Keywords

1 Introduction

Recent years have seen an explosive growth in the number of mobile devices such as smart phones and tablets. There are hundreds of thousands of mobile apps out there for iOS and Android users. According to Gartner’s report, it indicates that mobile apps revenues tipped to reach 26 billion dollars in 2013, and estimates that 103 billion mobile apps will be downloaded [20]. Gartner also predicts future trends in the market, claiming that by 2017, annual app downloads will reach 268.7 billion, and in-app purchases will generating 48 % of revenues. Due to the great potentials, users’ behavior of mobile apps is always an important issue both in the research area and practical area.

To date, many theories and the antecedents of consumer adoption in mobile marketing have been discussed, such as theory of reasoned action, theory of planned behavior, task-technology fit, trust, flow, playfulness, decision-making, and perceived usefulness etc. Most previous literature focuses on treating new service/technology adoption as a rational decision based on the functional needs of an individual. In many cases, however, new service/technology adoption is not due to functional needs but affective reaction. Gartner points out that five simple attributes into mobile apps to better engage our customers are as follows: recognize your customer, demonstrate that you value your relationship with your customer, create interactions that are inviting and fun, provide information sufficient for making a buy decision, and make payment easy. That is, mobile commerce apps should emphasize on the entire customer experience and satisfy their individual needs. Users with different motivations for adoption may lead to different outcome. In addition, we found there are few articles that can include functional factors and affective factors into consideration. Therefore, this study tries to understand what factors will affect an individual’s continuance intention to use and whether different perceived value (functional value vs. mental value) will affect the continuance intention to use of the new app.

In this paper, we aim to investigate affective factors that may affect the continuance intention to use of mobile apps. In order to provide a solid theoretical basis for examining the use of mobile apps, this paper proposes a research model that integrates the Task-technology Fit (TTF) and Theory of Reasoned Action (TRA), which are augmented with concepts of affective factors. TTF and TRA have been used in many studies to predict and understand user intention to adopt new information systems. Hence, they are also appropriate for analyzing the continuance intention to use mobile apps. The purposes of this paper are specified as:

  1. 1.

    To investigate whether affective factors significantly impact a user’s continuance intention to use mobile apps.

  2. 2.

    To evaluate whether the augmented technology adoption model can provide a better predictive power for the continuance intention to use mobile apps.

This paper proceeds as follows. Section 2 reviews related literature and describes our research framework. Section 3 outlines research method and instruments. Section 4 provides data analysis and results. Finally, Sect. 5 summarizes our findings and discusses potential implications.

2 Theory and Hypotheses

2.1 Fit

In recent years the fit concept has been widely predicted individual and organizational technology adoption and performance. Fit reveals different kinds of match in social science. For example, Task-Technology Fit (TTF) proposed by Goodhue and Thompson is more likely to have a positive impact on individual performance in organization and is used if the capabilities of the IT match the tasks that the user must perform [8–10, 24]. Fit-Appropriation Model (FAM) extended from TTF considers organizational context and argues a TTF is a necessary but not sufficient condition to improve performance. That is, TTF affects performance, but is moderated by appropriation [6]. Technology-Organization-Environment framework (TOE) identifies three aspects of an enterprise’s context that influence the process by which it adopts and implements a technological innovation [23]. Fit-Viability Model (FVM) proposed by Liang and Wei [16] combines TTF with the general notion of organizational viability of information technology. Viability refers to the extent to which the organizational environment is ready for the application. Fit refers to the extent to which the capabilities of IT meet the requirement of task. As mentioned above, besides TTF can both be used in the organizational and individual context, other theories are standing on organizational level.

