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Extensiveness of Cyberloafing for Nurse Managers

 

 Extensiveness of Cyberloafing for Nurse Managers

 

                                                 Chapter VI

                                                  Results

The purpose of this chapter was to describe the results of the study which was designed to explore whether there is a relationship between cyberloafing and employees' productivity from a nursing personnel perspective.

The program was used for statistical analysis is statistical Package for the Social Sciences (IBM-SPSS) (version 24.0). Descriptive (frequency, mean, and standard deviation) statistics was used to describe the characteristics of participants, how extent nurse managers engaged in cyberloafing behavior, and employees' productivity. Pearson Correlation and multilinear regression was used to determine the relationship between cyberloafing and employees' productivity. It will examine whether specific demographic characteristics (such as gender, age, education, occupation, marital status, and years of experience) have any association between study variables.

For inferential statistics and an Independent t-test were used to determine the relationship between the categorical variables. ANOVA test is a nonparametric that does not require that dependent variable has a normal distribution. However, it does assume that the distribution of the dependent variable has approximately the same shape in each group, which implies that the variance is approximately equal across the groups. A p value of P≤ 0.05 was considered statistically significant. A Two-way ANOVA test was computed to check for homogeneity of variance in the independent variable in order to reduce the risk o f a Type Ⅲ error.  A non-significant result (p >.05) meant that the variances of the two groups were assumed to be equal (Burns & Grove, 2005).

The study aim was to explore the relationship between cyberloafing and nurse managers' productivity in healthcare settings. In the following sections, the results of the descriptive and inferential statistical analyses of the study variables are presented as they relate to each research question.

The following questions were answered:

  1. What is the demographic profile of the ‘nurse managers’ in terms of age, nationality, educational background, years of managerial experience, marital status, working unit, hospital where employed?
  2. To what extent are ‘nurse managers’ engaged in cyberloafing behaviors?
  3. What is the ‘nurse managers’ attitude and behavior towards work performance and their productivity?
  4. What is the bivariate relationship between cyberloafing and productivity?
  5. Is there a difference among nurse managers' cyberloafing behavior and their productivity in the Two study settings?
  6. Is there an association between nurse managers' demographic characteristics and their cyberloafing behavior and productivity in the Two study settings?

 

 

Table 1. Response Rate

Analysis

Exclusion

Returned

Distributed

 

58

0

58

58

KFH

39

0

39

39

KAAH

97

0

97

97

Total

 

From all questionnaire sheets distributed (n = 97), 97 sheets had returned, with a response rate of 100%. All the filled sheets had been filled by nurse managers after a full explanation about each item to be easy for them to answer the whole questionnaire. So, 97 survey sheets had been used for analysis and result acquisition.

Demographic Data:

Selected Participants characteristics

A total of 97 nurse managers completely filled both the cyberloafing and productivity questionnaires. Participants’ demographic characteristics are summarized in Table (2) Fifty-eight nurse managers were recruited from KFH while 39 nurse managers were recruited from KAAH.

Research question 1

What is the demographic profile of the ‘nurse managers’ in terms of age, nationality, educational background, years of managerial experience, marital status, working unit, hospital where employed?

The majority of participants were; female nurses 85.6%, supporting the fact that nursing is perceived as a female-dominant position. In terms of the degree of recruited nurse managers, 68% of the respondents were head nurses, while 32% participants were nursing supervisors.

According to the highest educational level received, a total of 71.1% of the study participants reported receiving bachelor degree and certified as nursing specialists, whereas 28.9% were nurse managers received diploma as their highest degree of education and certified as nursing technicians.

According to the years of experience, about one third of the participants had one up to three years practicing nursing management in the nursing profession, accounting for a total of 38.1% of the study population. A total of 28.9% of nurse managers had four to six years of clinical experience. Less percentage of respondent had higher experience level of seven to nine years of experience (18.6% of nurse managers). Far less respondents had ten to twelve years of experience within the nursing profession, accounting for 9.3% of nurse managers from the total population.

Table 2. Descriptive Statistics of Participant’s Demographic Data (N=97)

Group

Frequency

Percent

Sex

Male

14

14.4

Female

83

85.6

Marital Status

Single

29

29.9

Married

56

57.7

Divorced

6

6.2

Widow

6

6.2

Education

bachelor degree

69

71.1

Diploma

28

28.9

Age

less than 30

12

12.4

30-35

45

46.4

36-40

20

20.6

41-45

13

13.4

greater than 45

7

7.2

Nationality

Saudi

55

56.7

Asian

41

42.3

Arab

1

1.0

Hospital

King Fahad Hospital

58

59.8

King Abdulaziz Hospital

39

40.2

Level

Head Nurse

66

68.0

Supervisor

31

32.0

Years of Practicing Management

1-3

37

38.1

4-6

28

28.9

7-9

18

18.6

10-12

9

9.3

13+

5

5.2

 

Total

97

100.0

 

Research question 2

To what extent are ‘nurse managers’ engaged in cyberloafing behaviors?

From the descriptive statistics of independent variable in tables (3-7) we can answer two of the research questions regarding to what extent Nurse Managers are engaged in cyberloafing behaviors. Herein, a report of the findings of the cyberloafing activity questionnaire in respect to certain questions related to nurses’ behavior.

Table 3. Descriptive Data of Cyberloafing

 

X

SD

ALCS

ALCS-A

1.68

0.76

ALCS-B

2.43

1.04

ALCS-C

1.73

0.74

SEtHC

2.01

0.69

Overall Cyberloafing Activity

1.96

0.67

 

Visiting non-related work websites via computer or laptop (ALCS-A):

Table (4) shows that how far the nurse managers are engaging in cyberloafing behavior by visiting nonwork-related websites via computer or laptop during office hours. The overall nurse managers are engaging in cyberloafing is considered low (1.68±0.76). The overall mean score for the engagement in cyberloafing behavior, including the seventeen ways was calculated by combining the scores of all the items in the cyberloafing behavior and dividing the combined scores by the number of items.

Additionally, Table (4) reveals that the item with the highest average score was “Instant message/chat online” (2.35±1.70) followed by “Visit general news websites” (2.22 ± 1.50). While the least behavior in both healthcare sittings is “Visit adult oriented (sexually explicit) websites” (1.03±0.17).

Descriptive statistical analysis

Table 4. Frequency, percentage, Mean and standard division of Nurse Managers engagement in cyberloafing behaviors during office hours, how often they did each of the following through a DESKTOP COMPUTER or LAPTOP. (No.97)

No.

Statement

 

Constantly

A few times a day

Once a day

A few times per week

A few times per month

Never

X

SD

1.                   

Visit nonjob related websites

Frequency

2

7

16

8

12

52

2.18

1.50

%

2 %

7%

16%

8%

12%

53%

   

2.                   

Visit general news websites

Frequency

3

6

15

9

16

48

2.22

1.50

%

3%

6%

15%

9%

16%

49%

   

3.                   

Visit entertainment websites

Frequency

0

3

17

7

14

56

1.94

1.28

%

0%

3%

18%

7%

14%

58%

   

4.                   

Visit sports related websites

Frequency

0

3

7

3

9

75

1.49

1.06

%

0%

3%

7%

3%

9%

77%

   

5.                   

Instant message/chat online

Frequency

5

11

14

4

12

51

2.35

1.70

%

5%

11%

14%

4%

12%

53%

   

6.                   

Download non-work related information

Frequency

0

3

1

4

12

77

1.36

0.87

%

0%

3%

1%

4%

12%

79%

   

7.                   

Look for employment

Frequency

0

4

2

7

7

77

1.44

1.01

%

0%

4%

2%

7%

7%

79%

   

8.                   

Shop online

Frequency

1

1

5

4

9

77

1.42

0.99

%

1%

1%

5%

4%

9%

79%

   

9.                   

Play online games

Frequency

0

1

1

1

9

85

1.19

0.60

%

0%

1%

1%

1%

9%

88%

   

10.               

Visit adult oriented (sexually explicit) websites

Frequency

0

0

0

0

3

94

1.03

0.17

%

0%

0%

0%

0%

3%

97%

   

11.               

Visit online discussion boards or forums

Frequency

0

0

1

5

10

81

1.24

0.59

%

0%

0%

1%

5%

10%

84%

   

12.               

Visit video sharing sites (Youtube, etc.)

Frequency

0

5

6

7

13

66

1.67

1.17

%

0%

5%

6%

7%

13%

68%

   

13.               

Visit social networking websites (Facebook, etc.)

Frequency

2

5

9

4

13

64

1.80

1.36

%

2%

5%

9%

4%

13%

66%

   

14.               

Visit investment or banking websites

Frequency

0

0

12

8

9

68

1.63

1.07

%

0%

0%

12%

8%

9%

70%

   

15.               

Check non-work related email

Frequency

1

5

11

7

9

64

1.84

1.34

%

1%

5%

11%

7%

9%

66%

   

16.               

Send non-work related email

Frequency

0

5

13

8

8

63

1.86

1.32

%

0%

5%

13%

8%

8%

65%

   

17.               

Receive non-work related email

Frequency

0

5

13

9

12

58

1.92

1.30

%

0%

5%

13%

9%

12%

60%

   

 

Mean

 

           

1.68

0.76

 

Visiting non-related work websites via cellphone:

As showing in Table (5) how much engagement in cyberloafing behavior by visiting nonwork-related website via cellphones during office hours by nurse managers. The overall nurse managers are engaging in cyberloafing is considered slightly low (2.43±1.04). The overall mean score including the seven ways was calculated by combining the scores of all the items in the cyberloafing behavior and dividing the combined scores by the number of items.

Furthermore, the item with the highest average score that appears in Table (5) was “Send or receive text messages” (3.64±1.65) followed by “Make phone calls” (3.58 ± 1.65). While the least behavior in both healthcare sittings is “Play games” (1.33±0.90).

Table 5. Frequency, percentage, Mean and standard division of Nurse Managers engagement in cyberloafing behaviors during office hours, how often they did each of the following through a CELL PHONE. (No.97)

No.

Statement

 

Constantly

A few times a day

Once a day

A few times per week

A few times per month

Never

X

SD

1         

Read/write nonwork email

Frequency

2

6

22

10

15

42

2.39

1.49

%

2%

6%

23%

10%

15%

43%

   

2         

Visit nonjob related websites

Frequency

1

8

18

5

12

53

2.16

1.50

%

1%

8%

19%

5%

12%

55%

   

3         

Visit social networking websites (Facebook, etc.)

Frequency

5

9

16

8

12

47

2.41

1.66

%

5%

9%

16%

8%

12%

48%

   

4         

Shop online

Frequency

1

1

7

6

7

75

1.51

1.07

%

1%

1%

7%

6%

7%

77%

   

5         

Make phone calls

Frequency

11

26

17

10

20

13

3.58

1.65

%

11%

27%

18%

10%

21%

13%

   

6         

Send or receive text messages

Frequency

14

23

17

12

19

12

3.64

1.65

%

14%

24%

18%

12%

20%

12%

   

7         

Play games

Frequency

1

1

3

3

8

81

1.33

0.90

%

1%

1%

3%

3%

8%

84%

   

 

Overall

 

           

2.43

1.04

 

Taking break time to cyberloaf:

As showing in Table (6), how often the nurse managers are taking break to cyberloaf either by computer, laptop, or cellphones. In general, break time for cyberloafing engagement by nurse managers is considered low (1.73±0.74). The overall mean score for the breaks that including the four periods was calculated by combining the scores of all the items in the cyberloafing behavior and dividing the combined scores by the number of items.

Moreover, Table (6) revealing the item with the highest average score which is “Take a quick break using a cell phone” (2.35±1.02) followed by “Take a quick break using a computer” (1.71±0.91). Hence, the least break is “Take a long break using a computer” (1.35±0.83).

Table 6. Frequency, percentage, Mean and standard division of Nurse Managers engagement in cyberloafing behaviors on a typical workday, how often they did each of the following. (No.97)

No.

