisticated categorization and inclusion of disabilities is needed. In order to improve sample representativeness, future research must take efforts to collect larger data sets from diverse groups of people with physical disabilities through multiple sampling sites. Next, people with disabilities have been considered media minorities with relatively lower levels of digital media literacy, mainly due to educational and economic inequalities. According to previous studies,57e59 media literacy is one of the most critical factors that determine individuals’ uses of digital technologies, especially smart devices. Therefore, researchers need to examine how media literacy levels might impact specific dimensions (i.e., accessibility, content creation skills, critical interpretation, etc.) of media literacy among people with physical disabilities. It will be helpful for developing practical strategies for media education targeted to such media minorities, aiding to overcome digital gaps as well as health gaps.60e62 Thirdly, it is also recommended for interested researchers to place more attention on the specific features and functions of smart devices. For the purpose of supporting people with physical disabilities, previous research has been devoted to the development and advancement of various assistive technologies (e.g., voice
Please cite this article as: Cho J, Lee HE, Post-adoption beliefs and continuance intention of smart device use among people with physical disabilities, Disability and Health Journal, https://doi.org/10.1016/j.dhjo.2019.100878
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recognition, eye movement recognition, etc.).3,4 Moreover, practitioners have also continuously taken great effort to developing numerous disability-aiding services that are provided through mobile apps (e.g., apps providing information regarding facilities for people with disabilities). Understanding of this topic regarding physical disabilities and smart devices will be further enriched with more focus given to the particular uses of assistive technologies and services and the effects such uses have on individuals in physical, mental, economic, and social aspects. Declaration of competing interest
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This manuscript has no conflict of interest and official funding. 30.
Acknowledgement 31.
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Please cite this article as: Cho J, Lee HE, Post-adoption beliefs and continuance intention of smart device use among people with physical disabilities, Disability and Health Journal, https://doi.org/10.1016/j.dhjo.2019.100878
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