The Modern App—2017 Technology Trends: Are I-O Psychologists Prepared
Tiffany Poeppleman and Evan Sinar
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:
- Highlight guiding technology principles that are being adopted by practitioners
- Raise awareness of cutting-edge research from academics and research agencies
- Identify technology-rooted gaps between research and practice to ensure a clear bridge for advancement
Evan and I we will evolve this column by:
- Actively working with technology-savvy research partners across SIOP to evolve our understanding of the opportunities that can stimulate future research and fill gaps in our knowledge of emerging tools. If you are one of those partners, please reach out to us directly on Twitter (Tiffany and Evan) or LinkedIn (Tiffany and Evan).
- Cross-referencing and seeking opportunities to complement and supplement the work of other regular TIP columnists such as Richard Landers and his Crash Course series, and The Bridge: Connecting Science and Practice by Mark Poteet, Craig Wallace, and Lynda Zugec.
- Drawing awareness to and implications from trends that will shape the future workplace, through a regular section called Tech Avenue sharing current industry reports and what they mean for I-O; the first of these sections follows below.
In October 2016, Gartner released its advisory report, “Gartner’s Top 10 Strategic Technology Trends for 2017,” to bring attention to emerging and disruptive technologies for a broad business audience. We feel that SIOP members can use these trends as one barometer of possible workplace futures. As you’ll see below, our field is intersecting with a few of these trends already, while many others are still beyond our horizon. In order to gauge current I-O coverage of the technology areas in the report, we searched for the trend keywords (e.g., “artificial intelligence”, “virtual reality”, “blockchain”) in six top I-O psychology and management journals (Nadler et al., 2015): Academy of Management Journal, Journal of Applied Psychology, Journal of Business and Psychology, Organizational Behavior and Human Decision Processes, Journal of Management, and Personnel Psychology. We also searched programs from SIOP’s Annual Conferences and Leading Edge Consortia (LEC) between 2014 and 2016.
We review the trends by first providing Gartner’s verbatim definition (to describe these trends as a non-I-O audience views them). We then cite relevant I-O publications and presentations (where available) and propose questions we can ask and answer to deepen organizational understanding and successful incorporation of the trend into the workplace.
We’ve grouped the summaries by the same three categories as Gartner: Intelligent, Digital, and Mesh.
Gartner “Intelligent” Category Trends
First, three trends that center around the creation of intelligent, autonomous systems, well-known versions of which include IBM’s Watson and the recommendation engines of Pandora, Netflix, and Amazon.
Trend #1: Artificial Intelligence and Advanced Machine Learning
What it is: “Artificial intelligence (AI) and advanced machine learning (ML) are composed of many technologies and techniques (e.g., deep learning, neural networks, natural-language processing [NLP]). The more advanced techniques move beyond traditional rule-based algorithms to create systems that understand, learn, predict, adapt and potentially operate autonomously” (Panetta, 2016).
Coverage by I-O and management: Of the Gartner trends, machine learning has been covered most by I-O publications and conferences, albeit typically as a subtopic within a larger discussion of big data or data science. Notable articles include George, Haas, & Pentland (2014) and George, Osinga, Lavie, & Scott (2016). Machine learning has been featured in several SIOP sessions (e.g.,Illingworth, 2016; Tonidandel, 2014) and LEC presentations (Alexander & Van Buren, 2016; Mondragon, 2016; Taylor, 2016; Putka, 2016), as well as in Tonidandel, King, & Cortina’s 2015 book, “Big Data at Work.”
Key questions: The most revealing word from the definition above is “autonomously.” As we continue to adopt machine learning techniques for workplace data, we must do so while considering that the ultimate goal for many organizations is independent operation of the employee decision algorithms. We must target and tackle questions such as:
- How can construct frameworks be created and applied to ground any observed relationships in, if not psychological theory, explanatory clarity?
- What approaches are effective for communicating the “black box” nature of AI and ML models to those affected by their outcomes to maintain some semblance of procedural and distributive justice?
- What form and documentation of validity evidence will be produced to justify autonomous use of AI- and ML-based systems for personnel decision making?
