Purpose Following the call for awareness of accepted reporting practices by Ringle, Sarstedt, and Straub in 2012, the purpose of this paper is to review and analyze the use of partial least squares structural equation modeling (PLS-SEM) in Industrial Management & Data Systems (IMDS) and extend MIS Quarterly (MISQ) applications to include the period 2012-2014. Design/methodology/approach Review of PLS-SEM applications in information systems (IS) studies published in IMDS and MISQ for the period 2010-2014 identifying a total of 57 articles reporting the use of or commenting on PLS-SEM. Findings The results indicate an increased maturity of the IS field in using PLS-SEM for model complexity and formative measures and not just small sample sizes and non-normal data. Research limitations/implications Findings demonstrate the continued use and acceptance of PLS-SEM as an accepted research method within IS. PLS-SEM is discussed as the preferred SEM method when the research objective is prediction. Practical implications This update on PLS-SEM use and recent developments will help authors to better understand and apply the method. Researchers are encouraged to engage in complete reporting procedures. Originality/value Applications of PLS-SEM for exploratory research and theory development are increasing. IS scholars should continue to exercise sound practice by reporting reasons for using PLS-SEM and recognizing its wider applicability for research. Recommended reporting guidelines following Ringle et al. (2012) and Gefen et al. (2011) are included. Several important methodological updates are included as well.

We investigated the role automated behavior plays in contributing to security breaches. Using different forms of phishing, combined with multiple neurophysiological tools, we were able to more fully understand the approaches participants took when they engaged with a phishing campaign. The four participants of this pilot study ranged in their individual characteristics of gender and IT experience while controlling for age. It seems the biggest factor for awareness and successfully resisting a phishing campaign may be proximity of security training to engagement with that campaign. Neurophysiological tools helped illustrate the thought processes behind participants' statements and actions; combined with consideration of individual characteristics, these tools help shed more light on human behavior. In the future, we plan to further enhance our testing environment by incorporating an emergent model that considers work task complexity and incorporate more industry participants with a range of IT experience.

With the proliferation of mobile device types and variety of tasks being performed on those devices, it is necessary to examine how this pairing changes with individuals. NeuroIS offers complementary tools to traditional survey tools helping researchers delve into users' perceptions while they are engaged in different tasks. Through analysis of neurophysiological data we may better understand activities performed on mobile devices and help provide more customized user experiences. A two-part preliminary study is described as a pre-cursor to a larger, focused experiment utilizing EEG and eye-tracking on mobile device usage.