Background
Workplace bullying has become a serious threat for employees’ social, physical, and psychological well-being [
1‐
3]. Workplace bullying refers to a situation in which a person is repeatedly subjected to harassment, abuse, or social exclusion in his/her workplace over a period of time and finds it difficult to defend him/herself against these maltreatments [
4,
5]. The development of information and communication technology (ICT) devices and the spread of the coronavirus disease 2019 (COVID-19) pandemic have accelerated the trend of teleworking [
6,
7]. The COVID-19 pandemic has a huge impact on people’s mental health all across the world [
8‐
10]. In addition, it is feared that workplace cyberbullying (CB), which involves electronic forms of contact for bullying, will increase as more people begin working remotely [
11‐
13]. In this manuscript, bullying victimization in which electronic forms of contact are not specifically involved is defined as traditional bullying (TB). Exposure to workplace bullying can shatter victims’ basic assumption of self-worth and increase negative views of themselves, others, and the world [
14]. Victims of CB report higher levels of turnover intention and anxiety, less optimism, worse performance, and reduced psychological and physical well-being [
5,
11,
15‐
17].
CB is relatively a new phenomenon that shares the same nature as TB. It has been pointed out that CB can occur as an extension of TB [
18]. However, CB has three distinguishing characteristics that are not found in TB. The first one is anonymity. Sometimes the victim of CB does not know who the perpetrator is. This anonymity allows the perpetrator of CB to escape social constraints and responsibilities [
19,
20]. The second is dissemination. For example, if abuse or slander is leaked through videos or text messages, it can be exposed to a very large number of people [
21]. The third is that it is location- and time-independent; CB can occur anywhere and anytime throughout the day [
22]. Research on CB has primarily emerged as a problem to solve for adolescence; research on CB among employees remains limited [
23].
A meta-analysis demonstrated that a considerable number of employees are being victimized by workplace bullying [
24]. Precedent studies have reported that CB victimization rates differ greatly by sample populations, reporting periods (6–12 months), and measurement scales [
18,
25,
26]. For example, in a survey of Australian manufacturing workers, 10.7% of respondents were cyberbullied on at least a weekly basis in the last 6 months [
27]. A survey of British trainee doctors showed that 46.2% had experienced some form of CB in the past 6 months [
28]. In a study of New Zealand workers, 2.8% participants experienced at least two forms of CB at least weekly for the last 6 months [
15]. Although few studies of CB at work have been reported from Asian countries, Park and Choi [
29] reported that the prevalence of CB victimization among nurses in Korea was 8.0%.
Understanding the factors underlying CB is important to develop effective countermeasures. Regarding individual factors, a presence of CB victimization in the workplace has been shown to be associated with male sex, managerial position, poor physical health, and lower levels of optimism [
15,
16]. Some studies have also reported that certain professions, such as politicians, are at a higher risk for CB victimization [
30]. CB victimization is sometimes recognized by those who enjoy online activities, such as spending time on social networking sites (SNSs) [
31,
32]. Workers with higher levels of neuroticism and conscientiousness and low levels of agreeableness are more likely to report TB victimization [
33]. Although no relationship between CB victimization and personality traits has been reported among workers, studies of young adults have shown that extroversion and openness are predictors of CB victimization [
34]. Regarding work-related factors, precedent studies have reported a significant association between low support from managers or colleagues and CB victimization [
15,
35]. In addition, a poor organizational climate [
36] described as “distrustful and suspicious” and “rigid and rule-based”, has been shown to be associated with CB behavior [
35]. In the current study, we also tested the effect of gratitude at work, defined as the tendency to recognize and appreciate the impact of various aspects of work on one’s life [
37,
38], on workplace bullying. Although precedent research has proposed that gratitude is a promising resource to protect adolescents from adverse outcomes caused by CB victimization [
39], it was not examined in working population.