Statement

 

Five or more times a day

Three to four times a day

Once or twice a day

Less than once a day

Never do this

X

SD

1         

Take a quick break using a computer

Frequency

1

6

6

35

49

1.71

0.91

%

1%

6%

6%

36%

51%

   

2         

Take a quick break using a cell phone

Frequency

4

9

22

44

18

2.35

1.02

%

4%

9%

23%

45%

19%

   

3         

Take a long break using a computer

Frequency

2

2

4

12

77

1.35

0.83

%

2%

2%

4%

12%

79%

   

4         

Take a long break using a cell phone

Frequency

4

3

2

20

68

1.51

0.99

%

4%

3%

2%

21%

70%

   

 

Oveall

 

         

1.73

0.74

 

Self-Efficacy to Hide Cyberloafing (SEtHC):

As participants were asked for their ability to hide cyberloafing from their co-workers and supervisors and not being caught, so, the results of their answers are revealing in Table (7).

The overall self-efficacy to hide cyberloafing from co-workers is considered slightly low (2.01±0.69). The overall mean score for their ability to hide cyberloafing behavior, including the two items was calculated by combining the scores of all the items in the self-efficacy to hide cyberloafing behavior and dividing the combined scores by the number of items. The item “I COULD hide my computer activity if I wanted to” score is (2.20±1.70) which considered the highest average score. While is “I COULD pretend to be working on my computer and people would never know” recorded as the lowest (1.82±1.39) in both healthcare settings.

Table 7. Frequency, percentage, Mean and standard division of Nurse Managers engagement in cyberloafing behaviors during office hours, how often they could Hide Cyberloafing (Self-Efficacy to Hide Cyberloafing Scale). (No.97)

No.

Statement

 

Agree very much

Agree moderately

Agree slightly

Disagree slightly

Disagree moderately

Disagree  very much

X

SD

1         

I COULD pretend to be working on my computer and people would never know

Frequency

4

3

6

9

12

63

1.82

1.39

%

4%

3%

6%

9%

12%

65%

   

2         

I COULD hide my computer activity if I wanted to

Frequency

10

4

6

8

16

53

2.20

1.70

%

10%

4%

6%

8%

16%

55%

   

 

Overall

 

           

2.01

0.69

 

Research Question 3

What is the ‘nurse managers’ attitude and behavior towards work performance and their productivity?

From descriptive statistics of independent variable in Table (8) we can answer question three of research questions regarding attitude and behavior of nurse managers towards work performance and productivity. Herein, the findings of EWPS questionnaire in respect to certain questions related to nurse managers’ behavior.

 

Table 8. Nurse managers’ attitude and behavior towards work performance and their productivity

   

Almost Always

Often

Sometimes

Rarely

Never

X

SD

During the past week, how frequently did you Arrive at work late or leave work early?

Frequency

1

3

19

26

48

1.79

0.94

%

1%

3%

20%

27%

49%

   

Take longer lunch hours or coffee breaks?

Frequency

4

1

12

26

54

1.71

1.01

%

4%

1%

12%

27%

56%

   

Just do no work at times when you would be expected to be working?

Frequency

1

4

21

28

43

1.89

0.96

%

1%

4%

22%

29%

44%

   

Find yourself daydreaming, worrying, or staring into space when you should be working?

Frequency

0

2

25

27

43

1.86

0.88

%

0%

2%

26%

28%

44%

   

Have to do a job over because you made a mistake or your supervisor told you to do a job over?

Frequency

1

3

23

28

42

1.90

0.94

%

1%

3%

24%

29%

43%

   

Waste time looking for misplaced supplies, materials, papers, phone numbers, etc.?

Frequency

5

9

22

35

26

2.30

1.12

%

5%

9%

23%

36%

27%

   

Find you have forgotten to call someone?

Frequency

2

6

25

43

21

2.23

0.93

%

2%

6%

26%

44%

22%

   

Find you have forgotten to respond to a request?

Frequency

0

3

25

50

19

2.12

0.75

%

0%

3%

26%

52%

20%

   

Become annoyed with or irritated by coworkers, boss/supervisor, client/customers/ vendors, or others?

Frequency

3

4

37

32

21

2.34

0.97

%

3%

4%

38%

33%

22%

   

Become impatient with others at work?

Frequency

5

7

24

36

25

2.29

1.09

%

5%

7%

25%

37%

26%

   

Avoid attending meetings?

Frequency

3

1

10

27

56

1.64

0.94

%

3%

1%

10%

28%

58%

   

Avoid interaction with coworkers, clients, vendors, or supervisors?

Frequency

0

1

11

26

59

1.53

0.74

%

0%

1%

11%

27%

61%

   

Have a coworker redo something you had completed?

Frequency

1

2

20

31

43

1.84

0.90

%

1%

2%

21%

32%

44%

   

Find it difficult to concentrate on the task at hand?

Frequency

2

0

31

33

31

2.06

0.91

%

2%

0%

32%

34%

32%

   

Fall asleep unexpectedly or become very sleepy while at work?

Frequency

0

0

16

35

46

1.69

0.74

%

0%

0%

16%

36%

47%

   

Become restless while at work?

Frequency

2

5

19

27

44

1.91

1.02

%

2%

5%

20%

28%

45%

   

Notice that your productivity for the time spent is lower than expected?

Frequency

2

0

22

44

29

1.99

0.85

%

2%

0%

23%

45%

30%

   

Notice that your efficiency for the same spent is lower than expected?

Frequency

0

3

28

41

25

2.09

0.82

%

0%

3%

29%

42%

26%

   

Lose interest or become board with your work?

Frequency

3

7

32

29

26

2.30

1.04

%

3%

7%

33%

30%

27%

   

Work more slowly or take longer to complete tasks than expected?

Frequency

0

5

23

27

42

1.91

0.94

%

0%

5%

24%

28%

43%

   

Have your boss/ coworkers remind you to do things?

Frequency

2

3

39

40

13

2.39

0.84

%

2%

3%

40%

41%

13%

   

Not want to return phone calls or put off returning calls?

Frequency

0

4

26

26

41

1.93

0.93

%

0%

4%

27%

27%

42%

   

Have trouble organizing work or sitting priorities?

Frequency

1

5

29

36

26

2.16

0.92

%

1%

5%

30%

37%

27%

   

Fail to finish assigned tasks?

Frequency

0

0

25

39

33

1.92

0.77

%

0%

0%

26%

40%

34%

   

Feel too exhausted to do your work?

Frequency

3

4

39

32

19

2.38

0.95

%

3%

4%

40%

33%

20%

   

Overall

           

2.01

0.54

 

Frequency of late arrival to work or early leave from work

All the items of the cyberloafing scale were analyzed to determine any patterns and behavior of nurses towards cyberloafing within the clinical setting.

Late arrival at work and early leave was reported to occur by a minority of respondent where only 1% reported that they ‘almost always’ arrive late or leave early. On the other hand, about half of the participants (49%) never have been later for work and never left work early before the end of their duty hours.

Prolonged lunch hours and coffee breaks

In terms of long lunch hours and coffee breaks, more than the half of nurse (56%) reported never taking too long during lunch hours or lengthen coffee breaks. On the other hand, 4% (4 nurse managers) almost always had longer lunch hours and/or coffee breaks. This could reflect their need to take some time off during stressful work.

No work at times when work is being expected

In terms of doing nothing related to work during duty hours, about half of study respondents (44%) reported never being lazy to do the job during their duty hours. However, only one nurse manager reported doing noting at times when she was expected to be working.

Annoyance and Irritation by co-workers, supervisors, clients, and others

Forty-three percent of respondents reported never doing a job just because the supervisor instructed them to do so. However, only a single nurse manager reported that almost always her supervisor was instructing her to do the job, otherwise she would not have done it. Surprisingly, (38%) of nurse managers reported being ‘sometimes’ irritated by co-workers, supervisors, clients or others, whereas, 7% of nurses were ‘often’ to ‘almost always’ being irritated by colleagues, bosses, or clients.

Being impatient with others at work

Noteworthy, 5% of study participants reported being ‘almost always’ impatient with other in the workplace. On the other hand, 25% of respondents were ‘sometimes’ impatient, while 26% of nurse managers are ‘never’ impatient with others.

Falling asleep or becoming very sleepy during work hours

As regards falling asleep unexpectedly or feeling sleeping during duty hours, 16% reported being ‘sometimes’ sleepy in the workplace, however, none of them were ‘almost always or often’ falling asleep during work. This could simply be contributable to the hard work environment they are placed in. As most nurses are at the front line by the patients’ bed 24/7, they are expected to feel exhausted and sleepy at times.

Self-assessment of expectations of work productivity and efficiency

As for the self-assessment of productivity during work among nurse managers, only 2% of respondents reported perceiving their productivity at work as ‘almost always’ lower than expected. On the other hand, about half of participants (45%) reported acknowledging their productivity at the clinical setting as ‘rarely less than expected. In terms of efficiency of work in the clinical setting, none of the respondents reported efficiency of their work as “almost always’ less than expected. Furthermore, 42% of the study participants reported their efficiency as ‘rarely’ lower than expected.

Loss of interest or becoming bored with work

Surprisingly, a total of 10% of nurse managers in the current study reported losing interest or becoming bored with work “often to almost always”. Moreover, 33% of respondents reported ‘sometimes’ losing interest in work. These findings point out the extra load put on nurses in patient care. As most nurses are expected and instructed to do nothing less than perfect as regards patient care, they may often get the feeling of being burnt out, which could manifest as losing interest in the job or being bored at the same time.

Feeling exhausted to do work

Consistently, 7% of participating nurse managers reported that ‘often’ to ‘almost always’ they feeling exhausted during duty hours. Furthermore, about 40% of nurse managers reported being ‘sometimes’ exhausted.

Failure to finish assigned tasks

That being said, about half of nurse managers (40%) reported that they ‘rarely’ fail to finish their assigned tasks, as they are expected to do nothing less than perfect during work. Moreover, none of them reported being ‘almost always’ or ‘sometimes’ unable to finish the tasks. This finding also makes it clear that the intention of nurse managers to prove compliant to difficult tasks makes them exhausted and subsequently feeling lack of interest in work.

Putting off returning calls

Of note, none of the nurse managers in the current study reported ‘almost always’ putting off on returning calls, whereas only (42%) would return calls.

Research Question 4

What is the bivariate relationship between cyberloafing and productivity?

CORRELATION BETWEEN CYBERLOAFING AND PRODUCTIVITY

Two correlations were tested to answer this research question, and the results are presented in Table (9). The results indicated that there was a correlation between internet surfing and employee productivity, r (97) = - 0.550. Although a negative relationship existed between internet surfing and employee productivity, (p-value = 0.000). So, there is significant relationship between internet surfing and employee productivity.

Upon investigating the correlation between internet surfing (cyberloafing) activity and employee productivity among nurse managers, the statistical analysis revealed a significant negative relationship between both factors (P <0.0005). Therefore, we suggest that our findings support the existing literature in regards to the negative correlation between the act of cyberloafing within the clinical setting and its impact on the employee’s productivity. Such association could be explained by the negative effect of escaping from work in the clinical setting by acting through cyberloafing on performing job-related tasks effectively and on time which means it leads to decrease productivity.

Table 9. Pearson Correlation Coefficient

 

ALCS

EWPS

Pearson Correlation

       - 0.550**

Sig. (2-tailed)

0.000

N

97

                ** Correlation is significant at the 00.010.

Linear Regression Analysis Findings

We performed linear regression analysis between the dependent variable (employee’s productivity) and a group of independent variables including some of the basic demographic characteristics of study participants.

As shown in Table (10), we found that age, nationality, and marital status to be significantly correlated with nurse managers' productivity. That being said, age was negatively correlated with nurse managers’ productivity, as an increase in age would reflect a reduction in the outcome of productivity (P =0.002). As for marital status, we found that being single is positively correlated with better productivity outcome (P =0.004) while being married was negatively associated with productivity (P =0.01). However, that finding requires further confirmation by further studies to control any confounding variable.

