| Literature DB >> 34940098 |
Patrick Allen Rose1, Suzana Brown2.
Abstract
This article explores how after almost two years of government-imposed work from home (WFH) for the purpose of curbing the spread of COVID-19, South Korean managers' general attitudes towards WFH may have been reconstructed and if this change influenced their expectations that WFH would persist for the long run. Before COVID-19, WFH was rare, and the country was well known for having one of the most hierarchical and rigid work cultures, with long hours at the office being the norm. The results of this study are based on survey responses from 229 South Korean managers and executives. Using means comparisons and hierarchical linear multiple regression models to answer three research questions, the present study evaluates theorized predictors of WFH take-up, general attitudes towards WFH, and the likelihood that WFH will continue post-COVID-19. The results indicate that forced WFH adoption during COVID-19 had statistically significant positive effects on the attitudes of South Korean managers and their intentions to continue working from home in the future. This study has practical implications for companies and governments that are interested in taking advantage of WFH and implementing it more permanently. It provides interesting findings on how managers from a country with minimal WFH prior to COVID-19 perceive the benefits of WFH and how they respond to its mandated adoption.Entities:
Keywords: COVID-19; South Korea; attitudes; culture; job feasibility; job satisfaction; managers; outcomes; policy; productivity; telework; trade-offs; trust; work from home
Year: 2021 PMID: 34940098 PMCID: PMC8730337 DOI: 10.3390/bs11120163
Source DB: PubMed Journal: Behav Sci (Basel) ISSN: 2076-328X
Figure 1Proposed Conceptual Model.
Frequencies of WFH Take-Up Before, During and Expected After COVID-19 by Demographic Subgroups.
| One or More Days WFH Per Week | ||||
|---|---|---|---|---|
| Time |
| Before COVID-19 | During COVID-19 | Expected after COVID-19 |
| Total | 229 | 13%/0.3 | 59%/1.6 | 44%/1.1 |
| Gender | ||||
| Male | 134 | 11%/0.2 | 56%/1.5 | 47%/1.2 |
| Female | 91 | 16%/0.5 | 62%/1.7 | 39%/1.0 |
| Age | ||||
| 18–29 Years Old | 29 | 7%/0.3 | 59%/1.8 * | 45%/1.3 ** |
| 30–39 Years Old | 42 | 14%/0.4 | 67%/2.0 * | 54%/1.6 ** |
| 40–49 Years Old | 57 | 16%/0.4 | 70%/1.8 * | 53%/1.2 ** |
| 50+ Years Old | 97 | 13%/0.3 | 48%/1.2 * | 33%/0.7 ** |
| Married | ||||
| Yes | 163 | 13%/0.3 | 59%/1.6 | 42%/1.0 |
| No | 60 | |||
| Education Level | ||||
| High School Graduate | 13 | 15%/0.5 | 38%/1.1 | 30%/0.8 |
| 2-year College | 9 | 22%/0.4 | 44%/0.9 | 33%/0.7 |
| 4-year University | 126 | 12%/0.4 | 65%/1.7 | 47%/1.2 |
| Masters degree | 61 | 13%/0.3 | 51%/1.4 | 46%/1.0 |
| PhD or Doctoral Degree | 16 | 19%/0.3 | 60%/1.6 | 27%/1.0 |
| Length of Employment | ||||
