| Literature DB >> 36046404 |
Guopeng Xiang1, Qian Chen1, Qiucheng Li1.
Abstract
Continued use intention of customers is a critical factor in the development of tourism mobile platforms (TMP), which reflects the degree of users' attachment to the platforms. However, existing research in this field intends to investigate users' attachment to a TMP by focusing on the overall cognitive satisfaction of the users, which deviates from the "cognition-affect" framework in psychology. Following the stimulus-organism-response (S-O-R) framework, this paper draws upon the attachment theory and the user experience theory, and proposes a model depicting how service experience of TMP affects users' intention to keep using the TMP through the mediation effect of platform attachment. The empirical results (N = 276) showed that functional experience and social experience positively affect TMP users' development of platform attachment (i.e., platform dependence and platform identity), which in turn enhance their intention to continuously obtain and provide tourism information via the TMP. This study expands the research on the continued use of TMP from an attachment perspective and contributes to the field in both theoretical and practical levels.Entities:
Keywords: SOR framework; attachment theory; continued use intention; tourism mobile platform; user experience
Year: 2022 PMID: 36046404 PMCID: PMC9421155 DOI: 10.3389/fpsyg.2022.995384
Source DB: PubMed Journal: Front Psychol ISSN: 1664-1078
Figure 1Conceptual model of the study.
Measurements of the constructs.
| Construct | Items | Sources |
|---|---|---|
| Functional experience (FE) | FE1: Reliable information of tourist routes and scenic spots |
|
| FE2: Convenient and reliable reservation of tourism products | ||
| FE3: Like the pushed content and update it timely | ||
| FE4: Perfect payment function and transaction security | ||
| FE5: During the use period, the account personal information is guaranteed | ||
| Social experience (SE) | SE1: I can have very good communication with people who are also interested in traveling | |
| SE2: This platform can communicate with others around their favorite tourist destinations | ||
| SE3: This platform can make me meet new friends who have the same hobby of traveling | ||
| Altruistic experience (AE) | AE1: Sharing travel information on this platform can help others | |
| AE2: Sharing travel strategies and writing travel notes through this platform can help other friends who like to travel | ||
| AE3: Online hotel or scenic spot evaluation through this platform | ||
| Platform dependency (PD) | PD1: The platform can meet the actual needs of our travel process | |
| PD2: This platform can provide professional services and help for my travel | ||
| PD3: This platform solves the specific problems encountered in the tourism process for me | ||
| Platform identity (PI) | PI1: I feel that this platform is a part of my travel life |
|
| PI2: This platform records my travel experience | ||
| PI3: This platform reflects my values | ||
| PI4: I have a strong identification with this platform | ||
| Intention to continuously obtain information (ICO) | ICO1: I will continue to use the tourism platform to obtain tourism information | |
| ICO2: I will continue to browse tourism related information on this platform | ||
| ICO3: I will continue to seek help on this platform | ||
| Intention to continuously provide information (ICP) | ICP1: I will continue to share my travel experience on this platform | |
| ICP2: I will continue to answer questions from other users seeking travel help | ||
| ICP3: I will continue to share my travel knowledge on this platform in an effective way |
Confirmatory factor analysis results.
| Construct | Item | Parameter significance estimation | Standard loading | SMC | CR | AVE | Cronbach’s α | |||
|---|---|---|---|---|---|---|---|---|---|---|
| Unstd. | S.E. | |||||||||
| FE | FE1 | 1 | 0.831 | 0.691 | 0.873 | 0.581 | ||||
| FE2 | 0.930 | 0.066 | 14.13 | *** | 0.783 | 0.613 | ||||
| FE3 | 0.728 | 0.063 | 11.589 | *** | 0.668 | 0.446 | 0.872 | |||
| FE4 | 0.875 | 0.062 | 14.003 | *** | 0.778 | 0.605 | ||||
| FE5 | 0.848 | 0.064 | 13.156 | *** | 0.740 | 0.548 | ||||
| SE | SE1 | 1.000 | 0.777 | 0.604 | 0.790 | 0.557 | ||||
| SE2 | 0.991 | 0.101 | 9.812 | *** | 0.741 | 0.549 | ||||
| SE3 | 0.967 | 0.099 | 9.749 | *** | 0.720 | 0.518 | 0.789 | |||
| AE | AE1 | 1.000 | 0.787 | 0.619 | 0.845 | 0.645 | ||||
| AE2 | 1.149 | 0.090 | 12.802 | *** | 0.869 | 0.755 | 0.843 | |||
| AE3 | 0.966 | 0.079 | 12.177 | *** | 0.749 | 0.561 | ||||
| PD | PD1 | 1.000 | 0.775 | 0.601 | 0.811 | 0.588 | ||||
| PD2 | 1.009 | 0.093 | 10.857 | *** | 0.811 | 0.658 | 0.809 | |||
| PD3 | 0.882 | 0.084 | 10.519 | *** | 0.712 | 0.507 | ||||
| PI | PI1 | 1.000 | 0.811 | 0.658 | 0.824 | 0.542 | ||||
| PI2 | 0.871 | 0.081 | 10.741 | *** | 0.675 | 0.456 | ||||
| PI3 | 0.835 | 0.081 | 10.348 | *** | 0.651 | 0.424 | 0.821 | |||
| PI4 | 0.971 | 0.079 | 12.317 | *** | 0.793 | 0.629 | ||||
| ICO | ICO1 | 1.000 | 0.824 | 0.679 | 0.832 | 0.624 | ||||
| ICO2 | 0.999 | 0.083 | 12.084 | *** | 0.796 | 0.634 | 0.831 | |||
| ICO3 | 0.972 | 0.083 | 11.735 | *** | 0.747 | 0.558 | ||||
| ICP | ICP1 | 1.000 | 0.845 | 0.714 | 0.684 | 0.867 | ||||
| ICP2 | 0.842 | 0.060 | 13.936 | *** | 0.786 | 0.618 | 0.865 | |||
| ICP3 | 1.002 | 0.068 | 14.684 | *** | 0.849 | 0.721 | ||||
Square root and correlation coefficient of mean extracted variance.
| AVE | SE | PI | PD | FE | ICP | ICO | AE | |
|---|---|---|---|---|---|---|---|---|
| SE | 0.557 | 0.746 | ||||||
| PI | 0.542 | 0.710 | 0.736 | |||||
| PD | 0.588 | 0.481 | 0.629 | 0.767 | ||||
| FE | 0.581 | 0.632 | 0.669 | 0.518 | 0.762 | |||
| ICP | 0.867 | 0.630 | 0.642 | 0.578 | 0.544 | 0.931 | ||
| ICO | 0.624 | 0.662 | 0.653 | 0.607 | 0.596 | 0.592 | 0.790 | |
| AE | 0.645 | 0.527 | 0.477 | 0.478 | 0.474 | 0.560 | 0.601 | 0.803 |
Figure 2Structural model results. ***p < 0.001, *p < 0.05.