How Important Are Enjoyment and Mobility for Mobile Applications?

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How Important Are Enjoyment and Mobility for Mobile Applications?

Article in Journal of Computer Information Systems · July 2016

DOI: 10.1080/08874417.2016.1181463

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How Important Are Enjoyment and Mobility for Mobile Applications?

June Lu^a, Chang Liu^b, and June Wei^c

aUniversity of Houston–Victoria, Sugar Land, TX, USA;^bNorthern Illinois University, DeKalb, IL, USA;^cThe University of West Florida, Pensacola, FL, USA

ABSTRACT

This article investigates how perceived enjoyment and mobility drive user continuance intentions toward using mobile applications. A second-stage continuance model was developed after a thorough literature review. A survey instrument was deployed to collect data from 584 smartphone users. The model was empirically tested using structural equation modeling procedures. Data analyses show that the salience of disconfirmation and beliefs in enjoyment and mobility serve as the primary driver of the changes in satisfaction and attitude toward continuance intentions. Furthermore, perceived enjoyment, mobility and satisfaction jointly explained over 60% of the variance in post-usage attitude. As an initial effort to revise and test the expanded IS continuance model, this study deepens our understanding of enjoyment and mobility at post-usage stage of mobile experience. It urges mobile application providers to forge continuance by devoting more resources and efforts toward creating a truly enjoyable as well as mobile experience.

KEYWORDS Mobile applications;

continuance intention;

mobility; enjoyment;

expectation–confirmation theory

Introduction

When iPhone from Apple emerged in the marketplace in 2007, it quickly caught the attentions of the individual consumers with its eye-catching touch-screen and highly perso- nalized, location-sensitive and context-aware applications [33]. Mobile applications refer to an assortment of computer programs designed to run on smartphones, tablet computers or other mobile devices supported by proprietary mobile operating systems [61]. Taking advantage of Wi-Fi and 4G Internet connections, the simple-to-use user interfaces and much better designed and more data-focused mobile apps, people can do most things on a mobile platform and keep in touch with their friends anywhere and anytime [32].

Currently, the US smartphone penetration hits 64% [37] and mobile app users exceed mobile Web users [61]. The smartphone-based apps have become so popular that they have become an integral part of the American life. To add to those practical values, the fancy looks of smartphones and attractive designs and features in mobile apps make it really appealing to own and to show off in the public.

However, the wireless broadband and mobile Internet today still suffer from usability problems including weak interoperability, unstable connection, and latency of data transmission [60]. Mobile apps are still constrained by bat- teries, memory, device, operating systems, and distribution limits [28]. Evidence has shown that mobile users have high expectations for mobile apps. Connectedness enabled by mobility and enjoyable experiences are among the most expected qualities in mobile applications [46, 61, 63]. If their expectations are not met in reality, the fate of mobile apps could be questionable [61]. Existing IS literature also shows

that users tend to evaluate to which extent their initial cognition agrees or disagrees with actual experience, and then revises their cognition to gain a higher level of agreement [1, 26, 62]. Long-term viability of an IS and its eventual success depend on its continued use rather than first-time use [3]. From a marketing perspective, retaining current cus- tomers is easier and more cost effective as well. Thus, we argue that it is the high time to pay special attention to these two aspects, to examine disconfirmation of expectations and to understand how changes in beliefs affect their mobile apps continuance intentions.

Our preliminary literature review reveals that user continuance intentions in mobile context are under-studied; and none uses the context of mobile applications [11,52]. Of those published studies, most examined the effect of hedonic motivation without paying enough heed to the mobility needs [6, 8, 57]. Very few studied mobility, but either did not show perception changes during the usage stage [20], or without considering the effect of hedonic needs [2]. Different from organizational use, mobile apps are mostly designed for individual uses at leisure time or on the go. Thus, using a mobile app becomes a manner of satisfying both hedonic and utilitarian needs. And satisfaction of hedonic needs, to a great extent, depends on mobility—being connected independent of location and time [46], a most desirable utilitarian strength of a mobile app. In fact, many mobile apps such as location- based services, mobile social media, and mobile search engines facilitate a dual purpose of providing for information or communication needs as well as taking pleasure from using them [33]. Without including both mobility and enjoyment in the same research model, thus, is impossible to gain correct understanding of mobile app user continuance intentions.

CONTACTJune Lu [email protected] University of Houston–Victoria, 14000 University Boulevard, Sugar Land, TX 77479, USA Color versions of one or more of the figures in the article can be found online atwww.tandfonline.com/ucis.

2017, VOL. 57, NO. 1, 1–12

http://dx.doi.org/10.1080/08874417.2016.1181463

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Furthermore, mobile apps are expanding and undergoing many changes. Mobile users are constantly adjusting their perceptions based on their usage experience, which can either enhance or lead to ex post reversal of their initial decision [3].

Thus, this study is specifically designed (1) to reveal if the utilitarian and hedonic determinants are both important in the continuance decision process toward mobile apps; (2) to explore if actual usage experience changes user perceptions toward continuance intentions; and (3) to discover if satisfaction of hedonic needs is influenced by perceived mobility.

Answers to those questions should contribute to discovering salient motivations and relationships underlying continuance intention toward mobile apps.

In this article, we first discuss the relevant literature and present a research model with a set of hypotheses. We then propose our research methodology and data analysis plan to guide our model testing. After reporting the test results, implications for research and practice are also discussed, prior to research limitations and future research directions.

Theory background

Users’post-adoption behaviors have emerged as a key topic in IS research in recent years. Prior studies proposed several important theory models, including the expectation–confirmation theory (ECT), technology acceptance model (TAM), uni- fied theory of acceptance and use of technology, its updated model (UTAUT, UTAUT2), and the latest integration of UTAUT and ECT in the expanded two-stage model of IS continuance. Those theories have one thing in common—to explain how human perceptions or beliefs are related to each other and determine IS usage for IS users. Those general models help to lay the nomological network of our research model.