As mentioned above, research on CB in the workplace has been accumulating in the last decade. The main limitation of precedent research is that most studies have been conducted in Western countries, even though TB/CB in the working population is perceived as a serious problem in other cultural contexts, such as in Japan. The Japanese work environment is based on simultaneous recruiting of new graduates and lifetime employment [
40], and mid-career hiring and job change opportunities are still developing. The Japanese government cites examples of workplace bullying such as hitting things, yelling, verbal abuse, invasion of privacy, neglect, and not giving work [
41]. They mentioned that it is difficult to draw a line between workplace bullying and work-related guidance, especially when it was given by a supervisor to a subordinate. In the government survey, the one third of victims said they took no actions after being harassed in the workplace, because they thought “it would not solve anything” or “it would be detrimental for my job” [
42]. This situation carries a risk that workplace bullying could persist. Regarding CB, a young woman’s suicide due to slander on SNSs, who was on a popular reality TV show, sparked national debate in 2020 [
43]. The number of consultations received by the Illegal Harmful Hotline, a government-supported consultation service for illegal and harmful information on the Internet, has increased from 1,337 in fiscal year (FY) 2010 to 5,198 in FY 2019 [
44]. However, despite its widespread recognition, no survey has revealed the prevalence of CB using an internationally used assessment tool, its antecedents, or its negative health outcomes in Japan. The percentage of employees who engage in teleworking doubled from 9.8% in FY 2019 to 19.7% in FY 2020 [
45]. Considering that teleworking is becoming increasingly common and has been accelerated by the COVID-19 pandemic, more information on CB in the workplace is needed to fill the knowledge gap and develop effective countermeasures.
Our research questions are the following: What percentage of workers were suffering from CB? What kinds of factors are associated with CB victimization? Is CB victimization associated with frequencies of teleworking? Is CB victimization associated with psychological well-being? To our knowledge, no studies have attempted to answer these questions in Japan. To address these issues, we conducted an anonymous, cross-sectional, Internet-based survey targeting nationwide workers in Japan. At that time, the Tokyo metropolitan area was under its second emergency declaration due to the spread of COVID-19 (from January 8 to March 21, 2021).
Our analysis is divided into two parts. First, we define participants as having been victimized if they experienced any kind of event suggested by the questionnaire at least weekly during the last 6 months [
4] and examine the following hypothesis. Although our primary focus was on CB, we also examined TB to gain a better understanding of the characteristics of CB.
Hypothesis 1
Factors such as sociodemographic characteristics, personality traits, information dissemination on SNSs, and work circumstances make people vulnerable to CB victimization.
In the second part of analysis, we focus on the negative consequences of CB on employees’ psychological well-being. The limitations of the majority of the precedent studies are that they did not consider the consequences of the combination of TB and CB. Because TB and CB have been shown to coexist, we considered it more appropriate to treat them as mutually dependent variables. We used a two-step cluster analysis to identify groups of respondents with similar patterns of TCB (traditional and cyber bullying) victimization. We expected that it would reveal distinctive patterns of TCB victimization, as precedent studies have shown in a latent class cluster model [
46,
47]. Therefore, our second hypothesis is the following.
Hypothesis 2
The co-existence of TB and CB victimization demonstrate severe effects on psychological well-being (e.g., psychological distress, insomnia, and loneliness).
The present study aims to replicate and add to the existing literature in the context of the Japanese workforce. Workplace bullying is increasingly recognized as a public health concern for policymakers and stakeholders. Since the use of the ICT in the workplace is expected to expand during the COVID-19 pandemic, this study could be the first step in clarifying the actual condition of CB victimization among employees, which would also be helpful for the early detection and prevention of the negative outcomes of workplace bullying victimization.
