Introduction
In today’s digital era, access to the internet is available to almost everyone, everywhere, whether at home, at work, or at school. Despite the enormous benefits that internet technology offers in terms of learning, the ‘inadvisable, excessive and uncontrolled use’ of this platform can be harmful (Yılmaz et al.,
2015). Cyberloafing is one of the terms used to describe the disruptive behavior of frequent internet usage for personal, non-work-related activities during working hours, often under the guise of doing actual work (Blanchard & Henle,
2008; Lim,
2002), and for non-academic purposes during course hours (Kalaycı,
2010). Such counterproductive behaviors (Dursun et al.,
2018; Lim,
2002) include visiting news and discussion sites, social-networking platforms and other virtual communities; checking e-mails; downloading files, including music; online gaming/gambling; and online shopping (Yılmaz & Yurdugül,
2018). Cyberloafing has been observed both at work and in school classrooms, including computer laboratory teaching settings, where students cyberloaf while they perform tasks or listen to their instructors.
Numerous studies have reported on the increases in student motivation and achievement observed in connection with the use of technology in the classroom (Anwaruddin,
2013; Estapa, & Nadolny,
2015; Kalanzadeh et al.,
2014; Mackinnon & Vibert,
2002). While schools cannot ignore the opportunities that digital tools provide for enhancing the learning experience (Tindell & Bohlander,
2012), particularly among today’s tech-savvy younger generation, studies have also pinpointed certain adverse effects that accompany the use of technology in the classroom. Computers have been cited as a source of distraction (Fried,
2008) that leads to increases in cyberloafing (Yılmaz et al.,
2015) and decreases in learner engagement in connection with increases in opportunities to engage in non-education-related, off-task personal activities (Sivrikova et al.,
2019; Skolnik & Puzo,
2008). Not only does multitasking during lessons pose a threat to comprehension (Sana et al.,
2013) and lead to impaired learning on the part of the cyberloafer (Ravizza et al.,
2013), classmates and teachers also suffer from decreased motivation and distraction as a result (Akgün,
2020). When compared to regular classrooms, students seem to feel freer to cyberloaf in computer laboratories, and they tend to do so even when they have assignments to complete (Yaşar & Yurdugül,
2013). University students have been found to cyberloaf not only with their computers, but with their mobile phones as well (Yılmaz et al.,
2015). They often multi-task while studying, using their mobile phones, tablets, laptops, or desktop PCs. Worryingly, the extent of cyberloafing is expected to increase as the number of high-tech mobile devices and opportunities for online connectivity increase and more courses start to require mobile devices and computers (Akbulut et al.,
2016).
While the literature includes many studies on cyberloafing in a work environment, the number of published studies on cyberloafing in an educational setting, particularly in a classroom where computers are easily accessible, are relatively limited. In a recent study conducted by Varol and Yıldırım (
2019), four factors were found to have an influence on cyberloafing in a school setting, namely (a) the instructor (i.e., instructional methods, material usage, content knowledge and communication skills), (b) course content relevance, student attentiveness and motivation levels, and (c) the learning environment (i.e., classroom seating, lighting and temperature).
The current study investigates the demographic, psychological and academic factors affecting cyberloafing in computer laboratory teaching settings in light of Van Doorn’s (
2011) theoretical framework described below. The following research questions were formulated accordingly:
1.
Do students cyberloaf during their computer laboratory classes? If so, what types of cyberloafing activities do they engage in, and to what extent?
2.
Are any of the following predictive of the overall cyberloafing activities identified in the first research question?
a.
student demographic factors (gender, internet skills, internet experience, and internet use frequency),
b.
student-related individual factors (personality, internal–external locus of control, sense of belonging, attitude towards cyberloafing),
c.
organizational factors (classroom norms, instructor respect for students),
3.
Are any of the above-mentioned factors predictive of the specific types of cyberloafing activities identified in the first research question?