ECT of IS continuance

The ECT of IS Continuance model explains continued IS usage [3, 34]. This model proclaims that continuance intention is influenced by user satisfaction and post-acceptance perceptions. It also declares that user satisfaction is determined by confirmation of expectations from users’prior usage and confirmation of expectations is also influenced by post-usage perceptions. This model has been revised to study many IS-related problems including post-usage satisfaction [41], IS continuance, changes in users’ beliefs and attitudes during the course of usage [3,4], and extended IS use in complex contexts [21].

However, this model is designed for cross-sectional study, and unable to guide study of changes in beliefs.

Based on the ECT of IS Continuance, Bhattacherjee and Premkumar [4] proposed a two-stage model to study the change in cognitive beliefs and their influences during the course of IS usage. This model posits disconfirmation (the dissonance between users’ original expectations and observed performance) equivalent to confirmation in essence [59] and satisfaction as determinants of continuance intention by affecting post-usage beliefs and post-usage attitudes. However, perceived usefulness, a typical utilitarian belief, was incorporated as the only usage-related construct. This approach neglects the effects of a variety of possible influencers on IS continuance.

UTAUT/UTAUT2 and expanded two-stage model of IS continuance

Venkatesh, Morris, Davis, and Davis [49] developed UTAUT model to consolidate previous TAM and TAM-related studies.

As a most mature stream of IS research, IS acceptance research through UTAUT categorize acceptance into early adoption and post adoption usage [50]. The strength of UTAUT for explaining usage is that it allows inclusion of additional determinants besides perceived usefulness and ease of use. UTAUT has been validated using data from workplaces at multiple time periods, and has been supported in a number of studies using different research contexts [36, 49, 55, 54]. However, the focus of UTAUT is mostly on IS acceptance in workplaces. Recently, UTAUT2 [52] extends UTAUT by using a consumer context and by incorporating constructs beyond utilitarian considerations—hedonic motivation (equivalent to perceived enjoyment), price value and habit. For all the changes in UTAUT2, the UTAUT models have not been built to discover disconfirmation of user beliefs, or changes in satisfaction, thus, not powerful enough for explaining continuance intention in a consumer context.

Venkatesh and his colleagues later extended Bhattacherjee and Premkumar’s model by incorporating predictors including effort expectancy, social influence, facilitating conditions and trust as a contextual belief from the UTAUT model. Such extension resulted in a more comprehensive set of beliefs [4,35,51].

The expanded model enables in-depth study of disconfirmation of all the included beliefs. However, some typical predictors emerged from mobile literature have not been studied using this approach.

Model and hypotheses development

To enhance our understanding of the post-adoption behavioral decisions toward mobile application usage, a revised continuance model is developed (Figure 1). Drawing on the ECT theory and the expanded model of IS continuance, this revised model emphasizes the usage stage of the continuance decision process. Drawing on UTAUT models, besides the two well-known cognitive beliefs—performance expectation (PE) and effort expectation (EE), this model emphasizes two major beliefs—perceived enjoyment from hedonic system acceptance model [27] and UTAUT2, and mobility from recent mobile technology literature [2, 56]. The argument is that many mobile applications are in fact designed to fulfill users’hedonic needs to take pleasure, in addition to satisfying their utilitarian needs to access information and to commu- nicate anywhere, any location and anytime. Disconfirmations of expectations for mobility and enjoyment have a positive influence on post-usage perceptions; such disconfirmation should influence satisfaction as the post-usage perceptions;

meanwhile, perceived mobility should provide an essential utilitarian basis for perceived enjoyment. Understanding those relationships is vital to comprehend their ultimate influences on continuance intentions in mobile app context.

Our model excludes facilitating conditions and social influence constructs presented in the UTUAT model. Facilitating conditions refers to the degree to which an individual believes

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that organizational and technical infrastructure can facilitate the IS usage. Social influence is the degree to which an individual perceives that others important to him or her believe that he or she should use the technology [49]. The impacts of these two constructs on users’behavioral intention have been extensively tested and discussed both in the original UTAUT model and other empirical studies. With high popu- larity of mobile smartphones in the United States, it would be safe to drop these two constructs to focus on mobility and enjoyment in the current research model, for their promi- nence in explaining mobile app user continuance. Similarly, it would be appropriate to exclude price, value and habit in UTAUT2 from our research model.

Mobility

Mobility was once regarded as an external constraint in the mobile environment, because of the concern that ubiquitous mobile information access might not necessarily be guaran- teed in reality. Thus, perceived mobility was explained as the extent to which mobile context is perceived as being able to provide pervasive and timely connections [20]. Rooted in the decomposed theory of planned behavior [43], Hong and his colleagues positioned mobility as a behavioral control variable in their research model, a construct with the belief that this factor might directly impede or facilitate the formulation of continuance intention. However, supporting evidence of such proposition was only found in the information content cate- gory. As the rapid proliferation of mobile technology in Korea, mobility caught the attention of Korean researchers as a most significant technical support of mobile Internet service [2]. Defined by Baek, Park, and Lee [2] as the degree of availability of on-the-move Internet service, mobility was technically believed including instant connectivity (by wireless and mobile networking) and location awareness, with a focus on the former, since the research context is mobile broadband. Baek and his colleagues further argued that the technical characteristics of a mobile service in general comprise mobility, high speed, diversity and convenience. This heavily

influenced the definition of usability in Wang and Li’s study [56]. Wang and Li proposed the concept of usability as a key attribute of mobile commerce comprising network ubiquity (vs. instant connectivity), location-awareness and convenience. This line of research regarded mobility as an indispensable attribute comprising separate functions to support human needs for mobile services. This line of research believed that attributes of mobile services work on behavioral intention through attitude change. The improvement in attributes will enhance evaluation of use experience and thus lead to changes in behavioral intentions [56].

