# Applying the Decomposed Theory of Planned Behavior to Explore the Influencing Factors of NTC App Usage Intention

Authors: Chih-Wei Lin; Yi-Xuan Tsai; Yu-Shan Chang; Yun-Jhih Ding; Jou-Chun Liu; Yu-Sheng Lin
Journal: Journal of Function Spaces
 (2021)

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## Abstract

This research is aimed at exploring the influencing factors of users’ behavioral intention toward the Nike Training Club (NTC) app, using the decomposed theory of planned behavior as the main research framework. In this study, users of the NTC app were the research objects. A total of 300 questionnaires were distributed on the Internet by the purposive sampling method. After excluding invalid questionnaires, a total of 280 copies were obtained. The effective questionnaire recovery rate was 93.33%. All data were verified by descriptive statistics and multiple regression analysis. The results of this study were as follows. First, the NTC app users were mostly female, aged between 19 and 25 years, were students, had a monthly income below 20,000, and exercised one to two times a week. Second, the application of the decomposed theory of planned behavior found the following: perceived usefulness and perceived ease of use had positive effects on attitude; interpersonal influence and electronic word of mouth had a positive influence on subjective norms; self-efficacy and technology facilitation had positive effects on perceptual behavior control; and attitude, subjective norms, and perceptual behavior control all had a positive impact on behavioral intention. The NTC app users thought that the program interface was easy to use and that the app could help them effectively achieve the effect of exercise and fitness, which increased the benefits of continuously using the program.