Theoretical framework
Van Doorn (
2011) offers the only available theoretical framework for cyberloafing. He describes the phenomenon in an organizational setting as a multidimensional construct consisting of different types of activities and behaviors and influenced by organizational policies, the relationship between job demands and resources, additional work and family responsibilities, and individual personalities. Li and Chung (
2006) identify four main types of cyberloafing activities: social, which includes both self-expression (e.g., Facebook, Twitter, Instagram) and information-sharing (e.g. Blogger.); informational (e.g., internet searches); leisure (e.g., online gaming, downloading music, downloading software); and virtual emotional activity (e.g., online shopping, dating sites and other unclassifiable activities). Cyberloafing behavior, on the other hand, describes cyberloafing in terms of its consequences, either positive or negative. Positive behaviors include cyberloafing for development, i.e., as a source of learning and skill improvement (Belanger & Van Slyke,
2002) and for recovery, i.e., as a means of reducing discomfort and positively impacting on individual health (Lim & Chen,
2012; McLean et al.,
2001), whereas negative behaviors include deviant behavior that reduces productivity (Weatherbee,
2010) and addictive behavior (Young,
2010), i.e., behavior that is problematic and habitual.
Studies of cyberloafing in the workplace have examined how organizational policies related to internet availability, work hours and use of one’s own device at work may impact cyberloafing either positively or negatively. With regards to whether or not policies limiting employee usage of the internet are effective in preventing cyberloafing, the results are inconclusive (e.g., Blanchard & Henle,
2008; Lim & Teo,
2005). Lim et al., (2002) and Anandajaran and Simmer (2004) emphasize that both organizational control and individual responsibility play a role in dictating cyberloafing behaviors. The recent trend of allowing/expecting employees to use their own devices for work as part of an effort to reduce organizational expenses on technological infrastructure is a question addressed by Van Doorn (
2011), who suggests that employees may be more likely to engage in increased cyberloafing when they use their own devices, as this makes it easier to access personal sites and links. Flexibility in terms of work hours and location represents another emerging trend that may trigger cyberloafing. As Kurland and Bailey (
1999) point out, flexible work schedules characterized by a lack of supervision and monitoring may increase the tendency of employees to cyberloaf, especially, when they are not sufficiently informed about the rules.
The relationship between job demands and resources has also been put forward as having a role in cyberloafing behavior (Van Doorn,
2011). Job demands are defined as the work-related stimuli requiring cognitive, emotional and/or physical effort (Jones & Fletcher,
1996), whereas job resources describe the factors that make it possible to deal with these demands (Hobfoll,
2001). The Demand-Induced Strain Compensation (DISC) model describes job demands and resources in terms of their cognitive, emotional and physical aspects (De Jonge & Dormann,
2006) and suggests that high demands combined with high resources result in active learning and growth in the workplace. By contrast, cyberloafing has reported to be one consequence of a lack of balance between job demands and resources (Henle & Blanchard,
2008; Lim,
2002; Robinson & Bennett,
1995). Specifically, low demand and high resources prompt employees to search for non-work-related activities to kill time when they have no tasks to complete, whereas in cases of high demand and low resources, employees engage in cyberloafing as a form of deviant behavior, either as a means of escape, which carries the potential of addiction (LaRose et al., 2010), or to replenish their resources, which carries the potential of recovery (Weatherbee,
2010). Janssen et al. (
2004) noted a connection between high psychological job demands and a lack of balance between work and family, suggesting an interaction between these two factors could lead to cyberloafing, either directly or indirectly.
The “Five Factor “Theory provides a comprehensive explanation of personality traits in terms of five basic dimensions: neuroticism, openness to experience, conscientiousness, extraversion, and agreeableness (Bacanlı et al.,
2009; Costa, & McCrae,
2011). These dimensions have been suggested as forming the basis of human personality and encompassing the characteristics of human thought, emotion and behavior. While they might manifest in different degrees, all people share the same basic traits regardless of gender, age or culture, (Novikova,
2013). Van Doorn (
2011) theorizes the expected relationships between personality traits and cyberloafing based on the relationships identified for internet usage. According to Landers and Lounsbury (2006), neuroticism and openness to experience are unrelated to internet usage, whereas agreeableness, conscientiousness and extraversion are correlated with less frequent usage. Wyatt and Phillips (
2005) infer that less agreeable and more introverted individuals are more frequent users of the internet, which represents less of a distraction for more conscientious people.
