Recent work, in journals such as MIS Quarterly and Management Science, has highlighted the importance of evaluating the influence of common method bias (CMB) on the results of statistical analysis. In this research note, we assess the utility of the unmeasured latent method construct (ULMC) approach in partial least squares (PLS), introduced by Liang et al. (2007). Such an assessment of the ULMC approach is important, because it has been employed in 76 studies since it appeared in MIS Quarterly in early 2007. Using data generated via Monte Carlo simulations, we use PLS structural equation modeling (SEM) to demonstrate that the ULMC approach of Liang et al. is neither able to detect, nor control for, common method bias. Method estimates using this approach resulted in negligible estimates, regardless of whether there were some, large, or no method bias introduced in the simulated data. Our study contributes to the IS and research methods literature by illustrating that, and explaining why the ULMC approach does not accurately detect common method bias in PLS. Further, our results build on prior work done using covariance-based SEM questioning the usefulness of the ULMC technique for detecting CMB.

As the highest level of cloud computing delivery model, Software-as-a-Service (SaaS) has gained considerable popularity in the industry as a new way of deploying IT solutions, due to its low cost and high elasticity. However, the new business model associated with SaaS highlights the importance for SaaS vendors to understand how to retain customers in a hyper-competitive market. In particular, increasing customer retention and preventing customers from replacing the adopted SaaS applications has become a crucial task for all SaaS vendors. In this study, using a mixed-methods approach, and drawing on the cognitive–affective–conative– action (CACA) framework, we investigate the IS replacement phenomenon in the context of SaaS-delivered applications. Our qualitative study allows us to develop an IS-centric view of customer commitment by differentiating between organizations’ commitment to the SaaS application and to the cloud computing technology in general, while the subsequent quantitative study validates the difference between the two types of commitment and helps understand how they together influence organizations’ intentions to replace a SaaS application. Our results generate important theoretical implications for research on IS replacement and clarifies the concept of customer commitment. We also offer practical guidelines to SaaS vendors on how to retain customers so as to survive/thrive in this competitive market.

Phishing is a major threat to individuals and organizations. Along with billions of dollars lost annually, phishing attacks have led to significant data breaches, loss of corporate secrets, and espionage. Despite the significant threat, potential phishing targets have little theoretical or practical guidance on which phishing tactics are most dangerous and require heightened caution. The current study extends persuasion and motivation theory to postulate why certain influence techniques are especially dangerous when used in phishing attacks. We evaluated our hypotheses using a large field experiment that involved sending phishing messages to more than 2,600 participants. Results indicated a disparity in levels of danger presented by different influence techniques used in phishing attacks. Specifically, participants were less vulnerable to phishing influence techniques that relied on fictitious prior shared experience and were more vulnerable to techniques offering a high level of self-determination. By extending persuasion and motivation theory to explain the relative efficacy of phishers' influence techniques, this work clarifies significant vulnerabilities and lays the foundation for individuals and organizations to combat phishing through awareness and training efforts.