Results
The frequencies of S-NAQ and ICA-W responses are shown in Table
1. The most common S-NAQ response was “Someone withholding information that affects your performance”, with 5.0% of the respondents experiencing this at least once a week. The most common response on the ICA-W was “Your e-mails, phone calls, or messages are ignored at work”, with 5.0% of the respondents experiencing this at least once a week. The factor loading by factor analysis is shown on the right side of Table
1. It was confirmed that each item of the S-NAQ and ICA-W loaded onto each scale separately. Cronbach’s α was 0.94 for the S-NAQ and 0.93 for the ICA-W.
Table 1
Frequency of being victimized weekly or daily basis, and the factor loadings of S-NAQ and ICA-W items among regular employees in Japan (n = 1,200)
S-NAQ items |
a. Someone withholding information that affects your performance | 5.0 | .62 | .00 |
b. Spreading of gossip and rumors about you | 2.7 | .80 | .04 |
c. Being ignored or excluded | 3.4 | .80 | .05 |
d. Having insulting or offensive remarks made about your person (i.e., habits and background), attitude, or private life | 3.0 | .82 | .03 |
e. Being shouted at or being the target of spontaneous anger (or rage) | 3.3 | .82 | -.05 |
f. Repeated reminders of your errors or mistakes | 2.8 | .80 | .01 |
g. Being ignored or facing a hostile reaction when you approach | 3.3 | .84 | .05 |
h. Persistent criticism of your work and effort | 3.8 | .86 | -.06 |
i. Practical jokes carried out by people you do not get along with | 2.2 | .81 | .03 |
ICA-W items |
A. Your e-mails, phone calls, or messages are ignored at work | 5.0 | .10 | .43 |
B. Your e-mails are forwarded to third parties in order to harm you | 1.6 | .05 | .81 |
C. Your work is criticized publicly by means of ICT | 1.5 | -.01 | .89 |
D. Somebody is withholding e-mails or files you need, making your work more difficult | 2.9 | .08 | .61 |
E. Rumors or gossip is being spread about you by means of ICT | 1.2 | -.01 | .93 |
F. You are being insulted, threatened, or intimidated by means of ICT | 1.4 | .03 | .88 |
G. Constant remarks are being made about you and your private life by means of ICT | 1.8 | .01 | .89 |
H. Your personal information is hacked and used to harm you | 1.3 | -.02 | .87 |
I. Somebody shares photos or videos of you on the Internet to make fun of you | 1.2 | -.04 | .87 |
J. Somebody takes over your identity | 1.1 | -.06 | .86 |
The Spearman’s rank correlation coefficients of S-NAQ and ICA-W items are shown in Table
2. The correlation coefficients were ranged from 0.211 to 0.467. Relatively strong correlation coefficients were found between “c. Being ignored or excluded” and “B. Your e-mails are forwarded to third parties in order to harm you” (0.465), and between “i. Practical jokes carried out by people you do not get along with” and “F. You are being insulted, threatened, or intimidated by means of ICT” (0.467).
Table 2
Spearman’s rank correlation coefficientsa between S-NAQb and ICA-Wc items (n = 1,200)
S-NAQ Items |
a | .305 | .295 | .290 | .309 | .250 | .300 | .289 | .237 | .250 | .211 |
b | .269 | .400 | .338 | .334 | .362 | .422 | .393 | .359 | .342 | .341 |
c | .311 | .465 | .382 | .341 | .394 | .441 | .420 | .387 | .359 | .377 |
d | .270 | .389 | .364 | .290 | .378 | .423 | .405 | .361 | .340 | .348 |
e | .274 | .358 | .326 | .283 | .326 | .393 | .356 | .323 | .307 | .313 |
f | .255 | .353 | .354 | .284 | .317 | .364 | .349 | .312 | .315 | .296 |
g | .305 | .451 | .394 | .329 | .401 | .448 | .407 | .394 | .352 | .364 |
h | .294 | .361 | .335 | .300 | .322 | .373 | .354 | .320 | .307 | .307 |
i | .305 | .452 | .403 | .348 | .436 | .467 | .432 | .426 | .377 | .370 |
The characteristics of the participants and the percentages of TB/CB victimization are shown in Table
3. The results of the chi-squared test regarding the association between the percentages of TB/CB victimization and characteristics of the participants are listed together. The percentages of participants who had experienced TB and CB victimization were 11.3% and 8.0%, respectively. Sex and managerial position were significantly associated with TB victimization. The percentages of TB victimization were significantly higher for males than for females and for managers than for non-managers. Age, educational attainment, position, average working hours per week, and frequency of teleworking were significantly associated with CB victimization. The percentages of CB victimization were significantly higher for managers than for non-managers. No statistical significance was found in regarding to residential area, type of industry, type of work, or number of employees in the workplace.