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## Body

## 1. Introduction

In recent years, the Internet has become increasingly developed, mobile networks have become more widespread, and smartphone applications have become more diversified. The time spent by people using mobile phones has increased year by year, and the activities carried out on mobile phones have become more diverse [1]. With the popularization of smartphones, various types of applications (apps) derived from mobile devices have developed rapidly. According to a survey from App Annie (an app analysis agency), the number of global app downloads exceeded 175 billion in 2017, and every smartphone user installs an average of 80 kinds of apps (App [2]). In addition, a survey by the American College of Sports Medicine (ACSM) on global fitness trends showed that wearable devices with sports training apps are still the first choice for people’s favorite fitness exercises [3]. This survey showed that people’s lives are becoming more and more inseparable from smart technology. The combination of information technology with sports has opened up business opportunities for fitness apps in the market. The fun, interactivity, and effectiveness of sports fitness apps are breaking through the limitations of venues, space, and money.At present, many companies in the market have launched exclusive branded apps, not only for brand communication such as creating brand topics, strengthening brand interaction, building brand image and popularity, and delivering brand messages but also for building a new communication and interaction platform. The world-renowned sports brand Nike has designed apps with different functions for different sports groups. In 2016, Nike designed the Nike Training Club (NTC) app, which provides 185 free exercise plans and can be based on personal needs. NTC app users can formulate different training plans and adjust them according to their different needs and goals. Users can customize courses according to their personal time and needs. In addition, the NTC app will ask about the user’s feelings when completing each training course and make appropriate adjustments to the next training course. It can guide users on how to conduct each training session, clearly introduce the key points, and reduce the occurrence of errors [4].The development of an intelligent cloud system can allow the collection and monitoring of user-related data, help prevent the occurrence of chronic diseases, and reduce the consumption of medical resources [5]. At present, people around the world are still being attacked by COVID-19, which has severely affected people’s lifestyles. Venues such as gyms or other sports-related venues have temporarily closed, making sports-related apps the most economical and efficient way to engage in physical activity [6, 7]. In Germany, about 19% of people engaged in fitness activities at home during the COVID-19 period, and many of them used the Internet or sports technology resources to assist in exercising [8]. Through the app, the combination of exercise and information helps users record their exercise process and status. According to the personal exercise history recorded by the app, people can achieve sports benefits more quickly and effectively, improve their physical fitness and prevent diseases, and at the same time promote the development of the sports and information technology industries. Therefore, this study used the NTC app as the research theme to explore user influence factors.The advancement of technological information has caused people to become more dependent on technological products in their lives, and their behavior is now affected by many factors. Therefore, how to effectively understand and how predict whether people can accept and use various technology products are the concerns of technology-related industries and developers. Past research presented many theories to understand and predict human behavioral intention, of which the most commonly used theory is the theory of reasoned action (TRA). This theory was put forward by Ajzen and Fishbein [9], who used the perspectives of social psychology to explore the causal relationships among behavior attitude, subjective norms, behavior intention, and actual behavior. Attitude implies people’s feelings or opinions about something. In contrast to negative feelings, subjective norms are the social pressure that individuals receive from people who have an influence on them when they engage in a certain behavior, which results in the individual’s intention and motivation to participate in the behavior and which in turn affects the actual behavior. Ajzen [10] proposed the theory of planned behavior (TPB). TRA ignores many internal and external factors that are not controlled (such as time and money); therefore, TPB adds perceptual behavior control as a potential variable that is mainly used to control behavior ability and control resources and opportunities. Davis [11] developed the technology acceptance model (TAM) based on the TRA model theory and mainly applied it to people’s acceptance of information systems and potential influencing factors. Therefore, external influencing variables are divided into the two independent aspects of perceived usefulness and perceived ease of use, which in turn affect individuals’ subsequent attitudes and behavioral intentions toward using technology products. According to the three theoretical models above, it is insufficient to choose only one theory to explain people’s behavioral intentions, because there are many environmental change factors that affect people’s lives; this study tries to choose a theory that was more suitable to discuss this research topic.Taylor and Todd [12] proposed a theoretical model suitable for technological products called the decomposed theory of planned behavior (DTPB). In DTPB, attitude, subjective norms, and perceptual behavior control are determined by a single belief aspect, but the relationships among them are not necessarily significant. Ajzen [13] pointed out that in TPB, attitude, subjective norms, and perceptual behavior control are all unidimensional dimensions. This may lead to the effect of offsetting positive and negative beliefs in the same dimension, which maybe will result in insignificant final measurement results when predicting behavioral intentions. After improving the TPB, the multidimensional attitude, subjective norms, and control of perceptual behavior are consistent, and the offset between the aspects is reduced through deconstruction. The relationships among the various variables are clearer and easier to understand, and the belief structure is more stable, allowing a better understanding of the specific factors that may affect behaviors.In this study, DTPB was used to incorporate the TPB model structure into TAM and the innovation diffusion theory (IDT) proposed by Rogers and Marcus [14] and to decompose TPB’s attitude, subjective norms, and perceptual behavior control into a multistructural orientation, thereby making the theoretical framework more suitable for examining the intention of using information technology.In order to meet the research theme, the external influence of the subjective norm dimension was reduced to the electronic word of mouth dimension, because the Internet could be used to quickly search for related usage information of the NTC app, which could be found in blogs, social networking sites, industry websites, and the App Store online review system. Online feedback system can show the past user experience information in real-time and with a more in-depth understanding of a product’s functions and details, replacing convenience in self-efficacy with technological convenience, in order to more accurately measure and use the resources and technologies in the App program. In the past research, DTPB has gained better explanatory power in verifying the behavioral intention of the use of scientific and technological information [15–18]. Nike’s NTC app has been chosen by many people to use at home for exercise and fitness; therefore, this study chose the deconstructive behavior theory to explore the influencing factors of NTC users’ intention to use the NTC app and then analyzed the willingness of users to continue using it.


### 1.1. Purpose of the Study

The research objectives were as follows:(1)
To explore the impact of perceived usefulness and ease of use on attitude(2)
To discuss the influence of interpersonal influence and external influence on subjective norms(3)
To explore the influence of self-efficacy and technology facilitation on perceptual behavior control(4)
To explore the influence of user attitude, subjective norms, and perceptual behavior control on behavioral intention


