| Literature DB >> 35645942 |
Haowen Li1, Muhammad Ali2, Muhammad Waqas Amin3, Haoshen Liang4.
Abstract
Despite the larger interest of information systems scholars in excessive ESM usage, little is known about how excessive ESM usage is related to employee performance. This study focused on excessive ESM usage and investigated its impact on employee performance. Based on the status quo perspective with the integration of social cognitive theory, this study first proposed that excessive ESM usage has a positive and negative relationship with employee performance through ESM usage regret and ESM usage inertia. Furthermore, COVID-19 threat moderates the direct relationship between excessive ESM usage and ESM usage regret, and ESM usage inertia. Time-lagged, multi-source data collected in China support most of our hypothesis. Results reveal that excessive ESM has a positive and negative indirect effect on employee performance via ESM usage regret and ESM usage inertia. Furthermore, the COVID-19 threat moderates the positive direct effect of excessive ESM usage on ESM usage inertia. In the later section, theoretical contributions and practical implications are discussed.Entities:
Keywords: COVID-19 threat; ESM usage inertia; ESM usage regret; employee performance; excessive ESM usage
Year: 2022 PMID: 35645942 PMCID: PMC9138881 DOI: 10.3389/fpsyg.2022.884946
Source DB: PubMed Journal: Front Psychol ISSN: 1664-1078
FIGURE 1Conceptual model of the study.
Results of confirmatory factor analysis.
| Variables | CR | AVE | Cronbach’s alpha | Loadings |
| EESMU | 0.74 | 0.50 | 0.74 | 0.66–0.76 |
| ESMU-regret | 0.95 | 0.87 | 0.95 | 0.54–0.85 |
| ESMU-inertia | 0.75 | 0.51 | 0.75 | 0.91–0.96 |
| COVID-19 threat | 0.76 | 0.51 | 0.75 | 0.64–0.77 |
| Employee performance | 0.90 | 0.59 | 0.90 | 0.66–0.83 |
N = 310, EESMU = excessive ESM usage.
Correlation matrix.
| Variables | Mean | Standard deviation | EESMU | ESMU-regret | ESMU-inertia | COVID-19 threat | Employee performance |
| EESMU | 3.38 | 0.79 | 0.70 | ||||
| ESMU-Regret | 3.41 | 1.00 | 0.19 | 0.93 | |||
| ESMU-Inertia | 4.69 | 0.95 | 0.27 | 0.12 | 0.71 | ||
| COVID-19 threat | 2.97 | 0.82 | 0.02 | 0.07 | –0.08 | 0.71 | |
| Employee performance | 3.49 | 0.45 | 0.14 | 0.30 | −0.11 | 0.10 | 0.77 |
N = 310, EESMU = excessive ESM usage, square roots of AVE in diagonal cells. *p = 0.05, **p = 0.01.
Results of regression analysis.
| Independent variables | Dependent variables | Effect | SE | t |
| EESMU | ESMU-inertia | 0.34 | 0.07 | 4.95 |
| EESMU | ESMU-regret | 0.23 | 0.07 | 3.46 |
| ESMU-Inertia | Employee performance | –0.06 | 0.02 | −2.62 |
| ESMU-Regret | Employee performance | 0.11 | 0.03 | 4.47 |
| Gender | Employee performance | 0.03 | 0.03 | 0.94 |
| Age | Employee performance | –0.01 | 0.02 | –0.59 |
| Education | Employee performance | 0.01 | 0.05 | 0.10 |
| ESMU -Experience | Employee performance | 0.04 | 0.02 | 1.70 |
| Job tenure | Employee performance | 0.00 | 0.02 | –0.14 |
| COVID-19 threat | ESMU-regret | –0.01 | 0.03 | –0.17 |
| Interaction 1 | ESMU-regret | 0.00 | 0.04 | 0.02 |
| COVID-19 threat | ESMU-inertia | –0.07 | 0.07 | –1.12 |
| Interaction 2 | ESMU-inertia | 0.17 | 0.08 | 2.14 |
N = 310, EESMU = excessive ESM usage, Interaction 1 = EESMU X COVID-19 threat, Interaction 2 = EESMU X COVID-19 threat. *p = 0.05, **p = 0.01, ***p = 0.001.
FIGURE 2Results of hypotheses test. *p = 0.05, **p = 0.01, ***p = 0.001.
FIGURE 3Interactive effect of COVID-19 threat and excessive ESM usage on ESM usage inertia.