Table 3
Characteristics of the participants and percentage of TBa/CBb victimization (n = 1,200)
Overall | 1,200 | 11.3 | | 8.0 | |
TB victim |
No | 1,064 | | | 5.4 | < .001 |
Yes | 136 | | | 28.7 |
CB victim |
No | 1,104 | 8.8 | < .001 | | |
Yes | 96 | 40.6 | |
Sex |
Male | 800 | 12.6 | .046 | 8.9 | .11 |
Female | 400 | 8.8 | 6.3 |
Marital status | | | | | |
Not married | 497 | 10.3 | .33 | 7.2 | .42 |
Married | 703 | 12.1 | 8.5 |
Annual household income, JPY |
4 million or less | 264 | 11.4 | .76 | 6.4 | .19 |
4–8 million | 591 | 11.3 | 7.4 |
8–12 million | 249 | 12.4 | 11.2 |
12 million or more | 96 | 8.3 | 7.3 |
Educational attainment |
High school | 250 | 9.2 | .48 | 2.8 | .001 |
College, etc | 157 | 11.5 | 5.7 |
University/graduate school | 793 | 12.0 | 10.1 |
Active dissemination via SNSs, blog, or video-sharing site |
No | 1,057 | 10.4 | .006 | 6.1 | < .001 |
Yes | 143 | 18.2 | 21.7 |
Residential area |
Hokkaido/Tohoku | 83 | 15.7 | .24 | 6.0 | .18 |
Tokyo | 254 | 15.4 | 10.6 |
Kanto (excluding Tokyo) | 370 | 9.5 | 6.2 |
Chubu | 146 | 9.6 | 6.2 |
Kansai | 207 | 10.1 | 11.1 |
Chugoku/Shikoku | 73 | 11.0 | 8.2 |
Kyusyu/Okinawa | 67 | 9.0 | 4.5 |
Type of industry |
Construction | 62 | 6.5 | .33 | 11.3 | .53 |
Manufacturing | 333 | 11.4 | 6.9 |
Information/communication | 107 | 11.2 | 11.2 |
Transportation | 66 | 7.6 | 10.6 |
Wholesale/retail trade | 96 | 17.7 | 7.3 |
Finance/insurance/real estate | 110 | 12.7 | 9.1 |
Healthcare/welfare | 90 | 13.3 | 3.3 |
Services | 132 | 9.1 | 7.6 |
Public sector | 64 | 10.9 | 10.9 |
Academic research | 40 | 2.5 | 2.5 |
Others | 100 | 14.0 | 9.0 |
Type of work |
Professional/technical position | 296 | 10.1 | .30 | 10.1 | .26 |
Clerical position | 489 | 10.6 | 8.6 |
Sales position | 77 | 18.2 | 5.2 |
Service position | 98 | 15.3 | 4.1 |
Production engineering | 91 | 9.9 | 4.4 |
Others | 149 | 10.7 | 8.1 |
Position |
Non-manager | 946 | 10.4 | .04 | 6.6 | < .001 |
Manager | 254 | 15.0 | 13.4 |
Number of employees in the workplace |
5 or fewer | 175 | 11.4 | .71 | 4.6 | .33 |
6–9 | 161 | 11.2 | 8.1 |
10–19 | 242 | 9.1 | 7.0 |
20–29 | 124 | 10.5 | 9.7 |
30 or more | 498 | 12.7 | 9.2 |
Average working hours per week |
30 or fewer | 59 | 16.9 | .09 | 15.3 | .04 |
30–39 | 252 | 10.7 | 7.1 |
40–49 | 649 | 9.6 | 6.5 |
50–59 | 142 | 14.8 | 12.0 |
60 or more | 98 | 16.3 | 10.2 |
Frequency of teleworking |
Almost never | 777 | 11.1 | .25 | 4.5 | < .001 |
About 1 to 3 times a month | 79 | 17.7 | 19.0 |
About 1 to 3 times a week | 203 | 9.4 | 15.3 |
Almost every day | 141 | 12.1 | 10.6 |
The mean scores for each scale regarding TB/CB victimization are shown in Table