### 1.2. Research Hypothesis

In recent years, scholars have often used deconstructive planning behavior theory as the theoretical basis for exploring the continuous use of various information technologies, and they have begun to use it to explore the continuous use of new information technology products. In the study of TAM research, it can be found that perceived usefulness, perceived ease of use, and attitude can effectively and significantly affect users’ or consumers’ behavioral intentions [19, 20]. The subjective norms for the personal use of information technology products will also be affected by external factors or others [21] because new high-tech products with sufficient and complete information are the main factors affecting the control of perceptual behavior [16]. Based on the above-mentioned literature discussion, the hypotheses of this research were as follows:
(H1)
Perceived usefulness has a significant positive effect on attitude(H2)
Perceived ease of use has a significant positive effect on attitude(H3)
Interpersonal influence has a significant positive influence on subjective norms(H4)
Internet word of mouth has a significant positive impact on subjective norms(H5)
Self-efficacy has a significant positive effect on perceptual behavior control(H6)
Technological convenience has a significant positive impact on perceptual behavior control(H7)
The user’s attitude has a significant positive impact on behavioral intention(H8)
The user’s subjective norms have a significant positive impact on behavioral intention(H9)
The user’s perceptual behavior control has a significant positive impact on behavioral intention


## 1.1. Purpose of the Study

The research objectives were as follows:(1)
To explore the impact of perceived usefulness and ease of use on attitude(2)
To discuss the influence of interpersonal influence and external influence on subjective norms(3)
To explore the influence of self-efficacy and technology facilitation on perceptual behavior control(4)
To explore the influence of user attitude, subjective norms, and perceptual behavior control on behavioral intention


## 1.2. Research Hypothesis

In recent years, scholars have often used deconstructive planning behavior theory as the theoretical basis for exploring the continuous use of various information technologies, and they have begun to use it to explore the continuous use of new information technology products. In the study of TAM research, it can be found that perceived usefulness, perceived ease of use, and attitude can effectively and significantly affect users’ or consumers’ behavioral intentions [19, 20]. The subjective norms for the personal use of information technology products will also be affected by external factors or others [21] because new high-tech products with sufficient and complete information are the main factors affecting the control of perceptual behavior [16]. Based on the above-mentioned literature discussion, the hypotheses of this research were as follows:
(H1)
Perceived usefulness has a significant positive effect on attitude(H2)
Perceived ease of use has a significant positive effect on attitude(H3)
Interpersonal influence has a significant positive influence on subjective norms(H4)
Internet word of mouth has a significant positive impact on subjective norms(H5)
Self-efficacy has a significant positive effect on perceptual behavior control(H6)
Technological convenience has a significant positive impact on perceptual behavior control(H7)
The user’s attitude has a significant positive impact on behavioral intention(H8)
The user’s subjective norms have a significant positive impact on behavioral intention(H9)
The user’s perceptual behavior control has a significant positive impact on behavioral intention


## 2. Research Methodology

### 2.1. Research Objects and Scope

This research mainly focused on individuals who had used the Nike Training Club app. The questionnaire was distributed through using the posts and private messages on the Facebook fan page, Dcard, Instagram, and other platforms related to the NTC app. In addition, this study asked NTC app users to forward the questionnaire to family and friends who also used the NTC app. The questionnaires were filled in anonymously to protect the personal privacy of the respondents. This study mainly adopted intentional sampling and snowball sampling to select the research objects. Online questionnaires can improve regional issues and the use of anonymity and the concealment of the Internet prompted some users who were unwilling to reveal their identities to answer with peace of mind. According to Nunnally [22], the relative scale is judged by taking the sample size required for each measurement variable, and the most common principle is 10 : 1. The highest number of measurement variables in this study was five questions, so the proportion needed to be at least 50 questionnaires. The pretest questionnaire for this study was distributed online from December 16, 2019, to December 30, 2019, and a total of 100 questionnaires were collected and pretested. The official questionnaire was distributed from January 22 to March 8, 2020, and snowball sampling was used to distribute questionnaires through the Internet on a daily basis. A total of 300 copies were collected. After excluding invalid questionnaires, a total of 280 copies were valid, and the effective return rate of the collected questionnaires was 93.3%.


