Unpacking the Top 10 I/O Psychology Trends for 2014
Each month, the Society for Industrial & Organizational Psychology releases a newsletter describing current events in I/O Psychology. In the February issue, I noticed a brief article on an intriguing little study conducted by the SIOP Media Subcommittee, part of the SIOP Visibility Committee. Via the SIOP newsletter, the SIOP website, and social media, the committee asked I/O psychologists what they considered to be the top workplace trends for 2014. Since most of the trends they identified are within my research area, I thought I’d explore my take on this what these items mean and how much progress I/O psychology has made so far in exploring them. Here’s the list of trends, in order from hot to sizzling.
- Alternatives to Full Time Work. Temporary, part time, and contractor work is becoming an increasingly common model for modern businesses. As employees shift to part-time work, do their motivations for performing “good work” change? I/O certainly has models of job performance and its predictors, but most of the empirical work on these constructs was developed and tested on permanent positions. To what extent do these findings hold with temporary workers? Of all areas of I/O, I expect discussion of culture and climate to turn to this problem first.
- Telework. As technology improves, it is becoming more obvious that completing the technical requirements of many jobs does not require workers to be physically present in the office. So why not let them work from home, saving money on office space and other resources? The research literature on virtual teams has grown quite a bit in the last few years, but our understanding of completely remote workers is still quite limited. Much to be done here!
- Social Media for Employment-related Decisions. Social media is one of my personal areas of interest, in terms of both organizational learning and employee selection (evidenced by my lab’s work on social media in selection at SIOP this year). On both fronts, we know very little so far. It seems like we can get some degree of job-relevant information from social media, but it remains unclear what the best way to go about this is, or what the legality of that information ultimately is. Should we trust hiring managers to ignore information about protected class membership (sex, race, national origin, skin color, religion, disability, and others) when scanning social media for information about job applicants? Even if we trust hiring managers to ignore this information, would the courts believe us? I suspect not.
- Work-Life Balance. Of the list of top trends, this is probably the most well-explored on the list within I/O. Work-family conflict (and family-work conflict) are both linked with a variety of negative outcomes for workers. But as technology becomes omnipresent in people’s lives, the line between work and home continues to blur. For many (like myself!), there essentially is no line. With Twitter, our private lives and public lives are often the same. What effect does this have on well-being and job performance? Will people burn out faster than ever before?
- Integration of Technology into the Workplace. Another of my research areas. Employees are now able to reach out via social media to others in their organizations when they need help – via email, via instant messaging, via teleconference – but are more tempted than ever by the siren song of easy, quick social interaction. How can social media be leveraged within organizations to bring the potential benefits without the drawbacks? We don’t know yet – and that’s what I aim to learn. Technology is increasingly being used to monitor employees in ways not even conceivable 10 years ago – including tracking specific employee movements throughout the workday. What impact does all this dehumanization have upon productivity and retention?
- Gamification. Another research area! Gamification has taken the business world by storm, with gamification “gurus” and “experts” rising up all over the place. The dirty truth, of course, is that no one yet really know what makes it work. Nearly zero empirical research is available to explore these effects, although there are three at SIOP 2014 (two of which are from my lab). The big problem is that many gamification efforts do fail – and fail badly – with a wide variety of unintended negative consequences. Does gamification need to crash before people use it selectively and carefully, when there is a clear reason to do so? I hope the crash can be avoided, but it looks like we’re headed that direction.
- New Ways to Test. Yet another research area, with more SIOP presentations! Many people want to complete assessments on whatever device they have handy. That means tablets and mobile phones. The problem with BYOD (bring-your-own-device) is that your assessment needs to be reliable and valid on all of them – and this is notoriously difficult to test because of the sheer variety of such devices. Even desktop and laptop computers vary a bit in how they present the web – but this variation is nothing compared to the incredible range of mobile device interfaces (from 2″ to 7″ screens!). Fortunately, early research indicates that mobile and traditional devices don’t differ much in terms of reliability and validity. But more work is needed.
- The Talent Question. Now that organizations compete for talent on the Internet (and thus on a global level), how do you identify and attract the best of the best? If Google and Apple can snap up all the best IT talent from every school in the world, where does that leave everyone else? And as specialized skills that require deep levels of training become more common needs for organizations, how can organizations find these people at all? Unfortunately, online recruiting is probably the oldest yet understudied area on this list.
- Increasing Efficiency. The traditional I/O approach to helping organizations run lean has been to improve selection systems (you don’t need as many people if they are better people), improving training (if people know their jobs well, you don’t need as many backups) and to solve any specific personnel problems (troublemakers harming group performance, etc.). But post-recession, many organizations are already running lean – and need to do even more. At what point is there nowhere left to go? When is the only solution remaining to hire more personnel?
- Big Data. #1 on the list is Big Data. This is a bit of a hard concept for I/O’s I’ve talked to to deal with, because many believe we already “do” Big Data. “But I collected a 500-person validation study! That’s the sort of skill Big Data requires!” they say. It isn’t. I’ve only worked on one Big Data project myself – a 13.5 million case dataset. SPSS would not even open it, and that’s not even all that “big” in terms of Big Data, which starts at the point where traditional data analysis programs can’t handle the analyses you want to run. Big Data is not synonymous with data mining, but that’s mostly how it is used for because the people with data mining skills happen to be the people with the ability to process these kinds of data sets – computer and information scientists. I/O could theoretically do a lot of good here with its focus on rigorous construct development, but graduate training (in both I/O and management) is not generally sufficiently grounded in computer programming to get us there just yet. Big Data analysis is not done by clicking buttons in a statistics program. It is done by writing algorithms to process massive data sources, and letting those programs run for days at a time. The closest we get are Monte Carlo simulations. I teach computer programming to our graduate students, but I’m an outlier – it is not (yet) a common skill in I/O. If we want to be on top of Big Data – and of this list, I suspect this is the trend that will stick around the longest – this needs to change.
There you have it. Technology, and the impact of technology, is what current I/O’s are worrying about. It’s a lot of change very quickly, but that just means it’s an especially exciting time to be an I/O psychologist!
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Hey Richard,
Great post. It makes me want to collect big data with cell phone games to select organizational talent from home. 🙂
I think the link on #8 – Social Media for Employment-Related Decisions is wrong – it goes to the RFID page.
Whoops! Copy-paste mistake. Thanks, fixed now.
I completely agree with number 1. I wrote an article for TIP’s recent January edition on big data where I attempted to establish a common definition for I-Os and then provide different ways that I-O could become more involved. Programming may not be essential for all I-Os but I think its definitely useful. Out of curiosity, what language do you teach? It appears that its usually R or Python. I prefer Python, but see utility in knowing both.
Well, the course isn’t on data science, but instead on programming more broadly. My goal is give students a broad understanding of programming languages in general – after seeing the similarities between a few, it is much easier to pick up a new one, after all. We actually cover all languages needed to operate a website with server-side data access. So we’re using PHP (which permits multiple programming paradigms like Python, but with weak data typing and a curly-brace structure), SQL (for database administration), and a little JavaScript (for UX). That enables us to start with an imperative programming approach and transition into object-oriented (which I think makes a lot more sense than the opposite direction). Between Python and R, I would probably go with Python these days, just because it is a bit more general purpose.
Another added benefit of starting with web stack skills is that it allows one to produce something tangible and shareable without having to deal with GUIs and operating systems.
But I think its great that you offer a programming course, and I look forward to seeing your labs presentations at SIOP.