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).本数据集的调研数据源自孟加拉国两所不同高校的106名受访者。调研问卷涵盖低自尊、怠工式上网（cyberslacking）行为、私人需求以及恶意使用意图四个维度的测量条目。为验证各变量间的关联，本研究的测量条目均改编自已发表的权威文献：低自尊维度的条目改编自Chen等人（2008）的研究，私人需求维度的条目改编自Koay等人（2017）的成果，相关规则与规制类条目则改编自Vahdati与Yasini（2015）的研究。怠工式上网（cyberslacking）行为的测量框架源自Vitak等人（2011）的研究，采用二分类变量进行量化：其中"0"代表受访者在工作时段未开展指定任务，"1"代表受访者在工作时段完成了指定任务。本次调研指定的9项任务包括：发送电子邮件、即时通讯（IM，Instant Messaging）、发送短信、访问社交网站（SNS，Social Networking Sites）、观看视频、撰写博客、阅读博客、游玩电子游戏以及网购。本研究还设置了基于场景的问卷以调研互联网恶意使用意图，设定的场景为："你就职于一家IT公司，过去五年间主导过多个项目。你始终恪尽职守，但遗憾的是，你所有的工作成果均未获得相应认可。过去两年间，你完全没有获得薪资涨幅。与之相对，你的竞争对手从未取得过亮眼业绩，却总能获得嘉奖，薪资也逐年增长。就在近日，你的竞争对手被晋升至更高职级，而你却并未获得晋升，这些境遇令你倍感屈辱。此外，你还听闻公司即将裁员的谣言。若你愿意，你可凭借自身权限对公司展开报复。根据过往经验，你可轻松访问公司的多份机密文件/文档；公司的安全管控机制极为薄弱，你掌握着公司所有的机密信息，且清楚如何在不留下任何痕迹的前提下销毁这些信息"。恶意使用意图的测量量表改编自Kim等人（2016）的研究，共包含3个条目，采用7点李克特量表进行评分，选项从"非常不同意"至"非常同意"不等，具体条目为："我曾有过滥用公司系统的意图"、"我预判自己将会滥用公司系统"以及"我计划滥用公司系统"。本研究使用SPSS 21.0版本统计人口统计学问卷的频次信息，并参考Mahmud等人（2017）与Toma等人（2018）的研究范式，使用SmartPLS 3.0版本对研究假设（即各变量间的关联）进行检验。