### 2.2. Research Tools

This questionnaire used a Likert seven-point scale. All items were analyzed by the decision value. Bartlett’s test and the KMO value were used to test the discrimination of the items, and the internal consistency of the scale was tested using Cronbach’s alpha coefficient. The significance level of the statistical test in this study was set asα=.05 (i.e., confidencelevel=95%). Perceived usefulness (PU), perceived ease of use (PEU), perceptual behavior control (PBC), and behavioral intention (BI) mainly referred to the measurement items of Taylor and Todd [12] and the measurement methods of Wu and Jiang [23] and Lin et al. [16]. Attitude (ATT), subjective norms (SN), interpersonal influence (INP), and Internet word of mouth (EWM) mainly referred to the measurement items of Bhattacherjee [24] and Lin et al. [16]. Self-efficacy (SE) and technological convenience (TF) mainly referred to the measurement items of Taylor and Todd [12] and the measurement method of Lin et al. [16]. The semantics of the above scale questions were modified to conform to the meaning of this research. The results of the study showed that the Bartlett’s test of each variable in this study was significant (p<.05), and the KMO value was between .782 and .878. Finally, the reliability was between .879 and .950, indicating the scale of this study had good reliability and validity.


## 2.1. Research Objects and Scope

This research mainly focused on individuals who had used the Nike Training Club app. The questionnaire was distributed through using the posts and private messages on the Facebook fan page, Dcard, Instagram, and other platforms related to the NTC app. In addition, this study asked NTC app users to forward the questionnaire to family and friends who also used the NTC app. The questionnaires were filled in anonymously to protect the personal privacy of the respondents. This study mainly adopted intentional sampling and snowball sampling to select the research objects. Online questionnaires can improve regional issues and the use of anonymity and the concealment of the Internet prompted some users who were unwilling to reveal their identities to answer with peace of mind. According to Nunnally [22], the relative scale is judged by taking the sample size required for each measurement variable, and the most common principle is 10 : 1. The highest number of measurement variables in this study was five questions, so the proportion needed to be at least 50 questionnaires. The pretest questionnaire for this study was distributed online from December 16, 2019, to December 30, 2019, and a total of 100 questionnaires were collected and pretested. The official questionnaire was distributed from January 22 to March 8, 2020, and snowball sampling was used to distribute questionnaires through the Internet on a daily basis. A total of 300 copies were collected. After excluding invalid questionnaires, a total of 280 copies were valid, and the effective return rate of the collected questionnaires was 93.3%.


## 2.2. Research Tools

This questionnaire used a Likert seven-point scale. All items were analyzed by the decision value. Bartlett’s test and the KMO value were used to test the discrimination of the items, and the internal consistency of the scale was tested using Cronbach’s alpha coefficient. The significance level of the statistical test in this study was set asα=.05 (i.e., confidencelevel=95%). Perceived usefulness (PU), perceived ease of use (PEU), perceptual behavior control (PBC), and behavioral intention (BI) mainly referred to the measurement items of Taylor and Todd [12] and the measurement methods of Wu and Jiang [23] and Lin et al. [16]. Attitude (ATT), subjective norms (SN), interpersonal influence (INP), and Internet word of mouth (EWM) mainly referred to the measurement items of Bhattacherjee [24] and Lin et al. [16]. Self-efficacy (SE) and technological convenience (TF) mainly referred to the measurement items of Taylor and Todd [12] and the measurement method of Lin et al. [16]. The semantics of the above scale questions were modified to conform to the meaning of this research. The results of the study showed that the Bartlett’s test of each variable in this study was significant (p<.05), and the KMO value was between .782 and .878. Finally, the reliability was between .879 and .950, indicating the scale of this study had good reliability and validity.


## 3. Results

### 3.1. Subject Data Analysis

The majority of the Nike Training Club app users in this study were women (58.6%), aged between 19 and 25 (76.1%), and mostly students (53.6%). Regarding the monthly income, 40.4% were below NTD 20,000. Lastly, 51.8% had been introduced by relatives and friends; and 75.4% used it one to two times a week.


