Exploring the Role of Demographics and Psychological Variables in Internet Addiction

Abstract

Background and Aim:

In today’s fast changing world, the Internet plays a major role in people’s lives. Apart from its benefits, the Internet also has serious negative consequences which include the issue of Internet addiction. This study explores the role of demographics (e.g., age, gender, and occupational position) in relation to Internet addiction. In addition, the influence of individual psychological variables (loneliness, shyness, and proactive personality) on Internet addiction was also examined.

Methods:

A structured questionnaire consisting of items representing loneliness, shyness, proactiveness, Internet addiction, and demographics was used to collect the data from 1,000 working adults in selected organizations. Chi-square tests were used to investigate the effect of gender, age, and occupational positions on Internet addiction. The influence of variables such as loneliness, shyness, and proactiveness on Internet addiction was analyzed using partial least squares structural equation modeling (PLS-SEM).

Results:

The results demonstrated no significant differences in terms of Internet addiction between the genders. However, significant differences were found in Internet addiction depending on age and occupational positions. The study also found positive relationship between loneliness, shyness, proactiveness, and Internet addiction.

Conclusion:

The findings of this study add empirical evidence to the existing literature in respect of the correlation between age, gender, occupational position, psychological characteristics (loneliness, shyness, and proactiveness), and Internet addiction.

Keywords

Internet addiction loneliness shyness proactiveness age occupational position

The revolution of the Internet has brought about many technological innovations. The innovations have received many critiques, some favorable and some more negative. Among the favorable effects of the Internet is the ability to bring individuals and organizations together (Laprise & Musiani, 2015) and change in the way communication is conducted (Kiesler, 2014). One of the negative connotations related to Internet is the issue of Internet addiction. There are studies (e.g., Randler, Horzum, & Vollmer, 2014; Leung & Lee, 2012) illustrating the rise in the number of individuals who are addicted to the Internet. Thus, it has become increasingly urgent to study this kind of addictive behavior.

Internet addiction generally consists of four elements: excessive use, withdrawal, tolerance, and negative social repercussions (Block, 2008). Excessive use is associated with a loss of a sense of time or a neglect of basic drives when using the computer, whereas withdrawal is related to the feelings of anger, tension, and/or depression when the computer is inaccessible. Tolerance refers to the need for more advanced computer equipment and software and/or more hours of use, whereas negative social repercussions may include feelings of depression and stress or other negative feelings.

The question that arises then is “What individual characteristics influence Internet addiction behavior?” Following this line of questioning, the main objective of this study is to explore the influence of individual characteristics on Internet addiction behavior. This study focuses on shyness, loneliness, proactiveness, and three demographic profiles (age, gender, and occupational position). The rationale for choosing these variables is discussed in the following paragraph.

Shyness and loneliness were chosen as they have been proven to be significant predictors of Internet addiction behavior (Ang, Chong, Chye, & Huan, 2012; Odaci & Celik, 2013; Öztunc, 2013; Tokunaga & Rains, 2010). This study expands existing literature on Internet addiction behavior as there are no existing studies that studied the effect of loneliness, shyness, and proactiveness on Internet addiction behavior. A few studies (Öztunc, 2013; Odaci & Celik, 2013) focused on loneliness and shyness but not proactiveness (Hung, Chen, & Lin, 2015). Thus, an examination of the relationship between proactiveness and Internet addiction will add to existing knowledge about behavioral addiction. This study also explores the effects of age, gender, and occupational position on Internet addiction behavior. There have been a number of studies that have found that males are more likely to be addicted to the Internet than females (Mottram & Fleming, 2009; Randler et al., 2014). Randler, Horzum, and Vollmer (2014) found no significant relationship between age and Internet addiction, but Tonioni et al. (2012) found otherwise. Hence, it would be interesting to study the relationship between age and Internet addiction in the Malaysian context and among working adults. There is, however, little empirical evidence about the relationship between Internet addiction and occupational position. Therefore, it would beneficial to see the effect of occupational position on Internet addiction as most past studies focused on students (Ang et al., 2012; Varshney et al., 2015; Yeap et al., 2015) whereas this study focused on working adults.

