In today’s digital world, it is common for students to bring their Internet-connected devices to classes. However, using these devices for non-class-related purposes during lessons can be distracting and detrimental to students’ academic performance as well as frustrating for instructors. Defined as the use of organizational resources for work-related purpose (in this case, studies-related purpose; Lim, 2002), cyberloafing has a negative impact on the learning environment as it causes distraction and affects students’ attention and ability to focus. In this study, we examined students’ cyberloafing behavior through the lens of the theory of planned behavior (TPB). We also examined two new constructs, namely class engagement and habit. In addition, this study tested the role of two specific subjective norms, i.e., descriptive and prescriptive norms, in predicting students’ intention to cyberloaf. Data from 238 university students were analyzed with Consistent Partial Least Squares (PLSc) analysis using SMART PLS. Results showed that both habit and intention were significantly related to cyberloafing behavior. Attitude, prescriptive norms and perceived behavioral control were significant in predicting students’ intention to cyberloaf. However, descriptive norms were not significantly related to intention. Finally, class engagement played an important role in determining students’ attitude towards cyberloafing, descriptive norms and perceived behavioral control. In general, results of this study provided support that the extended theory of planned behavior is useful in explaining students’ cyberloafing behavior. Implications of the findings for theory and practice are discussed.
2. Current cyberloafing research in educational settings
4. Data analysis
Nowadays, students typically bring their Internet-connected devices such as laptops, smartphones and tablets to classes for class-related and/or non-class related reasons (Ragan, et al., 2014). When a student engages in electronically-mediated activities during classes that his or her instructors do not consider class-related, it is labeled as cyberloafing (Gerow, et al., 2010). Examples of cyberloafing activities include sending personal e-mail messages, playing online games, visiting deal-of-the-day Web sites, posting status updates on social networks and watching videos online. Some professors have resorted to banning their students from using Internet-connected devices in classrooms because it distracts both students and instructors. Indeed, various research findings note that cyberloafing disrupts students’ concentration during lessons and annoys lecturers (Hembrooke and Gay, 2003; Ravizza, et al., 2013). Studies also found that students who do not cyberloaf in the classroom perform significantly better in memory tests (Hembrooke and Gay, 2003) and comprehension tests (Sana, et al., 2013). In addition, Ravizza, et al. (2013) reported that multitasking (which includes students’ cyberloafing in classes) disrupts students’ concentration during lessons and subsequently affects their academic performance.
As mobile usage in the classroom becomes increasingly pervasive amongst students, educators need to explore ways to manage the adverse impact of technology so that it does not impede students’ learning experiences. Furthermore, as the habit of cyberloafing becomes engrained in their lives, these students are likely to continue the habit of cyberloafing in their workplaces, consequentially affecting the performance and productivity of the labor force. Thus, given the significant consequences of cyberloafing, it is useful to carry out empirical research on the factors influencing students’ intentions to cyberloaf.
The remainder of this paper is organized as follows. First, we will discuss the literature and past studies regarding cyberloafing research in education settings to date, as well as theoretical and managerial contributions. Second, we will draw from the theory of planned behavior to develop our hypotheses. Third, methodology and data analysis will be presented. Last, we will explain our research findings and contributions, concluding with limitations of research and future recommendations.
2. Current cyberloafing research in educational settings
A significant number of exploratory cyberloafing studies in academic settings have been conducted using demographics as predictors of cyberloafing (e.g., Baturay and Toker, 2015; Karaoğlan Yılmaz, et al., 2015). For example, Baturay and Toker (2015) examined demographics including gender, grade, Internet skills level, Internet usage and Internet experience as predictors of cyberloafing and conceptualized cyberloafing as a three-dimensional construct (personal business, news follow-up and socialization). The results of the study revealed that gender, grade and Internet experience are significantly related to three dimensions of cyberloafing. Internet skills was only significantly related with socialization and Internet usage was only significantly related with personal business and socialization. Similar findings were reported in the study of 288 Turkish high school students by Karaoğlan Yılmaz, et al. (2015), which revealed a significant difference in the degree of cyberloafing in terms of gender, departments of study and Internet use frequency. However, these demographic-based studies do not provide insights in understanding the social-psychosocial motivations of students’ cyberloafing behavior.
Some studies have advanced beyond demographics in order to provide deeper insights into explaining why people cyberloaf. For example, Yasar and Yurdugul (2013) examined four antecedents of cyberloafing, namely recovery, development, deviant behavior and addiction on a sample of 215 higher education students at a Turkish university. Surprisingly, they reported that addiction was the only significant factor for cyberloafing, while recovery, development and deviant behavior were not significant predictors Another study used a multi-factor survey on 451 university students in the United States to investigate the predictors of students’ intention to cyberloaf (Gerow, et al., 2010). Their results revealed that social norms, multitasking and cognitive absorption were significant key factors driving cyberloafing behavior, while awareness of instructor monitoring, distraction by other students’ cyberloafing behavior were not significant. Taneja, et al. (2015) used the theory of planned behavior to study cyberloafing activities of 267 college students in the United States. Taneja, et al.’s study revealed that cyberloafing attitude, social norms and descriptive norms were significant predictors of the intention to cyberloaf, while distractions by others’ cyberloafing behavior, escapism, consumerism, lack of attention and cyberloafing anxiety were significant factors influencing the attitude towards cyberloafing.
Table 1: Summary of past cyberloafing studies.