### 3.2. Analysis of the Current Status of Research Variables

The analysis of the perceived usefulness and perceived ease of use showed that perceived ease of use (M=5.49, SD=1.05) scored slightly higher than perceived usefulness (M=5.30, SD=1.02), meaning that most of the NTC app users thought that the system was easy to use and could assist them in exercising. The analysis of the interpersonal influence and electronic word of mouth showed that electronic word of mouth (M=5.13, SD=1.07) scored higher than interpersonal influence (M=4.81, SD=1.16), meaning that the NTC users believed that the support of family and friends was very important. Besides that, the recommendations from social websites and online reviews were also helpful in motivating the respondents to continue using the app. According to the analysis of self-efficacy and technology facilitation, technology facilitation (M=5.76, SD=1.04) scored higher than self-efficacy (M=5.65, SD=1.20), meaning that most users thought that their personal smartphones could operate the NCT app smoothly. Through the analysis of the various aspects of planning behavior theory, the attitude had the highest score (M=5.68, SD=1.02), followed by perceived behavior control (M=5.61, SD=1.04), behavioral intention (M=5.28, SD=1.15), and subjective norms (M=4.71, SD=1.18). These results indicated that the users had mostly positive comments about the NTC app, found it easy to operate, were willing to give positive word of mouth on the Internet, and would recommend others to use it. The average and standard deviation values of each variable are shown in Table 1.Table 1
Means and standard deviations of the research variables.
VariableItemMSDVariableItemMSDPUPU15.191.19PEUPEU15.501.25PU25.281.16PEU25.351.23PU35.211.20PEU35.541.16PU45.291.22PEU45.561.17PU55.551.10Overall5.301.02Overall5.491.05INPINP14.661.38EWMEWM15.341.26INP24.751.31EWM24.741.32INP34.981.22EWM35.111.21INP44.861.38EWM45.331.21Overall4.811.16Overall5.131.07SESE15.661.20TFTF15.661.20SE25.681.28TF25.681.28SE35.561.41TF35.561.41SE45.701.27TF45.701.27Overall5.651.20Overall5.761.04ATTATT15.651.06SNSN14.671.37ATT25.661.09SN24.581.41ATT35.761.13SN34.851.33ATT45.641.11SN44.731.39Overall5.681.02Overall4.711.18PBCPBC15.751.12BIBI15.351.28PBC25.731.21BI25.371.29PBC35.551.16BI35.651.18PBC45.411.22BI44.781.59Overall5.611.04Overall5.281.15


### 3.3. The Impact of Research Variable Analysis

This research used the DTPB model to explore the impact of each aspect of the NTC app users. Perceived usefulness, perceived ease of use, interpersonal influence, electronic word of mouth, self-efficacy, and technology facilitation were the independent variables, while attitude, subjective norms, and perceptual behavior control were the dependent variables. Then, attitude, subjective norms, and perceptual behavior control were taken as independent variables, and behavioral intention was taken as a dependent variable. The regression analysis results are shown in Table2.Table 2
NTC app regression analysis summary.
DVATTSNPBCBIVIFt-testIVPU.4381.8910.23∗PEU.49311.52∗INP.4831.429.83∗EWM.3416.94∗SE.4423.678.90∗TF.4959.96∗ATT.4052.537.79∗SN.3001.248.25∗PBC.3012.455.90∗∗p<.05.First of all, in the analysis of the perceived usefulness and perceived ease of use, both subfacets reached a significant level (F2,277=378.221, R2=.730, p<.05), indicating that the two substructures had significant explanatory power for attitude (73%). The standardized regression equation was as follows: attitude=.438∗perceivedusefulness+.493∗perceivedeaseofuse. Interpersonal influence and electronic word of mouth both reached a significant level for subjective norms (F2,277=604.659, R2=.526, p<.05), indicating that the two substructures had significant explanatory power for subjective norms (52.6%). The standardized regression equation was as follows: subjectivenorm=.483∗interpersonalinfluence+.341∗electronicwordofmouth. Self-efficacy and technological facilitation both reached a significant level for perceptual behavior control (F2,277=604.659, R2=.812, p<.05), indicating that the two substructures had significant explanatory power for perceptual behavior control (81.2%). The standardized regression equation was as follows: perceptualbehaviorcontrol=.442∗self‐efficacy+.495∗technologicalfacilitation. Lastly, attitude, subjective norms, and perceived behavior control reached a significant level for behavioral intention (F3,276=220.542, R2=.702, p<.05), indicating that the three substructures had significant explanatory power for behavioral intention (70.2%). The standardized regression equation was as follows: behavioralintention=.405∗attitude+.300∗subjectivenorms+.301∗perceivedbehaviorcontrol. The path coefficients between the variables in this study are shown in Figure 1.Figure 1
Path coefficient diagram of the NTC app users.


