The Modern App: Don’t Believe (Most of) the Technology Hype
Evan Sinar, DDI, and Tiffany Poeppelman, LinkedIn
It’s nearly a daily occurrence in media where we hear phrases like “technology is advancing exponentially, disrupting our world, and at a faster pace than ever before!” But what if most of these writers and technology commentators are creating a level of technology hype and making claims about this “accelerated pace” that aren’t actually much different from our past technology advances. Or, are they neglecting to factor in practical and operational considerations that will heavily shape how or even if a technology is actually implemented?
We as I-O psychologists must providing a counterweight to the pressures of mass media, especially when it comes to the technology domain. This article will explore the dynamic and evolving perceptions of technology as well as considerations for refuting (or validating) hype. We feel this will help our field advance technology discourse by guiding our business constituents to move past outdated, overstated, and simplistic assumptions propagated in the media. Although we can’t risk being passive or dismissive about technology trends, nor can we shirk our responsibility to represent—through our research and practice—a sophisticated approach to managing technology change.
But how do we begin assessing the technology winners and those that are just getting press for their sexy appeal or good marketing tactics? One resource to analyze trends effectively is published regularly by Gartner, a research, advisory, and information technology firm in their Hype Cycle: a compelling graphical research methodology to uncover which technology advances have merit as a viable opportunity for industry and which are just noise (2017). This branded approach was designed to:
- Represent the maturity and adoption of specific technologies—describing current trends and the likelihood of which the suggested technology will drive change in business environments.
- Provide a source of insight for companies, and their executives to ensure deployment of technologies in relation to their business goals.
- Aid investors understand the risks in the market versus viable products that might be worth the leap.
As practitioners and researchers, we can draw on this approach to understand the repeatable, often predictable cycles that surround technology change. The cycle offers a series of relative expectations along a time continuum which include: Innovation Trigger, Peak of Inflated Expectations, Trough of Disillusionment, Slope of Enlightenment, and a Plateau of Productivity. As a generalized form, below is Gartner’s graphic representation showcasing the five core Hype Cycle phases for interpreting a technology's life cycle.
Gartner Hype Cycle
To summarize each phase on the cycle (the 2017 version of which is shown in Panetta, 2017):
- Innovation Trigger: This typically comes in the form of a technology that hasn’t quite been proven yet through a working product but offers some viability in the commercial marketplace. Such examples cited in Gartner’s recent 2017 report include 4D printing and human augmentation, which appear to be on the rise.
- Peak of Inflated Expectations: Typically showcased in the media with companies who claim success initially but often follow shortly with instances of failure. Smart robots and machine learning are currently showcased by Gartner as starting to see success in some organizations, but not yet being implemented successfully at broad scale.
- Trough of Disillusionment: Often highlighted by a lack of success in experiments and implementations, which leaves much to be desired and reduces interest from investors. With many beginning to fail in adoption or proof, the reality starts to set in that these investments may not work in the long term. One notable example Gartner highlights today is augmented reality. Without the proof of value for consumers, investments may begin to fade.
- Slope of Enlightenment: This is the point within the technology hype cycle where organizations and investors start to see consistent benefits. For those technologies that make it here, consumers begin to see products appear and more investors see a need for additional implementations. Gartner calls out virtual reality as one such technology that is starting to take shape after addressing initial phases of skepticism.
- Plateau of Productivity: At the final stage, full adoption starts to take off as the technology has proven to be viable, clearly defined, and applicable to many consumers.
I-O Psychologists’ Responsibility to Challenge Assumptions
So what does this mean to I-O psychologists? To fully understand our impact in each of these spaces, we need to ensure we are ahead of the curve (literally and conceptually) to understand what is taking shape in technological advancements and which will impact work environments. Some attention to hype is instructive because it can point us to understand why the advances are being sought out in industry (what organizational challenges still exist for which technology can offer an aid) and how we can help advise companies: ignoring hype means neglecting workplace trends. Additionally, being ahead of the hype curve can give us an edge to learn from—and sidestep—past failures or common misconceptions on what is happening around us.
Like other jobs in the workplace, our roles as I-O professionals are constantly evolving and require a sharp edge as a business consultant and advisor. Additionally, it’s paramount that researchers partner with industry and practitioners to leverage research and gain unconventional perspectives to guide and advise organizations on what is noise and what could lead to profound challenges or advances to an organization executing on a technology-driven transformation.