In this research, we focus on the study of individual continuance intention to use under the mobile commerce context. The satisfaction of Individual needs deriving from the unique features of mobile apps (such as customization, personalization, and social integration etc.) becomes more and more important. Therefore, TTF which is one of appropriate theories will be considered in our research. Although TTF effectively uses a user evaluation perspective to explain individual performance after information technology/service adoption, it neglects user’s attitudes and intention in its model. To some extent, it is the concept of “cognitive fit”, because whether task and technology fit for each other depends on individual’s personal perception. If user perceived task and technology are fit for each other, it would affect user’s intention of technology adoption. Meanwhile, if user perceive task and technology are fit for each other, it would also positive affect user’s attitude toward technology usage. Therefore, factors affecting users’ attitude and intention to use IT will be both considered in our theoretical model. In the use of mobile commerce context, the cognitive dimension, fit, measures the whether mobile app fits for the tasks that the individual needs to perform. Therefore, we propose our first two hypothesis.

Hypothesis 1: Task-Technology Fit positively affect user’s attitude toward using mobile apps.

Hypothesis 2: Task-Technology Fit positively affect user’s continuance intention to use mobile apps.

Most previous literature focuses on treating new service/technology adoption as a rational decision based on the functional needs of an individual. In many cases, however, new technology/service adoption is not due to functional needs but affective needs. That is, the other individual perception we concerned in this research. We have thought that a user who adopts a service was desire to gain a reward or avoid a negative outcome. However, we found an alternative behavior occur while people have other particular needs [11, 13, 15, 20]. For example, people are playing a game because they find it exciting, joining a charity event due to increase social status, and participating in a sport to gain a social identity [12]. It is called perceived intangible value. On the contrary, people are participating in a sport in order to win awards, and competing in a contest for winning a scholarship. This means perceived tangible value. Therefore, Value-Technology Fit is defined by this study as the extent that technology functionality matches perceived value of individual. If technology functionality and perceived value of individual fit for each other, it will affect individual attitude and intention to use new technology. Thus, the followings are our hypothesis.

Hypothesis 3: Value-Technology Fit positively affect user’s attitude toward using mobile apps.

Hypothesis 4: Value-Technology Fit positively affect user’s continuance intention to use mobile apps.

2.2 Theory of Reasoned Action

TRA was derived from social psychology and proposed by Ajzen and Fishbein [1, 7]. It is a models that have been used to interpret and predict the intention of technology use in the information systems area. The components of TRA are three general constructs: behavioral intention, attitude, and subjective norm. Behavioral intention measures a person’s relative strength of intention to perform a behavior. Attitude consists of beliefs about the consequences of performing the behavior multiplied by his or her evaluation of these consequences. Subjective norm (SN) refers to the social pressure exerted on an individual to perform or not perform a particular behavior [7]. Consequently, the social pressure causes the relevant behavior to become the individual’s normative beliefs with which he/she would comply. Motivation to comply refers to he/she wanting or being willing to comply with these beliefs. That is, a user may exhibit different motivations for complying with the opinions of relevant people on the adoption of mobile apps. This theory has been applied to study many information technology applications and is certainly appropriate for investigating the continuance intention to use mobile apps.

Hypothesis 5: Attitude toward mobile apps positively affect user’s continuance intention to use mobile apps.

Hypothesis 6: Subjective Norm positively affect user’s continuance intention to use mobile apps.

2.3 Research Model

To summarize, our theoretical model examines effects of (1) task-technology fit to user attitude, (2) task-technology fit to continuance intention to use, (3) vale-technology fit to user attitude, (4) value-technology fit to continuance intention to use, (5) user attitude to continuance intention to use, and (6) subjective norms to continuance intention of using mobile apps. Figure 1 shows the theoretical framework.

Fig. 1.
figure 1

Theoretical framework

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3 Instrument Development and Research Methodology

3.1 Instrument Development

Existing measures from previous studies were adapted with slight modifications to fit our context. The measures used five-point Likert scales. All measures are listed as follows (Table 1).