Method
This study was designed as a correlational study (Creswell,
2012) in order to evaluate the relationships among the 11 factors listed above (independent variables) and three types of cyberloafing, i.e., socialization, personal business and news follow-up (dependent variables). Sample size was determined based on purposive and convenience sampling and the requirements for structural equation modelling (SEM). A number of authors (Boomsma,
1985; Kline,
1998) have suggested a minimum or moderate sample size for SEM to range between 100 and 200, while others have recommended 5–10 observations per estimated parameter (Bentler & Chou,
1987; Bollen,
1989) and 10 cases per variable (Nunnally,
1967). Power analysis conducted prior to data collection for this study indicated that between 245 (0.80 power) and 493 participants (0.99 power) were required for SEM (MacCallum et al.,
1996). Therefore, data collection was halted when 272 participants were reached (SEM: 0.847 power, df = 8, RMSEA-H0 = 0.10 (mis-fit), and RMSEA-H1 = 0.030 (good-fit), p = 0.05).
Study participants
A total of 272 students at a university in Turkey voluntarily participated in the study. All participants had at least one social-networking account and had completed a mandatory computer literacy course in a computer laboratory classroom. After receiving a brief explanation of the study purpose, participants were asked to complete a questionnaire (see below, “Measurement tools” section).
Demographic characteristics of the participants, including gender and information on internet usage, are given in Table
1. Of the 272 participants, 122 (44.9%) were male, and 150 were female (55.1%). The average age of participants was 20.95 years (SD = 1.55). Mean overall GPA of participants and mean grade in the computer literacy course referenced for cyberloafing behavior were 2.91 (SD = 0.46) and 71.24 (SD = 15.64), respectively. Most participants (60.3%) rated their internet skills as “intermediate”. More than 85% of participants (n = 239, 87.9%) stated that they used the internet on a daily basis, and the remaining participants (n = 33, 12.1%) stated that they used the internet either weekly or monthly. Whereas 77.9% of participants (n = 212) said they had an average to high level of experience using the internet, the remaining 22.1% (n = 60) said they had little to no experience.
Table 1
Study participants
Gender | | |
Female | 150 | 55.1 |
Male | 122 | 44.9 |
Internet skill level |
Beginner | 27 | 9.9 |
Intermediate | 164 | 60.3 |
Advanced | 68 | 25.0 |
Expert | 13 | 4.8 |
Internet experience |
No experience | 11 | 4.0 |
Low level | 49 | 18.0 |
Average level | 129 | 47.4 |
High level | 58 | 21.3 |
Very high level | 25 | 9.2 |
Frequency of internet usage |
Daily-several times | 219 | 80.5 |
Daily-once | 20 | 7.4 |
Weekly-several times | 21 | 7.7 |
Weekly-once | 5 | 1.8 |
Monthly-several times | 4 | 1.5 |
Monthly-once | 3 | 1.1 |
Data was collected using a questionnaire that was self-implemented by participating students in a computer laboratory course during the 2019–2020 Fall semester. The questionnaire was comprised of the following:
This section collected information on gender and internet skill, experience, and frequency of usage in order to determine the impact of these factors on cyberloafing.
Adjective Based Personality Scale (ABPT) (40 items)
The ABPT was developed by Bacanlı et al. (
2009) to measure personality. It is based on instruments used to measure the Big Five personality traits used by psychologists to describe human personality (Costa & McCrae,
1992; Goldberg,
1993). Construct validity of the ABPT was demonstrated by oblimin rotation factor analysis. The ABPT also demonstrated concurrent validity with the Sociotropy-Autonomy Scale, which measures such things as separation anxiety, efforts to satisfy others, and disapproval in relationships (Beck et al.,
1988; Savaşır & Şahin,
1997); Reactions to Conflicts (Demirci,
2004); Positive and Negative Affect Schedule (PANAS) (Gençöz,
2000; Watson et al.,
1988); and State-Trait Anxiety Inventory (STAI) (Öner & ve Le Compte,
1998; Spielberger,
2010). Test–retest reliability and Cronbach’s α for internal consistency indicated the ABPT to have satisfactory psychometric properties and to be suitable for assessing personality traits of undergraduate and graduate students.