So far, mobility has been treated as a first-order conceptual construct or under the umbrella of other construct in the studies. Findings on mobility are intertwined. Network ubiquity and location awareness are found to have positive effect on intentions to adopt mobile commerce [25,58]. Studies also showed mobility a significant determinant of mobile adoption intentions [22]. Baek and his colleagues [2] studied mobility as a first-order antecedent in a TAM-based model. The empirical data strongly supported the effect of mobility on continuance intentions mediated by usefulness and ease of use. However, usability (comprising network ubiquity, location-awareness and convenience) was not found to have any significant effect on attitudinal changes [56].

Using a system acceptance perspective, we believe that perceived mobility is the degree to which a person believes that using mobile apps would enhance his or her capability to cope with tasks and activities while on the move. In mobile contexts, perceived mobility represents an overall feeling toward the effect of mobile system characteristics.

Such perception could be even more relevant and instrumental to acceptance than the famous IS acceptance determinants of performance expectancy (usefulness) and effort expectancy (ease of use). However, mobility is never men- tioned in any established theory models such as ECT, TAM, or UTAUT. For the importance of mobile economy at present, perceived mobility deserves adequate attention in theory development. Moreover, anecdotal evidence [5] and field observations [27] from previous studies reveal that

H1e

Disconfirmation

H1f H2e

H2d H2b

H2a

Post-usage Beliefs PE

H1b H1c

H2c H1d

EE

Enjoyment

Mobility

H1a

EE

Mobility

Enjoyment

Satisfaction Post-usage

Attitude

Continuance Intention

Figure 1.A revised mobile application continuance model.

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perceived technical quality change serves as a key influence on hedonic perceptions. The explanation is that improved technological features and capabilities embedded in a system tend to provide users greater opportunities for deriving delight or excitement from the usage experience. Increased mobility, as the result of improved mobile systems and applications, indisputably enhances perceived enjoyment since mobile users can access their chosen data services anytime, anywhere and anyplace.

As indicated in the ECT and the expanded IS continuance model, users’ pre-usage perceptions undergo a disconfirmation process, which, in turn, influence post-usage perceptions and satisfaction. The adjusted post-usage perceptions and satisfaction, subsequently, influence post-usage attitude and continuance intention. Disconfirmation may be positive or negative depending on whether the observed performance is above or below initial expectations. By this logic, positive disconfirmation of perceived mobility will promote post- usage mobility perception. Such disconfirmation should positively relate to satisfaction as the post-usage perceived benefits, because it implies affirmation of achieving the expected benefits of system use. To keep in line with the findings in adoption and post adoption studies, we posit the following hypotheses:

H1a: Positive disconfirmation of perceived mobility has a positive influence on post-usage perceived mobility of mobile applications.

H1b: Positive disconfirmation of perceived mobility has a positive influence on satisfaction with mobile applications.

H1c: Post-usage perceived mobility has a positive influence on satisfaction with mobile applications.

H1d: Post-usage mobility has a positive influence on post- usage perceived enjoyment of mobile applications.

H1e: Post-usage perceived mobility has a positive influence on post-usage attitude toward mobile applications.

H1f: Post-usage perceived mobility has a positive influence on continuance intention toward mobile applications.

Enjoyment

Perceived enjoyment is defined as the excitement and happi- ness derived from use of a system in its own right [47].

Therefore, it refers to the degree of hedonic pleasure that users have experienced in using mobile applications in this article. Enjoyment has been extensively researched in the domain of consumer behavior. Enjoyment as a specific hedonic experience refers to the sensations derived from the experience of using products—the fun and the resulting feeling of pleasure [18, 53]. Hedonic enjoyment is intrinsic, in that it is an end in itself, and personally meaningful [17].

Such intrinsic characteristic of hedonic enjoyment is positively related to an inner need to keep an individual at an optimal, preferred state of comfort, congruent with external stimulation [13]. If the experience of use confirms or positively disconfirms the desired pleasant feeling, enjoyment is reinforced; otherwise, such enjoyment is lowered or destroyed.

Enjoyment, as an intrinsic motivator, is found positively related to usage of the early mobile services [12, 45]. One plausible reason is that mobile users mostly use MDSs to kill the time, for leisure purposes or on the move. The wide variety of information incorporating multimedia features on the wireless Internet coupled with aesthetic designs of application interfaces facilitate fulfillment of such need. Lin and Bhattacherjee [27] in their hedonic system acceptance model found that enjoyment works through user attitudes in the context of hedonic systems.

As a marketing strategy, mobile application providers are increasingly developing programs that incorporate enjoyable and entertaining features in product designs, content and functions, and thus providing contextual space for personal hedonistic fun and gratification. A mobile application user may enjoy hedonic rewards due to the availability of the desired stimuli that arouse the individual’s attention to process such stimuli in online gaming, interesting information feeds, social networking experience, etc. Empirical studies have yielded some positive findings. In using mobile phones, the effect of perceived usefulness can be marginal when compared to enjoyment [19]. Though not included in the original TAM, perceived enjoyment is recently identified the strongest of the four key predictors of mobile shopping website use [28]. Studies using revised TAM models recognize that both utilitarian (i.e., usefulness) and hedonic constructs (enjoyable) predict adoption intention or system use regardless of research contexts or countries [7, 44,29].

Our literature review shows that Korean researchers are among the earliest to report the positive effects of perceived enjoyment on the intention to reuse in mobile context.

Kim, Park, and Oh [23] recently found enjoyment one of the four major factors that directly affect Korean mobile phone users’continued intention to use SMS. By UTAUT2, the most sophisticated version of TAM, Venkatesh, Thong and Xu [52] declared hedonic motivation a more critical driver than performance expectancy for continued mobile Internet use. However, enjoyment is not regarded as a continuance predictor in the stream of ECT models; not even in the expanded IS continuance model. Understanding of how perceived enjoyment works in the continuance decision process is just beginning. According to the ECT- related process models, confirmation or disconfirmation of expectations from prior use determine post-usage satisfaction; satisfaction, changes in users’ beliefs and attitudes during the course of usage contribute to formation of continuance intention. Assuming the correctness of those process models, perceived enjoyment as a hedonic determinant should follow the same pattern. Thus, we postulate the following hypotheses:

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H2a: Positive disconfirmation of perceived enjoyment has a positive influence on post-usage perceived enjoyment of mobile applications.