Table 1. Measures of constructs
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3.2 Measure and Data Collection

The targets of this research are office workers in Taiwan. The voluntary users were invited to join the Project 2 weight-loss challenge. Each volunteer had to record his/her daily exercise and food via a health-related app, JustFit. JustFit is one of the most popular apps in Taiwan. It not only can help people to record daily food, exercise and mood easily, but also provide over 120,000 local food data. An online survey was conducted to gather data after using the app for three months (from 11 July to 31 October in 2014). Finally, a total of 278 volunteers (170 females and 108 males) were recruited. Their ages ranged from 31 to 45 years old (43.5 %). 70.2 % of the subject had at least a master degree, and 75.2 % of them were sitting at their desks for over 5 h per day. There were over 120 people (50.4 %) who think his/her body type is a little fat, and over 123 people (44.3 %) who don’t satisfy their body (shown in Table 2).

Table 2. Demographic characteristics of participants
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4 Analysis of Results

4.1 Measurement Model

A confirmatory factor analysis using the Partial Least Squares (PLS) was conducted to assess the validity and reliability of our data. Reliability and convergent validity of the factors were estimated by composite reliability and average variance extracted (AVE). The acceptable composite reliability value is suggested to exceed 0.7, and the AVE value to exceed 0.5. Discriminant validity verifies whether the squared correlation between a pair of latent variables is less than the AVE for each variable. As can be seen in Tables 3 and 4, all constructs satisfies the criteria, thus requiring no changes to the constructs.

Table 3. Reliability, convergent validity
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Table 4. Discriminant validity
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4.2 Structural Model

The results show that the combined model can interpret user attitude toward mobile apps and users’ continuance intention of mobile app. The model indicates that Task-Technology Fit and Value-Technology Fit can explain 46.5 % of the variance in attitude and the attitude along with subjective norm can explain 50.3 % of the variance in continuance intention. Attitude was affected by Task-Technology Fit and Value-Technology Fit, and continuance intention to use was affected by attitude and subjective norm. However, Task-Technology Fit and Value-Technology Fit had no significant influence on the continuance intention to use mobile app. Therefore, hypotheses 1 to 6 are partially supported. That is, the integrated model can predict 50.3 % of the continuance intention to use mobile app (Fig. 2).

Fig. 2.
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Path analyses of mobile app

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5 Conclusion

Mobile devices such as mobile phones and tablets have become a part of human life. The mobile apps market seems have the feeling of a gold rush. Juniper Research claimed that 80 billion mobile apps will be downloaded in 2013, rising to 160 billion by 2017, but only around 5 % of apps will be paid by 2017 [21]. That is why many researchers attempted to investigate the issue of factors affecting users’ adoption behavior. Given that the adoption of mobile app is purpose-sensitive, this paper aims to analyze user’s continuance usage of mobile apps by providing an integrated TTF and TRA model and augmented the model with affective factors. Using a health-related mobile app as example, our specific goal is to examine to what extent our model can explain the continuance usage of mobile apps. After the empirical study and data analysis, we have obtained the following findings.

TTF and VTF both had significant impact on attitude towards using the mobile app. However, the coefficients of variation of VTF is higher than TTF’s. It indicates that people had more positive attitudes toward using a new technology while their affective needs were satisfied [20]. SN and attitude had strong significant impacts on users’ continuance intention to use the app. However, TTF and VTF had no significant effect on the continuance intention to use the app. The further analysis, we found most of people are not satisfied with their bodies even they have a standard body shape. Any app which could help them to manage and control their weight is viewed as a good app. It will increase users’ positive attitude toward the app. Furthermore, some of people who use the lose-weight app are trying to connect with others, or gaining a sense of identity. They expect to have more confidence via increasing opportunities of communication with other people. This implies that a good app should not only provide the right technical services, but also satisfy the mental needs. Besides, those people who care about other people’s opinions, especially colleagues and friends, are more willing to continue using the app. The research findings have suggestions for the mobile apps and future research studies.

One potential limitation of this research surrounds the size of the sample collected. Also, the convenient sampling used to solicit respondents for the survey may not be as perfect as random sampling. Another measurement limitation is that only two affective effects were investigated in this study. Other affective factors may affect users’ intentions and future research could usefully identify and explore the effects of these factors.