Many empirical studies of employee engagement show positive relationships with desirable work‐related outcomes, yet a consistent understanding of the construct remains elusive (Saks & Gruman, 2014). We propose that this lack of clarity is leading to an increased risk that employee engagement is becoming overly generalized and that, as a consequence, its utility in both theory and practice is compromised. Indeed, our study of 472 information technology (IT) professionals working in community hospitals reveals that, even though the two measures of employee engagement examined in this study are conceptually based on Kahn's (1990) needs‐satisfaction framework, they are nomologically different, evidence different predictive properties (with regard to workplace stress and burnout), and suggest different workplace interventions. As hypothesized, both measures of employee engagement reveal negative relationships with workplace stress, and burnout has a mediating effect on those relationships. Further, the relationships are significantly different, but these differences are understood only when examining the dimensional levels of each engagement measure. Our findings also clarify the highly debated relationship between employee engagement and burnout and challenge those engagement measures that are conceptually grounded in a burnout‐antithesis framework. Implications and avenues for future research are presented.

This paper presents best practices for conducting survey research using Amazon Mechanical Turk (MTurk). Readers will learn the benefits, limitations, and trade-offs of using MTurk as compared to other recruitment services, including SurveyMonkey and Qualtrics. A synthesis of survey design guidelines along with a sample survey are presented to help researchers collect the best quality data. Techniques, including SPSS and R syntax, are provided that demonstrate how users can clean resulting data and identify valid responses for which workers could be paid.

This paper presents best practices for conducting survey research using Amazon Mechanical Turk (MTurk). Readers will learn the benefits, limitations, and trade-offs of using MTurk as compared to other recruitment services, including SurveyMonkey and Qualtrics. A synthesis of survey design guidelines along with a sample survey are presented to help researchers collect the best quality data. Techniques, including SPSS and R syntax, are provided that demonstrate how users can clean resulting data and identify valid responses for which workers could be paid.