Cyberloafing (12 items)
This scale was originally developed by Blanchard and Henle (
2008) and adapted to Turkish by Kalaycı (
2010). The original instrument included minor and major cyberloafing behaviors as two sub-factors, and applied explanatory factor analysis only. In the Turkish version, both explanatory and second-order confirmatory factor analyses indicated a three-factor scale comprised of personal business, socialization and news follow-up sub-scales, with reliability measures of 0.83, 0.85, and 0.66, respectively, whereas the overall reliability of the scale was 0.88.
Sense of belonging to the department (4 items)
This scale measured the extent to which participants felt they belonged to the department in which they were studying. Construct validity was determined using EFA [KMO Measure of Sampling Adequacy (0.647); Bartlett’s Test of Sphericity (χ2 (6) = 453.269, p < 0.01)]. EFA communalities ranged between 0.504–0.721. A single factor explained 62.09 percent of total variance, with item loading ranging between 0.710–0.849, and a Cronbach’s α of 0.792.
Attitudes towards cyberloafing (3 items)
This scale measured participant attitudes towards cyberloafing. EFA was used for construct validity [KMO Measure of Sampling Adequacy (0.702); Bartlett’s Test of Sphericity (χ2 (3) = 368.755, p < 0.01)]. EFA communalities ranged between 0.734–0.838. A single factor explained 77.13% of total variance, with item loadings ranging between 0.857–0.915, and a Cronbach’s α of 0.851.
Loafing (4 items)
This scale measured the daily loafing behavior of participants. EFA was used for construct validity [KMO Measure of Sampling Adequacy (0.656); Bartlett’s Test of Sphericity χ2 (6) = 147.854, p < 0.01)]. EFA communalities ranged between 0.294–0.614, indicating good fit to data. A single factor explained 48.33 percent of total variance, with item loadings ranging between 0.543–0.783. A Cronbach’s α of 0.631 indicated satisfactory internal consistency.
Computer lab teaching settings (9 items)
This scale measured participant perceptions of factors related to computer lab teaching settings. EFA was used for construct validity [KMO Measure of Sampling Adequacy (0.703); Bartlett’s Test of Sphericity χ2 (36) = 247.038, p < 0.01)], with communalities ranging between 0.307–0.592. Three factors—computer lab teaching settings, instructor responsibility, instructor monitoring of students during class—explained 51.89 percent of total variance. Item loadings for these factors ranged between 0.410–0.742, 0.634–0.749 and 0.491–0.700, respectively. The scale had a Cronbach’s α of 0.422, indicating moderate internal consistency.
Norms for cyberloafing (6 items)
This scale measured peer group and instructor norms related to cyberloafing. EFA was used for construct validity [KMO Measure of Sampling Adequacy (0.817) Bartlett’s Test of Sphericity (χ2 (15) = 774.528, p < 0.01)], with communalities ranging between 0.719–0.805. Two factors—peer norms and instructor norms—accounted for 76.15 percent of total variance. Item loadings for the factors ranged between 0.854–0.857 and 0.862–0.908, respectively. The scale had a Cronbach’s α of 0.857, indicating satisfactory internal consistency. Lower scores on the scale indicate little presence of either instructor or peer-group norms.
Teacher evaluation (15 items)
This scale measured student evaluations of their instructors in computer literacy courses. EFA was used for construct validity [KMO MSA 0.818; Bartlett’s Sphericity (χ2 (78) = 800.761, p < 0.01)], with communalities ranging between 0.499-0.662. Four factors—respect, activities, communication, and motivation—accounted for 58.97 percent of total variance, with item loadings ranging between 0.709–0.811 (respect), 0.553–0.807 (activities), 0.606–0.758 (communication) and 0.592–0.841 (motivation). The scale had a Cronbach’s α of 0.831, indicating a satisfactory level of internal consistency.