Note: This table is extended from Huma, et al. (2017)
Author(s) Year Context Theory base Methodology Key findings Galletta and Polak 2003 Employee TPB Survey Attitude (Job satisfaction and Internet addiction) and subjective norms (supportive peer culture and supportive supervisor culture) are significant to cyberloafing. Woon and Pee 2004 Employee TIB and TPB Survey TIB has greater explanatory power over TPB for cyberloafing intention. Seymour and Nadasen 2007 Employee TPB Survey Attitude and subjective norms are not significant to cyberloafing. Peceived behavioral control (less managerial supervision) is partially significant to cyberloafing. Pee, et al. 2008 Employee TIB and TPB Survey All constructs of the modified TIB significant to cyberloafing. Moody and Siponen 2013 Employee TIB Survey The TIB model explained 86.7 percent of the variance on cyberloafing. Only facilitating conditions was found to have no significant relationship with cyberloafing and have no moderating effect on intention and cyberloafing behavior. Betts, et al. 2014 Employee SET and TIB Survey The combined model of SET and TIB explained 46.4% of the variance on cyberloafing intention. Askew, et al. 2014 Employee TPB Survey Cyberloafing attitudes, ability to hide, descriptive norms are significant predictors of cyberloafing. Koay, et al. 2017 Employee TIB Survey Perceived consequences, social factors and affect are significantly related to cyberloafing intention except private demands. Huma, et al. 2017 Employee TIB Survey The path coefficients of perceived consequences and facilitating conditions on cyberloafing are significantly different between two samples from public and private organizations. Gerow, et al. 2010 Students Field Theory Survey Social norms, cognitive absorption, and multitasking significantly influence students’ intention to cyberloaf. Also, cognitive absorption mediates the relationship between multitasking and intention to cyberloaf. Yasar and Yurdugul 2013 Students No Survey Only addiction dimension of cyberloafing behaviors is significantly related to cyberloafing activities. Baturay and Toker 2015 Students Demographics Survey Gender, grade and Internet experience are significantly related to three types of cyberloafing (personal business, news follow-up, and socialization) except Internet skills was only significant to socialization and Internet usage was only significant to personal business and socialization. Karaoğlan Yılmaz, et al. 2015 Students Demographics Survey There is a significant difference in the degree of cyberloafing in terms of gender, departments of study, Internet use frequency. Taneja, et al. 2015 Students TPB Survey Attitude, subjective norm and descriptive norm are significant to cyberloafing intention whereby consumerism, escapism, lack of attention, cyber-slacking anxiety, distraction by others are significant to attitude. Lastly, intrinsic motivation, extrinsic motivation, class engagement, apathy towards course material are significant to lack of attention.
The summary of past studies on cyberloafing is presented in Table 1. It can be seen that the theory of planned behavior (TPB) and the theory of interpersonal behavior (TIB) are both commonly used in explaining employees’ cyberloafing behavior. Although both are different theories, yet “TPB and TIB are similar as both include expectancy-value constructs (e.g., attitude of TPB and perceived consequences of TIB) and normative belief constructs (i.e., subjective norm in TPB and social factors in TIB)” . Furthermore, both posit that environmental factors play a determining role on the likelihood of a behavior is able to be executed. To data, only one study has tested the TPB model in the educational setting. Hence, this study advanced our understanding on cyberloafing in an educational setting through extending the theory of planned behavior (TPB) with additional variables such as class engagement, habit, prescriptive and descriptive norms.
This paper makes several contributions to the academic literature. First, this paper tests an extended TPB model to predict students’ cyberloafing behavior using Malaysian samples, which is a new setting. Second, this paper examines the roles played by class engagement (of students) and habit. Both these variables are not part of the original TPB model. Thirdly, in order to provide a thorough explanation of the role of social influence in predicting the intention to cyberloaf, subjective norms is conceptualized into two specific norms, namely descriptive norms and prescriptive norms. To date, prescriptive norms has not been empirically tested as a predictor of cyberloafing in educational settings. Fourth, this paper applied the latest analytical procedure, which is consistent PLS, to validate the model. Last, the findings of this research are expected to extend our understanding of the drivers of cyberloafing at the social-psychological level as well as to serve as a reference for educators to formulate strategies to manage students’ non-class-related technology use in the classroom.
2.1. Literature review
2.1.1. Theory of planned behavior
The theory of planned behavior (TPB) was extended from the theory of reasoned action and “ has been successful in predicting important behaviors in a wide variety of domains” . According to TPB, intention is the proximal predictor of actual behavior. A person’s intention to engage in a particular behavior is driven by three key factors: his or her attitude towards the behavior, perceived behavioral control and subjective norms (Ajzen, 1991, 1985; Fishbein and Ajzen, 1975). Attitude refers to individuals’ negative or positive perception towards the outcome of a particular behavior; in this study, cyberloafing during lessons (Ajzen, 1991; Askew, et al., 2014). Attitude includes both the instrumental (e.g., convenience or troublesome) and the affective (e.g., exciting or dull) assessment of the behavior. Subjective norms refer to an individual’s perceived social pressure not to perform or to perform the behavior and his or her motivation to comply with the perceived social pressure (Ajzen, 1985). Perceived behavioral control refers to an individual’s perceived difficulty or ease to perform the behavior (Ajzen, 1991; Askew, et al., 2014). According to Ajzen (1991), the TPB theory should be developed further and extended with new predictors that are theoretically supported and contribute a convincing amount of unique variance. In this research, we attempt to incorporate four additional variables, namely class engagement, descriptive norms, prescriptive norms and habit, into the TPB model in order to study investigate students’ cyberloafing behavior.
For a comprehensive perspective, we should note that cyberloafing activities also occur at the workplace (Lim, 2002; Lim and Teo, 2005; Lim and Chen, 2009). Cyberloafing describes online behavior engaged by an employee that is not job-related (Askew, et al., 2014). There is considerable debate on whether cyberloafing constitutes a counter-productive work behavior. Many scholars argue that cyberloafing impedes employees’ productivity, while others advocate that cyberloafing improves creativity and job satisfaction. Many cyberloafing studies have used the TPB. For example, Lee, et al. (2004) tested a modified TPB model and reported that attitude, perceived behavioral control, subjective norm and denial of responsibility were significant for the intention to cyberloaf, but employees’ perceived moral correctness on cyberloafing, referred as moral obligation, was not significant. Another study based on TPB (Bock, et al., 2010) found that perceived benefit and cost were significantly related to attitude; while attitude as well as subjective norms were significantly related with intention to cyberloaf regardless of the level of control mechanism (e.g., Internet usage policies, monitoring software and disciplinary measurement). That study also revealed that the significant relationship between intention and cyberloafing behavior disappears when habit is included to predict cyberloafing behavior. This indicates that cyberloafing can become a routine behavior that does not require conscious decision-making. In fact, habit was reported to be the most significant predictor of cyberloafing in many studies (e.g., Pee, et al., 2008; Moody and Siponen, 2013; Koay, et al., 2017). Hence, we included habit as a variable in this study.