Internet Addiction

The Internet addiction disorder emerged for the first time in 1996 which was introduced by Goldberg (1996) who related it to substance dependence. Among the symptoms of Internet addiction include tolerance, withdrawal, lack of control, relapse, and large amounts of time spent online, negative consequences, and continuation of use irrespective of problem awareness. According to Kuss, Griffiths, Karila, and Billieux (2014) in their “Systematic Review of Epidemiological Research on Internet Addiction,” there are several terms that have been used to describe Internet addiction such as problematic Internet use, Internet dependency, compulsive computer use, virtual addiction, and Internet addiction disorder. Internet addiction is defined as a maladaptive pattern of Internet use, generally time-consuming, that leads to clinically significant impairment or distress. In general, Internet addiction has been conceptualized as a behavioral control problem. It refers to Internet users’ inability to control their use of the medium, which in turn causes one’s marked distress and functional impairment in daily life (Shek, Sun, & Yu, 2013; Yao & Zhong, 2014).

Review of literature on Internet addiction illustrated that Griffiths and Young (Griffiths, 1996; Young, 1996) are the pioneers to conduct empirical research on Internet addiction. Since then, empirical research (Kuss, Griffiths, Karila, & Billieux, 2014) on Internet addiction has greatly increased. Various factors such as sociodemographic variables, Internet use variables, psychosocial factors, and comorbid symptoms were studied in relation to Internet addiction (Kuss et al., 2014).

Shyness, Loneliness, Proactiveness, and Internet Addiction

Coplan et al. (2013, p. 862) implied that “shyness is a temperamental trait characterized by excessive wariness and feelings of unease in the face of social novelty and perceived social-evaluation.” Someone who is shy possesses a sense of uneasiness in the presence of others (Öztunc, 2013). Moreover, Odaci and Celik (2013) believed that those individuals who are shy would not feel unease and wary if they interact with other people virtually rather than physically. Individuals who are shy are presumed to be able to express themselves better online, rather than in face-to-face interactions, thus have greater possibility to be addicted to Internet usage compared to those who are not shy. Ryan and Xenos (2011) found that people who are shy tend to spend more time on the Internet using Facebook. Young, Griffin-Shelley, Cooper, O’mara, and Buchanan (2000) also reported that the anonymity provided by virtual environments gives shy people a safe and secure platform for social interaction. Thus, it is hypothesized that:
Hypothesis 1: Shyness has a significant positive effect on Internet addiction.

Loneliness is known as an individual’s state of being alone. It occurs when one feels detached from the world. Individuals who suffer from loneliness feel that they are isolated from the society. They experience difficulties in establishing meaningful contact with other people (Öztunc, 2013). Loneliness is one of the main predictors of Internet addiction, as the Internet provides a channel to escape from stressful situations and relieve undesirable feelings associated with it (Zou, Yaduvanshi, & Malik, 2015). Loneliness has been found to be positively associated with Internet addiction in some studies (Tokunaga & Rains, 2010; Yao & Zhong, 2014). In addition, Kim, LaRose, and Peng (2009) found that loneliness is not only the cause of problematic Internet use among American college students, but it is also an effect of the problematic usage. Therefore, the hypotheses are postulated as follows:
Hypothesis 2: Loneliness has a significant positive effect on Internet addiction.

Proactiveness describes an individual’s personal trait that reflects their willingness to undertake actions that would improve current situations without being instructed by anyone. According to Hung, Chen, and Lin (2015), proactive individuals actively seize opportunities to make adjustments and choose to adapt to existing environments, and they have a forward-looking perspective which is then accompanied by innovative activity and risk taking.

In relation to Internet usage, individuals who are proactive is expected to use the Internet continuously for various activities such as information about new products, services, and innovations, information about competitors, and so on. These activities would allow them to increase their knowledge, thus enabling them to take appropriate actions which would bring beneficial results (Belschak & Hartog, 2010). Hence, it is expected that the individuals would spend more time on the Internet. As mentioned earlier, there are no studies that have attempted to look at the relationship between proactiveness and Internet addiction. Proactiveness has, however, been used to study work performance and productivity. For example, Hung et al. (2015) found that proactiveness lessens the effect of technostress on productivity among mobile phone users. Therefore, this study aims to investigate the influence of proactiveness on Internet addiction.
Hypothesis 3: Proactiveness has a significant positive effect on Internet addiction.