Locus of control (29 items)
The Internal–External Locus of Control scale was originally developed by Rotter (
1966). Psychometry and clinical psychology experts reviewed the original version, translating it to Turkish and adjusting it for cultural relevance (Dağ,
1991). Reliability of the Turkish version of the scale was estimated by calculating Cronbach’s α (0.71 for 532 participants), KR-20 (0.68 for 99 participants) and test–retest reliability correlation coefficients (0.83 for 99 participants). EFA yielded seven factors (chance control, political external control, chance and interpersonal control, external control on school achievement, external control on interpersonal relationships, fatalism, political and school achievement external control) that explained 47.7 percent of total variance. Concurrent validity with Rosenbaum’s Learned Resourcefulness Scale (Rosenbaum, 1980) and Symptoms Checklist (SCL-90-R) was obtained, with correlation coefficients of 0.29 (p < 0.001) and 0.21 (p < 0.001), respectively.
Scale of motivation in education (Echelle de Motivation en Éducation) (12 items)
The original scale was developed in French by Vallerand et al. (
1992) to measure student motivation in an educational setting. A total of 12 items loaded on four factors that described different types of motivation (intrinsic, integrated, introjected, amotivated). The Turkish version of the scale (Kara,
2008) maintained the original factor structure and accounted for 63.48% of variance, with factor loading ranging between 0.380–0.750. Internal consistency reliability Cronbach’s α values were 0.84 for the entire scale and between 0.78–0.80 for the individual factors.
Data analysis
Questionnaire data was entered into an electronic spreadsheet and transferred to the software programs SPSS 22.0 and AMOS 22.0 for analysis. Descriptive analysis (e.g., frequency, percent, mean, standard deviation) was used to identify the types and extent of student cyberloafing behavior during their computer laboratory classes (Research Question 1). Multiple linear regression analysis was performed using the measures described above as independent variables and general cyberloafing behavior as the dependent variable in order to determine the factors affecting cyberloafing in general (Research Question 2). Assumptions were explored prior to the analysis, and no issues were detected. Structural equation modelling (SEM) analysis was performed using the measures described above as independent variables and personal business, socialization and news follow-up cyberloafing as dependent variables in order to determine the factors affecting the three different types of cyberloafing behavior (Research Question 3).
Discussion
Despite the existence of a theoretical framework and numerous studies on cyberloafing in work settings, studies in educational settings are scarce. The present study attempted to take the theoretical framework developed by Van Doorn (
2011) specifically for work settings and adapt it to educational settings. In this regard, instructor norms and respect for students were examined rather than supervisor norms to represent organizational policies, and in place of job resources, a construct that is not described in detail in Van Doorn’s theory, motivation was investigated as a resource-related construct in an educational setting. In addition to demographic characteristics, which included gender as well as factors related to internet-usage, sense of belonging and locus of control were examined as possible individual antecedents to cyberloafing, and attitudes towards cyberloafing and loafing were examined along with cyberloafing activities and behaviors.
The final model developed included six factors identified as being associated with cyberloafing behavior. Internet skill level had the greatest impact, followed by gender, instructor respect for students, instructor norms regarding classroom cyberloafing, student amotivation, and student attitude towards cyberloafing. The remaining factors examined had no demonstrable effect on cyberloafing behavior.
The finding that increases in internet skill levels are accompanied by increases in cyberloafing behavior is not surprising and has been demonstrated by several previous studies (Arabaci,
2017; Baturay & Toker,
2015; Blanchard & Henle,
2008). However, whereas previous studies demonstrated a uniform effect on cyberloafing, the current study found cyberloafing for personal business and socialization to be affected more than cyberloafing for news follow-up (Arabaci,
2017; Baturay & Toker,
2015). Internet experience and frequency of use were also analyzed as potential factors involved in cyberloafing, but no associations were identified.