H2b: Positive disconfirmation of perceived enjoyment has a positive influence on satisfaction with mobile applications.

H2c: Post-usage perceived enjoyment has a positive influence on satisfaction with mobile applications.

H2d: Post-usage perceived enjoyment has a positive influence on post-usage attitude toward mobile applications.

H2e: Post-usage perceived enjoyment has a positive influence on continuance intention toward mobile applications.

Performance expectancy, effort expectancy, satisfaction, post-usage attitude and continuance intention

PE and EE have been found instrumental predictors of IT/IS acceptance intention in TAM, UTAUT and other revised models. The expanded two-stage model of IS continuance [51] keeps these two constructs and argues that their roles in disconfirmation and post-usage are likely to hold true in continuance contexts. Satisfaction, as an affect construct, is regarded the result of expectation–confirmation and cognitive beliefs and a predictor of post adoption behavioral decisions in the IS Continuance Model [3]. Bhattacherjee and Premkumar, in their two-stage model of belief and attitude [4], discovered satisfaction as an emergent construct affecting attitudes that, in turn, influence continuance intention. They, therefore, suggested that satisfaction need to be included in future process models of IT usage. The latest expanded IS continuance model [51] contains the constructs of satisfaction, attitude and continuance intention to help achieve their research objective. For the same reason our revised model retain those stated constructs, not as the focus of our study but as the indispensable parts of the entire decision process.

Methodology

Instrument development

We used an online survey to collect data on mobile users’ perceptions and continuance intentions toward mobile applications. Almost all the items are adapted from those used in previous studies. Specifically, items on mobility were adapted from the instruments developed by Hong and his colleagues [20]. Items measuring perceived enjoyment were adapted from Moon and Kim’s study [30]. Items on satisfaction, post-usage attitude and continuance intention were borrowed from Venkatesh and his colleagues [51]. Those items were modified to fit the mobile applications context that we examined. Seven-point Likert scales, with anchors ranging from

“strongly disagree” to “strongly agree,” were used for most scale questions to ensure consistency with previous studies, except the items measuring disconfirmation of enjoyment and mobility. Those items and scales were modeled after relevant

items by Venkatesh and his colleagues [51]. For easy refer- ence, we list the items and scales used in the Appendix.

Though most constructs in the model had been validated in previous studies, a pilot study was conducted in the beginning of spring 2012 using 100 MBA students taking MIS course from a regional university in Texas, mostly because the items on disconfirmation constructs were newly created for this study. Ninety-one participants provided valid data with some very good comments on wording and format changes. Some recommended changes were integrated in the final version. The scale reliability tests revealed Cronbach’s alpha values of above .70 on all the model constructs, except that for disconfirmation of EE (0.61). Two items were found using a scale in reversed order.

Sample

Our target population was the voluntary users of smartphone mobile applications in the United States. Table 1 lists the demographic descriptives of our sample. Majority (80.3%) of our 584 survey participants was between the ages of 21 and 40. According to the statistics from Yahoo Advertising Team based on the latest report from Pew Internet and American Life Project (2013), 57% of the American adults use mobile data services on the smartphone platform. Smartphone own- ers are mostly in the age range of 18–44. Our respondents seemed to be within this age range with higher percentage of mobile apps usage. The majority of the respondents had college or university education (55%), closely followed by those with graduate education (39%), which is mostly in line with the description of mobile Internet users—well-educated adults in last year’s report [39]. Our survey covered more of

Table 1.Descriptives of sample and their MDS uses.

Variable Frequency Percentage (%)

Gender

Male 273 46.4

Female 311 53.1

Age≤20 24 4.1

21-30 285 48.8

31-40 184 31.5

41-50 60 10.3

>50 31 5.3

Education

High school 35 6.0

College/university 321 55

Graduate 228 39

Occupation

Manual worker 17 2.9

Line employee 16 2.7

Unemployed 17 2.9

Managerial 95 16.3

Self-employed 41 7

Educational 28 4.8

Student 139 23.8

Professional 230 39.4

Mobility

High 195 33.4

Medium 290 49.7

Low 97 16.6

Length of use

≤1 Year 70 12

1–2 years 162 27.7

3–5 years 200 34.2

>5 years 139 23.8

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professionals (39%) and students (24%) with medium to high level of mobility. This was closely associated with our data collection method, since many of our respondents were MBA students with a full-time job.

Data collection

Data were collected using an online survey from undergrad- uate and graduate students from three universities in Florida, Illinois, and Texas, respectively. Those students were encour- aged to invite their social circles to participate as well. This survey was active from April to June 2012. Year of 2012 showed a great surge number of mobile application uses.

The variety and quality of mobile apps have been growing steadily recently. But no significant changes have been noticed in the mobile app architecture [61]. The needs for mobility and enjoyment should not disappear. Seven-hundred and seventy people responded to our survey. Among them, 626 were smartphone users with 584 valid data points. Data from 533 (69%) mobile search users were used to test the research model. Statistical packages IBM SPSS 20 and Amos Graphics 20 were used to perform scale reliability tests, factor analyses, and structural equation modeling procedures.

Results

We first examined the data distribution and the general pattern of the data collected on the construct variables. All the univariate skew indexes and kurtosis values indicate a minor non-symmetric distribution (with average skewness of–0.869 and average kurtosis of 1.72). According to Sheng and Sheng [40], non-symmetric distributions with positive kurtosis usually does not result in a larger alpha value than normal distribution, but probably a much smaller average of alpha with a larger SE. Since our valid sample size is 533, such non- symmetric distribution situation should not be a major concern [15].

In order to ensure that the variables in our study were internally consistent, Cronbach’s alpha was used to assess reliability. Detailed alpha values are included in Table 2.

Internal consistency reliability coefficients for research constructs under study are mostly above the commonly accepted level of 0.70 [24], except that for Disconfirmation of ease of use (0.60). Alpha, being a function of the number of items on a scale, tends to produce a lower value with fewer items 10].

