Full length article In search of a measure to investigate cyberloafing in educational settings Yavuz Akbulut a, * , € Ozcan € Ozgür Dursun a , Onur D € onmez b , Yusuf Levent S ¸ ahin a a Department of Computer Education & Instructional Technology, Faculty of Education, Anadolu University, Yunusemre Campus, 26470, Eskisehir, Turkey b Department of Computer Education & Instructional Technology, Faculty of Education, Ege University, _ Izmir, Turkey article info Article history: Received 5 June 2015 Received in revised form 14 October 2015 Accepted 3 November 2015 Available online xxx Keywords: Cyberloafing Cyberslacking Scale development Media in education abstract Cyberloafing is among the problematic tech-trends in contemporary work-based and educational set- tings. The current study administered an existing three-factor scale to three samples. The factor structure was not confirmed among high school teachers (n: 33), high school students (n: 479) and un- dergraduates (n: 86). A new and more comprehensive scale to address contemporary cyberloafing be- haviors during lectures was developed through literature review, expert panels and observations. Data from undergraduate students (n: 471) were used for construct validation with an exploratory factor analysis (EFA), which revealed a five-factor structure and explained 70.44% of the total variance. Factors were sharing, shopping, real-time updating, accessing online content and gaming/gambling. The scale was administered to another undergraduate student sample (n: 215) and a social networker student group (n: 515). The structure was validated in these new samples through confirmatory factor analyses (CFA). The scale and current findings are expected to facilitate further cyberloafing research in educa- tional settings. © 2015 Elsevier Ltd. All rights reserved. 1. Introduction Proliferation of internet technologies brought about many socio-psychological phenomena such as technology anxiety, Internet addiction and cyberbullying. Among these phenomena, intentional use of Internet access for personal purposes during work or lectures has become an issue of concern. Referred to as cyberslacking (Block, 2001; Greengard, 2000) or cyberloafing (Lim, 2002; Polito, 1997), this counterproductive use is one of the most common ways employees waste time at work (Weatherbee, 2010). Moreover, the density of cyberloafing is expected to trend upward due to constant advances in online connectivity opportunities and increasing availability of high-tech mobile devices. Prevalence and predictors of cyberloafing in work-based set- tings have been documented well with empirical studies (Andreassen, Torsheim, & Pallesen, 2014; Garrett & Danziger, 2008; Sheikh, Atashgah, & Adibzadegan, 2015; Vitak, Crouse, & LaRose, 2011). While some scholars considered it as a counterproductive act which can cause economic loss (Block, 2001; Greengard, 2000) and reduced system performance due to excessive use of band- width (Sipior & Ward, 2002), others addressed its restorative and pleasurable consequences as well (Lim & Chen, 2009; Mastrangelo, Everton, & Jolton, 2006; Page, 2015). Recent work further investi- gated countermeasures to address cyberloafing such as blocking websites in the black list, providing reminder mechanisms to reduce misuse (Glassman, Prosch, & Shao, 2015), employing secu- rity systems to monitor Internet activity or enforcement of sanc- tions on those who caught cyberloafing (Ugrin & Pearson, 2013). Though the issue has been primarily investigated in work-based settings, cyberloafing is catching attention from the field of edu- cation owing to massive technology integration investments and students' increasing access to digital technologies. Nevertheless, cyberloafing studies in educational settings are relatively novel. Online searches through relevant terms (i.e., cyberloafing, cyber- slacking) reveal only a few studies in educational settings where university teachers (Zoghbi-Manrique-de-Lara, 2012), classroom teachers (McBride, Milligan, & Nichols, 2013), or in-service teacher training students (Page, 2015) are taken into consideration. That is, work-based settings are again the primary source of empirical data as observed in the previous literature. Recent studies began to evaluate the non-academic technology * Corresponding author. E-mail addresses: yavuzakbulut@anadolu.edu.tr (Y. Akbulut), oodursun@ anadolu.edu.tr ( € O. € O. Dursun), onur.donmez@ege.edu.tr (O. D€ onmez), ylsahin@ anadolu.edu.tr (Y.L. S ¸ ahin). Contents lists available at ScienceDirect Computers in Human Behavior journal homepage: www.elsevier.com/locate/comphumbeh http://dx.doi.org/10.1016/j.chb.2015.11.002 0747-5632/© 2015 Elsevier Ltd. All rights reserved. Computers in Human Behavior 55 (2016) 616e625