Previous studies have highlighted the fact that cyberloafing behavior is more frequent among men than women (Baturay & Toker,
2015; Dursun et al.,
2018; Hargittai & Shafer,
2006; Lim & Chen,
2012; Vitak et al.,
2011; Yılmaz et al.,
2015). The present study supports this assumption. Moreover, when types of cyberloafing are examined in detail, it becomes clear that men and women exhibit different patterns of cyberloafing behavior. For example, this study found that males tend to spend more time cyberloafing for personal business and news follow-up, which is in line with a previous study (Baturay & Toker,
2015). Cyberloafing for socialization, gaming and gambling, and shopping (Akbulut et al.,
2017; Andreassen et al.,
2014; Dursun et al.,
2018) have also been reported to occur more frequently among men than women; however, according to Arabacı (
2017), cyberloafing for news follow-up is more common among women. While it appears that there are differences in the cyberloafing behavior of men and women, more research is required to understand these differences in detail.
Similar to Van Doorn’s findings for organizational antecedents, this study found instructor norms in computer lab settings to be associated with decreases in cyberloafing. This is in line with the literature that states the presence of norms, whether those of managers in a work setting or instructors in an educational setting, is associated with decreased cyberloafing. With regard to work settings, Anandarajan and Simmers (
2004) demonstrated that supervisor norms may affect cyberloafing in general, and Blanchard and Henle (
2008) reported a more specific association between supervisor norms and minor cyberloafing, such as checking and sending non-work-related emails; visiting news, financial and sports sites; shopping online; and participating in online auctions. In an educational setting, the literature suggests that classroom management, monitoring and controlling processes may prevent cyberloafing (Blanchard & Henle,
2008; Dursun et al.,
2018; Henle et al.,
2009; de Lara et al.,
2006).
Although norms may play a large role in determining cyberloafing behavior, this construct needs to be examined with great care in relation to short-term versus long-term impact. Establishing overly harsh norms carries with it the huge potential of generating unintended mild or severe consequences, including amotivation, dissatisfaction, sabotage and espionage as forms of payback. The enforcement of harsh rules with harsh punishment has been shown to have a negative effect on individuals (Lim,
2002; de Lara et al.,
2006). According to Ugrin et al. (
2018). Cyberloafing may function as a form of revenge behavior in response to disciplinary action, as individuals justify their cyberloafing behavior as a legitimate response to punishment and a means of balancing power.
It should also be noted that cyberloafing may be a behavior used to cope with stress (Lim & Chen,
2012), regain energy (Bridegan,
2008) and support creativity (Oravec,
2002). While some managers feel that employees should never engage in personal use of the web at work, others believe this can help employees maintain balance in their lives. Striking a balance in the implementation of norms that falls somewhere between that of cyber-bureaucrats, who belief in strict adherence to all norms, and cyber-humanists, who recognize that cyberloafing can help individuals recover from emotional and physical exhaustion (Van Doorn,
2011), may help to avoid the adverse “payback” effects mentioned above.
According to Yılmaz and Yurdugül (
2018), student cyberloafing may be an attempt at behavior that balances a perceived lack of respect on the part of instructors as well as strict instructor norms against cyberloafing. A similar situation has been observed in work settings where job demands exceed job resources (Lim,
2002; Robinson & Bennett,
1995). Under such circumstances, cyberloafing may become a deviant behavior used to avoid an unpleasant situation (Van Doorn,
2011). Cyberloafing activities and behaviors that are triggered and/or legitimized by an unpleasant situation can spread rapidly among students. In other words, when instructors do not show respect for their students, their students may respond by cyberloafing in their classes.
Studies have reported that students will start to engage in cyberloafing when they lose motivation and interest and are unable to concentrate on their lessons (Sana et al.,
2013; Yılmaz & Yurdugül,
2018). In line with these findings, the current study found amotivation to be associated with increased cyberloafing, especially for personal business. It is possible that amotivation and cyberloafing behavior both increase when students are unsure about why they are attending university and don’t see potential benefits of their education. Interestingly, no other types of motivation (intrinsic, integrated, introjected) were associated with cyberloafing, either positively or negatively. As Yılmaz and Yurdugül (
2018) note, motivation is a complicated construct that may be indirectly affected by numerous factors, including classroom setting and atmosphere, attitude, and teaching and learning strategies. An example of this complicated interaction can be found in Barry et al. (
2015), in which students gave the need to pay attention during a limited amount of class time, particularly when the lesson was demanding, as the reason for not using mobile phones during their lessons.