4. The results of a
t-test for the difference in mean scores between TB/CB victim and non-victim are listed together. Regarding job stress, TB/CB victims had significantly higher mean scores for stress-enhancing factors than did non-victims. Regarding personality traits, TB/CB victims had significantly lower mean scores for agreeableness than did non-victims. Regarding psychological distress, TB/CB victims had significantly higher mean K6 scores than did non-victims. Regarding loneliness, TB/CB victims had significantly higher mean TIL-J scores than did non-victims. Regarding organizational climate, TB/CB victims had significantly higher mean scores on the tradition scale than did non-victims.
Table 4
Mean scores of each scale for TBa/CBb victimization (n = 1,200)
Age | 21–64 | 40.64 | 39.57 | .26 | 40.90 | 36.00 | < .001 |
Japanese version of the Ten-Item Personality Inventory |
Extraversion | 2–14 | 7.17 | 7.63 | .02 | 7.19 | 7.53 | .13 |
Agreeableness | 2–14 | 9.47 | 8.38 | < .001 | 9.39 | 8.77 | .007 |
Conscientiousness | 2–14 | 7.84 | 7.74 | .66 | 7.80 | 8.08 | .26 |
Neuroticism | 2–14 | 8.03 | 8.60 | .008 | 8.11 | 8.01 | .71 |
Openness | 2–14 | 7.30 | 7.66 | .09 | 7.28 | 8.06 | < .001 |
Brief Scales for Job Stress |
Quantitative workload | 1–4 | 2.14 | 2.43 | < .001 | 2.14 | 2.49 | < .001 |
Qualitative workload | 1–4 | 2.11 | 2.47 | < .001 | 2.11 | 2.61 | < .001 |
Job control | 1–4 | 2.51 | 2.35 | .02 | 2.49 | 2.57 | .31 |
Support from colleagues and superiors | 1–4 | 2.46 | 2.24 | .001 | 2.43 | 2.48 | .42 |
the 12-item Organizational Climate Scale |
Tradition scale | 1–2 | 1.31 | 1.52 | < .001 | 1.33 | 1.44 | .001 |
Organizational environment scale | 1–2 | 1.35 | 1.32 | .32 | 1.35 | 1.36 | .73 |
Gratitude at Work Scale |
Gratitude for supportive work environment | 1–5 | 3.07 | 2.71 | < .001 | 3.04 | 2.99 | .63 |
Gratitude for meaningful work | 1–5 | 3.12 | 2.96 | 0.052 | 3.09 | 3.18 | .36 |
the 6-item Kessler Psychological Distress Scale | 0–24 | 4.91 | 9.58 | < .001 | 5.19 | 8.23 | < .001 |
Athens Insomnia Scale | 0–24 | 5.82 | 9.77 | < .001 | 6.09 | 8.29 | < .001 |
Japanese version of the Three-Item Loneliness scale | 3–9 | 4.70 | 6.18 | < .001 | 4.81 | 5.51 | < .001 |
The results of hierarchical binomial logistic regression analysis using TB victimization as the dependent variable are shown in Table
5. Extraversion, agreeableness, qualitative workload, and support from colleagues and superiors were entered into step 1 by forward selection. In step 2, CB victimization was added. All explanatory factors except qualitative workload maintained statistical significance.