Instead of measuring social norms as a general construct, in his study on cyberloafing, Askew, et al. (2014) used two specific norms, namely prescriptive norms (what is socially acceptable behavior by referent groups) and descriptive norms (what referent groups actually do). In addition, Askew and his team of researchers conceptualized perceived behavioral control as two constructs namely self-efficacy (employees’ ability to overcome obstacles to engage in cyberloafing behavior) and ability to hide (employees’ ability to prevent being spotted or monitored). Based on a sample representing the general working population, Askew, et al.’s study showed that attitude, prescriptive norms, descriptive norms, self-efficacy and ability to hide were significantly related to employees’ intention to cyberloaf. Among the four models tested, the model that yielded the highest explained variance of 32 percent in the intention to cyberloaf consisted of three factors, namely attitude, the ability to hide and descriptive norms. Testing and validating the main TPB model developed by Askew, et al. (2014) in the context of Iranian copper industry, Sheikh, et al. (2015) reported that attitude, descriptive norms and the ability to hide had a significant relationship with intention to cyberloaf and accounted for 32 percent of the variance. In summary, the TPB-based models have been used to effectively predict cyberloafing behavior at the workplace and have demonstrated suitability for use of cyberloafing studies in educational settings. In the next section, we provide our theoretical arguments for each of the proposed hypotheses.
2.2.2. Theoretical framework and research hypotheses
In this paper, intention refers to a student’s willingness and conscious plans to carry out cyberloafing activities during lessons (Fishbein and Ajzen, 1975; Ajzen, 1991). People are more likely to perform a particular behavior when they have strong intentions to perform it. Various studies have demonstrated that intention is a reliable and significant factor to predict behavior (e.g., Lee, et al., 2004; Moody and Siponen, 2013; Koay, et al., 2017). Thus, the following hypothesis is postulated:
H1: Higher levels of intention to cyberloaf will be positively related to high levels of cyberloafing behavior
Habit refers to situational-behavioral sequences that have become self-starting and occur without the need for mindful decision-making in order to respond to specific cues (Triandis, 1980). When the habit of cyberloafing is formed, the act of cyberloafing is carried out routinely without conscious effort and consideration of its implications. In other words, when the habit of cyberloafing is strong, the influence of intention to cyberloaf is reduced, suggesting that students may no longer need to assess their attitudes and subjective norm prior to enactment of cyberloafing behavior (Ajzen, 2002; Askew, et al., 2014). So long as other key considerations do not exist (e.g., phone malfunction, lecturers banned electronic devices, no mobile signal) to disrupt students to perform their habitual cyberloafing behaviors, habit can be a powerful predictor of cyberloafing. Extant studies have consistently reported that habit to be a strong predictor of behavior (Pee, et al., 2008; Moody and Siponen, 2013; Koay, et al., 2017). Moreover, habit is treated conceptually similar to addiction (LaRose and Eastin, 2004); Internet addiction was found to have a significant impact on employees’ cyberloafing behavior in the workplace (Chen, et al., 2008). Based on these arguments, the following hypothesis is postulated:
H2: Higher levels of habit will be positively related to higher levels of cyberloafing behavior
According to the TPB, attitude refers to the extent to which a student has a positive or negative opinion towards cyberloafing behavior (Fishbein and Ajzen, 1975; Taneja, et al., 2015). Favorable attitude is developed when the outcomes of cyberloafing are perceived positively such as fun, enjoyable and beneficial. Students with favorable attitude of cyberloafing tend to have stronger intentions to cyberloaf. Several studies have reported that attitude is significant to adolescents’ intention to use online social networking (Pelling and White, 2009; Baker and White, 2010). Attitude has also been consistently recorded to have a significant relationship with intentions to cyberloaf in the workplace (Pee, et al., 2008; Moody and Siponen, 2013). However, this relationship of attitude and intention has not been widely tested for cyberloafing during lessons. This leads to the following hypothesis:
H3: Higher levels of positive attitude towards cyberloafing will be positively related to higher levels of students’ intention to cyberloaf during lessons
220.127.116.11. Subjective norms
Subjective norms refer to a student’s perception of whether important referent groups view cyberloafing during lessons as socially acceptable and his or her motivation to comply with these views (Fishbein and Ajzen, 1975; Taneja, et al., 2015). Individuals often take cues from the environment to behave within socially “appropriate behavior” (Gerow, et al., 2010). When students perceive that cyberloafing during lessons to be socially acceptable, they are more likely to cyberloaf without being perturbed about violating group norms (Galluch and Thatcher, 2011). For students in a college environment, their peers are typically the most salient and relevant referent group because students want to belong to and gain acceptance of peers (Borsari and Carey, 2001). Many extant studies have proposed that subjective norms to be separated into two different types; prescriptive norms (what is perceived to be acceptable behavior by referent groups) and descriptive norms (what referent groups actually do) (e.g., Borsari and Carey, 2003; Brauer and Chaurand, 2010). Such a distinction is important because, for example, peers (sitting beside the person) may indulge in cyberloafing during lessons, although these people may consider that cyberloafing to be not socially acceptable (in other words, descriptive norms is different from prescriptive norms). Askew, et al. (2014) conducted two studies using different samples and reported that both descriptive and prescriptive norms were significant with intention to cyberloaf at the workplace. As mentioned previously, prescriptive norms have not been tested in the education environment. Hence, this study conceptualizes subjective norms into both descriptive and prescriptive norms and proposes the two hypotheses:
H4: Higher levels of prescriptive norms will be positively related to higher levels of students’ intention to cyberloaf during lessons
H5: Higher levels of descriptive norms will be positively related to higher levels of students’ intention to cyberloaf during lessons
18.104.22.168. Perceived behavioral control
This pertains to the amount of control that students perceived they are able to carry out cyberloafing activities during lessons (Fishbein and Ajzen, 1975). It is more likely for individuals to perform the desired behavior when they perceive they have sufficient means to carry out the behavior. For example, Gerow, et al. (2010) found that the ability to multitask (defined as the ability to handle multiple tasks at the same such as cyberloafing while participating in the classroom) can significantly impact intention to cyberloaf. In addition, external factors can hinder or facilitate the behavior. For instance, if instructors forbid students to use Internet devices during lessons, students will feel constrained from being able to cyberloaf. Hence, a hypothesis is postulated as:
H6: Higher levels of perceived behavioral control will be positively related to higher levels of students’ intention to cyberloaf during lessons
22.214.171.124. Class engagement
Class engagement refers to practices used by instructors to motivate students to devote their physical and psychological energy to participate in academically related activities during lessons (Dean and Jolly, 2012; Taneja, et al., 2015). Class engagement is reported to be crucial for learning performance (Trees and Jackson, 2007), increases attendance in classes, promotes active learning and facilitates academic success (Carini, et al., 2006; Gunuc and Kuzu, 2015). Examples of class engagement include giving feedback during the lesson, ensuring students comprehend lecture contents and developing closer relationships with them.