## 3.1. Subject Data Analysis

The majority of the Nike Training Club app users in this study were women (58.6%), aged between 19 and 25 (76.1%), and mostly students (53.6%). Regarding the monthly income, 40.4% were below NTD 20,000. Lastly, 51.8% had been introduced by relatives and friends; and 75.4% used it one to two times a week.


## 3.2. Analysis of the Current Status of Research Variables

The analysis of the perceived usefulness and perceived ease of use showed that perceived ease of use (M=5.49, SD=1.05) scored slightly higher than perceived usefulness (M=5.30, SD=1.02), meaning that most of the NTC app users thought that the system was easy to use and could assist them in exercising. The analysis of the interpersonal influence and electronic word of mouth showed that electronic word of mouth (M=5.13, SD=1.07) scored higher than interpersonal influence (M=4.81, SD=1.16), meaning that the NTC users believed that the support of family and friends was very important. Besides that, the recommendations from social websites and online reviews were also helpful in motivating the respondents to continue using the app. According to the analysis of self-efficacy and technology facilitation, technology facilitation (M=5.76, SD=1.04) scored higher than self-efficacy (M=5.65, SD=1.20), meaning that most users thought that their personal smartphones could operate the NCT app smoothly. Through the analysis of the various aspects of planning behavior theory, the attitude had the highest score (M=5.68, SD=1.02), followed by perceived behavior control (M=5.61, SD=1.04), behavioral intention (M=5.28, SD=1.15), and subjective norms (M=4.71, SD=1.18). These results indicated that the users had mostly positive comments about the NTC app, found it easy to operate, were willing to give positive word of mouth on the Internet, and would recommend others to use it. The average and standard deviation values of each variable are shown in Table 1.Table 1
Means and standard deviations of the research variables.
VariableItemMSDVariableItemMSDPUPU15.191.19PEUPEU15.501.25PU25.281.16PEU25.351.23PU35.211.20PEU35.541.16PU45.291.22PEU45.561.17PU55.551.10Overall5.301.02Overall5.491.05INPINP14.661.38EWMEWM15.341.26INP24.751.31EWM24.741.32INP34.981.22EWM35.111.21INP44.861.38EWM45.331.21Overall4.811.16Overall5.131.07SESE15.661.20TFTF15.661.20SE25.681.28TF25.681.28SE35.561.41TF35.561.41SE45.701.27TF45.701.27Overall5.651.20Overall5.761.04ATTATT15.651.06SNSN14.671.37ATT25.661.09SN24.581.41ATT35.761.13SN34.851.33ATT45.641.11SN44.731.39Overall5.681.02Overall4.711.18PBCPBC15.751.12BIBI15.351.28PBC25.731.21BI25.371.29PBC35.551.16BI35.651.18PBC45.411.22BI44.781.59Overall5.611.04Overall5.281.15


