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).

本研究数据采集自孟加拉国两所不同高校的106名受访者，测量变量涵盖低自尊、私人需求、网络摸鱼行为（cyberslacking）以及系统滥用意图。为探究各变量间的关联，研究改编了已有权威文献中的量表条目：低自尊量表条目改编自Chen等人（2008）的研究，私人需求量表条目改编自Koay等人（2017）的研究，相关规则与规范改编自Vahdati与Yasini（2015）的研究。

网络摸鱼行为（cyberslacking）的界定源自Vitak等人（2011）的研究，采用二分类变量进行测量："0"代表受访者在工作期间未实施所询问的行为，"1"代表受访者在工作期间实施了对应行为。所询问的9项行为分别为：发送电子邮件、即时消息（Instant Messages, IM）、短信、访问社交网站（Social Networking Sites, SNS）、观看视频、撰写博客、阅读博客、游玩电子游戏以及购物。

研究纳入基于情景的调查问卷以探究互联网滥用意图，设定情景如下："你供职于一家IT企业，过去五年间负责管理多个项目。你始终对工作尽职尽责，但遗憾的是并未因各项工作成果获得认可。过去两年间，你的薪资完全没有上调。与此同时，你的竞争对手虽未取得亮眼业绩，却总能获得赞誉，其薪资也逐年递增。突如其来的是，你的竞争对手获得晋升并担任更高职位，而你却未能获得同等机会。这些处境令你倍感屈辱。你还听闻一则谣言，称公司可能很快将解雇你。若你愿意，便可凭借自身拥有的极高权限对公司实施报复。根据过往经验，你知晓可以轻松访问公司的多份机密文件/资料，且公司的安全管控状况不佳。你掌握公司的全部机密信息，同时也清楚如何在不留下任何痕迹的前提下销毁这些信息。"

系统滥用意图采用Kim等人（2016）开发的量表进行测量，该量表包含3个条目，采用7点李克特量表（Likert scale）进行评分，选项从"完全不同意"到"完全同意"。所纳入的条目分别为："我曾打算滥用公司系统"、"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 v.21统计软件对人口统计学问卷的频数进行计算，并参考Mahmud等人（2017）与Toma等人（2018）的研究方法，采用SmartPLS 3.0对研究假设（各变量间的关联关系）进行检验。