Method

Procedure

The data set for this study was collected using a questionnaire survey. Several steps were taken to distribute the questionnaire to the targeted respondents, namely, working adults. The study first identified organizations with addresses in Kuala Lumpur from the Malaysian Yellow Pages (www.yellowpages.my). Kuala Lumpur was chosen for the study as it is the capital city of Malaysia, and it has the highest number of working adults in the country (Department of Statistics, 2015).

The contact person listed in the organizations’ website was then contacted to act as a liaison for the survey. After 7 days, a total of 75 liaisons agreed to participate in the survey out of the 250 organizations contacted. Once the organizations’ contact person (liaison) agreed, the questionnaires were distributed to them; and to ensure consistency, the researchers went through the questionnaire with them. The number of questionnaires distributed to each organization was dependent on the number of workers employed in the organizations. A minimum of 10 and a maximum of 20 questionnaires were distributed to the liaison of each organization. In total, nearly 1,400 questionnaires were distributed.

Participants

The liaisons were required to distribute the questionnaires to (1) employees of the organization and (2) those who are using Internet in their daily life. In order to ensure that the respondents are Internet users, a question, that is, Internet usage was included in the questionnaire. Thus those who were not Internet users were not included in the study. Working adults were chosen as respondents as most of the previous studies (Varshney et al., 2015; Yeap et al., 2015) on Internet addiction behavior used students as their respondents. Consequently, little is known about the occurrence of Internet addiction among working adults. Therefore, this study targeted working adults as its participants.

After 6 weeks, 1,020 questionnaires were returned from 67 organizations, however, only 1,000 responses were used for further analysis. Nearly 75% of the respondents were between 21 and 39 years old, 50.3% were male and in terms of position, 38%, 26.5%, and 30% were in middle management, lower management, and nonmanagement respectively.

Measures

The questionnaire consisted of five main sections, namely, shyness, loneliness, proactiveness, Internet addiction behavior, and demographic profile. The items were taken from past studies and have therefore been tested for validity and reliability. Nevertheless, exploratory factor analysis was performed to check for validity and the results showed that most items’ factor loadings were greater than 0.5 and each loads strongly on the associated factors.

There are many instruments that can be used to measure Internet addiction (Laconi, Rodgers, & Chabrol, 2014). Among the various instruments, the most popular one is by Young (1998). This study adapts Young’s 20 items. Shyness was measured using 3 items adapted from Crozier (2005) who devised the Revised Cheek and Buss Shyness Scale. Loneliness was measured using 3 items adapted from Wu and Yao (2008) and proactive items were adapted from Hung et al. (2015).

From the exploratory factor analysis, it was found that the reliability of the variables shyness, loneliness, and proactiveness were found to be good with the Cronbach’s α coefficient value of .85, .86, and .95 respectively. The scale for Internet addiction has been validated in adult populations and showed a good internal reliability across studies (Mittal, Dean, & Pelletier, 2013). For the current study, the construct reliability for the scale was high, with the Cronbach’s α coefficient value of .92.

Statistical Analysis

Chi-square test was used to investigate the effect of gender, age, and occupational positions on Internet addiction. The influence of variables such as loneliness, shyness, and proactiveness on Internet addiction was analyzed using partial least squares (PLS) technique. SmartPls 3.0 software (Hair, Hult, Ringle, & Sarstedt, 2013) was used to test the hypothesis. The measurement model was evaluated using criteria such as average variance extracted, composite reliability, Fornell-Larcker criterion, and outer loadings. The structural model was evaluated using path coefficient and t values. The t values were calculated using bootstrap method.

Ethics

Before the questionnaires were distributed, it was viewed by a panel of assessors at the department level. In addition, at the front cover of the questionnaire, it was stated that the respondents are given the choice either to take part in the survey or otherwise. Thus, by completing the questionnaire, the respondents have given their consent. In addition, the respondents are also informed that the data would be used in an aggregate form.

Results

Level of Internet Addiction

The level of Internet addiction is divided into three levels: low (mild), medium (moderate), and high (severe). As shown in Table 1, 59.9% of the individuals have moderate Internet addiction behavior and only 13.3% are considered as having severe Internet addiction behavior. Table 1 also indicates that different age-groups have different levels of Internet addiction behavior. The largest proportions (22.8% and 22.8%) of individuals who have a moderate level of Internet addiction behavior belong to one of two age-groups, from 21 to 29 and from 30 to 39. Moreover, none of the individuals who have a severe level of Internet addiction is more than 60 years old. The effect of age on the Internet addiction behavior was tested using chi-square and it was found to be significant (χ² = 31.343; p = .001). Thus, age affects Internet addiction behavior. Those in middle age (21–39 years old) are more likely to have Internet addiction than those in the other age categories.