Table 10. linear regression Analysis for cyberloafing behaviour in relation with employees' productivity

 

 

Pearson Correlation

 
 

EWPS 1to25

 

EWPS 1to25

1.000

 

ALCS

.550

.000

Group

.143

.081

Sex

-.135

.094

education

.110

.141

Age

-.285

.002

nationality

.294

.002

organization

-.153

.067

Level

.143

.081

experience

-.113

.134

Single

.268

.004

Married

-.235

.010

Divorced

.111

.139

Widow

-.140

.086

 

Research Question 5

Is there a difference among nurse managers' cyberloafing behavior and their productivity in the Two study settings?

As showing in Table (11), it was observed that the EWPS is sig=0.135 > 0.05 which demonstrate no Significance level for average Sample answers by participant in each organization. Moreover, in the ALCS and its sub-axes the sig is (0.794) > 0.05 showing that there is no Significance level for average sample answers by participant in each organization. So, there was no significant difference among nurse managers' cyberloafing behavior in the two study settings (p>0.05).

Table 11. Significance level for average Sample answers in the two settings (T-test)

 

Hospital

NO.

X

SD

t

Df

Sig

 

Employee productivity (EWPS) 1to25

King Fahad Hospital

58

1.9386

0.5715

-1.509

95

0.135

no Significance

King Abdulaziz Hospital

39

2.1067

0.4831

Cyberloafing (ALCS)

 

ALCS-A 1to17

King Fahad Hospital

58

1.6440

0.7688

-0.570

95

0.570

no Significance

King Abdulaziz Hospital

39

1.7345

0.7628

ALCS-B 1to7

King Fahad Hospital

58

2.4483

0.9698

0.192

95

0.848

no Significance

King Abdulaziz Hospital

39

2.4066

1.1553

ALCS-C 1to4

King Fahad Hospital

58

1.7112

0.7777

-0.293

95

0.770

no Significance

King Abdulaziz Hospital

39

1.7564

0.6942

SETHC 1to2

King Fahad Hospital

58

2.0776

1.4624

0.578

95

0.565

no Significance

King Abdulaziz Hospital

39

1.9103

1.2971

Overall of (ALCS)

 

King Fahad Hospital

58

1.8695

0.6771

-0.261

95

0.794

no Significance

King Abdulaziz Hospital

39

1.9060

0.6674

P value 0.794 > 0.05

 

Research Question 6

Is there an association between nurse managers' demographic characteristics and their cyberloafing behavior and productivity in the Two study settings?

The answer for this question is showing in Table (12) to Table (15).

  1. Level:

Table (12) presents a result of descriptive statistics of nurse mangers' and cyberloafing behavior in the two study settings, and presenting Two-way ANOVA test as related to their level.

The results as showing in Table (12), in the association between Nurse managers' level and their cyberloafing behavior in the two study settings the result revealed that, there is no evidence to assume that if there is any association (0.109>0.05).

Table 12. Frequency, Mean and standard division of Nurse Managers and their productivity and the association between them in the Two study settings as related to their level group             

Hospital

X

SD

N

Hospital * level

F

Sig

king fahad hospital

Head nurse

1.9274

.60197

38

 

 

nursing supervisor

1.9600

.52283

20

Total

1.9386

.57147

58

king abdulaziz hospital

Head nurse

1.9886

.42845

28

nursing supervisor

2.4073

.50273

11

Total

2.1067

.48309

39

Total

Head nurse

1.9533

.53240

66

nursing supervisor

2.1187

.55199

31

Total

2.0062

.54143

97

2.619

0.109

                       

 

 

Table (13) is presenting a result of descriptive statistics of nurse mangers' and their cyberloafing in the two study settings, and Two-way ANOVA test as related to their level.

Table (13) is showing, the result revealed than there is no evidence to assume that if there is association between Nurse managers' level either head nurse or nursing supervisor and their cyberloafing behavior in the Two study settings sig = 0.087>0.05.

Table 13. Frequency, Mean and standard division of Nurse Managers and their cyberloafing behavior and the association between them in the Two study settings as related to their level group

                                                                                                           

Hospital

X

SD

N

Hospital * level

F

Sig

king fahad hospital

Head nurse

1.7632

.66850

38

 

 

nursing supervisor

2.0717

.66300

20

Total

1.8695

.67712

58

king abdulaziz hospital

Head nurse

1.6810

.50041

28

nursing supervisor

2.4788

.71683

11

Total

1.9060

.66741

39

Total

Head nurse

1.7283

.60006

66

nursing supervisor

2.2161

.69920

31

Total

1.8842

.66998

97

2.983

0.087

 

                                                                                                           

 

  1. Education:

By performing Two-Way ANOVA test to determine the association between nurse managers' cyberloafing behavior and productivity in related to Nursing education level, the result in Table (14) and (15) revealed that there is no significant difference between Nurse's education level and their cyberloafing behavior and productivity (p>0.05)                                   

Table 14. Frequency, Mean and standard division of Nurse Managers and their productivity and the association between them in the Two study settings as related to their education group     

Hospital

X

SD

N

Hospital * education

F

Sig

king fahad hospital

bachelor degree

1.9902

.51682

41

 

 

Diploma

1.8141

.68731

17

Total

1.9386

.57147

58

king abdulaziz hospital

bachelor degree

2.1229

.44912

28

Diploma

2.0655

.58276

11

Total

2.1067

.48309

39

Total

bachelor degree

2.0441

.49144

69

Diploma

1.9129

.64911

28

Total

2.0062

.54143

97

0.231

0.632

 

 

Table 15. Frequency, Mean and standard division of nurse managers and their cyberloafing behavior and the association between them in the Two study settings as related to their education group

                                                                                                            

Hospital

X

SD

N

Hospital * education

F

Sig

king fahad hospital

bachelor degree

1.8911

.63670

41

 

 

diploma

1.8176

.78476

17

Total

1.8695

.67712

58

king abdulaziz hospital

bachelor degree

1.9321

.73184

28

diploma

1.8394

.48963

11

Total

1.9060

.66741

39

Total

bachelor degree

1.9077

.67197

69

diploma

1.8262

.67369

28

Total

1.8842

.66998

97

0.004

0.951

                                                                                                                                                

 

  1. Gender:

As a result of classifying participants according to their gender, table (16) and (17) shows Two-way ANOVA test for no differences between either male or female in their engagement in cyberloafing and level of productivity in the two study settings (p > .05).        

 

 

Table 16. Frequency, Mean and standard division of Nurse Managers and their productivity and the association between them in the Two study settings as related to their gender group

 

Hospital

X

SD

N

Hospital * Gender

F

Sig

King Fahad Hospital

Male

2.1771

.62646

7

 

 

Female

1.9059

.56227

51

Total

1.9386

.57147

58

King Abdulaziz Hospital

Male

2.1886

.48920

7

Female

2.0888

.48776

32

Total

2.1067

.48309

39

Total

Male

2.1829

.54002

14

Female

1.9764

.53920

83

Total

2.0062

.54143

97

0.301

0.585

 

 

Table 17. Frequency, Mean and standard division of Nurse Managers and their cyberloafing behavior and the association between them in the Two study settings as related to their gender group

 

 

                                               

Hospital

X

SD

N

Hospital * Gender

F

Sig

King Fahad Hospital

Male

2.3524

1.09829

7

 

 

Female

1.8033

.58379

51

Total

1.8695

.67712

58

King Abdulaziz Hospital

Male

2.0714

.54414

7

Female

1.8698

.69367

32

Total

1.9060

.66741

39

Total

Male

2.2119

.84536

14

Female

1.8289

.62512

83

Total

1.8842

.66998

97

0.814

0.369

                                 

  1. Marital status:

As a result of classifying participants according to their marital status, table (18) and (19) are showing the descriptive statistics of participants' marital status, and presenting Two-Way ANOVA test which shows there is no association between nurse manager marital status and productivity and cyberloafing behavior in the two study settings (p > 0.05).                                                

Table 18. Frequency, Mean and standard division of Nurse Managers and their productivity and the association between them in the Two study settings as related to their marital status group

 

                       

Hospital

X

SD

N

Hospital * status

F

Sig

King Fahad Hospital

 

Single

2.2244

.43259

18

 

 

Married

1.7937

.58059

38

Divorced

2.6800

 

1

Widow

1.5600

 

1

Total

1.9386

.57147

58

King Abdulaziz Hospital

Single

2.2327

.57238

11

Married

2.1178

.49267

18

Divorced

2.1520

.32422

5

Widow

1.7440

.22379

5

Total

2.1067

.48309

39

Total

Single

2.2276

.48025

29

Married

1.8979

.57019

56

Divorced

2.2400

.36133

6

Widow

1.7133

.21379

6

Total

2.0062

.54143

97

1.093

0.356

 

 

Table 19. Frequency, Mean and standard division of Nurse Managers and their cyberloafing behavior and the association between them in the Two study settings as related to their marital status group

                                                                                                            

Hospital

X

SD

N

Hospital * status

F

Sig

King Fahad Hospital

Single

2.0926

.61735

18

 

 

Married

1.7632

.70356

38

Divorced

1.9667

 

1

Widow

1.8000

 

1

Total

1.8695

.67712

58

King Abdulaziz Hospital

Single

2.1030

.52841

11

Married

1.9185

.83636

18

Divorced

1.8133

.41001

5

Widow

1.5200

.29684

5

Total

1.9060

.66741

39

Total

Single

2.0966

.57545

29

Married

1.8131

.74470

56

Divorced

1.8389

.37203

6

Widow

1.5667

.28906

6

Total

1.8842

.66998

97

0.187

0.905

                                                                           

 

  1. Age:

Table (20) and (21) present results of descriptive statistics and Two-way ANOVA test for analyzing the association between nurse managers and their cyberloafing behavior and productivity in the age group. The result revealed that there is no evidence to assume if there is an association between Nurse managers and the two study variables (p>0.05).

Table 20. Frequency, Mean and standard division of Nurse Managers and their productivity and the association between them in the Two study settings as related to their age group

                                                                                                                        

Hospital

X

SD

N

Hospital* age

F

Sig

King Fahad Hospital

less than 30

2.1771

.73787

7

 

 

30-35

2.0229

.50858

28

36-40

2.1200

.56631

10

41-45

1.6089

.41026

9

greater than 45

1.2200

.23209

4

Total

1.9386

.57147

58

King Abdulaziz Hospital

less than 30

2.2800

.47917

5

30-35

1.9788

.53406

17

36-40

2.3200

.29814

10

41-45

2.2600

.53914

4

greater than 45

1.6267

.15144

3

Total

2.1067

.48309

39

 

30-35

2.0062

.51274

45

36-40

2.2200

.45227

20

41-45

1.8092

.53170

13

greater than 45

1.3943

.28606

7

Total

2.0062

.54143

97

1.220

0.308

 

 

Table 21. Frequency, Mean and standard division of Nurse Managers and their cyberloafing behavior and the association between them in the Two study settings as related to their age group                                                   

Hospital

X

SD

N

Hospital * age

F

Sig

king fahad hospital

less than 30

1.9381

.90522

7

 

 

30-35

2.0143

.67558

28

36-40

1.9100

.43887

10

41-45

1.5370

.72981

9

greater than 45

1.3833

.32830

4

Total

1.8695

.67712

58

king abdulaziz hospital

less than 30

1.7600

.49126

5

30-35

1.6725

.54253

17

36-40

2.2800

.79009

10

41-45

2.2750

.72998

4

greater than 45

1.7333

.69602

3

Total

1.9060

.66741

39

 

30-35

1.8852

.64434

45

36-40

2.0950

.65035

20

41-45

1.7641

.78357

13

greater than 45

1.5333

.50037

7

Total

1.8842

.66998

97

1.294

0.259

 

 

  1. Years of experience:

Tables (22) and (23) are showing no significant association between years of experience of the participants in regard to cyberloafing behavior and productivity. (p>0.05)

Table 22. Frequency, Mean and standard division of Nurse Managers and their productivity and the association between them in the Two study settings as related to their years of experience group

Hospital

experience

X

SD

N

Hospital * experience

F

Sig

King Fahad Hospital

0-3

2.0576

.59756

25

 

 

4-6

1.7929

.53315

17

 

 

7-9

2.0711

.52603

9

 

 

10-12

1.7867

.67686

3

 

 

13+

1.6300

.59003

4

 

 

Total

1.9386

.57147

58

 

 

King Abdulaziz Hospital

0-3

2.1200

.48841

12

 

 

4-6

2.0873

.61157

11

 

 

7-9

2.2178

.46994

9

 

 

10-12

2.0467

.22545

6

 

 

13+

1.5200

.