Table 5
Factors correlated with TBa victimization (n = 1,200)
Female (ref. Male) | 0.73 | (0.48–1.14) | 0.76 | (0.48–1.18) |
Age | 0.99 | (0.97–1.01) | 1.00 | (0.98–1.02) |
Extraversion | 1.15 | (1.06–1.25) | 1.14 | (1.04–1.24) |
Agreeableness | 0.83 | (0.76–0.91) | 0.84 | (0.77–0.92) |
Qualitative workload | 1.42 | (1.10–1.83) | 1.29 | (0.99–1.68) |
Support from colleagues and superiors | 0.67 | (0.50–0.89) | 0.62 | (0.45–0.83) |
Tradition scale | 1.32 | (1.20–1.47) | 1.30 | (1.17–1.45) |
CBb victim, Yes (ref. No) | | | 5.61 | (3.37–9.33) |
Nagelkerke R2 | 0.16 | 0.22 |
The results of hierarchical binomial logistic regression analysis using CB victimization as the dependent variable are shown in Table
6. Managerial position, active dissemination via SNSs, etc., openness, and qualitative workload were entered into step 1 by forward selection. Frequency of teleworking and TB victimization were selected to be added in steps 2 and 3, respectively. In the final step, the correlation between openness CB victimization was attenuated by adding other explanatory variables. On the other hand, managerial position (odds ratio [OR] = 1.90, 95% confidence interval [CI] = 1.09–3.30), active dissemination by SNSs, etc. (OR = 2.59, 95% CI = 1.42–4.30), qualitative workload (OR = 1.84, 95% CI = 1.34–2.52), and frequency of teleworking (“almost never” vs. “about 1 to 3 times a month”: OR = 3.09, 95% CI = 1.44–6.62; “about 1 to 3 times a week”, OR = 3.46, 95% CI = 1.96–6.11) maintained statistical significance. Regarding the frequency of teleworking, it is noteworthy that the difference between “almost never” and “almost every day” was not statistically significant.
Table 6
Factors correlated with CBa victimization (n = 1,200)
Female (ref. Male) | 0.87 | (0.53–1.45) | 0.89 | (0.53–1.49) | 0.89 | (0.52–1.53) |
Age | 0.95 | (0.92–0.97) | 0.95 | (0.93–0.98) | 0.95 | (0.93–0.98) |
Manager (ref. Non-manager) | 2.35 | (1.40–3.95) | 1.97 | (1.16–3.36) | 1.90 | (1.09–3.30) |
Active dissemination via SNSs, blog, or video-sharing site, Yes (ref. No) | 2.90 | (1.74–4.83) | 2.74 | (1.63–4.63) | 2.59 | (1.42–4.30) |
Openness | 1.11 | (1.001–1.23) | 1.10 | (0.995–1.23) | 1.08 | (0.97–1.20) |
Qualitative workload | 2.06 | (1.55–2.73) | 2.03 | (1.51–2.73) | 1.84 | (1.34–2.52) |
Frequency of teleworking (ref. Almost never) |
About 1 to 3 times a month | | | 3.44 | (1.70–6.99) | 3.09 | (1.44–6.62) |
About 1 to 3 times a week | | | 2.94 | (1.71–5.06) | 3.46 | (1.96–6.11) |
Almost every day | | | 1.95 | (1.001–3.79) | 1.96 | (0.97–3.95) |
TBb victim, Yes (ref. No) | | | | | 6.03 | (3.60–10.10) |
Nagelkerke R2 | 0.17 | 0.21 | 0.29 |
The two-step cluster analysis revealed three TCB-victimization clusters. The AIC was 8,502.32 when the number of clusters was two, 6,560.20 when the number of clusters was three, and 6,082.60 when the number of clusters was four. The ratio of the AIC change was 0.264 between two and three clusters and dropped to 0.065 between three and four clusters. A total of 81.0% (