Conversely, students may use cyberloafing to increase their motivation and interest as well as their ability to concentrate on a lesson. Bridegan (
2008) suggested that students may cyberloaf with the sole aim of clearing their minds while performing a task, which is similar to Wagner et al.’s (
2012) claim that cyberloafing in a work setting may help decrease an individual’s mental workload and provide cognitive relaxation. This seems to contradict Van Doorn’s (
2011) model with regard to the negative effects of demand-resource imbalances on well-being and motivation, but it is in line with the suggestion that cyberloafing should be tolerated to a certain extent (Yılmaz & Yurdugül,
2018). Based on the findings of the present study, it is impossible to state that convincingly that amotivated students are likely to cyberloaf and motivated students are not; however, the findings do provide preliminary evidence for a relationship between cyberloafing and amotivation, which may indeed be a reciprocal relationship.
The current study also confirmed the literature stating that a positive attitude towards cyberloafing has an effect on cyberloafing behavior in an educational setting (Knight,
2017; Soh et al.,
2018; Yılmaz & Yurdugül,
2018). According to Ajzen (
1991), student attitudes towards cyberloafing influence their intention to cyberloaf, which in turn influences their behavior, and several studies have demonstrated an association between attitude and intention to cyberloaf in workplace situations (Askew et al.,
2014; Moody & Siponen,
2013; Pee et al.,
2008). In the present study, a positive attitude towards cyberloafing was found to be associated specifically with cyberloafing for socialization, but not with cyberloafing for personal business or news follow-up. It is possible that the attitude towards cyberloafing stimulates the intention to perform cyberloafing for socialization, which then leads to cyberloafing behavior, but further studies are required to clarify this assumption.
Whereas sense of belonging and external locus of control were associated with increased cyberloafing in work settings (Blanchard & Henle,
2008; Van Dick, Tissington, et al.,
2009), and the personal traits of conscientiousness, extraversion, honesty, and agreeableness were associated with decreased cyberloafing in both traditional and distance work settings (O’Neill et al.,
2014; Van Doorn,
2011; Wyatt & Phillips,
2005), the present study found no evidence that these factors had any predictive value in determining cyberloafing behavior in an educational setting. This may be due to certain difference between educational and work settings.
Finally, this study found no relationship between loafing and cyberloafing, which may have to do with the age of the study population. As discussed by Van Doorn (
2011), loafing may be more specific to older individuals, who may take coffee breaks, smoke, read newspapers, or talk on the phone rather than engage in cyberloafing. While it is possible that age may explain the difference between loafing and cyberloafing, it is also possible that technological skill level plays a role. Younger individuals tend to have greater technological competence than older individuals, and an increase in cyberloafing in conjunction with an increase in internet skill level was noted in this study.
Implications for practice
The current study provides valuable clues on how cyberloafing can be monitored and exploited to improve student motivation. First, instructors need to establish clear rules for internet usage in computer-based classroom settings, but these rules need to be flexible enough to allow students to take advantage of cyberloafing to relax and eliminate their mental stress loads. Instructors should keep in mind that if students feel they are respected, they will be less likely to cyberloaf and more likely to pay attention to lectures. In order to maintain student interest, instructors can provide extra responsibilities to students who may be more inclined to perform cyberloafing, such as male students with high internet skill levels and low levels of motivation.
Students who are proficient internet users are especially prone to cyberloafing—and they may need to be reminded that, as the saying goes, “With great powers come great responsibilities.” Here, we might adapt another saying—“Drink Responsibly,” which is aimed at preventing drunk driving—to “Use Responsibly,” in order to prevent a different type of deviant behavior, cyberloafing. An effective training program that focuses on the negative consequences of cyberloafing and warns students about ineffective and inefficient use of technology during class hours (Soh et al.