1

 

 

Total

2.1067

.48309

39

 

 

Total

0-3

2.0778

.55841

37

 

 

4-6

1.9086

.57306

28

 

 

7-9

2.1444

.48973

18

 

 

10-12

1.9600

.40398

9

 

 

13+

1.6080

.51334

5

 

 

Total

2.0062

.54143

97

0.234

0.918

 

 

Table 23. Frequency, Mean and standard division of Nurse Managers and their cyberloafing behavior and the association between them in the Two study settings as related to their years of experience group

Hospital

X

SD

N

Hospital * experience

F

Sig

King Fahad Hospital

0-3

1.9853

.69762

25

 

 

4-6

1.5569

.50645

17

7-9

2.0556

.61531

9

10-12

2.2111

1.30909

3

13+

1.8000

.63654

4

Total

1.8695

.67712

58

King Abdulaziz Hospital

0-3

1.8056

.60550

12

4-6

1.6394

.47348

11

7-9

2.2889

.88081

9

10-12

2.1056

.58781

6

13+

1.4000

 

1

Total

1.9060

.66741

39

 

4-6

1.5893

.48653

28

7-9

2.1722

.74678

18

10-12

2.1407

.80446

9

13+

1.7200

.57956

5

Total

1.8842

.66998

97

0.402

0.807

 

 

Hence, there is no association between nurse managers' demographic characteristics and their cyberloafing behavior and productivity in the two study sittings (p > 0.05).

Summary

This chapter reported the findings of the current study, 97 nurse managers participated in this study, and most of the participants were female. Nurses perceived highly to OCB

(5.78±1.43). In order to reduce the risk of a Type Ⅲ error, Two-way ANOVA test was computed to check for homogeneity of variance. There were no association among the participants' demographic characteristics and CB and productivity. There is a significant relationship between nurse managers' cyberloafing behavior and productivity.

 

 

 

 

 

 

 

 

Findings Discussions chapter

The purpose of the current study was to determine the impact of cyber loafing on nurse managers' productivity. To reach this aim six research questions were stated and investigated. The results chapter showed the answer for each question, tabulated and presented statistically through descriptive and inferential statistical work. This chapter discusses the findings and relates them to other studies that have been conducted related to cyberloafing.

In respect to the first research question about demographic profile of the nurse managers, (Yilmaz, 2013) indicates that a good research study is one that groups the participants considering certain demographics like age, sex, marital status and education status. Doing this helps to ensure that the data collected is not biased and that it is well distributed thus helping achieve the best results. The majority of the study surveyed population was female nurses which supports the argument that nursing is a female dominant profession worldwide. Following this criteria, this study divided respondents in the survey study into various age groups: less than 30 years old; 30 to 35 years old; 36 to 40 years old; 41 to 45 years old; greater than 45 years to help define the extensiveness of cyberloafing in regard to age. Based on the marital status of our study participants, twenty-nine nurses were single; 56 were married; 6 were divorced; 6 were widows. Furthermore, according to the highest educational level received, sixty-nine of the study participants reported receiving bachelor degree, whereas 28 nurse managers received diploma as their highest degree of education.

On the second research question about the extent to which nurse managers engaging in cyberloafing, computer and internet based functioning is something that has greatly grown in many sectors over the years, most especially in the health care sectors. Using the internet helps to reduce costs and it helps to shorten the time taken in providing health services (Henle & Blanchard, 2008). This has also provided a considerable problem for the management that now has to deal with the issue of the employees engaging in non-work related activities for instance sending and receiving personal emails, playing online games, online shopping and engaging in social connections that affect the overall services provided within organizations (Weiser, 2000). In the health care system where the use of pen and paper has become obsolete, it is difficult for the nurses to avoid using the internet while on duty at the hospital. Many nurses are involved in cyberloafing which affects their work because they are always distracted while on duty (Henle & Blanchard, 2008). This study however disagree with this concept showing that the number of nurse managers that are engaged in cyberloafing is relatively low, with the highest non-work related activity being making phone calls where 27%  of the nurses indicate that they did this a few times in a day, followed by sending and receiving of messages where 24% of the nurses indicated that they are victims to this and the least behavior being playing of internet games with only 1% of the nurses admitting that they engage in this while at work.

Other activities like visiting social networks like Facebook and shopping online also happen but they are not frequent and only 9% of the nurses admitted to engaging in such activities while at work. Most of the nurse managers indicated that they are ready to hide their cyberloafing activities from their supervisors in case they are caught which indicates that they are aware that cyberloafing is not acceptable at work. Most of the times employees engage in cyber loafing to take some time off the stressful work setting in their various organizations (Zoghbi, 2012). Taking those few minutes to go through one’s social media changes one’s attitude helping them to relax and lighten up which helps them have more motivation once they get back to work (Zoghbi, 2012). This concept has been illustrated in this study where upon investigating the correlation between internet surfing (cyberloafing) activity and employee productivity in our population, there is significant positive relationship between both factors (P <0.0005). Consequently, the conclusion that any act of cyberloafing during working hours within the healthcare setting has a positive effect on employee’s productivity within the workplace, most probably secondary to the relief of the stressful environment the nurses work at as well as its effect in helping the nurses ability to cope with the demanding tasks and stress within the working place as they are in continuous work by the patients’ bedside 24/7.

The internet is in most cases used by staff as an educational tool where they look up various strategies to help improve their output while at work (Fox, 2007). A nurse manager could for instance look up the internet for best way to deal with an irritable patient while on duty which will in the overall help him or her conduct her duties appropriately. In regard to loosing work interest, a total of 10% of nurse managers in our study reported losing interest or becoming bored with work “often to almost always”. Moreover, 33% of respondents reported losing interest in work ‘sometimes’. These findings point out the extra load put on nurses in patient care. As most nurses are expected and instructed to do nothing less than perfect as regards patient care, they may often get the feeling of being burnt out, which could manifest as losing interest in the job or being bored at the same time and hence choosing to use the internet while on duty(Fox, 2007). Consistently, in our study, 7% reported feeling exhausted during duty hours from ‘often’ to ‘almost always’. Furthermore, the majority of nurse managers in our population reported being exhausted ‘sometimes’, accounting for 40% from the total population surveyed. The majority of nurses in our population ‘rarely’ fail to finish their assigned tasks, as they are expected to do nothing less than perfect during work. Moreover, none of the nurses reported being unable to finish the task either ‘almost always’ or ‘sometimes’. This finding also makes it clear that the intention of nurse managers to prove compliant to difficult tasks makes them exhausted and subsequently feeling lack of interest in work.

On the third research question about attitude and behaviors, Carmeli et al., (2008) establishes that nurses are just basically positively motivated to attend to their patients in the right manner and be at work at the right time and not letting the irritation that they get from other people affect their output, because this is how they are trained to function. This study shows that the attitudes of the nurse managers towards job performance are just naturally positive without them having to be pushed around by their supervisors to perform their duties. 43% of respondents reported never doing a job just because the supervisor instructed them to do so. However, only a single nurse manager reported that almost always her supervisor was instructing her to do the job, otherwise she would not have done it. Surprisingly, the majority of nurses reported being ‘sometimes’ irritated by co-workers, supervisors, clients or others, whereas, 7% of nurses were ‘often’ to ‘almost always’ being irritated by colleagues, bosses, or clients. A nursing career is not for people who are dedicated; it is for people who understand the concept of being self-disciplined and selfless (Carmeli et al., 2008). These are people that do not need to be pushed around to attend to patients and be at the right place at the right time (Carmeli et al., 2008). Nurses have to be time conscious ensuring that they are not wasting time taking long breaks and chatting with their colleagues while patients are suffering in their wards. In terms of long lunch hours and coffee breaks, the majority of nurse managers in our study (56%) reported never taking too long during lunch hours or lengthens coffee breaks. On the other hand, 4% (4 nurse managers) almost always had longer lunch hours and/or coffee breaks. This could reflect their need to take some time off during stressful work. In terms of doing nothing related to work during duty hours, the majority of study respondents (44%) reported never being lazy to do the job during their duty hours. However, only one nurse manager reported doing nothing at times when she was expected to be working.

  On the fourth research question about bivariate relationship between, the study by Fox, (2007) indicates that it is often common for employees to lose interest with work especially because this is something that they do day in day out. The work environment and roles can become monotonous leading workers to feel bored and this is one of the things that prompt them to cyberloaf. This concepts have been supported by this study where the linear regression analysis performed between the dependent variable (employee’s productivity) and a group of independent variables including some of the basic demographic characteristics of study participants; indicates that age and marital status to be significantly correlated with nurse managers' productivity. The study shows that age is negatively correlated with nurse managers’ productivity, as an increase in age would reflect a reduction in the outcome of productivity (P =0.002). Being married and having a family also negatively affects productivity, where nurses married have divided attention for work and family. Late arrival at work and early leave was reported to occur by a minority of respondent where only 1% reported that they ‘almost always’ arrive late or leave early and this is dependent on their marital status where the married ones always come late and leave early because they have other responsibilities as parents while the single ones always come in early and leave late which can be attributed to the fact that they do not have much responsibilities. On the other hand, the majority (49%) never have been later for work and never left work early before the end of their duty hours.

On the fifth research question about difference among nurse managers' cyberloafing behavior and their productivity in the Two study settings, the study by Henle et al., (2010) indicates that nurses are people expected to offer quality health care to patients without any form of bias. They often have to deal with stress as they attend to different types of patients who at times treat them in an inhumane manner which can greatly affect their productive. However the use of the internet for them, acts as an escape from these stress which helps them to rejuvenate and always perform their best (Weiser, 2000). This study in regard to self-assessment of productivity during work in the survey nurse managers, indicates that only 2% of respondents reported receiving their productivity at work as ‘almost always’ lower than expected meaning that their productivity is always above average. On the other hand, the majority of participants reported acknowledging their productivity at the clinical setting as ‘rarely less than expected (45%). In terms of efficiency of work in the clinical setting, none of the responded reported their efficiency of work as “almost always’ less than expected. Furthermore, 42% of the study population reported their efficiency as ‘rarely’ lower than expected. Employees use the internet as a way to help ease boredom and monotony at work (Weiser, 2000). This argument is supported by the findings from this study where the few nurse managers that engage in cyberloafing cite it as an effect of boredom while at work, which cause them to feel sleepy where 16% of them reported that they often feel sleepy wile at work. The nurses just like many other employees out there opt to cyberloaf as a way of keeping their minds engaged which in turn helps them to stay sharp and motivated at work (Weiser, 2000). It is important to note that none of the nurse managers in our study reported ‘almost always’ putting off on returning calls, whereas the majority (42%) would return calls. This shows that even though the nurses understand their responsibilities while at work, they are still attached to other activities outside of work that they do not feel restricted to attend to while still at work.