Trend #2: Intelligent Apps
What it is: “Intelligent apps such as Virtual Personalized Assistants (VPAs) perform some of the functions of a human assistant making everyday tasks easier (by prioritizing emails, for example), and its users more effective (by highlighting the most important content and interactions)” (Panetta, 2016).
Coverage by I-O and management: No direct coverage in the sources reviewed.
Key questions: The most intriguing implications of this trend are for work itself, as jobs become upskilled due to automation for more repeatable tasks, and as technology steps in to aid employees in processing information:
- How do these forces change the relative influences of various personal attributes on job effectiveness: cognitive, personality, and values?
- What is the interaction between individual differences (including protected group status) and the productivity “boost” projected to occur through use of the technology?
- How must performance and development systems be recalibrated to account for technology influences on productivity, isolating the true impact of the employee
Trend #3: Intelligent Things (Internet of Things)
What it is: “Intelligent things refer to physical things that go beyond the execution of rigid programing models to exploit applied AI and machine learning to deliver advanced behaviors and interact more naturally with their surroundings and with people” (Panetta, 2016).
Coverage by I-O and management: No direct coverage in the sources reviewed.
Key questions: Internet of Things devices will vastly will increase the scope and scale of data used to inform workplace decisions. Although ideally these data are with full awareness of relevant psychometric and ethical considerations, that may prove to be the minority of situations. We can shape discussion of this topic through questions such as:
- How do we extend our theories and research models to incorporate new data available through Internet of Things devices, which may bear little resemblance to the survey and assessment-centric sources that have historically dominated our field?
- How should employers communicate about data gathered from and about employees to elicit perceptions of developmental value rather than cynicism, violated trust, and compromised ethics?
- What will be the effects of an “always on” state of awareness and tracking of employee actions on stress and health outcomes? What individual differences and organizational interventions will moderate these effects?
Gartner’s “Digital” Category Trends
Gartner’s second set of trends deals with the shift toward a near-mirror match of the physical and digital worlds.
Trend #4: Virtual and Augmented Reality
What it is: “Immersive technologies, such as virtual reality (VR) and augmented reality (AR), transform the way individuals interact with one another and with software systems […] a flow of information that comes to the user as hyper personalized and relevant apps and services” (Panetta, 2016).
Coverage by I-O and management: Within the sources reviewed, several SIOP sessions discussed virtual reality’s use for employee training (e.g., Howard et al., 2015, Howard et al, 2016a, Howard et al., 2016b, Lee et al., 2016).
- Which interpersonal constructs, for example, empathy and extraversion, translate to virtual interactions among employees and with customers?
- What contaminating and extraneous factors occur as a result of these devices to mask true psychological constructs?
- How will the projected “flow of information” impact employee information processes and required cognitive and noncognitive skills? For those with less comfort engaging with this data firehose, how will their job effectiveness—and career prospects—change as a result?
- How must legally relevant principles of cross-employee consistency be maintained—for performance evaluation, promotion decision making, and compensation, for example—in an environment where each employee’s experiences are unique?
Trend #5: Digital Twin
What it is: “A digital twin is a dynamic software model of a physical thing or system that relies on sensor data to understand its state, respond to changes, improve operations and add value. Digital twins include a combination of metadata (for example, classification, composition and structure), condition or state (for example, location and temperature), event data (for example, time series), and analytics (for example, algorithms and rules).” (Panetta, 2016).
Coverage by I-O and management: No direct coverage in the sources reviewed. However, this topic was discussed briefly in Allen Kamin’s 2016 LEC session, where he proposed that we are nearing the point where various ambient datapoints about employees (e.g., from an HRIS, online profiles, wearable devices, and social network analyses) will be integrated to produce a parallel, digital-only model of the individuals.
Key questions: We feel that the digital twin concept will eventually extend to people as well as things, via residual data providing information about employee actions, relationships, and health. This trajectory raises questions such as:
- What is the fundamental validity of a “digital twin” model of employee behavior, bounded by reliability and comprehensiveness of the measures used to create the twin?
- How will the concept of employee development be retained, or bolstered, in an environment of constant reevaluations against an increasingly deterministic outcome?