| Less than 6 months | 16 | 25%/0.9 | 63%/2.2 | 50%/1.9 *** |
| 6–11 months | 19 | 16%/0.6 | 58%/2.0 | 53%/2.1 *** |
| 1–4 years | 61 | 10%/0.3 | 52%/1.6 | 38%/0.9 *** |
| 4–8 years | 20 | 15%/0.2 | 55%/1.4 | 26%/0.6 *** |
| More Than 8 years | 107 | 13%/0.3 | 61%/1.4 | 47%/1.0 *** |
| Employees Supervised | ||||
| One, Just Myself | 83 | 17%/0.4 | 64%/1.8 | 50%/1.2 |
| Under 5 People | 73 | 10%/0.3 | 56%/1.5 | 42%/1.1 |
| 5–19 People | 41 | 10%/0.3 | 56%/1.4 | 33%/0.8 |
| 20–49 People | 17 | 12%/0.2 | 40%/1.0 | 33%/0.6 |
| 50 or More | 10 | 20%/0.5 | 67%/1.7 | 63%/1.2 |
| Size of Organization | ||||
| One, Just me | 2 | 50%/1.0 | 50%/1.8 ** | 50%/1.5 * |
| Fewer than 10 | 40 | 18%/0.4 | 44%/1.2 ** | 29%/0.9 * |
| 10–49 People | 37 | 11%/0.2 | 25%/0.9 ** | 31%/0.6 * |
| 50–99 People | 28 | 25%/0.9 | 59%/2.2 ** | 46%/1.5 * |
| 100–499 People | 34 | 15%/0.4 | 76%/2.0 ** | 61%/1.3 * |
| 500–1000 People | 20 | 10%/0.4 | 65%/2.2 ** | 60%/1.8 * |
| More Than 1000 People | 62 | 6%/0.1 | 76%/1.6 ** | 45%/0.9 * |
Note: Correlations (* p < 0.05; ** p < 0.01; *** p < 0.001).
Frequencies of Organizations’ WFH Policies by WFH Take-Up Subgroups.
| Organizational Policy |
| 1 | 2 | 3 | 4 |
|---|---|---|---|---|---|
| Time WFH DuringCOVID-19 | 194 | 41% | 20% | 25% | 14% |
| 0 (None) | 36 | 64% | 19% | 11% | 6% |
| 1–3 Days Per Month | 40 | 45% | 23% | 23% | 10% |
| 1 Day Per Week | 44 | 59% | 14% | 20% | 7% |
| 2 Days Per Week | 20 | 35% | 20% | 35% | 10% |
| 3 Days Per Week | 22 | 14% | 23% | 36% | 27% |
| 4 Days Per Week | 15 | 7% | 27% | 40% | 27% |
| 5 Days Per Week | 17 | 6% | 24% | 29% | 41% |
| Eta Squared | 0.212 | ||||
| 8.40 | |||||
| Sig. | <0.001 |
Note: 1. Employees can WFH only when it is recommended by the government; 2. Employees will WFH until COVID-19 subsides even when it is NOT recommended by the government and afterwards they will return to the office; 3. After COVID-19 subsides employees will be allowed to continue WFH, but not as much as now during COVID-19; 4. After COVID-19 subsides, employees will be allowed to continue WFH the same levels as now during COVID-19.
Frequencies of Job Feasibility by WFH Take-Up Subgroups.
| Job Feasibility |
| 1 | 2 | 3 | 4 |
|---|---|---|---|---|---|
| Time WFH During COVID-19 | 217 | 16% | 32% | 40% | 13% |
| 0 (None) | 44 | 36% | 36% | 23% | 5% |
| 1–3 Days Per Month | 45 | 16% | 36% | 36% | 13% |
| 1 Day Per Week | 47 | 13% | 32% | 49% | 6% |
| 2 Days Per Week | 21 | 5% | 57% | 38% | – |
| 3 Days Per Week | 24 | 8% | 21% | 46% | 25% |
| 4 Days Per Week | 17 | – | 12% | 65% | 24% |
| 5 Days Per Week | 19 | 11% | 16% | 37% | 37% |
| Eta Squared | 0.155 | ||||
| 13.04 | |||||
| Sig. | <0.001 |
Note: 1. Most employees’ jobs cannot be done remotely; 2. Most employees can only do part of their jobs from home; 3. Most employees can work from home, but with difficulty; 4. Most employees can work from home without difficulty.
