Empirical Study on the Factors Affecting User Switching Behavior of Online Learning Platform Based on Push-Pull-Mooring Theory
Abstract
:1. Introduction
2. Literature Review
2.1. User Switching Behavior
2.2. Push-Pull-Mooring (PPM)
3. Research Framework
3.1. Research Foundation
3.2. Research Model
3.2.1. Push Effects
3.2.2. Pull Effects
3.2.3. Mooring Effects
4. Research Design
4.1. Measurement
4.2. Data Collection
5. Result and Discussion
5.1. Measurement Model
5.2. Structural Model
5.3. Expansibility Analysis
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Constructs | Items | Measures |
---|---|---|
Information overload [34,35,36,37,53] | IO1 | I feel overwhelmed with the large volume of information everyday. |
IO2 | Only a small part of the information is what I need. | |
IO3 | It is less efficient way of obtaining effective course information. | |
IO4 | I am frequently disturbed by push information | |
Dissatisfaction with tncumbent platform [15,38,39,40,54,55] | DS1 | Dissatisfied with the quality of teachers and course on the platform. |
DS2 | Dissatisfied with the speed and depth of the platform’s response to user questions | |
DS3 | Dissatisfied with the platform’s fees, refunds, and other financial protection issues | |
DS4 | Dissatisfied with the level of protection in personal privacy. | |
DS5 | Dissatisfied with the inability of timely feedback when using the platform’s recording format. | |
Functional value [4,42,56] | FC1 | I think the learning mode of another platform can bring better value to the capital |
FC2 | I think the learning mode of another platform is more scientific, and the scene is more vivid than the incumbent platform. | |
FC3 | I think the learning resources of another platform are more abundant and high quality. | |
FC4 | I think another platform will provide personalized services to better meet individual needs. | |
FC5 | I think the teaching explanation is more closely integrated with technology in another platform. | |
Network externality [10,28,32,43,44,57] | NE1 | Many of my friends are using another platform |
NE2 | My respected teachers and friends recommend another platform | |
NE3 | I anticipate many people will use another platform. | |
Switching cost [10,13,15,45,46,47] | SC1 | In general, it would spend a lot of money, time, and effort to switch to a new platform. |
SC2 | I would lose the accumulated credit and service in the current platform if I were to switch to a new platform. | |
SC3 | I would lose the benefits of being a long-term user of the current platform if I were to switch to a new platform. | |
SC4 | It would take a lot of time and effort to evaluate the learning resources and services of the new platform. | |
Affective commitment [15,50,51,58] | AC1 | I value some teachers and courses on the current platform |
AC2 | I like to communicate with teachers and other users on the current platform | |
AC3 | I really like the public image of the current platform. | |
AC4 | I feel emotionally attached to my current platform. | |
Switching behavior | SB1 | I would spend less time on my incumbent platform. |
SB2 | I am considering stopping using my incumbent platform. | |
SB3 | I am considering switching from my incumbent platform to another. | |
SB4 | I am determined to switch to another platform, and I am already trying to use another platform. |
Survey Object | Options | Quantity | Percentage |
---|---|---|---|
Gender | Male Female | 140 173 | 44.7% 55.3% |
Age | Under 18 | 61 | 19.5% |
18–25 26–40 Above 40 years old | 181 64 7 | 57.8% 20.4% 2.3% | |
Education | Postgraduate and above | 52 | 16.6% |
Undergraduate | 170 | 54.3% | |
Specialist | 15 | 4.8% | |
High school and below | 76 | 24.3% | |
Monthly income (including student allowance) | Below 1000 yuan | 102 | 32.6% |
1001–3000 yuan 3001–5000 yuan 5001–8000 yuan 8001 yuan and above | 90 66 32 23 | 28.7% 21.0% 10.2% 7.5% |