- How do various digital twins of employees interact in group systems, such as teams, and can the results of interactions be accurately modeled to generate predictions?
Trend #6: Blockchain and Distributed Ledgers
What it is: “Blockchain is a type of distributed ledger in which value exchange transactions are sequentially grouped into blocks. Each block is chained to the previous block and recorded across a peer-to-peer network, using cryptographic trust and assurance mechanisms.” (Panetta, 2016).
Coverage by I-O and management: No direct coverage in the sources reviewed.
Key questions: Blockchain is already beginning to influence HR information management, and we can see scenarios where this trend could extend to be a disruptive workplace force. Consider blockchain as an immense, highly secure method for verifying employee identities, experiences, and certifications—that is, as “blocks” exchanged across a “chain” of computer systems. This will raise questions such as:
- What qualifications and experience structures will serve effectively as a mutually exclusive, collectively exhaustive groundwork for a blockchain approach?
- Will increased consistency and presumably, accuracy of this information push employee qualifications beyond their current middling validity compared to other hiring tools?
- In a vast, cross-organizational system housing this information, who bears responsibility for establishing job-relatedness of the variables captured therein, and how can organizations confidently incorporate these data into their selection processes?
Gartner “Mesh” Category Trends
Gartner’s final four trends fall under the grouping, “Mesh,” referring to the “dynamic connection of people, processes, things and services supporting intelligent digital ecosystems” (Panetta, 2016). Examples of these include employee-facing chatbots such as those being developed by Talla. These trends share two characteristics: no direct coverage in I-O forums, and serving as broad rather than narrow workplace context; as a result, we group key questions together into one list below.
Trend #7: Conversational Systems
“The current focus for conversational interfaces is focused on chatbots and microphone-enabled devices (e.g., speakers smartphones, tablets, PCs, automobiles). […] an expanding set of endpoints people use to access applications and information, or interact with people, social communities, governments, and businesses” (Panetta, 2016).
Trend #8: Mesh App and Service Architecture
“In the mesh app and service architecture, mobile apps, web apps, desktop apps and IoT apps link to a broad mesh of back-end services to create what users view as an "application." (Panetta, 2016).
Trend #9: Digital Technology Platforms
“Digital technology platforms provide the basic building blocks for a digital business [...] five major focal points to enable the new capabilities and business models of digital business — information systems, customer experience, analytics and intelligence, the IoT, and business ecosystems (Panetta, 2016).
Trend #10: Adaptive Security Architecture
“The intelligent digital mesh and related digital technology platforms and application architectures create an ever-more-complex world for security. […] new vulnerability areas and often requiring new remediation tools and processes that must be factored into IoT platform efforts" (Panetta, 2016).
- How can our research design expertise be used to produce frameworks for efficiently aggregating data about employees, to enable use of these data fairly, ethically, and predictively?
- What new or adapted skills must employees possess to enable rather than add risk to (e.g., for security reasons) further applications of technology in the workplace?
- What is the new role of a leader within these technology-saturated environments as stewards of data use: What information is being gathered about whom, and for what purpose?
- At what point does the technology itself become an “employee” for the purposes of falling under similar rules and expectations for productivity, customer service, teamwork, adaptability, and continuous learning?
- If “digital twins” come to pass, is it IT or I-O expertise which will steer them toward optimal performance and productivity?
- Does the influence of technology expand or narrow the scope of employee constructs in which we’ll be expected to provide evidence-rooted recommendations? How much of our current know-how about employee behavior needs to be overhauled?
A final question looking across the full set of technology trends from this report: Given meager coverage of these topic in our current knowledge base, do we have a realistic path forward—robust enough and fast enough—to provide the prescriptions the workplace needs from us?
For upcoming issues, we intend to explore a host of notable hot topics at greater length such as the Internet of Things (IoT), open source technologies, and further subtopics of data science. For topics that involve tools and platforms, we will explore how they work and their benefits, and we will aim to cut across disciplines to ensure stronger collaboration and deeper partnerships for the long-term growth of our field.