2018) could be included in an “ethics in computing curriculum”, and development of such a comprehensive training program could be the subject of future design-based research studies.
Limitations of the study
This study has a number of limitations associated with the sample design. Data was collected using an online questionnaire, which represents a type of convenience sampling rather than random sampling; therefore, the study findings cannot be generalized. While purposive and convenience sampling strategies are unable to represent the population due to lack of randomness, their use is fairly common in educational research, where constraints such as time, money, and resources make random sampling infeasible (Wallen & Fraenkel,
2001).
Another limitation in connection with the online survey methodology used is that it relied on self-selection by participants, and thus a non-response rate cannot be calculated (Thompson et al.,
2003). For this reason, characteristics of respondents and non-respondents cannot be compared (Guerra,
2003), making inherent bias in the study sample unavoidable (Leigh & Tracey,
2010).
Finally, the self-reported nature of the survey data represents another limitation. Self-report surveys are considered appropriate when individuals’ personal experiences and opinions are the main emphasis of research ("Self-Report Method.,"
2008). Self-reported data is presumed to provide a picture of the actual feelings and opinions of study participants; however, the confidential, anonymous nature of self-reporting makes data verification impossible (Witucki,
2006).
As Wright (
2005) has stated, proper sampling is one of the major obstacles of survey research and has an impact on generalization. Future studies may attempt to collect data from different study populations and use larger sample sizes, and, if random sampling is possible, the study findings will then be generalizable to the wider population.
Conclusion
The current study examined various factors that could affect cyberloafing in an educational setting, namely a computer laboratory lecture class. Internet skills, gender (male), lack of instructor norms regarding cyberloafing, student amotivation and positive attitudes towards cyberloafing were associated with increases in cyberloafing behavior, whereas instructor respect for students was associated with decreases in cyberloafing behavior. When specific types of cyberloafing were examined, the study found internet skill level, gender, instructor norms and amotivation to be associated with cyberloafing for personal business; gender, instructor norms and amotivation to be associated with cyberloafing for news follow-up; and internet skill level, instructor respect for students, and student attitudes towards cyberloafing to be associated with cyberloafing for socialization. Considering the negative outcomes that may be associated with cyberloafing, future studies should be conducted that look at cyberloafing in different educational settings (e.g., in-person vs online learning) line and using different devices (e.g., mobile phones and tablets vs computers) in order to develop more effective interventions for limiting cyberloafing.
As noted by Varol and Yıldırım (
2019), educators will be able to provide more effective learning environments for their learners when they are able to establish more effective methods for preventing cyberloafing. In this regard, a number of areas can be suggested for future research to obtain additional data that can be used to extend and validate the model for educational settings developed by the present study.
First, the model developed by Von Doorn (
2011) for work settings that formed the basis of this study can be explored in different types of educational settings, including both live lectures and online classes. The various gender differences identified in this study can be further researched to include clustering gender-specific cyberloafing behaviors performed by either men, women, or both genders. Interactions between “internet skill level” and other constructs can also be investigated to provide a more detailed understanding of the mechanisms affecting different types of cyberloafing.
Considering that rules and punishment may actually encourage behavior as well as encourage it, an eclectic composite of instructors that include both cyber-bureaucrats and cyber-humanists may be included in future studies to identify the direction and magnitude of the association between cyberloafing and instructor norms/institutional policies. In this regard, studies may investigate the effects of different strategies for instituting norms. Similarly, since instructor respect for students was identified as another factor influencing cyberloafing, its positive impact on cyberloafing should be examined in the future.
Motivation and cyberloafing may have a reciprocal relationship, with amotivation leading to cyberloafing on the other hand, and cyberloafing being used to relax and regain motivation on the other. Future studies may examine both aspects of this relationship. Finally, as noted above, future studies should investigate the potential of attitude towards cyberloafing to excite intention as well as actual behavior, especially with regard to cyberloafing for socialization.
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