With only two items left of Disconfirmation of ease of use, this result is considered acceptable. We then conducted a principal components factor analysis adopting rotation method of varimax with Kaiser normalization. The seven factors extracted 78% of the variance.

Harman’s one-factor test was conducted to test significance of common method variance (CMV) [16]. The results indicate that the explained variance of a single factor is about 35%.

The effect of CMV is obvious but should not be significant in our study. Common latent factor (CLF) analysis was then conducted in Amos to detect precisely the effect of CMV and the sources among all observed variables in the model.

We observed that the method factor had a significant p value, though the average variance explained by the variance of the

indicators reached 0.701. We then compared the standardized regression weights from the measurement model tests with and without the CLF by subtraction. The subtraction results on the indicators in Satisfaction construct and on those in Attitude construct are greater than 0.2 which usually lead to a method variance of greater than 0.3. To ensure that our findings are not contaminated by CMV bias, we retained the CLF in the structural model. The hypotheses testing results are, thus, CMV factor adjusted.

Following the two-step analytical procedures of structural equation modeling (SEM) [15], we first examined the fit of measurement model and then of the structural model.

The measurement model

We first examined the validity for all the constructs in our proposed model using the confirmatory factor analysis (CFA).

The model included 38 observed variables (Two were dropped for low factor loading values) describing 11 latent constructs:

Disconfirmation of performance expectancy, disconfirmation of effort expectancy, disconfirmation of mobility, disconfirmation of enjoyment, post-usage performance expectancy, post-usage effort expectancy, post-usage mobility, post-usage enjoyment, satisfaction, post-usage attitude, and continuance intention. We present important descriptive results and factor

Table 2.Descriptives and factor loadings.

Scale item Item mean Item SD Item loading* Cronbach’s alpha Discon-Mobility 1 4.84 1.487 .192 (deleted) 0.72 Discon-Mobility 2 4.15 1.568 .553

Discon-Mobility 3 3.96 1.593 .713

Discon-Enjoy 1 4.84 1.487 .527 0.91

Discon-Enjoy 2 4.85 1.268 .620 Discon-Enjoy 3 4.76 1.234 .680 Discon-Enjoy 4 4.82 1.306 .684

Discon-Ease 1 5.13 1.410 .122(deleted) 0.61

Discon-Ease 2 5.29 1.121 .684

Discon-Ease 3 4.53 1.482 .795

Discon-Ease 4 5.12 1.517 .246(deleted)

Discon-Use 1 4.74 1.411 .513 0.90

Discon-Use 2 4.68 1.406 .620

Discon-Use 3 4.67 1.417 .676

Discon-Use 4 4.69 1.382 .644

Discon-Use 5 4.63 1.432 .530

Mobility 1 5.92 1.235 .241(deleted) 0.74

Mobility 2 4.88 1.555 .685

Mobility 3 3.97 1.830 .634

Enjoyment 1 5.47 1.290 .636 0.93

Enjoyment 2 5.40 1.339 .732

Enjoyment 3 5.21 1.346 .720

Enjoyment 4 5.29 1.242 .643

Ease 1 5.81 1.301 .620 0.88

Ease 2 5.55 1.431 .582

Ease 3 5.79 1.279 .696

Ease 4 5.79 1.232 .659

Useful 1 5.85 1.216 .639 0.87

Useful 2 5.66 1.348 .759

Useful 3 5.53 1.351 .693

Useful 4 4.88 1.612 .409 (deleted)

Useful 5 5.42 1.352 .418 (deleted)

Satisfaction 1 5.77 1.177 .610 0.94

Satisfaction 2 5.81 1.124 .751 Satisfaction 3 5.83 1.133 .719

Attitude 1 5.87 1.128 .683 0.95

Attitude 2 5.93 1.121 .737

Attitude 3 5.84 1.142 .694

Con-Intention 1 6.20 1.299 .836 0.91

Con-Intention 2 6.18 1.307 .833

*All the factor loading values are corrected controlling for CMV.

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loadings inTable 2. The average variance extracted (AVE) for every construct was over 0.5 [14]. Hence, the conditions for convergent validity were met.

The discriminant validity was also examined by comparing the square root of the AVE of each factor and the correlation coefficients with other factors, using Fomell and Larcker’s [14] criterion. As the square root of the AVE was larger than the corresponding correlation coefficient between the factors, we conclude acceptable discriminant validity (seeTable 3). As the MSV values and the ASV values are less than the AVE, the constructs validity is also confirmed.

The goodness of fit of the overall CFA model was also examined. Since the data set contains missing data, only incremental fit indices are reported. The results showed that an adequate model fit have been achieved using the empirical data (see Table 4). We are ready to move to the next level—hypotheses testing.

The structural model

To test our research model and hypothesized associations, we developed a path model accordingly. Specifically, we examined our proposed model fit and the hypothesized relationships by inspecting the significance and strength of hypothesized effects and comparing relative effect sizes for the dependent variables. Model indexes indicate a moderately acceptable fit (Table 4).

Results of the research model testing, including path coefficients, path significances, and variance explained (R² values) for every hypothesized relationship and the

relevant dependent variable, are shown in Figure 2.

Regression weights of path analysis reveal that both disconfirmation of perceived mobility and disconfirmation of perceived enjoyment strongly influenced the post-usage mobility and post-usage enjoyment (0.345***, 0.404***) as hypothesized, but neither has any significant direct impact on satisfaction in our study. Thus, hypotheses H1a and H2a are supported, H1b and H2b, rejected. Post-usage mobility influences post-usage enjoyment (0.162***) and post-usage attitude (0.104**) as posited. But its influence on satisfaction or continuance intention is not statistically significant.

Thus, H1d and H1e are supported, H1c and H1f, rejected.