On the sixth research question on whether there is an association between nurse managers' demographic characteristics and their cyberloafing behavior and productivity in the Two study settings the study by Zoghbi-Manrique-de-Lara, & Olivares-Mesa, (2010), there are many factors that affect the productivity of employees and this range from the age of the employee to their responsibilities away from work. When it comes to age, the young people tend to be more energetic as compared to the older generation which allows them to perform more work and hence making them more productive (Young, 2001).  In the study, most of the nurse managers that are in the age gap of 30 and below tend to use the internet while at work much more as compared to the nurse managers that are between the ages of 45 and above. Cyberloafing differs according to generations and hence the age factor. The baby boomers have a preference to balance work and family and they therefore are not frequently engaged in cyber loafing because they disconnect their smartphones while working (De Lara, 2009). The generation X on the other hand demands a balance between work, family and friends and they are often willing to switch between work and personal life while at work. The generation Y on the other hand believes in fun at all times even while working and so they are often in both professional and personal modes concurrently meaning hat hey engage in cyberloafing the most (De Lara, 2009). Most of the employees that have families have divided attention where they are constantly concerned about their families, even while at work which affects their productivity as compared to the people that are single (Zoghbi-Manrique-de-Lara, & Olivares-Mesa, 2010). This study shows that in regard to marital status, being single is positively correlated with better productivity outcome (P =0.004) while being married was negatively associated with productivity (P =0.01). `

Summary

This discussed the findings were discussed in accordance to the past studies on cyberloafing. It basically included discussions on the nurse managers that were included in the study and the overall representation of their standing on cyberloafing. This discussion has established that cyberloafing is something that cannot really be eradicated in healthcare especially with the increased development of technology that requires all health care facilities to be internet connected. The findings show that though cyberloafing interrupts with the workflow of a nurse, it does not really affect his or her output in the overall. This study and many others that have been cited indicate that even though nurse managers engage in cyberloafing, it is minimal and it is through engagement of minor activities like phone calls and text messages a few times a day. This helps to reduce the stressing impacts of their work as they chat and talk with their loved ones which help motivate them to work extra harder and hence better work performance. This study shows that cyberloafing does not negatively impact the healthcare sector and the overall productivity of the nurses.

  Chapter Ⅵ

Conclusion/Recommendations

The current study findings reveal that the majority of our nursing managers reported returning phone calls during work; however, this does not affect their work productivity and efficiency. Most nurse managers achieve tend to deliver work with high productivity and efficiency, while aiming for finishing assigned tasks on time with no delay. However, Result shows some negative outcomes on their personality, many of them reported to feel sleepy during work hours and a considerable percentage of them reported losing the interest to work. Moreover, cyberloafing behavior has been shown to be positively associated with employee’s productivity in the workplace.

Based on the findings of our study which prove that cyberloafing activity is associated positively with employee’s productivity in the workplace, the researcher recommends the following:

  • Nursing Education: Nursing personnel can manipulate the thinking of adults regarding the harmful effects of internet usage for personally-centered purposes in the healthcare setting in order to decrease the frequency of cyberloafing, if it harmfully affects employee’s productivity, and to improve physical and psychological health of the nursing staff
  • Nursing administrator can provide facilities and promote education to junior nurses and the nursing staff regarding the internet usage and internet addiction (cyberloafing).
  • Nurse administrator should ensure for the availability of the material like pamphlets, posters, charts, modules, guidelines related to prevention of internet addiction (cyberloafing), if it harmfully affects employee’s productivity.
  • There should be policy for supervision of staffs and adequate supply of the equipment to reduce addiction
  • Cyberloafing would be permitted under certain circumstances, in order to prevent burnout among the nursing staff and to allow nurses to get a way to escape the stressful work environment to better cope with their work for better patients’ outcomes.
  • Nurse managers should appropriately address cyberloafing behavior and identify whether or not it negatively impacts employee’s productivity and subsequently patients’ outcomes.
  • Nursing Research: Further research should be conducted in order to investigate this phenomenon and examine its impact on the productivity and efficiency of work of registered nurses in the workplace.
  • Nurses can take initiative to conduct more research regarding the internet usage, mobile usage, and other personal gadget usage in the workplace for non-work-related purposes.
  • Further research investigating the effect of cyberloafing/slacking on nurses’ productivity still warrants further investigation.
The study Limitations

Given the fact that the study questionnaire is self-reported, this highlights the risk of recall bias in our study. Worthy to mention, the small sample size of our study would affect the interpretation of our findings and limits the generalizability of our results. Therefore, much larger sample sizes are required in order to detect a real significant correlation. Moreover, we did not gather any information as related to nurse managers’ use of internet during duty hours or their behavior in their daily working hours. 

Summary

The act of using internet access for personal, non-work-related purposes, in the workplace, intimately called “cyberloafing or cyberslacking” is thought to be of major impact on employee’s productivity as well as the efficiency of work delivered.

Nurses, being at the patients’ bedside 24/7, have the highest liability of being intimidated by work stresses and then try to escape that by cyberloafing, and eventually affecting patients’ outcomes.

We conducted this survey study at the level of two nursing settings in KSA to investigate the extent of cyberloafing in these settings and to determine the association between both factors: cyberloafing and productivity. We used pre-validated questionnaires from previous studies to assess the extent and pattern of cyberloafing as well as employee’s productivity. Our survey was based on the responses of nurse managers “supervisors and head nurses” only and did not incorporate registered nurses.

We re-tested the validity of both questionnaires and both were shown to be highly valid. Our analysis revealed that most nurses did their best in order to deliver their work at time, where most reported ‘never’ failing to deliver work assignments. Also, most of them were deemed to deliver high productivity and work efficiency. As a consequence, we think that this in some way affected their sleep, where most of nurse managers reported ‘sometimes’ falling asleep or feeling sleepy during duty hours. Subsequently, this would have impacted their interest in work, as most nurses reported ‘sometimes’ losing interest in work. Furthermore, we investigated the association between cyberloafing in the clinical setting and nurse managers’ productivity and we found a significantly positive association between both factors. So, we can propose that the positive relationship between cyberloafing and productivity is a result of the nurses trying to find a ‘way’ to escape work stress in order to be able to deliver work efficiently.

Based on the rigorous and thorough literature review, there are very scarce reported investigating the effect of cyberloafing in the clinical setting on health care workers’ productivity. Therefore, we recommend conducting further research with high sample sizes in order to detect the actual and significant association between cyberloafing behavior in the work place and employees’ productivity. Also, usage of internet in clinical practice should be assessed in further work.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

References

Adamic LA (1999). The small world Web. In the 3rd European Conference on Research and Advanced Technology for Digital Libraries, Springer-Verlag, Paris, France, 99: 443–452.

Agarwal, U.A. (2014). Linking justice, trust and innovative work behavior to work engagement. Personnel Review, 43(1), 41–73. https://doi.org/10.1108/PR-02- 2012-0019

Ahmad, Z., & Jamaluddin, H. (2009, June). Employees’ attitude toward cyberloafing in Malaysia. Paper present at the International Business Information Management Association (IBIMA 12th), Kuala Lumpur, Malaysia.

Ajzen, I. (1985). From intentions to actions: A theory of planned behavior. In Action control (pp. 11-39). Springer, Berlin, Heidelberg.

Ajzen, I. (1991). The theory of planned behavior. Organizational behavior and human decision processes50(2), 179-211.

Ajzen, I. (2011). The theory of planned behaviour: reactions and reflections.

Aizen, K. (2002). Theory of Planned Behavior Model. Retrieved from https://www.google.com/url?sa=i&rct=j&q=&esrc=s&source=images&cd=&cad=rja&uact=8&ved=0ahUKEwjNtbfL76LYAhWBD8AKHf-WCHQQjRwIBw&url=https%3A%2F%2Fwww.utwente.nl%2Fen%2Fbms%2Fcommunication-theories%2Fsorted-by- cluster%2FHealth%2520Communication%2Ftheory_planned_behavior%2F&psig=AOvVaw1Ul4NEd5BIM9gkWppG-YIv&ust=1514212452261496

Akhtar (2008). What is Self-Efficacy? Bandura's 4 Sources of Efficacy Beliefs. Retrieved from http://positivepsychology.org.uk/self-efficacy-definition-bandura-meaning.

Alder, G. S., Noel, T. W., & Ambrose, M. L. (2006). Clarifying the effects of Internet monitoring on job attitudes: The mediating role of employee trust. Information & Management43(7), 894-903.

Ali-Hassan H, Nevo D, Wade M (2015) Linking dimensions of social media use to job performance: the role of social capital. J Strat Inf Syst 24(2):65–89

Allen, L. A. (2018). Experiences of internationally educated nurses holding management positions in the united states: Descriptive phenomenological study. Journal of Nursing Management, 26(5), 613-620. doi:10.1111/jonm.12591

Amble, B., 2004. Does cyberloafing undermine productivity? [online]. Management-Issues Ltd. Available from: http:// www.management-issues.com/2006/8/24/research/doescyberloafing-undermining-productivity.asp [Accessed 16 October 2008]

Anandarajan M and Simmers C (2004). Personal web usage in the workplace: A guide to effective human resources management. IGI Global, Hershey, USA.

Anandarajan, M., & Simmers, C. A. (2005). Developing human capital through personal web use in the workplace: Mapping employee perceptions. Communications of the Association for information Systems, 15(1), 41.

Anandarajan M, Paravastu N, and Simmers CA (2006). Perceptions of personal web usage in the workplace: AQ-methodology approach. CyberPsychology and Behavior, 9(3): 325-335.

Anandarajan, M. (2002). Internet abuse in the workplace. Communications of the ACM, 45, 53–54.

Anandarajan, M., Simmers, C. A., & D’Ovidio, R. (2011). Exploring the underlying structure of personal web usage in the workplace. Cyberpsychology, Behavior and Social Networking, 14, 577-583. doi:10.1089/cyber.2010.0136

Andreassen, C. S., Torsheim, T., & Pallesen, S. (2014). Predictors of use of social network sites at work-a specific type of cyberloafing. Journal of Computer-Mediated Communication19(4), 906-921.

 

Askew, K. L. (2012). The relationship between cyberloafing and task performance and an examination of the theory of planned behavior as a model of cyberloafing.

Askew, K., Buckner, J. E., Taing, M. U., Ilie, A., Bauer, J. A., & Coovert, M. D. (2014). Explaining cyberloafing: The role of the theory of planned behavior. Computers in Human Behavior, 36, 510–519.

Askew, K., Coovert, M. D., Taing, M. U., Ilie, A., & Bauer, J. (2012). Work environment factors and cyberloafing: A follow-up to Askew. Poster presented at SIOP, San Diego, CA.

Askew, K., Coovert, M. D., Vandello, J. A., Taing, M. U., & Bauer, J. A. (2011). Work environment factors predict cyberloafing. In Poster presented at the Annual Meeting of the American Psychological Society. Washington DC.

Askew, K., Vandello, J. A., & Coovert, M. D. (2012). Cyberloafing and social norms: The role of subjective prescriptive and descriptive norms. Unpublished manuscript.

Battaglia, M. (2008). Purposive sample. In P. J. Lavrakas (Ed.), Encyclopedia of survey research method (pp. 645-647). Thousand Oaks, CA: SAGE Publications, Inc. doi: 10.4135/9781412963947.n419

Baturay, M. H., & Toker, S. (2015). An investigation of the impact of demographics on cyberloafing from an educational setting angle. Computers in Human Behavior, 50, 358-366. doi: 10.1016/j.chb.2015.03.081

Bell, E., Bryman, A., & Harley, B. (2018). Business research methods. Oxford university press.

Beugre, C. (2006). Understanding dysfunctional cyberbehavior: The role of organizational justice. The Internet and Workplace Transformation. Pp. 223-239.

Blanchard, A. L., & Henle, C. A. (2008). Correlates of different forms of cyberloafing: The role of norms and external locus of control. Computers in Human Behavior24(3), 1067-1084.

Blau, G., Yang, Y., & Ward-Cook, K. (2006). Testing a measure of cyberloafing. Journal of Allied Health, 35(1), 9–17.

Bock GW, Ho SL (2009) Non-work related computing (NWRC). ACM

Burns, N. & Grove, S.K. (1999). Understanding Nursing Research. Philadelphia: W.B. Saunders Company.

Burns, N. & Grove, S.K. (2007). Understanding Nursing Research: building an evidance-based practice (4th ed.). St. Louis, MO: Saunders Elsevier, Bushry, A. (2002). International perspectives on rural nursing: Australia, Canada, U,S,A, Australian Journal of Rural Health, 10, 104-111. Cacchione, P. Z., (2007). What is Clinical Nursing Research? [Editorial]. Clinical Nursing Research, 16(3), 167-169.

Bryman, A. & Bell, E (2015), Business Research Methods. 4th Edition. Online Resource Centre.

Brock, M. E., Martin, L. E., & Buckley, M. R. (2013). Time theft in organizations: The development of the time banditry questionnaire. International Journal of Selection and Assessment, 21, 309-321. doi:10.1111/ijsa.12040

Broos, A. (2005). Gender and information and communication technologies (ICT) anxiety: Male self-assurance and female hesitation. CyberPsychology & Behavior, 8(1), 21–31.