Conversely, a lack of class engagement can result in boredom and lower class participation (Barry, et al., 2015; Taneja, et al., 2015). Students would tend to give less attention and distract themselves with cyberloafing activities. However, if students are required to participate in class activities and discussion, even if students have the urge to cyberloaf, they might not carry it out. As such, high levels of class engagement can impede students from cyberloafing. Given these arguments, we propose four hypotheses:
H7: Higher levels of class engagement will be negatively related to lower levels of attitude towards cyberloafing
H8: Higher levels of class engagement will be negatively related to lower levels of prescriptive norms
H9: Higher levels of class engagement will be negatively related to lower levels of descriptive norms
H10: Higher levels of class engagement will be negatively related to lower levels of perceived behavioral control
The final research model is presented in Figure 1.
Figure 1: Research model.
3.1. Procedure and sample
There were two phases in the data collection process. In the first phase, we conducted a pre-test for the questionnaire on six undergraduates to ensure that the instructions and questions asked could be easily understood in a Malaysian higher education setting (Bryman and Bell, 2015). The questionnaire was pre-tested using a paper-based method so that the pilot respondents could write down their comments and feedback in a blank space provided in each section. Based on their feedback, we made minor modifications on wording, font and layout. Moreover, the survey questionnaire was examined by two academic experts for content validity and psychometric properties of the instrument.
In the second phase, a convenience sampling method was employed by requesting several lecturers working in a large private university in Malaysia to collect data in their classes over two semesters. Given that these lecturers were working in the same faculty (faculty of management), students were informed not to complete the questionnaire if they had already completed it in other classes in order to avoid duplicate responses. Furthermore, they were told that their academic marks would not be affected by their responses and that their personal information would be kept confidential. Out of 300 survey forms distributed, 280 survey forms were returned. Through a visual inspection of the responses, we deleted data with serious missing values, straight lining and diagonal lining responses, leaving a total of 238 usable data for further analysis. Among these, 102 (42.9 percent) respondents were male and 136 (57.1 percent) were female. The majority were Malay (45.4 percent), followed by Chinese (26.5 percent), Indian (14.3 percent) and others (13.9 percent). Most of the respondents (82.7 percent) had more than five years of experience with the Internet. The demographics of the respondents are shown in Table 2.
Table 2: Respondents’ profile. Demographic Frequency Percentage Gender Male 102 42.9 Female 136 57.1 Race Malay 108 45.4 Chinese 63 26.5 Indian 34 14.3 Other 33 13.9 Internet skills Novice 6 2.5 Intermediate 83 34.9 Advanced 104 43.7 Expert 45 18.9
To ensure the reliability and validity of constructs, we adapted, validated and tested scales. We modified some wording to fit the educational context of this research. Each item was measured on a seven-point likert scale (1 = Strongly Disagree, 4 = Neutral, 7 = Strongly Agree).
Most of the cyberloafing scales in the current literature are developed for use in a workplace setting. Some scholars attempted to modify those scales, which were originally meant for use in a workplace setting, to measure students’ cyberloafing behavior (e.g., Kalaycı, 2010; Yasar and Yurdugul, 2013; Baturay and Toker, 2015). However, this approach is problematic for two reasons. First, the adapted scales might not be valid, nor suitable for an educational setting because students are more likely to engage in more novel types of cyber-activities such as live streaming, tweeting posts or taking selfies. Second, the rapid pace of technological advancement has given rise to new types of online activities, resulting in obsolescence of previous cyberloafing scales (e.g., Lim, 2002; Pee, et al., 2008). Given these reasons, we decided to adapt Moody and Siponen’s (2013) scale that asked general behavioral questions. The original scale was designed to assess employees’ cyberloafing behavior in the workplace context. We modified it to measure students’ cyberloafing behavior in the classroom. Students were asked the extent to which they agree with the statement regarding using the Internet during lessons for non-class-related activities. An example item is: “In general, I use the Internet during class for non-class-related purposes”.
The habit scale (Bock, et al., 2010) adapted for this study was originally used to measure employees’ cyberloafing behavior. We modified the items to assess students’ cyberloafing habits in the classroom. Students were asked to rate the degree to which they agree with four statements regarding their cyberloafing habits. An example item is: “ It has become a habit for me”.
Attitude was measured with a seven-point Likert-scale with three items adopted from Taneja, et al.’s (2015). Students were asked to rate how fun, enjoyable and good it is to use the Internet for non-class-related activities during lessons.