## 3.3. The Impact of Research Variable Analysis

This research used the DTPB model to explore the impact of each aspect of the NTC app users. Perceived usefulness, perceived ease of use, interpersonal influence, electronic word of mouth, self-efficacy, and technology facilitation were the independent variables, while attitude, subjective norms, and perceptual behavior control were the dependent variables. Then, attitude, subjective norms, and perceptual behavior control were taken as independent variables, and behavioral intention was taken as a dependent variable. The regression analysis results are shown in Table2.Table 2
NTC app regression analysis summary.
DVATTSNPBCBIVIFt-testIVPU.4381.8910.23∗PEU.49311.52∗INP.4831.429.83∗EWM.3416.94∗SE.4423.678.90∗TF.4959.96∗ATT.4052.537.79∗SN.3001.248.25∗PBC.3012.455.90∗∗p<.05.First of all, in the analysis of the perceived usefulness and perceived ease of use, both subfacets reached a significant level (F2,277=378.221, R2=.730, p<.05), indicating that the two substructures had significant explanatory power for attitude (73%). The standardized regression equation was as follows: attitude=.438∗perceivedusefulness+.493∗perceivedeaseofuse. Interpersonal influence and electronic word of mouth both reached a significant level for subjective norms (F2,277=604.659, R2=.526, p<.05), indicating that the two substructures had significant explanatory power for subjective norms (52.6%). The standardized regression equation was as follows: subjectivenorm=.483∗interpersonalinfluence+.341∗electronicwordofmouth. Self-efficacy and technological facilitation both reached a significant level for perceptual behavior control (F2,277=604.659, R2=.812, p<.05), indicating that the two substructures had significant explanatory power for perceptual behavior control (81.2%). The standardized regression equation was as follows: perceptualbehaviorcontrol=.442∗self‐efficacy+.495∗technologicalfacilitation. Lastly, attitude, subjective norms, and perceived behavior control reached a significant level for behavioral intention (F3,276=220.542, R2=.702, p<.05), indicating that the three substructures had significant explanatory power for behavioral intention (70.2%). The standardized regression equation was as follows: behavioralintention=.405∗attitude+.300∗subjectivenorms+.301∗perceivedbehaviorcontrol. The path coefficients between the variables in this study are shown in Figure 1.Figure 1
Path coefficient diagram of the NTC app users.


## 4. Discussion

This study found that the perceived usefulness and perceived ease of use of the NTC app positively affected attitude. The designs of new technology products are more complicated. If users can easily operate them and get started with a small amount of time and thinking, they will become relatively familiar with information technology products and have a positive feeling. [25]. The use of a simple operating system with continuous updates can improve the user’s perceived usefulness of the system at the same time [26]. Therefore, when designing a scientific and technological system, priority should be given to convenience and simple operation, as the higher the user’s ease of use, the higher the user’s perceived usefulness and thus attitude to use will be. Moreover, the research of Hsu and Mou [27] confirmed that the user’s ease of operating and using the Nike+ Running app has a positive impact on the usefulness and attitude of the smartphone. At present, college students are quite proficient in the operation and use of smartphones, and they think that smartphone assistance is useful for querying information about life or schoolwork [28]. The results showed that that the operating interface of the NTC app was easy to use and could achieve the effect of exercise. The users agreed that the program was quite practical and gave it a positive evaluation.This study found that the interpersonal influence and electronic word of mouth of the NTC app users positively influenced the subjective norms. The research of Lin et al. [29] pointed out that in terms of subjective norms, interpersonal influence is more influential than electronic word of mouth and found that Nike + Run Club app users believe that the evaluations and suggestions from family and friends are important and that users also hope to establish relationships with other people through this product. Especially for consumers with the intention to purchase a product, the recommendations of relatives or close friends will relatively increase their purchase probability [30]. Facebook mainly provides interactive links between friends, peers, relatives, and friends, and it also provides social interaction, thereby inducing individuals’ desire to engage in social activities [31]. Searching for relevant information through the Internet can help in better understanding the information people need and will also affect their subsequent behavioral tendencies [32]. The same reason can be proved that the interpersonal influence of family, friends, or social pressure will affect the subjective norms. From the above, it could be seen that the NTC app users could increase their confidence in the evaluations and recommendations found on the Internet and that adding the support and encouragement of relatives and friends would also increase the frequency of using the program.This study found that the self-efficacy and technology facilitation of the NTC app users positively affected their perceptual behavior control. Because mobile devices have the convenience of downloading applications at any time and because the price is low, users can decide whether to download or not according to their own needs. The study of Lee et al. [33] found that the more technical resources a potential user can master, the higher their confidence in the control of innovative information technology will be. As users become aware of the convenience of technology, they will spontaneously increase their frequency of use [34]. The higher the confidence of potential users in the control of innovative resource technology, the greater the chance of achieving full use of wearable information technology, and the higher their intention to use it. It could be seen from the above that the NTC app users could smoothly operate the various functional interfaces of the app with their smartphones, which increased their self-confidence and relatively increased their intention to participate in behavior.This study found that the NTC app users’ attitudes, subjective norms, and perceptual behavior control could effectively and positively affect their behavioral intention. Past research on technology products has confirmed that behavioral attitude has a significant positive impact on behavioral intention and that this impact is greater than that for other variables. In order to enhance users’ willingness to use, in addition to the functional integrity of its technology products, it is more important to allow users to have a positive attitude towards the products, which in turn affects the user’s motivation to use them [35, 36]. It could be seen from the above that when the NTC app users opened the application through their smartphones, they could operate the app independently and smoothly through the simple and clear operation interface and the sports training courses developed by professional fitness coaches. There was no need to spend too much time in the process of using the app, and it also enabled the research objects to utilize the most effective and economical training methods, thereby achieving effective fitness effects in a short time and making the users willing to recommend the app to others and give a highly positive evaluation on the Internet.