Most hypothesized relationships regarding post-usage enjoyment are supported by the empirical data (0.426***, 0.290***), except its impact on continuance intention. Thus, H2c and H2d are accepted and H2e, rejected. In line with the previous findings in literature, satisfaction strongly influenced post-usage attitude which, in turn, significantly influenced continuance intention. In comparison, post- usage effort expectancy is a stronger predictor of continuance intention in our study. Out of 11 hypothesized relationships, the data analysis results confirmed six. Use of a convenience sample without considering any mediating or moderating effects might have certain impact on the results.

Relevant details are listed in Table 5.

To detect the influence of the CMV bias in our research, we compared all hypothesis testing results from the structural model containing CLF with those from the test without CLF. Despite some minor differences in magnitude, all the findings remain the same except that for H1f. The post- usage perceived mobility would have some weak positive influence on continuance intention (estimate = .097, SE = .047, CR = 2.074, P = .038), if the structural model without CLF was used.

For the rejected hypotheses, we examined the indirect effects of the antecedents for mediation effects. The SEM test shows an indirect effect of .124 from disconfirmation of expected enjoyment to satisfaction. Sobel test reveals post- usage enjoyment having a strong mediation effect (4.852***).

The Sobel test also supports that the effect of post-usage enjoyment on continuance intention is mostly mediated by post-usage attitude (2.317**) in our study.

Table 4.Fit indices of the measurement model, the structural model, and recommended values.

Fit index X²/df RMSEA NFI RFI IFI TLI CFI

Measurement model 2.310 .050 .916 .902 .951 .942 .950 Structural model 2.439 .052 .907 .897 .943 .936 .943 Recommended value < 3 <.08 >.90 >.90 >.90 >.90 >.90 Note:X²/df– chi-square to degree of freedom ratio; RMSEA–root mean

square error of approximation; NFI –normed fit index; RFI– relative fit index, IFI – incremental fit index; CFI – Tucker–Lewis coefficient;

CFI–comparative fit index.

Table 3.Reliability, convergent and discriminant validity, and correlation matrix (N= 533).

Factor CR AVE MSV ASV DMO DEN DUS DEA MOB ENJ USE EAS SAT ATT CIN

DMO 0.69 0.644 0.410 0.355 0.802

DEN 0.84 0.702 0.422 0.298 0.096 0.838

DUS 0.83 0.702 0.487 0.374 0.082 0.211 0.838

DEA 0.74 0.616 0.431 0.384 0.038 0.007 0.017 0.785

MOB 0.69 0.610 0.455 0.391 0.311 0.041 0.153 0.014 0.781

ENJ 0.90 0.762 0.347 0.304 0.036 0.123 0.052 0.002 0.278 0.873

USE 0.86 0.761 0.287 0.239 0.003 0.010 0.085 0.011 0.153 0.181 0.872

EAS 0.83 0.675 0.447 0.324 0.033 0.115 0.092 0.223 0.214 0.197 0.190 0.822

SAT 0.87 0.839 0.291 0.160 0.084 0.066 0.056 0.035 0.146 0.362 0.183 0.319 0.877

ATT 0.91 0.858 0.175 0.142 0.122 0.028 0.016 0.017 0.096 0.394 0.183 0.288 0.460 0.881

CIN 0.94 0.882 0.130 0.105 0.070 0.210 0.170 0.015 0.008 0.207 0.165 0.546 0.359 0.268 0.939

Construct legend: DMO–Disconfirmation of Mobility, DEN–Disconfirmation of Enjoyment, DUS–Disconfirmation of Usefulness, DEA–Disconfirmation of Ease of Use, MOB–Mobility, ENJ–Enjoyment, USE–Usefulness, EAS–Ease of Use, SAT–Satisfaction, ATT–Attitude, CIN–Continuance Intention.

Note: CR–Composite Reliability, AVE = average variance extracted, MSV = Maximum Shared Squared Variance, ASV = Average Shared Squared Variance. Values indicating square roots of AVEs are bolded and listed diagonally in the matrix.

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Discussions

Discussion of findings

The purpose of this article is to validate a revised model, to reveal how actual usage experience impacts user perceptions toward mobile applications; and to explore whether the utilitarian and hedonic determinants are both important in the continuance decision process.

Our study reveals that in smartphone mobile apps context, positive disconfirmation of expectations for enjoyment and mobility strongly and positively affected formation of post- usage perceptions in the same aspects. Such influences are mediated onto satisfaction and attitude via the post-usage perceptions, which serve as strong predictors of attitude toward continuance intention. These findings, to a certain extent, support our argument that both the hedonic (enjoyment) and utilitarian (mobility) constructs play crucial roles in the post- usage decision making process. Although our empirical data

did not support these two factors as better predictors than the classic determinants of PE and EE, the connotation is clear that adequate attention much be given to these two expectancies when forging continuance with smartphone mobile apps.

In comparison with the effect of the utilitarian construct (mobility), the hedonic construct (enjoyment) seems to play a more stable and stronger role in our decision model—the impacts on satisfaction and attitude even outshine those from EE and PE. This finding seems to suggest that in smartphone context, mobile applications users in this country are more after its hedonic pleasure than its utilitarian value [45], and such value works through user attitudes [27]. On the other hand, the strong positive influence from post-usage mobility on post-usage enjoyment as shown in our study adds another piece of positive evidence that improvement in utilitarian value tends to enhance users’hedonic pleasure [27].

No doubt that perceived mobility and perceived enjoyment are two emergent drivers of changes in post-usage perceptions

Table 5.Hypotheses testing results.

