Is the Need of the Hour
Tilman L. Sheets
Louisiana Tech University
Bharati B. Belwalkar
Department of Civil Service, City of New Orleans
Author note:This submission was presented as an Alternate Type Session at the 2016 SIOP Annual Conference in Anaheim, CA. We decided to submit this to The Industrial Psychologist (TIP) after receiving positive feedback from the attendees, especially I-O graduate students.
We would like to thank Jason Marks (researcher at Amazon.com, Inc.) and Sonali Karnik (postdoc research scholar at Illinois Institute of Technology) for their reviews and feedback on the manuscript.
As we begin this article, we cannot help remembering a specific scene from the movie “Hidden Figures” when Dorothy Vaughan walks back from the IBM room to her desk. When the new electronic systems are brought in to replace NASA’s human computers, Dorothy is the first one to recognize the threat; she begins to learn and subsequently trains her staff in FORTRAN programming and coding. This is perhaps an allegory of the role of technology in jobs today. The advent of technology has changed the landscape of the modern workplace, and it will continue to do so! In this article, we have outlined how technology and I-O interact and what that means for I-O graduate students. We are building a case for technology-related training to develop well-rounded I-O professionals.
Every organizational operation (e.g., selection, training, performance management) has undergone a significant revolution due to changes in technology-enabled systems (Ensher, Nielson, & Grant-Vallone, 2003). It is no surprise that technology has consistently been listed on SIOP’s Top 10 Workplace Trends. Although it has dropped off this year’s list, it is certainly a part of other trends (e.g., #3 Big Data and #4 People Analytics). For those who would like to know more about how it all began, we recommend you read Craiger’s (1997) wonderful review of how technology in organizations evolved in the 20th century.
Role of Technology in Organizational Operations
Technology is changing I‑O practices in a number of ways (Harris & Hollman, 2013). As an example, take recruitment, which has become more targeted and strategic in nature (Madia, 2011). A short time ago, the use of various e-recruitment systems and job boards exploded, creating a global platform for job advertisements. Web 2.0 came next, followed shortly by social media and now mobile apps, placing new imperatives on targeted recruiting. Additionally, organizations have been using social media to recruit for over a decade (Singh & Finn, 2003).
In the area of personnel selection, we have been introduced to more “gamified” assessments, virtual interviewing platforms, and interactive simulations (Fetzer & Tuzinski, 2014; Tonidandel, Quiñones, & Adams, 2002). The popularity of these selection tools is growing due to their higher fidelity, as well as their higher validities in relation to many other robust selection methods (Lievens & Patterson, 2011; Thornton & Cleveland, 1990; Tsacoumis, 2007). Big players in I-O consulting (e.g., DDI, and SHAKER) have developed unique technology-enabled hiring solutions for their clientele.
In training and development, web-based, virtual training and assessment centers are gaining popularity (Harris & Hollman, 2013). Organizations are making use of intelligent computer-based tutoring systems, simulator-based performance measurement software, mobile tools for capturing real-time observer metrics, and multimodal systems that unobtrusively capture teamwork and infer team states (such as cohesion, trust, and shared understanding). Check out Moonbase, NASA’s simulation game that teaches soft-skills to flight controllers and new astronauts.
Technology and I-O Psychology
Needless to say, many of today’s I-O practitioners are required to work with information technology (IT) professionals on a regular basis. For instance, I‑O practitioners working for consulting firms that offer computer adaptive, high-fidelity assessments to their clients collaborate with IT personnel to develop the digital requirements for such platforms. Likewise, I-O practitioners working in the area of training and development frequently collaborate with IT personnel in the design and development of their technology-enabled training solutions. Taking things a giant step further, there is growing interest in using Automated Item Generation (AIG) to design computer software that builds test items on the fly (Doebler & Holling, 2015). While I‑O practitioners who develop such tests are required to have knowledge of IRT software, they may also need to communicate with IT folks during the data acquisition, storage, and retrieval processes. Similarly, with increasing momentum of big‑data for predictive modeling (Putka & Oswald, 2015), many I-O practitioners are required to use specialized analytics software for large datasets (e.g., SAS Enterprise Miner, SAS Enterprise Miner, Oracle Data Miner, Oracle R Advanced Analytics for Hadoop).
