| Literature DB >> 34550524 |
Nasir Ishfaq1, Huang Mengxing2.
Abstract
This study aims to explore the factors that affect consumers' behavior in adaptation and use of internet-based services (IBS) during the COVID-19 crisis. In this study, technology acceptance model (TAM) was applied to predict the behavioral intention of active social media users among the Pakistan population based on the revised model of the TAM model. And the data of external factors facilitating conditions (FC), social impact (SI), and task technology fit (TTF) were collected from active social media users of Pakistan by using structured questionnaires. After performing Pearson's correlation and linear regression on the collected data, findings have shown that the outcome variable, i.e., behavioral intention, exhibited significant correlation with all variables except for perceived ease of use (PEoU). Further analysis revealed mixed results wherein FC and TTF can make a significant influence on perceived usefulness (PU) and PEoU, respectively. In addition, PU can significantly affect attitude (ATT) towards the use of IBS while the use of IBS has been affected by behavioral INT during the outbreak of COVID-19 in Pakistan.Entities:
Keywords: Facilitating conditions; Social impact; Task technology fit; Technology acceptance model (TAM)
Year: 2021 PMID: 34550524 PMCID: PMC8457038 DOI: 10.1007/s11356-021-15868-1
Source DB: PubMed Journal: Environ Sci Pollut Res Int ISSN: 0944-1344 Impact factor: 5.190
List of proposed hypotheses
| Factors | Abbrev | Hypotheses |
|---|---|---|
| Facilitating conditions | (FC) | H1: FC influences PU to use internet-based services during COVID-19. |
| H2: FC influences PEoU to use internet-based services during COVID-19. | ||
| Social impact | (SI) | H3: SI influences PU to use internet-based services during COVID-19. |
| H4: SI influences PEoU to use internet-based services during COVID-19. | ||
| Task technology fit | (TTF) | H5: TTF influences PU to use internet-based services during COVID-19. |
| H6: TTF influences PEoU to use internet-based services during COVID-19. | ||
| Perceived usefulness | (PU) | H7: PU influences AT to use internet-based services during COVID-19. |
| Perceived ease of use | (PEoU) | H8: PEoU influences AT to use internet-based services during COVID-19. |
| Attitude | ATT | H9: AT influences INT to use internet-based services during COVID-19. |
Fig. 1Framework of relationship between attributes used in this study
Fig 2Research methodology steps
Reliability (Cronbach’s alpha coefficients)
| FC | SI | TTF | PU | PEoU | ATT | INT | Overall |
|---|---|---|---|---|---|---|---|
| .684 | .667 | .618 | .660 | .680 | .696 | .689 | .704 |
Pearson correlation matrix
Regression analysis coefficients
| Unstandardized coefficients ( | Sig. | ||
|---|---|---|---|
| (Constant) | 1.936 | 7.237 | .000 |
| FC | .097 | 1.956 | .052 |
| SI | .108 | 1.375 | .171 |
| TTF | .292 | 4.069 | .000 |
| PU | − .171 | − 2.501 | .013 |
| PEoU | .658 | 8.634 | .000 |
| ATT | .441 | 3.800 | .000 |
Proposed model evaluation
| Hypotheses | Standard error | Results | ||
|---|---|---|---|---|
| H1: FC → PU | .049 | 7.237 | .052 | Not supported |
| H2: FC → PEoU | .046 | 4.006 | .000 | Supported |
| H3: SI → PU | .079 | 1.956 | .171 | Not supported |
| H4: SI → PEoU | .074 | − 1.920 | .056 | Not supported |
| H5: TTF → PU | .072 | 4.069 | .000 | Supported |
| H6: TTF → PEoU | .067 | 2.876 | .004 | Not supported |
| H7: PU → ATT | .069 | − 2.051 | .013 | Not supported |
| H8: PEoU → ATT | .076 | 8.634 | .000 | Supported |
| H9: ATT → INT | .116 | 3.800 | .000 | Supported |