n = 972) of the respondents were assigned to cluster X, 14.3% (
n = 171) to cluster Y, and 4.8% (
n = 57) to cluster Z. The ratio of the biggest/smallest cluster size was 17.1. According to a precedent study [
72], the overall model quality was “good”, with an average silhouette of 0.7. Based on the between-cluster comparisons, as shown in Table
7, the respondents in cluster X scored almost 1 for all 19 items, which means they rarely experienced negative acts as illustrated by the S-NAQ and ICA-W. The respondents in cluster Y showed a different pattern compared with those in cluster X, with scores for S-NAQ items ranging from 1.88 to 2.48. The scores for ICA-W items were higher, but not that significant (1.01–1.67). Lastly, the respondents in cluster Z showed a distinct pattern among the three clusters, demonstrating the highest score for all 19 items of the S-NAQ (2.60–2.88) and ICA-W (2.53–3.05). In total, the respondents in cluster X were nearly free from both TB and CB victimization. The respondents in cluster Y experienced TB victimization more frequently, but that was not the case for CB victimization. The respondents in cluster Z were characterized as frequent targets of both TB and CB.
Table 7
Mean scores for each S-NAQa and ICA-Wb item by three clusters (n = 1,200)
S-NAQ items |
a | 1.28 | 2.48 | 2.72 | X < Y, Z |
b | 1.06 | 2.23 | 2.81 | X < Y < Z |
c | 1.03 | 2.16 | 2.88 | X < Y < Z |
d | 1.04 | 2.12 | 2.77 | X < Y < Z |
e | 1.10 | 2.33 | 2.60 | X < Y, Z |
f | 1.09 | 2.23 | 2.72 | X < Y, Z |
g | 1.02 | 2.15 | 2.91 | X < Y < Z |
h | 1.06 | 2.27 | 2.65 | X < Y, Z |
i | 1.02 | 1.88 | 2.60 | X < Y < Z |
ICA-W items |
A | 1.19 | 1.67 | 2.91 | X < Y < Z |
B | 1.01 | 1.19 | 2.95 | X < Y < Z |
C | 1.02 | 1.04 | 3.05 | X, Y < Z |
D | 1.07 | 1.36 | 3.02 | X < Y < Z |
E | 1.01 | 1.02 | 2.82 | X, Y < Z |
F | 1.00 | 1.06 | 3.11 | X < Y < Z |
G | 1.01 | 1.05 | 3.02 | X, Y < Z |
H | 1.00 | 1.03 | 2.67 | X, Y < Z |
I | 1.01 | 1.01 | 2.53 | X, Y < Z |
J | 1.01 | 1.06 | 2.53 | X, Y < Z |
To examine the potential differences among TCB-victimization clusters regarding the association with psychological well-being, we performed a series of hierarchical binomial logistic regression analyses. The results are shown in Table
8,
9 and
10. In model 1, with sex and age as the explanatory variables, clusters Y and Z had significantly higher ORs for psychological distress, insomnia, and loneliness, with cluster X as the reference. In model 2, after adjusting for possible confounders, clusters Y and Z maintained their statistical significance. As for cluster Y, the ORs for psychological distress, insomnia, and loneliness were 3.70 (95% CI = 2.37–5.80), 3.33 (95% CI = 2.18–5.07), and 2.83 (95% CI = 1.92–4.19), respectively. As for cluster Z, the ORs for psychological distress, insomnia, and loneliness were 12.63 (95% CI = 4.20–38.03), 6.26 (95% CI = 2.80–14.01), and 3.24 (95% CI = 1.74–6.04), respectively.
