Cyberslacking and Internet Abuse Intention Data Set

Data was collected from 106 respondents from two different universities in Bangladesh. The items measured low self-esteem, private demand, cyberslacking behavior and abusive intention. To measure the relationship with the variables, we adapted the items from established literature. Items for low self-esteem was adopted from the research of Chen et al. (2008), items for private demand was adopted from the work of Koay et al. (2017), rules and regulations were adopted from the research of Vahdati and Yasini, (2015). Cyberslacking was identified from (Vitak et al., 2011) and measured it using binary variables where “0” means respondents did not do the questioned tasks at work and on the other hand “1” means respondents did the questioned tasks while they at work. The questioned nine items are “sending emails”, IM (Instant messages)”, “Texts”, “Visiting a SNS (Social Networking Sites)”, “Watching video”, ”Writing blogs”, “Reading blogs”, ”Playing video games” and “Shopping”. We included a scenario based questionnaire to investigate the internet abuse intention. The scenario was “You work for an IT farm; and for last five years, you managed several projects. You are always dedicated to your work but unfortunately you are not appreciated with all of your successful aspects. Your salary increment is totally off for last two years. On the other hand, your rival is always credited without doing successful perspectives as well as his/her salary increment is going on day by day. All on a sudden, your rival is promoted for higher designation but you are not. These situations are humiliating for you. You also hear a rumor that the farm may be fired you soon. If you want, then you may take revenge against the company because of your high liberty. From your past experience, you know that you can access several confidential files/documents easily. The security control situation at the company is poor. You know all of the confidential information of your company and also know how to destroy all of those information without leaving any evidence”. Abuse intention was measured using the scale from (Kim et al., 2016) consisting of three items with response options varying from strongly disagree to strongly agree in the seven point Likert scale format. The included items are “I intended to abuse the systems”, “I predict I will abuse the systems” and “I plan to abuse the systems”. We used SPSS v.21 to calculate the frequency of demographic questionnaire. SmartPLS 3.0 was used to test the hypotheses (relationship between the variables) by following the research of Mahmud et al. (2017) and Toma et al. (2018).