I’m not sure how time can move so quickly from the heat of summer to the heat of an election (on that topic see the thought experiment in the October TIP by Jessica Deselms, Lauren Bahls, Kristie Campana, and Daniel Sachau) to the halcyon clear, crisp days on autumn. Right now we’re having what we referred to in my consulting days as “recruiting weather”—just don’t tell them about August in Houston.

The “Bridge” column strives to further connect science and practice by publishing articles on the subject of science and practice integration (see Poteet, Zugec, & Wallace, 2016, for more background information). In this column, we profile Huntington National Bank, a recent winner of the HRM Impact Award. Sponsored by SIOP, the SIOP Foundation, the Society for Human Resource Management (SHRM), and the SHRM Foundation, the HRM Impact Award was designed to recognize, reward, and publicize “best available evidence regarding the usefulness and impact of successfully implemented innovative HRM initiatives” (HRM Impact Award, n.d.b, para. 1). To prepare this article, the aforementioned column editors communicated with two SIOP members involved in this project, William Shepherd (“Will”) and Robert Ployhart (“Rob”), to obtain insights on the challenges, lessons learned, and best practices in designing and implementing an evidence-based approach across industry and academics. We begin by providing a description of the project and its results then focus more specifically on the research collaboration between Will (a senior vice president of talent and organizational effectiveness at Huntington Bank at the time) and Rob (a professor at University of South Carolina).

We are delighted to be back writing The Modern App after a short break and welcoming its new coauthor: Evan Sinar! During the SIOP 2016 closing plenary, SIOP President Mort McPhail challenged I-O with a call to action: “Our science and its application has been shown to be consistently innovative: We need to focus the attention on scanning and communicating about the horizon and identify the roadblocks to our preparation.” We as I-O psychologists need to stay ahead of the trends that are redefining the way we generate high-impact research, provide evidence-based recommendations to internal and external clients, and engage and connect on social channels. Accordingly, our Modern App—short for the modern application of social media and technology in the workplace—vision and goals are to:

This issue, I’ll be exploring a concept you’ve undoubtedly heard of but probably don’t know much about: machine learning. To many I-O psychologists, the term machine learning describes some complex, unknown, and potentially unknowable “black box” analytic procedures. You often hear the term “data mining” thrown around in a dismissive fashion. But I hope after you read this article, you’ll realize that the term machine learning in fact refers to a variety of analytic techniques that are either identical to or extensions of techniques that most I-O psychologists already know. In fact, as you dig into machine learning, you start to realize that there is an entire parallel vocabulary to refer to many concepts that I-Os already use.

In recent years, there is increasing research interest in workaholism (Clark, Michel, Zhdanova, Pui, & Baltes, 2014). However, if one has read research articles on this topic (e.g., Aziz & Zickar, 2006; Fassel, 1990; Machlowitz, 1980; Mudrack & Naughton, 2001; Ng, Sorensen, Feldman, 2007; Oates, 1971; Porter, 1996; Robinson, 1998; Schaufeli, Taris, & van Rhenen, 2008; Scottl, Moore, & Miceli, 1997; Snir & Harpaz, 2012; Spence & Robbins, 1992; Sussman, 2012), it is not difficult to notice that there is lack of consensus in definition of workaholism (Clark et al., 2014). Because of the situation of multiple definitions for workaholism, conclusions can not be clearly drawn from different research, because there is no guarantee as to whether the differences among these research originate from differences between research per se, or just from different definitions that researchers adopted, where the latter means that basically researchers are measuring different things. For example, according to Ng et al. (2007), workaholics should have career satisfaction and success; however, this would not be the case according to Spence and Robbins (1992). If someone looks at the definitions of workaholism these researchers used in their research, it will be clear why they would not agree with each other. In the definition given by Ng et al. (2007), workaholics enjoy working, whereas, in the definition provided by Spence and Robbins (1992), they do not. This is, suffice it to say, troublesome.