All of this growth in technology means that I‑O practitioners are in need of more technology-specific KSAs. These KSAs range from basic word processing to more sophisticated technology in areas like multimedia development (e.g., Final Cut, Adobe Premiere, Prezi), simulation design (ITyStudio, iSpring TalkMaster), data storage and retrieval skills (e.g., MySQL, Hadoop, Hive, Mongo DB), and software development processes (e.g., Agile, Waterfall). Entry-level I-O job postings frequently include intermediate to advanced knowledge/experience with analytical software (e.g., SPSS, SAS), human resource software (e.g., HRIS, ATS), database software and query language (e.g., SQL, Access), and others. In our experience, it is generally easier to learn these technologies on the job if one has had some training and/or hands-on experience with them during graduate school. So, we are curious, are I‑O graduate schools adequately preparing their students to tackle these challenges head on?
Technology-Related Training in I-O Psychology
A fairly recent article in TIP, Reinecke and Toaddy (2016), offered a glimpse of the future of work and the changing role of I-O psychology. What resonated with us was their plea to adjust skillsets to proactively accommodate the changing responsibilities. We speculate that the new breed of I‑O practitioners will be better prepared to handle these changes if they are given adequate technology-related training during graduate school. The recently developed and published Guidelines for Education and Training in Industrial-Organizational Psychology (SIOP, 2016) sporadically mention technology-related skills, which we believe do not do justice to the importance of this issue.
To gain some perspective on the issue, we solicited a fairly diverse panel of four individuals representing early, mid, and late career stages in I-O research or practice. Our panel was comprised of SIOP members Milt Hakel, Zack Horn (StitchFix), Meisha-Ann Martin (Flextronics), and Luke Simmering (Legasus Group). As the senior panelist, Milt provided us with an account of the evolution of technology in the I-O arena, whereas one of our younger panelists, Luke, presented his perspective on how technology has become ingrained in various spheres of I-O work, especially personnel selection. The other two panelists, Meisha-Ann and Zack, offered valuable advice on how to make the most of grad‑school experience in order to prepare oneself.
Disclaimer: We do not claim to offer you any novel information as discussions surrounding the role of technology in I-O psychology have been going on for decades. Our sole aim is to offer you food-for-thought on technology training in I-O psychology.
Sharing Points of Views
As someone who retrospectively realizes that his decision to avoid computer programming (i.e., FORTRAN) during graduate school was based on convenience, but carried large opportunity costs, Milt solemnly declared that technology has become an integral part of his work. Having served as an academician most of his career, Milt thinks that research design and quantitative analysis have been the cornerstones of I-O psychology programs, and that they still are. What has changed in the world of I-O psychology is the sheer quantity of information to be mastered and the rate at which that quantity is growing, subsequently changing the straight-out-of-school KSAs needed. Milt adds that I-O psychology has entered the era of big data and ubiquitous computing, and that this necessitates the need for technology training for I-O graduate students and professionals.
Indeed, there has been a recent push towards big data, adds Zack. But organizations now want to collect not only “big” data, but “better” data. Remember the three Vs of big data – volume, variety, and velocity? Now, add another V – veracity! Zack believes, that’s where I-O comes into picture. His current role involves working with a team of data scientists to accomplish this goal. To him, succeeding in this role would have been difficult without his curiosity and sheer affection for technology. Familiarity with technology, therefore, has been critical. Zack believes that marrying technological capabilities with I‑O expertise can create new possibilities for more intelligent, unobtrusive, and valid training and assessment techniques, and that this is all the more reason to introduce technology training in I-O psychology graduate programs.