## 5. Conclusion

The results of this study confirmed that the DTPB model could be used to explain the behavioral procedures of exercise participants using the NTC app. However, by combining the DTPB with TAM and TPB, attitude, subjective norms, and perceptual behavior control developed and provided important explanatory factors that influence the intent of the multifaceted item. In the research results, it was found that the overall model had a high degree of explanatory power; the users have higher attitude and perceptual behavior control, especially when the user has a high degree of control over the system operation mode; the phase shape is improved for the product. Therefore, enhancing attitude towards positive product identification is also an important factor in increasing the continuous use of the NTC app. The results of this research showed that users can easily operate and set their own exercise or fitness plans in the interface of the NTC app when using a smartphone. This is a priority factor for using the system, paying the most attention to the practicality and experience of the system. Positive ratings of the NTC app by relatives, friends, peers, or related community sites can provide a motivation to support the individuals’ continued use of the program.


## 6. Recommendations and Suggestions

According to the results of this study, it was found that attitude had the greatest influence on behavioral intention. It could be seen that the users first considered the product’s ease of use when choosing the NTC app, indicating that if a user cannot fully use the complete functions of the app at the first time, the user’s attitude will be affected. Therefore, it is important to let the user experience the app’s complete and diverse functions smoothly when using it for the first time. It is suggested to add more complete and detailed instructions or add prompt functions that can assist users in real-time operations, thus allowing users to easily and instantly use the required functions of the NTC app. According to the results of this study, the score for the perceptual usefulness scale was slightly lower than that for the perceived ease of use, indicating that when individuals use the NTC app, they might not feel it to be efficient or ideal. It may be because the design of the NTC app is more suitable for someone already engaging in fitness or exercise activities. Optimizing the functions when using the exercise plan by playing training videos for a longer time or by adding detailed voice instructions could help the user to directly learn the action during training, thereby improving the exercise efficiency. The results of this study revealed that interpersonal influence has a greater influence on subjective norms, and subjective norms positively influence behavioral intentions. This study found that the majority of the NTC app users were 19–25 years old, and there were fewer people over 36 years old. Therefore, it is recommended that the NTC app increases the number of training exercises that middle-aged and older users can complete, in order to increase the number of older people using it.Due to the prosperity of the fitness industry, there are a variety of fitness apps. This research only used the Nike Training Club app as the research theme. Therefore, this research suggests that future researchers explore the use of fitness apps launched by different brands, so as to explore the differences in the influence of the users’ behavioral intention. This research was distributed on social platforms by online questionnaires, and users of the Nike Training Club app were the research objects. The results showed that the sample distribution was dominated by female college students and service industry employees. Therefore, this study suggests that future researchers add a greater variety of samples to understand the influencing factors and behavioral intentions of users from different ethnic groups toward using the Nike Training Club app. This research focused on the use of the Nike Training Club app. NTC app and NTC physical courses both are led by the Nike coach, but one needs to learn on their own, and the physical course can receive the actual guidance of the teacher. Therefore, this study suggests that future researchers focus on the differences in the exercise behaviors and exercise benefits between the app and physical courses.



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*Source: 7045242-2021-12-31.xml*