Hypothesis Results (estimate, SE, CR, P)

H1a: Positive disconfirmation of perceived mobility has a positive influence on post-usage perceived mobility. Supported (.404, .103, 3.934, ***) H1b: Positive disconfirmation of perceived mobility has a positive influence on satisfaction. Rejected (.05, .048, 1.024, .306) H1c: Post-usage perceived mobility has a positive influence on satisfaction. Rejected (.037, .032, 1.159, .246) H1d: Post-usage perceived mobility has a positive influence on post-usage perceived enjoyment. Supported (.162, .046, 3.521, ***) H1e: Post-usage perceived mobility has a positive influence on post-usage attitude. Supported (.104, .033, 3.147, **) H1f: Post-usage perceived mobility has a positive influence on continuance intention. Rejected (.086, .048, 1.792, .074) H2a: Positive disconfirmation of perceived enjoyment has a positive influence on post-usage perceived enjoyment. Supported (.345, .059, 5.888, ***) H2b: Positive disconfirmation of perceived enjoyment has a positive influence on satisfaction. Rejected (.112, .052, 2.138, .053) H2c: Post-usage perceived enjoyment has a positive influence on satisfaction. Supported (.426, .049, 8.631, ***) H2d: Post-usage perceived enjoyment has a positive influence on post-usage attitude. Supported (.290, .043, 6.763, ***) H2e: Post-usage perceived enjoyment has a positive influence on continuance intention. Rejected (.100, .080, 1.251, .211)

.290***

.404***

Path significances: ***p<0.001; **p<0.01; *p<.05.

Parentheses indicate R²values for dependent variables.

A relation not supported in the current research is represented by A relation not the focus of the current research is represented by

Disconfirmation

EE

.104**

.345***

Post-usage Beliefs

EE

Continuance Intention (.35) Satisfaction

(.42)

Post-usage Attitude (.63)

H1d

Enjoyment Enjoyment

(.27)

Mobility (.16)

.162***

.426***

Mobility

Figure 2.Research model supported by empirical data.

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which help to predict user attitude toward continued use of smartphone mobile apps in the USA. Our study also confirms that post-usage attitude is explained jointly by satisfaction and post-usage beliefs, and serves as a critical mediator between post-usage perceptions and continuance intention [4,51].

Unlike the findings from Venkatesh and colleagues’ study [51], satisfaction in our project was explained mainly by the post-usage beliefs, instead of disconfirmation of expectations.

Disconfirmation of expectations mainly predicted post-usage beliefs in our study. These results seemed to suggest that post- usage beliefs are more influential to the formation of satisfaction.

This finding is different from what’s postulated in the original IT continuance model that users’extent of confirmation or disconfirmation is positively associated with their satisfaction with IS use [3]. There are two explanations for the differing results.

Oliver [34], in his original conceptualization of EDT, specifies that disconfirmation and initial expectation jointly determine user satisfaction or dissatisfaction with the product. In our model, initial expectation was not studied, which may, to certain extent, account for the results. On the other hand, in psychology, disconfirmation of expectation, in many situations, can be a confirmatory bias based on selective memory of information or biased interpretation [31]. The effect of such subjective adjust- ment in belief on satisfaction, in logic, can hardly be as strong as that of the cognitive beliefs adjusted by direct first-hand experience. IS researchers also claimed that the magnitudes of disconfirmation, belief change, are expected to decrease in magnitude as the user gains more direct experience in time [42]. The effect of disconfirmation on satisfaction, in turn, is likely to reduce as well, as the user accumulates more objective, experience based perception of the product. In our study, almost 60% of the participants had three to five or more years of experience using mobile apps on smartphones. The discovered weak effect of disconfirmation on satisfaction may simply confirm that changes in affect can be determined more by an experienced user’s post-usage cognitive beliefs, than by disconfirmation of expectations of the system. Since this finding is different from most prior findings regarding disconfirmation in the continuance decision making process, more evidence is needed to ascer- tain the true nature of the impact from disconfirmation.

Unlike the prior findings in the literature [2, 4, 51, 52], users’ continuance intention in our study were explained mostly by perceived post-usage EE (0.60, ***). Our finding, however, supports a previous finding on EE’s effect in mobile context [20]. As another additional finding, our study confirmed the influence of perceived mobility on EE as discovered before [2]. Such findings could be highly context dependent and explained by the selected expectations for mobile apps in the first place. Based on a recent global study [9], vast majority of the users (85%) preferred mobile apps over mobile websites for convenience, speed, and ease of use. Numerous sources confirm that those mobile apps dis- tributed on proprietary platforms such as App Store, Android Market, and others are mostly for leisure purposes and convenience. The strengthened effect of EE and the weakened effect of PE in our study, thus, could be simply a truthful reflection of reality. It is also plausible that such strong need for ease of use is not independent of the desired level of mobility. We admit that variance in continuance intention

explained by our model is not very high (.35). However, our findings are reported with CMV bias under control. Thus, the magnitude of findings in this study is still worth attention.

Limitations of the study

This study has some limitations: (1) the research context of smartphone-based mobile applications is broadly defined. Even though only the mobile search user data were analyzed, their responses could be somehow influenced by their general impres- sion of the applications on a specific mobile platform. (2) It has certain level of common method bias. Due to the way data was collected in our study, certain CMV bias might be unavoidable [38]. Fortunately, the CMV was not extremely high in our study.

And the research model was tested with CMV under control.

The comparison between the hypotheses testing results with and without the control also shows that the CMV does not have significant impact on our study results. (3) One mobility indi- cator (“Mobile applications are available to me anytime and anywhere I go.”) was deleted for low loading value during model testing. The same happened to Disconfirmation of Mobility construct. Only two indicators were used for hypotheses testing regarding the relevant constructs. This might affect the results to a certain extent. This construct, in comparison with others in our research model, is comparatively new. Further effort may be needed to research the topic of mobility and revise the items in future studies. (4) Despite incorporation of disconfirmation into two primary predictors in our research model, our study has a limitation for adopting a single-stage cross- sectional design. We are unable to estimate the magnitude of post-usage beliefs that is attributed to the pre-usage beliefs; and the findings on satisfaction might also be somehow affected.

Replication efforts using a longitudinal design may help to examine the robustness and the validity of our findings.

Implications for research

This study is able to add some value to the existing literature.

The potential contributions are in the following aspects:

First, this is one of the first efforts to revise and test the expanded IS continuance model. Thus, it is able to examine, to some extent, the value of the expanded model in the context of mobile applications. Since this study confirms and disconfirms certain hypothesized relationships, it is, thus, able to deepen our understanding of the dynamics in relative importance of some key beliefs at post-usage stage of experience.