Means Comparison Analysis of Association between Management Culture and WFH Take-Up Subgroups.
| Management Culture |
| Index Score | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 |
|---|---|---|---|---|---|---|---|---|---|---|
| Time WFH During COVID-19 | 221 | 2.6 | 2.8 | 3.0 | 2.7 | 3.0 | 3.2 | 2.9 | 3.0 | 2.4 |
| 0 (None) | 47 | 2.7 | 2.7 | 2.5 | 2.7 | 2.6 | 2.8 | 2.4 | 2.7 | 2.2 |
| 1–3 Days Per Month | 45 | 2.9 | 2.7 | 2.9 | 2.4 | 2.7 | 2.9 | 2.9 | 2.8 | 2.2 |
| 1 Day Per Week | 47 | 2.9 | 2.6 | 3.0 | 2.6 | 3.3 | 3.3 | 2.9 | 2.9 | 2.4 |
| 2 Days Per Week | 22 | 2.8 | 2.7 | 3.3 | 2.6 | 2.9 | 3.3 | 3.1 | 3.2 | 2.3 |
| 3 Days Per Week | 24 | 3.4 | 2.8 | 2.8 | 2.6 | 2.8 | 3.2 | 3.0 | 2.6 | 2.2 |
| 4 Days Per Week | 17 | 3.3 | 3.5 | 3.5 | 3.2 | 3.4 | 3.9 | 3.3 | 3.4 | 3.1 |
| 5 Days Per Week | 19 | 2.8 | 3.4 | 3.4 | 3.3 | 3.4 | 3.6 | 3.3 | 3.7 | 2.6 |
| Eta Squared | 0.115 | 0.095 | 0.094 | 0.092 | 0.091 | 0.086 | 0.077 | 0.069 | 0.068 | |
| 4.66 | 3.74 | 3.63 | 3.62 | 3.57 | 3.32 | 2.95 | 2.61 | 2.57 | ||
| Sig. | <0.001 | 0.001 | 0.002 | 0.002 | 0.002 | 0.004 | 0.009 | 0.019 | 0.020 |
Note: 1. Employees try hard to keep a distance from their managers; 2. Managers always have the last say in meetings; 3. Employees feel tense when they are with their managers; 4. Employees’ job contents are assigned directly by their managers; 5. Employees have to rely directly on their managers for assistance to complete work; 6. Managers make all decisions in their teams, whether they are important or not; 7. In the minds of managers, they believe an ideal subordinate is one who always obeys their wishes; 8. Managers ask employees to obey their instructions completely.
Means Comparison Analysis of Association between General Attitude towards WFH and WFH Take-Up Subgroups.
| General Attitude |
| 1 | 2 | 3 | 4 | 5 | 6 |
|---|---|---|---|---|---|---|---|
| Time WFH During COVID-19 | 221 | 2.5 | 2.6 | 2.5 | 2.0 | 4.4 | 2.2 |
| 0 (None) | 46 | 3.0 | 2.4 | 2.3 | 2.3 | 4.9 | 2.4 |
| 1–3 Days Per Month | 45 | 2.8 | 2.4 | 2.4 | 2.2 | 4.5 | 2.5 |
| 1 Day Per Week | 47 | 2.8 | 2.7 | 2.5 | 2.1 | 4.2 | 2.2 |
| 2 Days Per Week | 22 | 2.5 | 2.3 | 2.5 | 1.9 | 4.5 | 2.1 |
| 3 Days Per Week | 25 | 2.3 | 2.9 | 2.9 | 1.7 | 3.9 | 1.9 |
| 4 Days Per Week | 17 | 2.1 | 3.1 | 2.8 | 1.8 | 4.0 | 1.8 |
| 5 Days Per Week | 19 | 1.7 | 3.2 | 2.8 | 1.5 | 4.0 | 1.9 |
| Eta Squared | 0.167 | 0.124 | 0.109 | 0.107 | 0.097 | 0.062 | |
| 6.25 | 4.92 | 4.38 | 4.25 | 3.80 | 2.20 | ||
| Sig. | <0.001 | <0.001 | <0.001 | <0.001 | 0.001 | 0.045 |
Note: 1. Success of WFH for Organization; 2. Productivity while WFH; 3. Trust in Employees to WFH; 4. In Favor of WFH; 5. Positive and Negative Tradeoffs of WFH; 6. Colleagues’ Reaction to WFH.