Brouwer, S., Krol, B., Reneman, M. F., Bültmann, U., Franche, R. L., van der Klink, J. J., & Groothoff, J. W. (2009). Behavioral determinants as predictors of return to work after long-term sickness absence: an application of the theory of planned behavior. Journal of occupational rehabilitation19(2), 166-174.

Burford S and Park S (2014). The impact of mobile tablet devices on human information behaviour. Journal of Documentation, 70(4): 622-639.

Carmeli, A., Sternberg, A., & Elizur, D. (2008). Organizational culture, creative behavior, and information and communication technology (ICT) usage: A facet analysis. Cyberpsychology & Behavior: The Impact of the Internet, Multimedia and Virtual Reality on Behavior and Society, 11(2), 175–180.

Charoensukmongkol P (2014) Effects of support and job demands on social media use and work outcomes. Comput Hum Behav 36(7):340–349

Chesley, N. (2010). Technology use and employee assessments of work effectiveness, workload, and pace of life. Information, Communication & Society, 13(4), pp.485–514.

Church K and Oliver N (2011). Understanding mobile web and mobile search use in today's dynamic mobile landscape. In the 13th International Conference on Human Computer Interaction with Mobile Devices and Services, ACM, Stockholm, Sweden: 67-76.

Cialdini, R. B., Reno, R. R., & Kallgren, C. A. (1990). A focus theory of normative conduct: recycling the concept of norms to reduce littering in public places. Journal of personality and social psychology58(6), 1015.

Coker, B. L. (2011). Freedom to surf: The positive effects of workplace leisure browsing. New Technology, Work and Employment, 26, 238-247. Retrieved from http://onlinelibrary.wiley.com

Coker, B. L. (2013). Workplace internet leisure browsing. Human Performance, 26, 114-125. doi:10.1080/08959285.2013.765878

Contreras, F. K., de Oliveira, F. B., & Muller, E. S. M. (2012). Internet: Monitored freedom. Journal of Information Systems and Technology Management, 9, 459-472. doi:10.4301/S1807-17752012000300002

Cooke, R. A., & Rousseau, D. M. (1984). Stress and strain from family roles and work-role expectations. Journal of applied psychology69(2), 252.

Creswell, J. W. (2012). Qualitative inquiry & research design: Choosing among five approaches (4th ed.). Thousand Oaks, CA: Sage.

D'Abate, C. P. (2005). Working hard or hardly working: A study of individuals engaging in personal business on the job. Human Relations, 58, 1009-1032. doi:10.1177/0018726705058501

D’Arcy, J., Hovav, A., & Galletta, D. (2009). User awareness of security countermeasures and its impact on information systems misuse: A deterrence approach. Information Systems Research, 20(1), 79–98

David Colton and Robert W. Covert. Designing and Constructing Instruments for Social Research and Evaluation, Volume 6 of Research Methods for the Social Sciences. Wiley, 2007. ISBN:0787987840, 9780787987848

Davis, R. a., Flett, G. L., & Besser, A. (2002). Validation of a new scale for measuring problematic internet use: Implications for pre-employment screening. Cyberpsychology & Behavior: The Impact of the Internet, Multimedia and Virtual Reality on Behavior and Society, 5(4), 331–345.

De Lara, P.Z. (2009). Inequity, conflict, and compliance dilemma as causes of cyberloafing. International Journal of Conflict Management, 20(2), 188–201. https://doi.org/10.1108/10444060910949630

De Manrique Lara, J. (2006). Fear in organizations: Does intimidation by formal punishment mediate the relationship between interactional justice and workplace Internet deviance. Journal of Managerial Psychology, 21, 580–592.

Debt Cubed, 2006. Are your internet costs going through the roof? Debt Cubed, 21 (1), 10.

Endicott, J. & Nee, J. (1997). Endicott Work Productivity Scale (EWPS): A New Measure to Assess Treatment Effects. Psychopharmacological Bulletin, 33(1); 13-16. 

Everton, W. J., Mastrangelo, P. M., & Jolton, J. A. (2005). Personality correlates of employees’ personal use of work computers. CyberPsychology & Behavior, 8(2), 143–153.

Fallows D (2002) Email at work. PEW Internet & America Life Project.

Fallows, D., (2005). How women and men use the Internet. PEW Internet and American Life Project, December, 1–45.

Fox, A. (2007). Caught in the Web: Internet surfing takes on addictive qualities for some employees who may be hiding their abuse at work-at a cost to both themselves and their employers. HR MAGAZINE, 52(12), 34.

Garrett, R. K., & Danziger, J. N. (2008). On cyberslacking: Workplace status and personal internet use at work. Cyberpsychology & Behavior: The Impact of the Internet, Multimedia and Virtual Reality on Behavior and Society, 11(3), 287–292.

George, J. (1996). Computer-based monitoring: common perceptions and empirical results. MIS Quarterly, 20(9), 459–480.

Glassman, J., Prosch, M., & Shao, B. B. M. (2015). To monitor or not to monitor: Effectiveness of a cyberloafing countermeasure. Information & Management, 52, 170-182. doi:10.1016/j.im.2014.08.001

Goode, W.J., 1960. A theory of role strain. American Sociological Review, 25, 483–496.

Griffiths, M. (2003). Internet abuse in the workplace: Issues and concerns for employers and employment counselors. Journal of Employment Counseling, 40(2), 87–96.

Griffiths ,M.(2010) Internet abuse and internet addiction in the workplace. Journal of Workplace Learning. Sep 14; 22 (7): 463-72.

Grover, S. L. (2014). Fair workplace regulation of Internet usage. Asia Pacific Management Review, 19, 99-115. doi:10.6126/APMR.2014.19.1.06

Hallett, T., 2002. Christmas cyberloafing cost UK businesses £154 million [online]. CBS Interactive Ltd. Available from: http://hardware.silicon.com/servers/0,39024647,11036829, 00.htm [Accessed 16 October 2008].

Hargittai, E., & Shafer, S. (2006). Differences in actual and perceived online skills: The role of gender. Social Science Quarterly, 87(2), 432–448.

Harrington, S. (1996). The effect of code of ethics and personal denial of responsibility on computer abuse judgments and intentions. MIS Quarterly, 20(3), 257–278.

Hartijasti, Y. (2016). Is serious internet deviance a problem in Indonesian workplace? Vol. 8. 96-107.

Helga, J. S., Rieger, D., Reinecke, L. & Connor III, W. (2017). Watching Online Videos at Work: The Role of Positive and Meaningful Affect for Recovery Experiences and Well-Being at the Workplace. Mass Communication and Society. DOI: 10.1080/15205436.2017.1381264

Henle, C. A., & Blanchard, A. L. (2008). The interaction of work stressors and organizational sanctions on cyberloafing. Journal of Managerial Issues, 383-400.

Henle, C. A., Kohut, G., & Booth, R. (2009). Designing electronic use policies to enhance employee perceptions of fairness and to reduce cyberloafing: An empirical test of justice theory. Computers in Human Behavior25(4), 902-910.

Henle, C. A., Reeve, C. L., & Pitts, V. E. (2010). Stealing time at work: Attitudes, social pressure, and perceived control as predictors of time theft. Journal of Business Ethics94(1), 53-67.

Herath, T., & Rao, H. (2009). Encouraging information security behaviors in organizations: Role of penalties, pressures and perceived effectiveness. Decision Support Systems, 47(2), 154–165.

Ivarsson, L., Larsson, P. 2012. “Personal Internet Usage At Work: A Source of Recovery”. Journal of Workplace Rights, (16:1), pp. 63-81.

Jackson, L. A., Ervin, K. S., Gardner, P. D., & Schmitt, N. (2001). Gender and the Internet: Women communicating and men searching. Sex roles, 44(5–6), 363–379.

J-Ho, S. C., Gan, P. L. &Thurasamy, R. (2017). A Review of the Theories in Cyberloafing Studies. Advanced Science Letters, 23(9); 9174 – 9176.

Jia, H., Jia, R., & Karau, S. (2013). Cyberloafing and personality: The impact of the Big Five traits and workplace situational factors. Journal of Leadership & Organizational Studies, 1548051813488208. http://dx.doi.org/10.1177/ 1548051813488208.

Jia, R., & Jia, H.H. (2015). An individual trait-based investigation of employee cyberloafing. Journal of Information Technology Management, 26(1), 58–71.

Jian, G. (2013). Understanding the wired workplace: The effects of job characteristics on employees’ personal online communication at work. Communication Research Reports, 30, pp.22-33. doi:10.1080/08824096.2012.746221

Johnson, P. R., & Indvik, J. (2003, July). The organizational benefits of reducing cyberslacking in the workplace. In Allied Academies International Conference. Academy of Organizational Culture, Communications and Conflict. Proceedings (Vol. 8, No. 2, p. 53). Jordan Whitney Enterprises, Inc.

  1. Naughton, J. Raymond, K. Shulman, Cyberslacking, Newsweek, 134 (1999) 62-65.

Kim, K., Triana, M., Chung, K., & Oh, N. (2015). When do employees cyberloaf? An interactionist perspective examining personality, justice, and empowerment. Human Resource Management, 1-18. doi:10.1002/hrm.21699

Koay K, Soh P, Chew K (2017) Antecedents and consequences of cyberloafing: evidence from the Malaysian ICT industry. First Monday 22(3–6)

Koay, K. Y., & Soh, P. C. H. (2018, August). Does Cyberloafing Really Harm Employees’ Work Performance?: An Overview. In International Conference on Management Science and Engineering Management (pp. 901-912). Springer, Cham.

Koehler, N., Vujovic, O., & McMenamin, C. (2013). Healthcare professionals’ use of mobile phones and the internet in clinical practice. Journal of mobile technology in medicine2(1), 3-13.

König, C. J., & Caner de la Guardia, M. E. (2013). Exploring the positive side of personal internet use at work: Does it help in managing the border between work and nonwork? Computers in Human Behavior, 30, 355-360. doi:10.1016/jchb.2013.09.021

Krishnan, S., Lim, V.K., & Teo, T.S. (2010, December 12–15). How does personality matter? Investigating the impact of big-five personality traits on cyberloafing. Thirty First International Conference on Information Systems, St. Louis, MO.

Kuem, J, Siponen, M. 2014. “Short-Time Non-Work-Related Computing and Creative Performance”. Proceedings of the 47th Hawaii International Conference on Systems Sciences, 2014, Hawaii.

Kusumadewi, A.W. & Baridwan, Z & Hariadi, B. (2017). Study on Auditors’ Attitude in Using Information Technology for Auditing: Theory of Planned Behavior and Social Cognitive Theory Modification. Russian Journal of Agricultural and Socio-Economic Sciences. 66. 250-258. DOI: 10.18551/rjoas.2017-06.29.

Leedy, P. D., & Ormrod,(2012) Practical Research: Planning and Design. 10th ed. Pearson.

Li, H., Zhang, J., & Sarathy, R. (2010). Understanding compliance with internet use policy from the perspective of rational choice theory. Decision Support Systems, 48, 635–645.

Liberman, B., Seidman, G., McKenna, K. Y. A., & Buffardi, L. E. (2011). Employee job attitudes and organizational characteristics as predictors of cyberloafing Computers in Human Behavior, 27, 2192-2199. doi: 10.1016/j.chb.2011.06.015

Lilienfeld, S. O., Ritschel, L. A., Lynn, S. J., Cautin, R. L., & Latzman, R. D. (2013). Why many clinical psychologists are resistant to evidence-based practice: Root causes and constructive remedies. Clinical psychology review33(7), 883-900.

Lim VK. (2002) The IT way of loafing on the job: Cyberloafing, neutralizing and organizational justice. Journal of Organizational Behavior: The International Journal of Industrial, Occupational and Organizational Psychology and Behavior. Aug; 23 (5): 675-94.

Lim VK, Teo TS. (2005) Prevalence, perceived seriousness, justification and regulation of cyberloafing in Singapore: An exploratory study. Information & Management. Dec 31; 42 (8): 1081-93.