3.2.4. Prescriptive norms
The scale measuring prescriptive norms was adapted from Askew, et al. (2014) and Taneja, et al. (2015), which consist of three items. Unlike descriptive norms, prescriptive norms measure students’ perceived approval of their classmates regarding the use of the Internet for non-class-related activities during lessons.
3.2.5. Descriptive norms
Descriptive norms was measured by three items from Taneja, et al.’s (2015) scale. Descriptive norms focusses on students’ observation of their classmates’ actual cyberloafing behavior in the classroom. An example item is: “my classmates use the Internet during lessons for non-class-related activities”.
3.2.6. Perceived behavioral control
This construct was adapted from a three-item scale used in studies by Moody and Siponen (2013) and Taneja, et al. (2015). The construct measures students’ perceived control over cyberloafing behavior in the classroom. An example item is: “Using the Internet for non-class-related activities is entirely dependent on me”.
3.2.7. Class engagement
Class engagement was assessed with a three-item scale adopted from Taneja, et al. (2015). Students were required to rate the degree to which the class is appealing and engaging. An example item is: “Instructor does not encourage student participation and interaction”.
4. Data analysis
In this research, we conducted consistent partial least square (PLS) analysis (Dijkstra and Henseler, 2015a; 2015b), a variance-based structural equation modelling (SEM), with SMART PLS version 3.2.3 software to test the proposed research model. SEM handles more complex models than traditional regression analyses (Bagozzi and Yi, 2012). Furthermore, the PLS algorithm manages the model relatively well with non-normal data and small sample sizes (Hair, et al., 2017). Table 3 provides the mean, standard deviation and correlations for latent variables in our research.
4.1. Common method bias
It is important to check for the severity of common method bias when data for endogenous and exogenous variables are collected from the same persons via self-reported questionnaires because the variance of dependent variables might be explained by measurement method (common method variance or same source variance) rather than independent variables (Podsakoff, et al., 2003). As such, we examined the extent of common method bias using Harman’s single factor test (Podsakoff and Organ, 1986). To run the test, all items were entered into the un-rotated principal component factor analysis. The results showed that the first factor with the greatest eigenvalue accounted for 36.86 percent of the variance, which is less than 50 percent. Furthermore, by examining the principal constructs inter-correlations using the correlation matrix, none of the correlations are larger than 0.9 (Bagozzi, et al., 1991), as seen in Table 3. Therefore, we can conclude that common method variance was not evident in this research and the study results attributed to common method bias were minimal.
Table 3: Descriptive statistics, correlations, and Fornell-Lacker criterion.
Notes: Diagonal elements are the square root of the average variance extracted (AVE).
Mean SD 1 2 3 4 5 6 7 8 1. Attitude 4.10 1.32 0.872 2. Behavior 4.09 1.33 0.415 0.880 3. Descriptive norms 4.77 1.40 0.526 0.345 0.894 4. Class engagement 4.49 1.29 -0.332 -0.082 -0.379 0.850 5. Habit 3.85 1.43 0.454 0.639 0.354 -0.132 0.830 6. Intention 3.60 1.40 0.521 0.655 0.412 -0.095 0.708 0.889 7. Perceived behavioral control 4.45 1.45 0.438 0.366 0.352 -0.234 0.440 0.489 0.861 8. Prescriptive norms 3.90 1.40 0.420 0.439 0.461 -0.082 0.471 0.574 0.449 0.921
4.2. Measurement model
Before assessing the significance of the structural path coefficients, we have to examine the measurement model. The evaluation of the measurement model involves internal consistency, convergent validity and discriminate validity. The two common criterions for internal consistency are Cronbach’s alpha and composite reliability (Hair, et al., 2017). Given that Cronbach’s alpha tends to underestimate, while composite reliability tends to overestimate the internal consistency reliability, true reliability usually lies in between these two criterions. In this research, all the constructs scored higher than the recommended value of 0.7 for both criteria (Cronbach’s alpha and composite reliability) suggesting satisfactory internal consistency reliability of constructs is achieved, as shown in Table 3.
Convergent validity refers to the degree to which a measure is positively related to other measures representing the same theoretical construct. Convergent validity is assessed through two methods. Firstly, we checked for the indicator’s outer loading referring to the common variance between the indicator and its associated construct. Generally, an indicator should achieve the rule-of-thumb outer loading of 0.7 to ascertain sufficient convergent validity. Indicators with loadings lower than 0.4 should be removed, while indicators with loadings between 0.4 and 0.7 can be retained if their composite reliability and average variance extracted (AVE) satisfy the recommended values. In this research, the only item (Beh3) was removed from the construct of cyberloafing behavior because of extremely poor outer loading lower than 0.4. Secondly, we examined the values of average variance extracted (AVE) for each construct (Hair, et al., 2017). AVE indicates how much of the construct’s variance is explained by its associated indicators. In this research, all constructs have AVE value of above 0.5 indicating convergent validity, as shown in Table 4.
Discriminant validity refers to the extent to which a construct is theoretically distinct from other constructs in the model. There are two ways to assess discriminant validity by checking the Fornell-Lacker and Heterotrait-monotrait ratio (HTMT) criteria. For the Fornell-Lacker criterion, the square root of each construct’s AVE should be greater than the off-diagonal correlations of other constructs in their corresponding row and column, as shown in Table 5. The HTMT criterion is a new approach to assess discriminant validity because cross-loadings and Fornell-Lacker criteria performed poorly in detecting discriminant issues. According to Henseler, et al. (2015), a HTMT value above 0.85 or a confidence interval (95 percent) higher than the value of one suggests a lack of disciminant validity. Thus, as can be seen from Table 5, discriminant validity is supported in this study.
Table 4: Assessment results of the measurement model.
Notes: All latent variables were connected to generate latent variable scores when running the PLS algorithm.