Table 1.
Internet Addiction Level of the Respondents According to Age-Groups.

Level of Internet Addiction Total

Mild Moderate Severe

Age, in years 18–20 1 (0.1%) 17 (1.7%) 1 (0.1%) 19 (1.9%)

21–29 77 (7.7%) 228 (22.8%) 59 (5.9%) 364 (36.4%)

30–39 104 (10.4%) 228 (22.8%) 50 (5.0%) 382 (38.2%)

40–49 76 (7.6%) 100 (10.0%) 19 (1.9%) 195 (19.5%)

50–59 10 (1.0%) 25 (2.5%) 4 (0.4%) 39 (3.9%)

>60 0 (0.0%) 1 (0.1%) 0 (0.0%) 1 (0.1%)

Total 268 (26.8%) 599 (59.9%) 133 (13.3%) 1,000 (100.0%)

Similarly, the level of Internet addiction behavior according to occupational position also showed significant effects (Table 2). The largest proportions (20.5%) of individuals who have a moderate level of Internet addiction behavior are individuals who are holding a middle management position. The effect of occupational position on the Internet addiction behavior was tested using chi-square test, and it was found to be significant (χ² = 12.590, p = .05). Thus, occupational position affects Internet addiction behavior. Those in middle management position are more likely to have Internet addiction than those in other categories (i.e., lower and senior management as well as nonmanagement).

Table 2.
Internet Addiction Level of the Respondents According to Position Groups.

Category of IA Total

Position Count (%) Mild Moderate Severe

Not part of management Count 82 188 30 300

% 8.20 18.80 3.00 30.00

In lower management Count 58 170 37 265

% 5.80 17.00 3.70 26.50

In middle management Count 115 205 60 380

% 11.50 20.50 6.00 38.00

In senior management Count 13 36 6 55

% 1.30 3.60 0.60 5.50

Total Count 268 599 133 1,000

% 26.80 59.90 13.30 100.00

In terms of gender (Table 3), it was observed that the percentage of those with severe Internet addiction behavior is approximately the same for males (50.3%) and females (49.7%). This is substantiated by the chi-square test results which were found not to be significant (χ² = 5.375; p = .068). It is also evident from Table 3, that the percentages for moderate Internet addiction behavior are also very similar, indicating that there is no difference between males and females in terms of Internet addiction behavior.

Table 3.
Internet Addiction Level of the Respondents According to Gender Groups.

Level of Internet Addiction Total

Mild Moderate Severe

Gender Male 119 (11.9%) 317 (31.7%) 67 (6.7%) 503 (50.3%)

Female 149 (14.9%) 282 (28.2%) 66 (6.6%) 497 (49.7%)

Total 268 (26.8%) 599 (59.9%) 133 (13.3%) 1,000 (100.0%)

Hypothesis Testing

This study uses the PLS technique to perform confirmatory factor analysis to validate measurements and test the hypothesis. For this study, the assessment of measurement model is based on the evaluation criteria for reflective models. The indicator outer loadings should be higher than 0.708 (Hair, Hult, Ringle, & Sarstedt, 2013). Most of the indicators’ outer loadings were above the threshold value of 0.708, but few indicators had low loadings. Those items were deleted from the model which increased the composite reliability and average variance extracted (AVE) of the results. Loadings of some indicators were below 0.7. However, removal of these indicators did not change AVE or composite reliability, and therefore they were retained in the study. Table A1 in the Appendix summarizes the outer loadings of the indicators retained in the model for further analysis. The values of composite reliability and AVE are reported in Table 4. Results show that the values of the composite reliability are greater than 0.6 and AVE is greater than 0.5 for all the reflective constructs, so construct reliability and convergent validity is achieved.

Table 4.
Construct Reliability and Convergent Validity.

Construct AVE Composite Reliability

Internet addiction 0.513 0.932

Loneliness 0.790 0.919

Proactiveness 0.722 0.959

Shyness 0.696 0.901

Note. AVE = average variance extracted.