Second, by highlighting the roles of mobility and enjoyment, our research model is able to advance the understanding of the typical drivers of consumer mobile application uses.

This extension is a response to recent calls for incorporating constructs to assist the design of relevant interventions in different research contexts [48, 50]. It should also contribute to the growing body of research emphasizing the importance of both cognitive and affective elements in IS studies.

Third, the strong ECT element in our model helps not only to examine the dynamic effects of the beliefs during the disconfirmation process, the speculation of how different beliefs may influence each other during the process also helps to deepen our understanding of the behavioral decision process. Placing

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mobility, enjoyment, PE and EE at the same level in a continuance model, this study has discovered the impact of perceived mobility on post-usage enjoyment, as well as EE. This approach has enabled us to compare the magnitude of influences as well as any crossover effects from those beliefs during the decision process.

Finally, mobility represents an essential difference between desktop technologies and mobile technologies, and the growing need for connectedness. Such need has been ignored in most existing IS models. Thus, exploring how mobility acts on and interacts with other drivers should help to enrich IS continuance literature in general.

Implications for practice

This study provides some implications for managing mobile apps in practice. From a user retention perspective, mobile app providers should forge continuance intentions by increasing user satisfactions with the app quality and functionality.

To enhance user satisfaction, mobile app providers should devote more resources and efforts toward creating a truly enjoyable as well as mobile experience – connectedness and quality consistency independent of physical location. Our study results reveal an obvious space for increasing mobility of mobile apps. Almost 40% respondents thought that the quality was affected pending where they were. Obviously, great effort is need to enable larger network coverage, better throughput, and reduced wireless latency, and to offer more varied location-based data services. Mobile cloud computing and better roaming across platforms may be instrumental. Of course, higher level of mobility which provide for ubiquitous computing can only be forged by collaborative effort between mobile app providers, mobile networking operators, and reg- ulations and policy makers. Thus, adequate attention should also be given to improving the business alliances and relationship maintenance with policy makers.

Our study results were derived from responses of smartphone app users. Thus, how to keep them interested in and enjoy the actual experience of use becomes another critical measure for value. The respondents in our study covered mostly professionals, managers and students in the age range below 20s up to above 50s. They mostly represented matured communication and information seekers different from those only adhered to playing games and downloading pop songs. Thus, bringing them enjoyable experience raises the expectations for the design of user interfaces, provision of functions and variety of general and specific apps on the smartphone platforms. For this purpose, it is necessary for mobile app providers to explore seriously the mobile app enjoyment triggers among users of diverse types, and to facilitate their varied needs with adhered products.

In addition, our study results also have implications for adjusting and providing relevant tutoring and promotion programs. It is an important and continuous task to follow up with user disconfirmations of expectations and actual perceptions. For these two types of feedback are dynamic and critical to user continuance decisions. To forge positive disconfirmation of expectation and actual perceptions, instant tutoring hints should be available whenever a new version or

a new program is loaded. Any achievement in skill should be immediately recognized. A feeling of being rewarded leads to enjoyment as well.

Conclusions

The objective of this study was to validate a revised model of continuance, with balanced attention to the classic and emerging determinants. Despite some variations between individual path coefficients, our findings on perceived mobility and enjoyment mostly substantiate the hypothesized salience of those beliefs in driving satisfaction and attitude changes toward continuance intentions. Meanwhile, some findings different from prior studies may also help to broaden our vision of the dynamic nature of the continuance decision making process, especially in the smartphone mobile apps context. Overall, the findings of this study are valuable in enriching our understanding of post adoption phenomena. Since only 35% of the model power was jointly explained by EE, satisfaction, attitude, mobility and enjoyment, there must be other critical determinants waiting to be discovered to better explain continuance with mobile apps. Future research efforts should explore more in those unknown areas and at a more granular level. Future efforts should also be made in exploring the modifying effects as shown in UTAUT and in cross-culture, cross-country comparisons to deepen our understanding of consumer continuation intention in the mobile context on a global scale.

References

[1] Ajzen I, Fishbein M. 2005. The influence of attitudes on behavior.

In: Albarracín D., Johnson BT, Zanna MP, editors, The handbook of attitudes, Mahwah, NJ: Erlbaum; p. 173–221.

[2] Baek J, Park S, Lee C C, 2011. Identifying drivers for con- tinual usage of wireless broadband. Intnal Jf Mobile Commun.

9:317–340.

[3] Bhattacherjee A. 2001. Understanding information systems continuance: an expectation-confirmation model. MIS Q. 25:351–370.

[4] Bhattacherjee A, Premkumar, G. 2004. Understanding changes in belief and attitude toward information technology usage: a theo- retical model and longitudinal test. MIS Q. 28:229–254.

[5] Cassab H, MacLachlan DL. 2006. Interaction fluency: a customer performance measure of multichannel service. Int J Productivity Perform Manage. 55:555–568.

[6] Chen L, Meservy TO, Gillenson M. 2012. Understanding information systems continuance for information-oriented mobile applications. Commun Assoc Inf Syst. 30:127–146.

[7] Chtourou MS, Souiden N. 2010. Rethinking the TAM model: time to consider fun. J Consum Marketing 27:336–344.

[8] Chun H, Lee H, Kim D. 2012. The integrated model of smartphone adoption: hedonic and utilitarian value perceptions of smartphones among Korean college students. CyberPsychology Behav Social Networking 15:473–479.

[9] Compuware 2012. Mobile Apps: What Consumers Really Need and Want. Retrieved fromhttp://offers2.compuware.com/rs/com puware/images/Mobile_App_Survey_Report.pdf

[10] Cortina JM. 1993. What is coefficient Alpha. J Appl Psychol.

78:98–104.

[11] Dai H, Hu T, Zhang XH. 2014. Continued use of mobile technology mediated services: a value perspective. J Comput Inf Syst. 54:

99–109.

[12] Davis FD, Bagozzi RP. 1992. Extrinsic and intrinsic motivation to use computers in the workplace. J Appl Social Psychol. 22:1111–1132.