Construct Items Loadings Cronbach’s alpha Composite reliability AVE Attitude Att1 0.888 0.902 0.904 0.760 Att2 0.930 Att3 0.791 Prescriptive norms Pnorm1 0.973 0.943 0.943 0.848 Pnorm2 0.881 Pnorm3 0.906 Descriptive norms Dnorm1 0.872 0.923 0.922 0.798 Dnorm2 0.870 Dnorm3 0.938 Perceived behavioral control Pb1 0.839 0.895 0.896 0.742 Pb2 0.841 Pb3 0.903 Class engagement Ins1 0.883 0.885 0.886 0.722 Ins2 0.837 Ins3 0.828 Intention Int1 0.899 0.919 0.919 0.791 Int2 0.849 Int3 0.918 Habit Hab1 0.930 0.898 0.897 0.688 Hab2 0.832 Hab3 0.702 Hab4 0.838 Behavior Beh1 0.851 0.872 0.872 0.774 Beh2 0.908
Table 5: HTMT criteria. Attitude Behavior Descriptive norms Habit Intention Class engagement Perceived behavioral control Prescriptive norms Attitude Behavior 0.417
Descriptive norms 0.523
Class engagement 0.333
Perceived behavioral control 0.439
Prescriptive norms 0.424
4.3. Structural model
Following the guidelines by Hair, et al. (2017), the structural model was assessed in terms of the collinearity, goodness-of-fit, significance of path coefficients, coefficient of determination, effect size and predictive relevance. To ensure that collinearity was not a problem in this research, we examined the values of variance inflation factor (VIF) for endogenous variables that had more than one exogenous variable. In this case, the VIF value for the predictors of cyberloafing behavior was 2.005 and for the predictors of intention to cyberloaf ranged from 1.389 to 1.568. This indicated no evidence of a collinearity issue. The established criterion to assess the model’s goodness-of-fit in PLS path modelling was standardized root mean square residual (SRMR). Generally, an SRMR value less than 0.08 suggests adequate approximate model fit for PLS path models. The value for SRMR was 0.038, indicating an adequate model fit for this study.
To test the proposed hypotheses, a nonparametric bootstrapping procedure was performed with 5,000 re-samples to obtain stable parameter estimates and confidence intervals (Chin, et al., 2008). As shown in Table 6 and Figure 2, the results revealed significant effects for intention (β=0.407, t=3.715) and habit (β=0.351, t=3.292) on actual cyberloafing behavior, which supported H1 and H2. In addition, in line with the hypotheses attitude (β=0.263, t=3.066), prescriptive norms (β=2.846, t=4.479) and perceived behavior control (β=0.200, t=2.573) were found to be significantly related with students’ intention to cyberloaf. However, descriptive norms (β=0.040, t=0.451) did not have a significant relationship with students’ intention to cyberloaf. Thus, H3, H4 and H6 were supported while H5 was not supported. Class engagement was significantly related to attitude (β=-0.332, t=4.037) descriptive norms (β=-0.379, t=4.662) and perceived behavioral control (β=-0.234, t=3.198) which supported H7, H9 and H10. However, we found no support for the relationship between class engagement and prescriptive norms (β=-0.082, t=0.983) (H8).
Next, we examined the coefficient of determination (R2) which refers to the predictor variables’ explanatory power of the respective construct. Intention and habit predicted 49.1 percent of cyberloafing (R2 = 0.491) whereas attitude, prescriptive norms, descriptive norms and perceived behavioral control predicted 45.5 percent of intention (adjusted R2 = 0.455). Furthermore, class engagement predicted 11 percent (R2 = 0.110) of attitude, 0.7 percent (R2 = 0.007) of prescriptive norms, 14.4 percent (R2 = 0.144) of descriptive norms and 5.5 percent (R2 = 0.055) of perceived behavior control. The effect size (f2) of all the exogenous variables on its respective endogenous variable(s) was also reported in Table 6. The guideline for the interpretation of f2 values was as follows: 0.02, 0.15 and 0.35 respectively represent small, medium and large effect size of the exogenous variables. All endogenous variables in this research study had either small or medium effect size on its respective exogenous variable(s) except that descriptive norms and class engagement were found to have no significant effect size on intention and prescriptive norms respectively. Lastly, blindfolding procedure was performed to assess the predictive relevance. q2 values of 0.2, 0.15 and 0.35 indicate a small, medium and large effect respectively of an exogenous construct on an endogenous variable. All endogenous variables appeared to have had a small effect on its respective endogenous variable(s) except that descriptive norms and class engagement had no significant predictive relevance on intention and prescriptive norms respectively.
Figure 2: Structural model.
Table 6: Results of hypothesis testing. Hypotheses Path coefficient Standard error t-value p values f2 q2 H1 Intention → Behavior 0.407 0.11 3.715 0.000 0.162 0.105 H2 Habit → Behavior 0.351 0.106 3.292 0.001 0.120 0.082 H3 Attitude → Intention 0.263 0.093 2.846 0.002 0.081 0.050 H4 Prescriptive norms → Intention 0.355 0.079 4.479 0.000 0.157 0.103 H5 Descriptive norms → Intention 0.040 0.089 0.451 0.326 0.002 0.002 H6 Perceived behavioral control → Intention 0.200 0.078 2.573 0.005 0.053 0.035 H7 Class engagement → Attitude -0.332 0.082 4.037 0.000 0.124 0.073 H8 Class engagement → Prescriptive norms -0.082 0.083 0.983 0.163 0.007 0.002 H9 Class engagement → Descriptive norms -0.379 0.081 4.662 0.000 0.168 0.099 H10 Class engagement → Perceived behavioral control -0.234 0.073 3.198 0.001 0.058 0.035
In this section, we discuss the findings from the empirical study. First, this study found that intention and habit were both significant predictors of students’ cyberloafing behavior in the classroom, suggesting that cyberloafing behavior in the classroom was directed by both conscious and non-conscious cognitive processes. These results are consistent with findings of extant studies on employees’ cyberloafing at the workplace (e.g., Pee, et al., 2008; Moody and Siponen, 2013; Koay, et al., 2017). This study confirms the significant roles of both intention and habit in the context of cyberloafing behavior in the classroom.