The next evaluation criterion for reflective models is discriminant validity. One criterion for discriminant validity is that factor loadings of each item should be greater than the cross loadings of items of other constructs (Bhattacherjee & Sanford, 2006; Pavlou & Gefen, 2004). The cross loadings of constructs after revision of the constructs were stronger on their respective factors than on other constructs. The Fornell-Larcker criterion (Table 5) indicated that the square root of AVE for the constructs is greater than values for interconstruct correlation. This confirms discriminant validity.

Table 5.
Fornell–Larcker Criterion.

Construct Internet Addiction Loneliness Proactive Behavior Shyness

Internet addiction 0.716

Loneliness 0.299 0.889

Proactiveness 0.123 –0.142 0.850

Shyness 0.291 0.681 –0.038 0.834

Note. Diagonal elements are square root of average variance extracted.

Assessment of the Structural Model

The main criteria to assess the structural models are the R ² of endogenous latent values. This study finds R ² values for the endogenous latent variables Internet addiction is 12.9%. The results for the path coefficients and t values showed that shyness (t = 3.645), loneliness (t = 5.375), and proactive behavior (t = 5.034), all with t values greater than 2.67 significantly influences Internet addiction. Figure 1 shows the structural model with t values and a summary of the hypotheses testing is summarized in Table 6.

Figure 1.
The structural model. p < .01 (>2.58). p < .05 (>1.96). p < .10 (>1.645).

Table 6.
Summary of Hypotheses Testing.

Hypothesis β t Value Result

Hypothesis 1: Shyness has a positive influence on Internet addiction. 0.146* 3.645 Supported

Hypothesis 2: Loneliness has a positive influence on Internet addiction. 0.222* 5.375 Supported

Hypothesis 3: Proactiveness has a positive influence on Internet addiction. 0.161* 5.034 Supported

*p < .01 (>2.58). **p < .05 (>1.96). p < .10 (>1.645).

Discussion

This study produced mixed results in terms of the relationships between age, gender, position, psychological characteristics (loneliness, shyness, and proactiveness) and Internet addiction behavior. Of the three demographic characteristics, age and occupational position were found to have an effect on Internet addiction behavior. Those who are above 39 and below 21 years old generally are less addicted than the groups in between those ages, which are consistent with Randler et al. (2014) and Mottram and Fleming (2009). Similarly, the results showed that those in middle management position are more addicted to Internet than those in lower and upper management. This finding adds to the current literature as most studies (Ang et al., 2012; Varshney et al., 2015; Yeap et al., 2015) focused on students. Thus, there is lack of empirical evidence in terms of the correlation between occupational positions with Internet addiction. In terms of gender, the Internet addiction behavior is approximately the same for males (50.3%) and females (49.7%). Therefore, there is no difference between males and females in terms of Internet addiction behavior. This finding contradicts Randler et al.’s (2014) finding whereby males are found to be more prone to Internet addiction than females.

There is a positive and significant relationship between shyness and Internet addiction. The more lonely and shy a person is, the more likely he or she is to be addicted to the Internet. This finding is consistent with Odaci and Celik (2013) and Ryan and Xenos (2011). Likewise, loneliness was also found to be associated to Internet addiction. Tokunaga and Rains (2010) and Yao and Zhong (2014) also found similar relationships.

The study also found significant positive relationship between proactiveness and Internet addiction. As mentioned earlier, there is little evidence on the influence of proactive behavior on Internet addiction, thus this study adds to the existing Internet addiction literature.

The findings of this study add empirical evidence to the existing literature in respect of the relationship between age, gender, occupational position, psychological characteristics (loneliness, shyness, and proactiveness), and Internet addiction. Although some of these findings are similar to past studies, it has to be noted here that the participants involved in this study are working adults and not students. Nevertheless, the results cannot be generalized as the participants were working adults in selected organizations in Kuala Lumpur. Perhaps, the results would be different with participants from other parts of the country. Therefore, future studies should include participants from other parts of the country.

In addition, it is also suggested that besides loneliness, shyness, and proactiveness, other personality traits such as the Big 5 personality should be incorporated in future studies. Besides this, additional analysis can be carried out to include industry where the participants are attached to. It would be interesting to see whether it has an effect on Internet addiction.