Lim, V.K. (2005). The moderating effect of neutralization technique on organizational justice and cyberloafing. Pacific Asia Conference on Information Systems, PACIS 2005, Bangkok, Thailand.

Lim, V.K, & Chen, D. (2009). Browsing and emailing: Impact of cyberloafing on work attitudes. Proceedings of 23rd Australia and New Zealand Academy of Management.

Lim, V. K. G., & Chen, D. J. Q. (2009). Cyberloafing at the workplace: Gain or drain on work? Behaviour & Information Technology, 25(1), 1-11.

Lim, V. K., & Chen, D. J. (2012). Cyberloafing at the workplace: gain or drain on work?. Behaviour & Information Technology31(4), 343-353.

Lim, V. K., & Teo, T. S. (2005). Prevalence, perceived seriousness, justification and regulation of cyberloafing in Singapore: An exploratory study. Information & Management42(8), 1081-1093.

Lim, V., & Chen, D. (2009). Browsing and emailing: Impact of cyberloafing on work attitudes. Proceedings of 23rd Australia and New Zealand Academy of Management.

Lim, V., & Chen, D. (2009). Cyberloafing at the workplace: Gain or drain? Behaviour and Information Technology, 90(3), 1–11.

Lim, V.K. (2002). The IT way of loafing on the job: Cyberloafing, neutralizing and organizational justice. Journal of Organizational Behavior, 23(5), 675–694. https:// doi.org/10.1002/job.161

Lim, V.K.G. and Teo, T.S.H., 2005. Prevalence, perceived seriousness, justification and regulation of cyberloafing in Singapore – an exploratory study. Information and Management, 42, 1081–1093.

LoBiondo-Wood, G. and Haber, J. (2014), Nursing research : Methods and Critical Appraisal for Evidence-Based Practice. St. Louis, Missouri : Elsevier. 8th Edition.

Lu, Y., Zhou, T., & Wang, B. (2009). Exploring Chinese users’ acceptance of instant messaging using the theory of planned behavior, the technology acceptance model, and the flow theory. Computers in human behavior25(1), 29-39.

MacCormick, J. S., Dery, K., and Kolb, D. G. (2012). Engaged or just connected? Smartphones and employee engagement. Organizational Dynamics, 41(3), pp.194–201.

Macklem, K., 2006. You got too much mail. Maclean’s, 119 (5), 20–22.

Mahatanankoon P, Anandarajan M, Igbaria M (2004) Development of a measure of personal web usage in the workplace. Cyberpsychol Behav Impact Internet Multimed Virtual Real Behav Soc 7(1):93

Mahatanankoon, P. (2006). Predicting cyber-production deviance in the workplace. International Journal of Internet and Enterprise Management, 4, 314–330.

Martens, M. P., Herman, K. C., Takamatsu, S. K., Schmidt, L. R., Herring, T. E., Labuschagne, Z., & McAfee, N. W. (2016). An update on the status of sponsored research in counseling psychology. The Counseling Psychologist, 44(4), 450-478. doi:10.1177/0011000015626271

Maslach, C. and Leiter, M.P., 1997. The truth about burnout: how organizations cause personal stress and what to do about it. San Francisco: Jossey-Bass.

Mastrangelo, P. M., Everton, W., & Jolton, J. A. (2006). Personal use of work computers: Distraction versus destruction. CyberPsychology & Behavior9(6), 730-741.

Mathieson, K. (1991). Predicting user intentions: comparing the technology acceptance model with the theory of planned behavior. Information systems research2(3), 173-191.

McBride, D. L. (2015). Distraction of clinicians by smart-phones in hospitals: A concept analysis. Journal of Advanced Nursing, 71, 2020-2030. doi:10.1111/jan.12674

McBride, D. L., LeVasseur, S. A., & Li, D. (2015). Non-work-related use of personal mobile phones by hospital registered nurses. JMIR mHealth and uHealth3(1).

McBride, D., LeVasseur, S. A., & Li, D. (2015). Nursing performance and mobile phone use: are nurses aware of their performance decrements?. JMIR human factors2(1).

McBride, D. L., LeVasseur, S. A., & Li, D. (2013). Development and validation of a web-based survey on the use of personal communication devices by hospital registered nurses: pilot study. JMIR research protocols, 2(2).”

Menzel, D. C. (1998). www.ethics.gov: issues and challenges facing public managers. Public Administration Review, 58, 445–452.

Metin, U. B., Taris, T. W., & Peeters, M. C. (2016). Measuring procrastination at work and its associated workplace aspects. Personality and Individual Differences101, 254-263.

Mertens, D. M. & Ginsberg, P. E. (2009). Handbook of Social Research Ethics. Sage Publications.

Moody, G. D., & Siponen, M. (2013). Using the theory of interpersonal behavior to explain non-work-related personal use of the Internet at work. Information & Management50(6), 322-335.

Muijs, D. (2010). Doing quantitative research in education with SPSS. London: Sage. doi:10.4135/9781849209014.n1

O’Leary, Z. (2017). The Essential Guide to Doing Research. Sage Publication, New Delhi.

O’Neill, T. A., Hambley, L. A., & Chatellier, G. (2014). Cyberslacking, engagement, and personality in distributed work environments. Computers in Human Behavior, 40, 152–160.

O’Neill, T. A., Hambley, L. H., & Bercovich, A. (2014). Prediction of cyberslacking when employees are working away from the office. Computers in Human Behavior, 34, 291–298.

Öğüt, E., Şahin, M., & Demirsel, M. T. (2013). The relationship between perceived organizational justice and cyberloafing: Evidence from a public hospital in Turkey. Mediterranean Journal of Social Sciences, 4, 226-233. doi:10.5901/mjss2013.v4n10p226

Ono, H., & Zavodny, M. (2003). Gender and the Internet. Social Science Quarterly, 84(1), 111–121.

Oravec, J. A. (2002). Constructive approaches to Internet recreation in the workplace. Communications of the ACM45(1), 60-63.

Oravec, J.A., (2004). When work morphs into play: using constructive recreation to support the flexible workplace. In: M. Anandarajan, ed. Personal web usage in the workplace: a guide to effective human resource management. Hershey, PA: Idea Group Publishing, 46–60.

Ozler, D.E., & Polat, G. (2012). Cyberloafing phenomenon in organisations: Determinants and impacts. International Journal of eBusiness and eGovernment Studies, 4(2), 2146–0744.

Papadakos, P. J. (2013). The rise of electronic distraction in health care is addiction to devices contributing. J Anesthe Clinic Res4(3), e112.

Parahoo, K. (2014) Nursing Research: Principles, Process and Issues. Palgrave Macmillan, London.

Park, H. S., & Smith, S. W. (2007). Distinctiveness and influence of subjective norms, personal descriptive and injunctive norms, and societal descriptive and injunctive norms on behavioral intent: A case of two behaviors critical to organ donation. Human Communication Research33(2), 194-218.

Pelling, E. L., & White, K. M. (2009). The theory of planned behavior applied to young people's use of social networking web sites. CyberPsychology & Behavior12(6), 755-759.

Piana, V. (2001). Productivity. Retreived on 4th February, 2018 from: http://www.economicswebinstitute.org/glossary/prdctvt.htm

Pindek, S., Krajcevska, A., & Spector, P. E. (2018). Cyberloafing as a coping mechanism: Dealing with workplace boredom. Computers in Human Behavior86, 147-152.

Polit, D. F., & Beck, C. T. (2013). Essentials of nursing research: Appraising evidence for nursing practice. Philadelphia: Wolters Kluwer/Lippincott/Williams & Wilkins Health.

Quoquab F, Halimah S, Salam ZA (2015) Does cyberloafing boost employee productivity? In: International symposium on technology management and emerging technologies, pp 119–122

Rajah R., Lim V.K.G. 2011. “Cyberloafing, Neutralization and Organizational Citizenship Behavior”. PACIS 2011 proceedings, 2011.

Raman, J. (2015). Mobile technology in nursing education: where do we go from here? A review of the literature. Nurse Education Today35(5), 663-672.

Ramayah T (2013) Personal web usage and work inefficiency. Bus Strat 11(11):295–301

Restubog, S. L. D., Garcia, P. R. J. M., Toledano, L. S., Amarnani, R. K., Tolentino, L. R., & Tang, R. L. (2011). Yielding to (cyber)-temptation: Exploring the buffering role of self-control in the relationship between organizational justice and cyberloafing behavior in the workplace. Journal of Research in Personality45(2), 247-251.

Richtel, M. (2011). As doctors use more devices, potential for distraction grows. The New York Times14.

Robson, C. (2011). Real world research: A resource for social -scientists and practitioner- researchers. 3rd edition. Oxford: Blackwell Publishing.

Saleem, H., Beaudry, A., & Croteau, A.-M. (2011). Antecedents of computer selfefficacy: A study of the role of personality traits and gender. Computers in Human Behavior, 27(5), 1922–1936.

Saleh, M., Daqqa, I., AbdulRahim, M. B., & Sakallah, N. (2018). The effect of cyberloafing on employee productivity. International Journal of Advanced and Applied Sciences5(4), 87-92.

Saunders, M., Lewis, P. & Thornhill, A. (2009) Research methods for business students, 5th ed., Harlow, Pearson Education.

Schneider, Z. & Whitehead, D. (2013). Nursing and Midwifery Research: method and Appraisal for Evidence based Practice.  4th Edition. Elsevier Australia.

Schumacher, P., & Morahan-Martin, J. (2001). Gender, Internet and computer attitudes and experiences. Computers in Human Behavior, 17(1), 95–110.

Seymour L and Nadasen K (2007). Web access for IT staff: A developing world perspective on web abuse. The Electronic Library, 25(5): 543-557.

Shehu, M. M. & Salomon, M. G. (2016). Consideration of Future Consequences as an Antecedent of Employee Cyberloafing Behavior among selected working adults in Nigeria. International Journal of Business and Technopreneurship, 6(2); 319-334.

Shepherd, M. M., & Klein, G. (2012). Using deterrence to mitigate employee Internet abuse. 45th Hawaii International Conference on System Science (HISCC), 5261-5266. doi:10.1109/HICSS.2012.627

Simmers, C., Anandrajan, M. & D’Ovidio, R. (2008). Investigation of the underlying structure of personal web usage in the workplace. Academy of Management Proceedings. Pp. 1-1. Doi: 10.5465/AMBPP.2008.33649965

Sipior, J. C., & Ward, B. T. (2002). A strategic response to the broad spectrum of Internet abuse. Information Systems Management19(4), 71-79.

Sluiter, J. K., De Croon, E. M., Meijman, T. F., & Frings-Dresen, M. H. W. (2003). Need for recovery from work related fatigue and its role in the development and prediction of subjective health complaints. Occupational and environmental medicine60(suppl 1), i62-i70.

Son, J. Y., & Park, J. (2016). Procedural justice to enhance compliance with non-work related computing (NWRC) rules: Its determinants and interaction with privacy concerns. International Journal of Information Management, 36, 309-321. Doi: 10.1016/j.ijinfomgt.2015.12.005

Spector, P. E., Fox, S., Penney, L. M., Bruursema, K., Goh, A., & Kessler, S. (2006). The dimensionality of counterproductivity: Are all counterproductive behaviors created equal?. Journal of vocational behavior68(3), 446-460.

Stanton, J.M., 2002. Company profile of the frequent Internet user. Communications of the ACM, 45 (1), 55–59

Straub, D. (1990). Effective IS security: An empirical study. Information Systems Research, 1(3), 255–276.

Straub, D. W., & Welke, R. J. (1998). Coping with systems risk: security planning models for management decision making. MIS quarterly, 441-469.

Suárez-Mendoza, M. J., & Zoghbi-Manrique-de-Lara, P. (2008). The impact of work alienation on organizational citizenship behavior in the Canary Islands. International journal of organizational Analysis, 15(1), 56-76.

Tamilselvi A. & Reghunath R. (2014). A cross sectional study to measure patients' perception of quality of nursing care at medical wards. Nitte university journal of health science, 4 (1): 21-23

Taylor J (2013). Giving kids a break: How surfing has helped young people in Cornwall overcome mental health and social difficulties. Mental Health and Social Inclusion, 17(2): 82-86.