Extant studies showed that habit is a stronger predictor than intention for employees’ cyberloafing behavior at work (Pee, et al., 2008; Moody and Siponen, 2013; Koay, et al., 2017). Bock, et al. (2010) even reported that intention was not significantly related with cyberloafing, when habit was present. However, in this research, intention was a stronger predictor than habit in explaining students’ cyberloafing behavior. One possible reason is that the habit of cyberloafing amongst university students may not be strong. These students typically stay in the same classroom for only two hours in each lesson and move to other locations after the lesson. University students also typically do not have consecutive lessons throughout the day, five days a week. Furthermore, students may encounter a lack of a WiFi connection as well as instructors who prohibit Internet use during lessons. Such factors could inhibit the development of strong habits of cyberloafing behavior in students. Hence, the formation of habit is relatively slower and weaker for students as compared to employees who are stationed at the same office desk for more than seven hours per day, five days a week. Moreover, students’ duration in universities typically is three years, a much shorter period compared to employees’ work history. In addition, university students typically enjoy more freedom than employees and hence, their intention to cyberloaf would play a stronger influence in their cyberloafing behavior as compared with employees, whose intentions are tempered by concerns over job security, productivity, deadlines, security and their employers. In summary, our findings revealed that intentions play a more important role than habits in educational settings, unlike at the workplace where habit plays a more important role than intentions.
Our investigations also revealed that students’ intention to cyberloaf is significantly predicted by attitude; positive attitude drives the intention to perform the behavior. When students perceived cyberloafing in the classroom as fun, exciting and enjoyable, these positive outcomes served as motivation for them to carry out their behavior. The results corroborate with social networking studies by Pelling and White (2009) and Baker and White (2010) in which attitude was significantly related with young people’s use of social networking Web sites. Similarly, attitude was found to be an important predictor of employees’ cyberloafing behavior in the workplace (Askew, et al., 2014; Sheikh, et al., 2015). This finding confirms the importance of attitude for intention to cyberloaf in classroom settings.
In this study, we conceptualized subjective norms into two specific norms which were prescriptive norms and descriptive norms as proposed by Askew, et al. (2014). To the best of our knowledge, measuring subjective norm using both these norms has not been carried out in educational settings. Our findings revealed that perspective norms had a significant effect on intention to cyberloaf, but descriptive norms were not significant. This is contrary to research recording that descriptive norms are a strong predictor compared to prescriptive norms in the cyberloafing context at the workplace (Askew, et al., 2014). One possible explanation is that it is hard for students to clearly differentiate whether Internet use by their peers during lessons is class or non-class related because of the small screen size of a mobile phone and physical distance from each other in the lecture room. Furthermore, it is getting common for instructors to ask students during classes to use the Internet to facilitate class participation through online groups discussions or to search for information. Students may use the Internet for both class and non-class related purposes concurrently during classes. Hence, observing peers’ non-class-related Internet use as a clue about the extent to which cyberloafing is socially acceptable may not be effective. Instead, students’ perceived approval of peers on cyberloafing in the classroom played a more important role in affecting their actual behavior, than descriptive norms.
Consistent with theory of planned behavior and empirical studies, we found that perceived behavioral control has a significant effect on students’ cyberloafing behavior in the classroom. Students were more likely to cyberloaf when they perceived they had absolute control over behavior. The controlling factors included their mastery over the Internet, availability of electronic devices, perceived proximity between students and lecturers as well as the degree of concentration required for lessons. For example, Barry, et al. (2015) found that the reasons provided by students for not using mobile phone in classes included the need to pay attention in a given class and little time available particularly when the lesson was demanding.
We found that class engagement was significantly and negatively related to attitude (H7), descriptive norms (H9), and perceived behavioral control (H10), but not with prescriptive norms (H8). Hence, an engaging lesson reduced students’ tendency to cyberloaf. Not surprisingly, class engagement was found to be unrelated with prescriptive norms as the views of referent groups was not influenced by the level of students’ engagement in a lesson.
Many studies on students’ cyberloafing behavior focused on demographic variables such as gender, Internet skills, Internet usage and faculties to predict students’ cyberloafing behavior in the classroom (e.g., Baturay and Toker, 2015; Karaoğlan Yılmaz, et al., 2015). Moreover, those studies typically used samples from developed countries, predominantly in the United States and Turkey (Gerow, et al., 2010; Yasar and Yurdugul, 2013; Baturay and Toker, 2015; Taneja, et al., 2015; Karaoğlan Yılmaz, et al., 2015). Our study provides a more nuanced understanding of cyberloafing from the theoretical lens provided by the theory of planned behavior.
This study has several theoretical contributions to existing cyberloafing literature. First, it supports the theory of planned behavior to be an appropriate framework in explaining students’ cyberloafing behavior in the classroom; supported by high values of R2 for both intention and behavior. Second, our findings confirmed the appropriateness of including habit into the theory of planned behavior. Originally, both the theory of planned behavior and theory of reasoned action posit behavior is only predicted by intention. However, a number of studies have shown that habit also significantly predicts behavior. As such, we tested an extended version of TPB by including habit as a predictor of students’ cyberloafing behavior, as habit has not been empirically examined in the educational cyberloafing context. Our study demonstrated that habit was indeed a significant factor driving students to cyberloaf, a result that corroborates with extant research on habits (Vitak, et al., 2011; Moody and Siponen, 2013). Our third contribution to literature is extending the TPB model by finding habit to be an important predictor of cyberloafing apart from intention. Fourthly, our investigation conceptualizes subjective norms into prescriptive norms and descriptive norms; this reveals that prescriptive norms are significant to intention to cyberloaf, but not descriptive norms. Again, this is different from the context of cyberloafing at work. Fifth, this research also showed that, except for descriptive norms, attitude, prescriptive behavior control and perceived norms were significantly related to students’ intention to cyberloaf during lessons. Furthermore, class engagement had significant influence on attitude, descriptive norms and perceived behavioral control.