	Level of Internet Addiction	Total
Age, in years	18–20	1 (0.1%)	17 (1.7%)	1 (0.1%)	19 (1.9%)
21–29	77 (7.7%)	228 (22.8%)	59 (5.9%)	364 (36.4%)
30–39	104 (10.4%)	228 (22.8%)	50 (5.0%)	382 (38.2%)
40–49	76 (7.6%)	100 (10.0%)	19 (1.9%)	195 (19.5%)
50–59	10 (1.0%)	25 (2.5%)	4 (0.4%)	39 (3.9%)
>60	0 (0.0%)	1 (0.1%)	0 (0.0%)	1 (0.1%)
Total		268 (26.8%)	599 (59.9%)	133 (13.3%)	1,000 (100.0%)

		Category of IA	Total
Not part of management	Count	82	188	30	300
%	8.20	18.80	3.00	30.00
In lower management	Count	58	170	37	265
%	5.80	17.00	3.70	26.50
In middle management	Count	115	205	60	380
%	11.50	20.50	6.00	38.00
In senior management	Count	13	36	6	55
%	1.30	3.60	0.60	5.50
Total	Count	268	599	133	1,000
%	26.80	59.90	13.30	100.00

	Level of Internet Addiction	Total
Gender	Male	119 (11.9%)	317 (31.7%)	67 (6.7%)	503 (50.3%)
Female	149 (14.9%)	282 (28.2%)	66 (6.6%)	497 (49.7%)
Total	268 (26.8%)	599 (59.9%)	133 (13.3%)	1,000 (100.0%)

Construct	AVE	Composite Reliability
Internet addiction	0.513	0.932
Loneliness	0.790	0.919
Proactiveness	0.722	0.959
Shyness	0.696	0.901

Construct	Internet Addiction	Loneliness	Proactive Behavior	Shyness
Internet addiction	0.716
Loneliness	0.299	0.889
Proactiveness	0.123	–0.142	0.850
Shyness	0.291	0.681	–0.038	0.834

Hypothesis	β	t Value	Result
Hypothesis 1: Shyness has a positive influence on Internet addiction.	0.146***	3.645	Supported
Hypothesis 2: Loneliness has a positive influence on Internet addiction.	0.222***	5.375	Supported
Hypothesis 3: Proactiveness has a positive influence on Internet addiction.	0.161***	5.034	Supported

Footnotes

Appendix

Table A1.

Indicators Outer Loadings.

	Internet Addiction	Loneliness	Proactiveness	Shyness
IA10	0.649
IA11	0.661
IA13	0.729
IA14	0.737
IA15	0.760
IA16	0.700
IA17	0.781
IA18	0.769
IA19	0.738
IA20	0.745
IA6	0.643
IA8	0.696
IA9	0.686
L1		0.906
L2		0.918
L3		0.841
P1			0.807
P2			0.823
P3			0.829
P4			0.861
P5			0.864
P6			0.86
P7			0.877
P8			0.893
P9			0.83
S1				0.822
S2				0.834
S3				0.855
S4				0.826

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The authors would like to thank University of Malaya for funding the research under the HIR Social Science grant UM.C/625/1/HIR/ASH/033.

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		Level of Internet Addiction			Total
		Mild	Moderate	Severe	Total
Age, in years	18–20	1 (0.1%)	17 (1.7%)	1 (0.1%)	19 (1.9%)
	21–29	77 (7.7%)	228 (22.8%)	59 (5.9%)	364 (36.4%)
	30–39	104 (10.4%)	228 (22.8%)	50 (5.0%)	382 (38.2%)
	40–49	76 (7.6%)	100 (10.0%)	19 (1.9%)	195 (19.5%)
	50–59	10 (1.0%)	25 (2.5%)	4 (0.4%)	39 (3.9%)
	>60	0 (0.0%)	1 (0.1%)	0 (0.0%)	1 (0.1%)
Total		268 (26.8%)	599 (59.9%)	133 (13.3%)	1,000 (100.0%)

		Category of IA			Total
Position	Count (%)	Mild	Moderate	Severe	Total
Not part of management	Count	82	188	30	300
Not part of management	%	8.20	18.80	3.00	30.00
In lower management	Count	58	170	37	265
In lower management	%	5.80	17.00	3.70	26.50
In middle management	Count	115	205	60	380
In middle management	%	11.50	20.50	6.00	38.00
In senior management	Count	13	36	6	55
In senior management	%	1.30	3.60	0.60	5.50
Total	Count	268	599	133	1,000
Total	%	26.80	59.90	13.30	100.00