Means Comparison Analysis of Association between Job Satisfaction and General Attitude towards WFH.
| Job Satisfaction |
| 1 | 2 | 3 | 4 | 5 | 6 |
|---|---|---|---|---|---|---|---|
| Time WFH During COVID-19 | 224 | 2.5 | 2.7 | 4.4 | 2.5 | 2.2 | 2.0 |
| Very Satisfied | 41 | 1.8 | 3.1 | 3.7 | 2.9 | 1.7 | 1.7 |
| Somewhat Satisfied | 147 | 2.6 | 2.7 | 4.4 | 2.5 | 2.2 | 2.0 |
| Somewhat Dissatisfied | 33 | 3.1 | 2.1 | 5.1 | 2.2 | 2.8 | 2.4 |
| Very Dissatisfied | 3 | 5.0 | 1.0 | 5.9 | 1.3 | 4.0 | 3.3 |
| Eta Squared | 0.202 | 0.195 | 0.168 | 0.154 | 0.135 | 0.119 | |
| 16.46 | 17.51 | 14.71 | 13.32 | 10.81 | 9.82 | ||
| Sig. | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 |
Note: 1. Success of WFH for Organization; 2. Productivity while WFH; 3. Positive and Negative Tradeoffs of WFH; 4. Trust in Employees to WFH; 5. Colleagues’ Reaction to WFH; 6. In Favor of WFH.
Hierarchical Linear Multiple Regression Models.
| Factor | Time WFH during COVID-19 | In Favor | Time WFH |
|---|---|---|---|
| Control: Age | 0.023 * | 0.069 *** | 0.043 ** |
| Time WFH During COVID-19 | --- | 0.077 *** | 0.371 *** |
| In Favor of WFH | 0.033 * | --- | 0.100 *** |
| Organizational WFH Policy | 0.192 *** | 0.029 *** | 0.233 *** |
| Time WFH Before COVID-19 | 0.139 *** | 0.026 *** | 0.140 *** |
| Management Culture | 0.124 ** | 0.093 *** | 0.078 ** |
| Job Satisfaction | 0.090 ** | 0.104 *** | 0.039 ** |
| Job Feasibility | 0.060 ** | 0.071 *** | 0.118 *** |
Note: Simple linear regression statistics calculated for the separate effects of each independent variable on each dependent variable while controlling for age (* p < 0.05; ** p < 0.01; *** p < 0.001). Adjust R-square is calculated for the total effects of all independent variables as a set on the major dependent outcome variable in each hierarchical regression model.
Frequencies of Likelihood of WFH Continuation Post-COVID-19 by WFH Take-Up Subgroups.
| Likelihood Organization |
| Much More | Somewhat More Likely Now | No Change | Somewhat Less Likely Now | Much Less |
|---|---|---|---|---|---|---|
| Time WFH During COVID-19 | 196 | 17% | 33% | 14% | 11% | 25% |
| 0 (None) | 25 | 4% | 36% | 36% | 12% | 12% |
| 1–3 Days Per Month | 42 | 12% | 29% | 17% | 14% | 29% |
| 1 Day Per Week | 46 | 2% | 13% | 13% | 22% | 50% |
| 2 Days Per Week | 22 | 18% | 41% | 9% | 9% | 23% |
| 3 Days Per Week | 25 | 24% | 52% | 12% | 0% | 12% |
| 4 Days Per Week | 17 | 47% | 47% | 0% | 6% | 0% |
| 5 Days Per Week | 19 | 47% | 37% | 5% | 0% | 11% |
| Eta Squared | 0.292 | |||||
| 13.00 | ||||||
| Sig. | <0.001 |
Figure 2Final Conceptual Model. Note: Multiple hierarchical regression R-square Change and Total Adjusted R-square (* p < 0.05; ** p < 0.01; *** p < 0.001).