Viewing the role of technology from a selection and assessment perspective, Luke (another tech-enthusiast) mentions that knowledge of best practices (e.g., test development, validation, data analysis) is a must-have. However, the expectation to understand the technological delivery of the assessments is becoming more and more important as conventional paper-pencil employment testing is becoming less common. Consequently, technology has changed the work of external consultants due to the diversity in tools, technological processes, and client expectations. As a consultant in a SaaS-based company, Luke constantly explored ways to improve his competency around technology.
When asked about the role of technology in I-O, Meisha-Ann recollected some of her experiences in her first post-graduate I-O role. Her role involved utilizing human-capital metrics and analytics to improve people and firm performance. She believes that what helped her to evolve, survive, and thrive in the new environment – an ability to understand and to easily navigate between technology and I-O psychology – may help young graduates too. Nodding in agreement, Luke adds that such tech-savviness enables I‑O psychologists to innovate beyond the boundaries of their own discipline, contributing successfully as a part of multidisciplinary teams.
On the topic of multidisciplinary teams, all three practitioners on the panel (Meisha-Ann, Zack, and Luke) seemed to agree that applied I-O often involves either working with or managing such teams. Depending on the scope of any particular project, these multidisciplinary teams may involve different combinations of I‑O psychologists, sales/marketing managers, cognitive scientists, HTML developers, implementation consultants, computational linguists, mathematicians, software engineers, use-experience specialists, user‑interaction designers, and graphic designers, to name a few.
Our panelists acknowledge that the complexity of the within-team mix of KSAs and the external/internal client’s expectations about deliverables add up to a steep learning curve, especially when any facet of technology is part of the big picture.
Developing Technology KSAs
If you are still reading this article, chances are you are pondering where to start and how to gain the technology-related knowledge and skill sets. Our panelists, Milt, Zack, Meisha-Ann, and Luke, have offered some valuable advice here:
- “The surest way to learn a foreign language is to speak it,” said Milt, advising students to get hands-on applied project or internship experience to learn technology-related skills.
- In addition to knowing the basics of I-O and mastering the craft, Meisha-Ann advises students to cultivate intellectual curiosity and develop an open mind. Learn from data scientists from other fields.
- It may be helpful to take an IT course, says Luke. During the SIOP 2016 session, he shared with attendees his “technology for I-O” syllabus.
- Zack seconded Luke’s opinion; he, in fact, took college courses in web design and coding. Upon joining the data science team at Stitch Fix, he learned SQL and took a course in R on datacamp.com (which he highly recommends).
In addition to the advice from our four panelists, here are some more tips. Some of these tips have tremendously helped graduate students (which include one of us) in the past:
- During the writing of this article, we noticed that Richard Landers has begun writing a TIP column called Crash Course in I-O Technology. Having read his first article on R, we certainly think it is a good starting point!
- Read technical books and magazines. Although buying them can be expensive for a graduate student (sigh!), look for older editions or free copies (e.g., Amazon 1¢ plus $3.99 shipping).
- Use technology to learn technology. In other words, the internet is a great source of information. Check out websites like Coursera, edX, and Codeacademy for free IT courses.
- Seek a mentor with expertise in both I-O and technology.
- Familiarize yourself with IT-specific jargon; make conscious efforts to build your IT vocabulary. This is likely to happen naturally as you learn.
- Once you have acquired technology-related KSAs, deliberate practice is necessary to master them. Practice programming, writing codes (R and Python have great online support), building websites (learn a little HTML or PHP), using different analysis or data visualization software (Tableau is a popular choice if you’ve already mastered Excel).
Remember what Milt said, “The surest way to learn a foreign language is to speak it,” and, we add to it by saying “learn to speak it well by practicing a lot.”
Job seekers (especially fresh out-of-school candidates) are often asked: “What do you bring to the table?” We believe that unique technological skills, in addition to the required I-O-specific KSAs, will provide I-O job-seekers with a competitive advantage. If anything, technology-related KSAs probably won’t hurt your chances.