Taylor, W. C., King, K. E., Shegog, R., Paxton, R. J., Evans-Hundnall, G. L., Rempel, D., & Yancey, A. K. (2013). Booster breaks in the workplace: Participants’ perspectives on health-promoting work breaks. Health Education Research, 28(3), 414-425. doi:10.1093/her/cyt001

Teo, T. S., & Lim, V. K. (2000). Gender differences in internet usage and task preferences. Behaviour & Information Technology, 19(4), 283–295.

The Orlando Sentinel. (1999). 19 May. A costly pleasure: Net surfing is riding high at work and employees are waxing up their keyboards and checking out their personal interests at company expenses. The Orlando Sentinel, 19 May, E1.

The Straits Times. (2000). Cyberslackers at work. The Straits Times, 28 April, 4.

Ugrin, J. C., & Pearson, J. M. (2013). The effects of sanctions and stigmas on cyberloafing. Computers in Human Behavior29(3), 812-820.

Ugrin, J. C., Pearson, J. M., & Odom, M. D. (2008). Cyber-slacking: Self-control, prior behavior and the impact of deterrence measures. Review of Business Information Systems, 12(1), 75-88.

Van Doorn, O. N. (2011). Cyberloafing: A multi-dimensional construct placed in a theoretical framework. Eindhoven, Netherlands: Eindhoven University of Technology.

Vitak, J., Crouse, J., & LaRose, R. (2011). Personal Internet use at work: Understanding cyberslacking. Computers in Human Behavior, 27(5), 1751–1759.

Wagner, D. T., Barnes, C. M., Lim, V. K., & Ferris, D. L. (2012). Lost sleep and cyberloafing: Evidence from the laboratory and a daylight saving time quasi-experiment. Journal of Applied Psychology97(5), 1068.

Wan, H. C., Downey, L. A., & Stough, C. (2014). Understanding non-work presenteeism: Relationships between emotional intelligence, boredom, procrastination and job stress. Personality and Individual differences, 65, 86-90. doi: 10.1016/j.paid.2014.01.018

Weatherbee, T. G. (2010). Counterproductive use of technology at work: Information & communications technologies and cyberdeviancy. Human Resource Management Review20(1), 35-44.

Websense 2005 Asia Pacific/Latin America Web @ Work Survey. http: //www. websense. com/global/en/PressRoom/PressR eleases/PressReleaseDetail/?Release= 050509928.

Websense, Inc., 2006. Web@Work Survey 2006. Conducted by Harris Interactive (available at http://www.websense.com/).

Weiser, E. B. (2000). Gender differences in Internet use patterns and Internet application preferences: A two-sample comparison. CyberPsychology and Behavior, 3(2), 167–178.

West, J. G. (2013). How to Conduct a Survey: A Primer on Survey Research. National Business Research Institute. Retrieved from ww.NBRI.com

WikiHow. (2019, March 29). How to Calculate Sample Size. Retrieved from https://www.wikihow.com/Calculate-Sample-Size

Xiao-chun, Tu & Ya-ping, Chang. (2010). An Empirical Study on the Determinants of Cyberloafing: Data Analysis Based on Individual Factors. 2010 International Conference on E-Product E-Service and E-Entertainment, ICEEE2010.  Doi: 10.1109/ICEEE.2010.5660800.

Yilmaz, K. (2013). Comparison of quantitative and qualitative research traditions: epistemological, theoretical, and methodological differences. European Journal of Education, 48, 311-325. Retrieved from http://eu.wiley.com

Young, K. S. (2001). Managing employee Internet abuse: A comprehensive plan to increase your productivity and reduce liability. Employee Internet Management, 1-37.

Yu, X., Wang, P., Zhai, X., Dai, H., & Yang, Q. (2015). The effect of work stress on job burnout among teachers: The mediating role of self-efficacy. Social Indicators Research122(3), 701-708.

Zauszniewski, J. A., Suresky, M. J., Bekhet, A., & Kidd, L. (2007). Moving from tradition to evidence: A review of psychiatric nursing intervention studies. Online journal of issues in nursing12(2), 9.

Zoghbi, P. (2012). Reconsidering the boundaries of the cyberloafing activity: the case of a university. Behaviour & Information Technology, 31, 469-479. doi:10.1080/0144929X.2010.549511

Zoghbi Manrique de Lara, P., Verano Tacoronte, D., & Ting Ding, J. M. (2006). Do current anti-cyberloafing disciplinary practices have a replica in research findings? A study of the effects of coercive strategies on workplace Internet misuse. Internet Research, 16(4), 450-467.

Zoghbi-Manrique-de-Lara, P., & Olivares-Mesa, A. (2010). Bringing cyber loafers back on the right track. Industrial Management & Data Systems, 110(7), 1038–1053.

Zoghbi-Manrique-de-Lara, Pablo & Viera-Armas, Mercedes. (2018). Corporate Culture as a Mediator in the Relationship Between Ethical Leadership and Personal Internet Use. Journal of Leadership & Organizational Studies.  Vol. 24.  Doi: 10.1177/1548051817696877.

 

 

                                                                           

APPENDICES

Appendix I – Tables

 

Table 1. Scales Reliability; Cronbach’s Alpha (α)

Cronbach's Alpha

N of Items

Axis 

0.921

25

 

EWPS

0.916

17

ALCS-A

ALCS

0.847

7

ALCS-B

0.797

4

ALCS-C

0.763

2

SEtHC

0.916

30

All

0.941

55

Cronbach's Alpha all

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Appendix Ⅱ – Cyberloafing Scale by Askew and Lim

  1. Are you currently employed?
  2. No    Yes

 

  1. At work, do you have access to the internet? Access can be through a computer, smartphone, or both.
  2. No Yes

 

 

  • DEMOGRAPHICS

The following will enable me to identify trends among different people responding, so please provide me with answer to some basic questions about you

  1. Age: __________
  2. Nationality: _______________
  3. What organization do you work in? __________________________________
  4. Years of experience:
  1. 1-3     4-6              3.   7-9             4.   10-12             5.   13+
    1. Which of the following best describes your current level in the organization?
  2. Staff    Head nurse          3.   Supervisor       4.   Other ______________

 

  1. How many hours per week do you work?

0-10           2.   11-20           3.    21-30             4.   31-40            5.   41-50              6.   51+

 

 

 

 

 

  1. During office hours, how often do you do each of the following through a

DESKTOP COMPUTER or LAPTOP?

1

2

3

4

5

6

Never

A few times per month

A few times per week

Once a day

A few times a day

Constantly

 

_________ 1. Visit nonjob related websites

_________2. Visit general news websites

_________3. Visit entertainment websites

_________4. Visit sports related websites

_________5. Instant message/chat online

_________6. Download non-work related information

_________7. Look for employment

_________8. Shop online

_________9. Play online games

_________10. Visit adult oriented (sexually explicit) websites

_________11. Visit online discussion boards or forums

_________12. Visit video sharing sites (Youtube, etc.)

_________13. Visit social networking websites (Facebook, etc.)

_________14. Visit investment or banking websites

_________15. Check non-work related email

_________16. Send non-work related email

_________17. Receive non-work related email

 

 

 

 

Appendix Ⅲ
  1. During office hours, how often do you do each of the following though a CELL

PHONE?

1

2

3

4

5

6

Never

A few times per month

A few times per week

Once a day

A few times a day

Constantly

 

_________1. Read/write nonwork email

_________2. Visit nonjob related websites

_________3. Visit social networking websites (Facebook, etc.)

_________4. Shop online

_________5. Make phone calls

_________6. Send or receive text messages

_________7. Play games

  1. On a typical workday, how often do you do

1

2

3

4

5

Never do this

Less than once a day

Once or twice a day

Three to four times a day

Five or more times a day

 

_________1. Take a quick break using a computer

_________2. Take a quick break using a cell phone

_________3. Take a long break using a computer

_________4. Take a long break using a cell phone

 

 

Appendix Ⅳ- Self Efficacy to Hide Cyberloafing Scale

The Ability to Hide Cyberloafing (AtHC) refers to how well an employee can hide his or

her computer activity from coworkers and supervisors. Below is the AtHC scale

developed by Askew and his colleagues (Askew, Coovert, Vandello, Ilie, & Tang, 2010).

The scale has shown good reliability and criterion-related validity (Askew, 2010a, Askew

et al., 2010, Askew et al., 2012).

Rate your agreement with the following statements. Please respond to the statements with

respect to your present job.

1

2

3

4

5

6

Disagree  very much

Disagree moderately

Disagree slightly

Agree slightly

Agree moderately

Agree very much

 

______ 1. I COULD pretend to be working on my computer and people would never know.

______ 2. I COULD hide my computer activity if I wanted to.

 

 

 

 

 

 

 

 

 

 

Appendix Ⅴ – Endicott Work Productivity Scale (EWPS)

              This questionnaire is designed to help assess work activities during the past week.

 

Date __ __/__ __/__ __       Group # _______   Sex: 1 - Male   2 - Female        Marital status ____________      

 

Occupation: ____________________________ Education: ____________________________            

 

               Do you receive pay or other money for any type of work?       1. No   2. Yes   

 

                 Do you do volunteer work?                                         1. No   2. Yes

 

      

If you do not receive money for your work and do not do volunteer work, please indicate why you do not:

___ I am physically ill 

___ I am too upset, depressed, or nervous

___ I can't find work

___ Other (Please describe)

 

 

If you receive money for your work or do volunteer work, please complete the questionnaire, otherwise  stop here. 

 

Please describe the characteristics of your work setting by completing the following items: 

 

                       I am self-employed.                              

1. No    

2. Yes     

                       I work for someone else.                       

1. No   

2. Yes   

                       I have a boss/supervisor.                       

1. No   

2. Yes

                     I have co-workers with whom I must work.  

1. No   

2. Yes

                       I supervise others at work.                    

1. No   

2. Yes      

                      I deal with clients/customers/vendors.   

1. No   

2. Yes 

 

 

How many hours do you usually work or would you usually be expected to work?____ hours per week

 

How many hours did you work last week? _____  hours per week

 

If you missed time at work last week, please note all the reasons why:   

____ I had a day off (Holiday/vacation)

____ I was physically ill

     ____ Too upset, depressed, or nervous                                                                                      

Other_____________________________

                                                                                                      (Please describe)

 

 

(PLEASE COMPLETE PAGE 2 OF THE QUESTIONNAIRE)

 

E W P S

 

0

1

2

3

4

Never

Rarely

Sometimes

  Often 

Almost Always

      

_________1. During the past week, how frequently did you Arrive at work late or leave work early? 

_________2. Take longer lunch hours or coffee breaks? 

_________3. Just do no work at times when you would be expected to be working? 

_________4. Find yourself daydreaming, worrying, or staring into space when you should be working? 

_________5. Have to do a job over because you made a mistake or your supervisor told you to do a job over? 

_________6. Waste time looking for misplaced supplies, materials, papers, phone numbers, etc.? 

_________7. Find you have forgotten to call someone? 

_________8. Find you have forgotten to respond to a request? 

_________9. Become annoyed with or irritated by coworkers, boss/supervisor, client/customers/ vendors, or others? 

_________10. Become impatient with others at work? 

_________11. Avoid attending meetings? 

_________12. Avoid interaction with coworkers, clients, vendors, or supervisors? 

_________13. Have a coworker redo something you had completed? 

_________14. Find it difficult to concentrate on the task at hand? 

_________15. Fall asleep unexpectedly or become very sleepy while at work? 

_________16. Become restless while at work? 

_________17. Notice that your productivity for the time spent is lower than expected? 

_________18. Notice that your efficiency for the same spent is lower than expected? 

_________19. Lose interest or become board with your work? 

_________20. Work more slowly or take longer to complete tasks than expected? 

_________21. Have your boss/ coworkers remind you to do things? 

_________22. Not want to return phone calls or put off returning calls? 

_________23. Have trouble organizing work or sitting priorities? 

_________24. Fail to finish assigned tasks? 

_________25. Feel too exhausted to do your work?

 

 

 

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