Survey Object | Options | Quantity | Percentage |
---|---|---|---|
The use situation of online learning platforms | K12 education platform (such as homework help, Xueersi online school, ape tutoring, etc.) | 69 | 22.0% |
Higher education platforms (such as Chinese University MOOC, Postgraduate Entrance Examination Gang, TED, etc.) | 226 | 72.2% | |
Language learning platforms (such as Liulishuo, Netease Youdao Dictionary, Momoback words, etc.) | 231 | 73.8% | |
Vocational training platforms (such as Zhong Gong Education, Driving Test Baodian, China Accounting Online School, etc.) | 140 | 44.7% | |
Comprehensive online school platforms (such as NetEase Open Class, Tencent Class, New Oriental Online, etc.) | 102 | 32.6% | |
Quality education platforms (such as Squirrel AI, Programming Cat, etc.) | 34 | 10.9% |
Constructs | Items | Loading | Cronbach’s α | CR | AVE |
---|---|---|---|---|---|
Information Overload | IO1 | 0.772 | 0.841 | 0.842 | 0.571 |
IO2 | 0.782 | ||||
IO3 IO4 | 0.758 0.709 | ||||
Dissatisfaction | DS1 | 0.767 | 0.913 | 0.914 | 0.679 |
DS2 | 0.881 | ||||
DS3 | 0.835 | ||||
DS4 | 0.811 | ||||
DS5 | 0.822 | ||||
Functional Value | FV1 | 0.774 | 0.911 | 0.913 | 0.679 |
FV2 | 0.740 | ||||
FV3 | 0.850 | ||||
FV4 FV5 | 0.836 0.909 | ||||
Network Externality | NE1 | 0.855 | 0.878 | 0.878 | 0.706 |
NE2 | 0.849 | ||||
NE3 | 0.817 | ||||
Switching Cost | SC1 SC2 SC3 SC4 | 0.872 0.901 0.884 0.879 | 0.932 | 0.935 | 0.782 |
Affective Commitment | AC1 AC2 AC3 AC4 | 0.887 0.835 0.866 0.868 | 0.921 | 0.922 | 0.747 |
Switching Behavior | SB1 SB2 SB3 SB4 | 0.862 0.827 0.900 0.800 | 0.910 | 0.911 | 0.719 |
IO | DS | FV | NE | SC | AC | SB | |
---|---|---|---|---|---|---|---|
IO | 0.756 | ||||||
DS | 0.431 | 0.824 | |||||
FV | 0.503 | 0.527 | 0.824 | ||||
NE | 0.585 | 0.504 | 0.467 | 0.841 | |||
SC | −0.402 | −0.259 | −0.312 | −0.363 | 0.884 | ||
AC | −0.305 | −0.241 | −0.273 | −0.291 | 0.386 | 0.864 | |
SB | 0.536 | 0.533 | 0.536 | 0.547 | −0.475 | −0.440 | 0.848 |
Fitting Index | Recommended Value | Fitting Result |
---|---|---|
CMIN\DF | <3.0 | 1.356 |
GFI | >0.9 | 0.907 |
AGFI | >0.8 | 0.887 |
CFI | >0.9 | 0.981 |
NFI | >0.9 | 0.932 |
RFI | >0.9 | 0.922 |
IFI | >0.9 | 0.981 |
TLI | >0.9 | 0.978 |
RMSEA | <0.08 | 0.034 |
Hypothesis | Path Coefficient | S.E. | C.R. | P | Result |
---|---|---|---|---|---|
SB←IO | 0.158 | 0.068 | 2.142 | 0.032 | Positive |
SB←DS | 0.227 | 0.055 | 3.834 | *** | Positive |
SB←FV | 0.176 | 0.059 | 2.952 | 0.003 | Positive |
SB←NE | 0.155 | 0.077 | 2.2 | 0.028 | Positive |
SB←SC | −0.184 | 0.043 | −3.596 | *** | Negative |
SB←AC | −0.174 | 0.044 | −3.58 | *** | Negative |
Expansion Variables | Model 1 | Model 2 | |
---|---|---|---|
Independent variable | Information overload | 0.74 | 0.190 ** |
Dissatisfactory | 0.172 ** | 0.213 *** | |
Function value | 0.175 ** | 0.140 * | |
Network externality | 0.210 ** | 0.167 * | |
Switching cost | −0.219 *** | −1.77 ** | |
Affective commitment | −0.170 ** | −1.56 ** | |
Regression indicators | R2 | 0.715 | 0.745 |
Adj R2 | 0.498 | 0.543 | |
F | 38.184 | 46.479 |
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Xu, H.; Wang, J.; Tai, Z.; Lin, H.-C. Empirical Study on the Factors Affecting User Switching Behavior of Online Learning Platform Based on Push-Pull-Mooring Theory. Sustainability 2021, 13, 7087. https://doi.org/10.3390/su13137087
Xu H, Wang J, Tai Z, Lin H-C. Empirical Study on the Factors Affecting User Switching Behavior of Online Learning Platform Based on Push-Pull-Mooring Theory. Sustainability. 2021; 13(13):7087. https://doi.org/10.3390/su13137087
Chicago/Turabian StyleXu, Heng, Jingru Wang, Zhaodan Tai, and Hao-Chiangkoong Lin. 2021. "Empirical Study on the Factors Affecting User Switching Behavior of Online Learning Platform Based on Push-Pull-Mooring Theory" Sustainability 13, no. 13: 7087. https://doi.org/10.3390/su13137087