We would like all of you, especially graduate students and I-O educators, to give this topic some serious thought. Additionally, we would like SIOP to consider I-O technology in its Education and Training Guidelines.
Note. Please take this short survey to help us with our initiative(s) on technology training in I-O psychology. Additionally, if you feel strongly about this issue and would like to share your thoughts, please email us: email@example.com or firstname.lastname@example.org.
1 During conversations with one of us, Sonali (one of the friendly reviewers) remarked that schools cannot possibly prepare students for everything. Sometimes, students have to proactively learn and develop new skills. We thought that her point of view is worth mentioning although it is different from that of ours.
Craiger, J. (1997). Technology, organizations and work in the 20th century. Industrial and Organizational Psychologist, 36(3), 89-97. Retrieved from http://www.siop.org/Museum/TIP/Craiger%20(1997)%20technology.pdf
Doebler, A., & Holling, H. (2015). A processing speed test based on rule-based item generation: An analysis with the Rasch Poisson Counts model. Learning and Individual Differences, 52(4), 121-128. doi: 10.1016/j.lindif.2015.01.013
Ensher, E. A., Nielson, T. R., & Grant-Vallone, E. (2003). Tales from the hiring line: Effects of the internet and technology on HR processes. Organizational Dynamics, 31(3), 224-244. doi: http://dx.doi.org/10.1016/S0090-2616(02)00111-0
Fetzer, M. S., & Tuzinski, K. (2014). Simulations for personnel selection. New York, NY: Springer.
Harris, M. M., & Hollman, K. D. (2013). The TIP-Topics – Top Trends in I‑O Psychology: A Graduate Student Perspective. The Industrial-Organizational Psychologist, 50(4), 120-124. Retrieved from http://www.siop.org/tip/Apr13/19_TipTopics.aspx
Lievens, F., & Patterson, F. (2011). The validity and incremental validity of knowledge tests, low-fidelity simulations, and high-fidelity simulations for predicting job performance in advanced-level high-stakes selection. Journal of Applied Psychology, 96(5), 927-940. doi: http://dx.doi.org/10.1037/a0023496
Madia, S. A. (2011). Best practices for using social media as a recruitment strategy. Strategic HR Review, 10(6), 19-24. doi: http://dx.doi.org/10.1108/14754391111172788
Putka, D., J., & Oswald, F. L. (2016). Implications of the big data movement for the advancement of I-O science and practice. In S. Tonidandel, E. King, & J. Cortina (Eds.), Big data at work: The data science revolution and organizational psychology (pp. 181-212). New York: Routledge.
Reinecke, O., & Toaddy, S. R. (2016). The I-Opener: We Feel a Change Comin’ On: I-O’s Role in the Future of Work. The Industrial-Organizational Psychologist, 53(4), 24-28. Retrieved from http://www.siop.org/tip/april16/pdfs/iopen.pdf
Singh, P., & Finn, D. (2003). The effects of information technology on recruitment. Journal of Labor Research, 24(3), 395-408. doi: http://dx.doi.org/10.1007/s12122-003-1003-4
Society for Industrial and Organizational Psychology, Inc. (2016). Guidelines for education and training in industrial/organizational psychology. Bowling Green, OH: Author
Thornton, G. C., III, & Cleveland, J. N. (1990). Developing managerial talent through simulation. American Psychologist, 45(2), 190-199. doi: 10.1037/0003-066X.45.2.190
Tonidandel, S., Quiñones, M. A., & Adams, A. A. (2002). Computer-adaptive testing: The impact of test characteristics on perceived performance and test takers' reactions. Journal of Applied Psychology, 87(2), 320-332. doi: http://dx.doi.org/10.1037/0021-9010.87.2.320
Tsacoumis, S. (2007). Assessment centers. In D. L. Whetzel & G. R. Wheaton (Eds.), Applied Measurement: Industrial Psychology in Human Resources Management (pp. 259‑292). Mahwah, NJ: Lawrence Erlbaum Associates.