Table 8
Association between TCB-victimizationa clusters and psychological distressb (n = 1,200)
Female (ref. male) | 1.10 | (0.85–1.42) | 1.55 | (1.14–2.10) |
Age | 0.98 | (0.97–0.99) | 0.99 | (0.97–0.999) |
Cluster Y (ref. cluster X) | 6.46 | (4.28–9.75) | 3.70 | (2.37–5.80) |
Cluster Z (ref. cluster X) | 16.92 | (6.06–47.29) | 12.63 | (4.20–38.03) |
Active dissemination via SNSs, blog, or video sharing site, Yes (ref. No) | 1.85 | (1.16–2.93) |
Extraversion | | | 0.92 | (0.87–0.98) |
Agreeableness | | | 0.89 | (0.82–0.95) |
Neuroticism | | 1.23 | (1.15–1.31) |
Qualitative workload | | | 2.13 | (1.76–2.59) |
Support from colleagues and superiors | | | 0.63 | (0.51–0.79) |
Tradition scale | | | 1.11 | (1.03–1.21) |
Gratitude for meaningful work | | | 0.76 | (0.64–0.91) |
Nagelkerke R2 | 0.18 | 0.40 |
Table 9
Association between TCB-victimizationa clusters and insomniab (n = 1,200)
Female (ref. male) | 0.92 | (0.71–1.18) | 1.09 | (0.82–1.44) |
Age | 0.99 | (0.98–1.00) | 1.00 | (0.98–1.01) |
Cluster Y (ref. cluster X) | 4.92 | (3.30–7.35) | 3.33 | (2.18–5.07) |
Cluster Z (ref. cluster X) | 6.81 | (3.18–14.58) | 6.26 | (2.80–14.01) |
Not married (ref. married) | | | 0.73 | (0.55–0.96) |
Manager (ref. non-manager) | | | 1.49 | (1.06–2.10) |
Neuroticism | | | 1.19 | (1.12–1.26) |
Quantitative workload | | | 1.31 | (1.05–1.63) |
Qualitative workload | | | 1.48 | (1.18–1.87) |
Job control | | | 0.81 | (0.68–0.97) |
Organizational environment scale | | | 0.91 | (0.84–0.98) |
Gratitude for supportive work environment | | | 0.72 | (0.60–0.86) |
Nagelkerke R2 | 0.12 | 0.27 |
Table 10
Association between TCB-victimizationa clusters and lonelinessb (n = 1,200)
Female (ref. male) | 1.00 | (0.77–1.29) | 1.11 | (0.81–1.51) |
Age | 0.99 | (0.98–1.00) | 1.00 | (0.98–1.01) |
Cluster Y (ref. cluster X) | 4.07 | (2.88–5.74) | 2.83 | (1.92–4.19) |
Cluster Z (ref. cluster X) | 3.00 | (1.73–5.20) | 3.24 | (1.74–6.04) |
High school (ref. university/graduate school) | | | 0.73 | (0.50–1.05) |
College, etc. (ref. university/graduate school) | | | 1.47 | (0.97–2.23) |
4 million or less (ref. 4–8 million) | | | 1.63 | (1.15–2.32) |
8–12 million (ref. 4–8 million) | | | 1.04 | (0.73–1.50) |
12 million or more (ref. 4–8 million) | | | 0.97 | (0.55–1.71) |
Extraversion | | | 0.84 | (0.79–0.89) |
Agreeableness | | | 0.86 | (0.81–0.93) |
Neuroticism | | | 1.16 | (1.08–1.24) |
Qualitative workload | | | 1.36 | (1.12–1.65) |
Job control | | | 0.54 | (0.43–0.68) |
Tradition scale | | | 1.18 | (1.09–1.28) |
Organizational environment scale | | | 0.86 | (0.79–0.93) |
Nagelkerke R2 | 0.09 | 0.34 |
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