There are several practical implications stemming from these findings. This study confirms that the attitude of students is significantly related with their intention to cyberloaf. Some educators have advocated the banning of cyberloafing during classes. Banning the use of electronic devices during lessons is being implemented in many institutions. In the long term, this may be counterproductive because students can find other means to cyberloaf so long as they can hide the practice to cyberloaf. Also, because students’ positive attitude towards cyberloafing is unaffected, students upon entering the workforce will be tempted to cyberloaf. A more effective way to counter cyberloafing is to educate students on class ethics and the negative ramifications of cyberloafing in order to influence students’ attitudes towards cyberloafing. If students’ attitudes towards cyberloafing can be changed, then they will voluntarily refrain from cyberloafing in classrooms. Such an attitude would also counter Internet addiction and bodes well when these students enter the workforce and voluntarily refrain from cyberloafing at work.
The findings also revealed that students’ attitudes towards cyberloaf can be influenced by class engagement. A well-planned lecture coupled with interactive teaching techniques will reduce students’ tendencies to cyberloaf for two reasons; first, students will be more interested in the lesson being taught. Second, students will have less opportunity to cyberloaf because they want to pay attention and participate in class activities. In the study of Barry, et al. (2015), boredom was found to be the main reason for students to engage in cyberloafing behavior. Thus, higher levels of class engagement can reduce students’ tendencies to cyberloaf. Thus, in order to foster a productive classroom full of engaged students, educators need to encourage active participation among students as well as make the learning environment interesting.
Habit reflects an individual’s past behaviors and is shaped over time. Repeated cyberloafing behavior in the classroom can lead to habitual behavior, whereby the action is carried out without a need for conscious decision-making (LaRose, 2010). On the positive side, it is also an opportunity for instructors to facilitate the good habit of refraining from cyberloafing during lessons. Students who are aware of the negative ramifications of cyberloafing and voluntarily refrain from cyberloafing in classes will in time form good habits which they will bring with them to their workplace.
5.2. Limitations and future directions
This study has several limitations. First, data were collected at the same point of time; known as cross sectional data, resulting in a lack of power to draw causal inferences. However, this limitation was mitigated to some extent by the fact that TPB antecedents have been reported to be causal in other domains and that TPB-based interventions were successful in changing behaviors (Ajzen, 2006).
Second, self-reported questionnaires can be susceptible to common method variance which can potentially deflate or inflate relationships between constructs under investigation. Nonetheless, we conducted two statistical tests (i.e., Harman’s single-factor test and checking correlation matrix) that did not revealed any indications of serious common method variance. Future researchers are advised to develop a mobile application which has the function to track students’ Internet access (duration and types of cyberloafing activities) during classes.
Another limitation was related to sample data as we collected surveys only from students in a large, private university in Malaysia. Hence, it may not be appropriate to generalise results to the entire college students in Malaysia. Notwithstanding, this data collection method is commonly applied in this field of research (Baturay and Toker, 2015; Galluch and Thatcher, 2011; Karaoğlan Yılmaz, et al., 2015). Future researchers may want to consider collecting data from students from both private and public universities as well as running a multi-group analysis to identify any differences in data sets. In addition, another potential weakness of this study was that the cyberloafing scale used in this study only measured students’ general cyberloafing behavior instead of specific cyberloafing activities. Future studies should contemplate adapting the cyberloaf scale developed by Akbulut, et al. (2016) which covers most of the temporary cyber-activities performed by youngsters.
In conclusion, our findings suggested that the extended theory of planned behavior tested in this study is useful in explaining cyberloafing by students in the classroom. In addition to intention, habit was found to be a unique predictor of cyberloafing behavior in the classroom. Moreover, class engagement seems to be a very important factor affecting students’ attitude, descriptive norms and perceived behavioral control. The way that instructors conduct their classes plays a determining role in student behavior in the classroom. Perhaps, adopting a game-based learning approach in which students learn through playing video games might be an alternative teaching method that not only can impede students from engaging in cyberloafing but also increase class engagement (Huizenga, et al., 2009; Sabourin and Lester, 2014) and perceived learning (Hamari, et al., 2016). Taken together, this study provides useful insights on why students to cyberloaf during lessons and offer suggestions on how to counteract them.
About the authors
Patrick Chin-Hooi Sohis a senior lecturer at the Faculty of Management, Multimedia University, Malaysia. His research interests include Internet usage, addiction, electronic commerce and business. He received his Ph.D. in Internet usage from Multimedia University and a Master’s of Science in information systems at Malaysia University of Science and Technology.
E-mail: chsoh [at] mmu [dot] edu [dot] my
Kian Yeik Koay is a Ph.D. candidate at the School of Business, Monash University, Malaysia campus. His research interests are organizational behavior, service quality and branding.
E-mail: koaydarren [at] hotmail [dot] com
Vivien K.G. Lim is Professor and Deputy Head of the Department of Management and Organization, National University of Singapore (NUS) Business School. She obtained her Ph.D. in organisational behaviour from the University of Pittsburgh. Her research interests include workplace health, leadership, job loss, employee misbehaviours and discipline and the impact of technology on the workplace..
E-mail: : bizlimv [at] nus [dot] edu [dot] sg
This work was supported by a research grant from the Malaysian Higher Ministry of Education under the Fundamental Research Grant Scheme (FRGS) (Grant number: FRGS/2/2014/SS03/MMU/03/1).
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Received 18 April 2017; revised 3 February 2018; accepted 13 March 2018.
This paper is licensed under a Creative Commons Attribution 4.0 International License.
Understanding cyberloafing by students through the lens of an extended theory of planned behavior
by Patrick Chin-Hooi Soh, Kian Yeik Koay, and Vivien K.G. Lim.
First Monday, Volume 23, Number 6 - 4 June 2018