Of course, we always welcome topic recommendations for future issues! If you or anyone you know would like us to highlight or explore a technology, tool, or trend, please contact us with your suggestions. Additionally, we are always looking for partners if you’d like to collaborate, either as an interviewed expert on a topic you’re focused on or as a coauthor for a future column.
Contact us on LinkedIn: Tiffany Poeppelman & Evan Sinar
Contact us on Twitter: @TRPoeppelman, & @EvanSinar
Below is the full list and timeline of our The Modern App columns over the past 3 years:
Alexander, A. & Van Buren, M. (2016, October). Applying predictive analytics to application data: Higher quality for less effort. Presented at the 12th Annual Leading Edge Consortium of the Society for Industrial and Organizational Psychology, Atlanta, GA.
George, G., Haas, M., & Pentland, A., (2014). Big data and management. Academy of Management Journal, 57(2), 321-326.
George, G., Osinga, E., Lavie, D., & Scott, B. (2016). Big data and data science methods for management research. Academy of Management Journal, 59, 1493-1507.
Howard, M., Lee, J., Rose, J., Dogru, E., Millard, L., & Mahla, E. (2016, April). A theory of training–technology fit and virtual reality: A meta-analysis. Poster presented at the 31st Annual Conference of the Society for Industrial and Organizational Psychology, Anaheim, CA.
Howard, M., Lee, J., Dogru, E., Rose, J., Mahla, E., & Millard, L. (2016, April). A meta-analysis of virtual reality hardware, software, and participant populations. Poster presented at the 31st Annual Conference of the Society for Industrial and Organizational Psychology, Anaheim, CA.
Howard, M., Resnick, T., Kutz, N., Mahla, E., Nestor, L., & Bet, J. (2015, April). Are head-mounted virtual reality systems useful for training and education? Poster presented at the 30th Annual Conference of the Society for Industrial and Organizational Psychology, Philadelphia, PA.
Illingworth, A. J. (2016, April). Machine learning in I-O psychology: Introduction, application, and future directions. Panel discussion at the 31st Annual Conference of the Society for Industrial and Organizational Psychology. Anaheim, CA.
Kamin, A. (2016, October). Enhancing data availability to improve employee experience and better understand talent. Presented at the 12th Annual Leading Edge Consortium of the Society for Industrial and Organizational Psychology, Atlanta, GA.
Lee, J., Howard, M., Dogru, E., Rose, J., Millard, L., Mahla, E., & Gui, F. (2016, April). Testing pretraining interventions for virtual reality training: Investigating seductive details. Poster presented at the 31st Annual Conference of the Society for Industrial and Organizational Psychology, Anaheim, CA.
Mondragon, N. (2016, October). Data science and I-O: Birds of a feather or lone wolves? Presented at the 12th Annual Leading Edge Consortium of the Society for Industrial and Organizational Psychology, Atlanta, GA.
Nadler, J., Bartels, L., Naumann, S., Morr, R., Locke, J., Beurskens, M., Wilson, D., & Ginde, M. (2015). Sampling strategies in the top I-O journals: What gets published? The Industrial-Organizational Psychologist, 53(2), 139-147.
Panetta, G. (2016, October 18). Gartner’s top 10 strategic technology trends for 2017. Retrieved from http://www.gartner.com/smarterwithgartner/gartners-top-10-technology-trends-2017/.
Putka, D. (2016, October). Big data mythbusters: Benefiting from big data analytic methods with your small data. Presented at the 12th Annual Leading Edge Consortium of the Society for Industrial and Organizational Psychology, Atlanta, GA.
Taylor, B. (2016, October). The super-human era: The latest in HR data science innovation. Presented at the 12th Annual Leading Edge Consortium of the Society for Industrial and Organizational Psychology, Atlanta, GA.
Tonidandel, S. (2014, May). The promise and perils of big data in I-O psychology. Roundtable discussion at the 29th Annual Conference of the Society for Industrial and Organizational Psychology. Honolulu, HI.
Tonidandel, S., King, E., & Cortina, J. (Eds.). (2015). Big data at work: The data science revolution and organizational psychology. New York, NY: Routledge.