Representative Examples of Technology’s Overhyped Impact
To illustrate cases of technology-centric hype, we focus on two topics that have a) drawn considerable attention in the general media and b) are predicated on conventional wisdom we feel is worth challenging. We list both below with a representative quote along with number (at the time when this article was written) of Google News Results and overall Google Results for that exact search term. For each, we briefly review the prevailing media tone and propose alternative viewpoints that we feel are essential to informing a more reasoned and ultimately, more effective approach to drive technology change that sticks.
Topic #1 - “Unprecedented Technological Change!”
[413 Google News Results; 30,600 Google Results]
An initial premise—either implied or directly stated—for many technology-centric articles is that current forces of technological disruption, including but not limited to the workplace, are entirely unprecedented in scope and scale. This framing certainly serves its role to “build the pain” and sense of urgency to recognize and rapidly address—often through investing in new technological platforms and employee upskilling—the clear and present workplace voids being created now like in no other time. The effect is predicted to be particularly strong and unique for “occupational churn” through which massive and never-before-seen proportions of employees are claimed to be displaced from outdated jobs while new jobs are created.
But What if It’s Not?
Technology change impacting the workforce can be sizeable without being seismic historically; recent research by two economists (Atkinson & Wu, 2017) integrates 165 years of data toward a compelling case against the latter. That is, at least in the U.S., occupational churn is actually lower than throughout most of our workforce history. Atkinson and Wu also challenge the common assumption that new technologies are more disruptive than ever, concluding that instead, these forces were weaker between 2010 and 2015 than in almost any past period. That is, technology’s impact on the workforce is actually less monumental in recent years than it was in the past. Not only is technology disruption of jobs not unique in our history, it’s also much less severe than it’s been.
Topic #2 -“Artificial Intelligence Will Change Everything!”
[182 Google News Results; 80,800 Google Results]
Artificial intelligence (AI) as a topic has surged in attention in technology-focused publications and more broadly in the media. With this spike in topical interest has come a series of extreme and at times alarmist propositions about the scope of change AI will produce in the workplace: for autonomous driving, for drone-based commerce, for blockchain finance, and for robotic coworkers among other potential implications. The vast majority of the media attention on this topic focuses on the disruptive pressures being placed on the workplace as a result of these technologies in their fully realized forms, extrapolating from the isolated instances within which they’re currently deployed to scale across the entire economy.
But What If It Won’t?
Projections of AI-driven disruption are often overly optimistic and fail to take into account the vast depth of complexity involved in translating theoretical benefits to operational realities. Reasons for a more guarded approach to the pace and pervasiveness of AI’s impact—drawing on the points summarized in a recent Knowledge@Wharton article (Knowledge@Wharton, 2017)—include the much slower progress possible (compared to the virtual aspects of new AI technology) in the physical and mechanical worlds, the sheer amount of high-quality data needed to calibrate the AI engine, the social/moral/ethical dilemmas that must be managed to implement systems in a public environment (not even including the political hurdles to overcome), and the expectation of not just high-accuracy but also transparent and explainable decisions resulting from AI.
Change the Conversation: How to Ride and Own the Hype Cycle
Our intent in this article is certainly not to recommend that our field take a naive or dismissive approach to the concept of technological change. The risks of doing so and as a result appearing out of touch, poorly adaptable, or irrelevant are simply too high. We do, however, advocate for an approach that’s more balanced than breathless about the scope and pace of changes, and how they can be managed by working through the implementation and practicality facets of the disruption. Though these considerations don’t get nearly the media attention as hyperbolic claims of unprecedented and all-encompassing impact, ultimately they’re likely to play a much stronger role in whether business leaps over the technology chasm successfully.
The most pressing question to continue to ask ourselves is, how do we challenge the assumptions and showcase our expertise and sense making? What is our role to initiate and engage in research on both sides of the hype curve, neither solely as a concept is ascending (like conversational user interfaces or smart robots in Gartner’s 2017 Hype Cycle) or descending (like autonomous vehicles or blockchain)? Do we have the courage to represent these issues through our practice and not by standing and watching, crossed-arms and disapprovingly as the trains of progress pass us by? Advancing the discussion will also ensure that we explore technology’s impact on organizations through credible and trustworthy research (Grand et al., in press). Whether advising or guiding work to implement new technologies, we can positively foster our recognition in the business community as capable advisors.
We’d like to hear from you! What recent trends have you seen in industry that you believe are hyped and overstated? Which technology risks are underrecognized? What technologies do you see just starting to emerge within organizations and warranting proactive, prescriptive research? Contact or follow us on the below social channels:
LinkedIn: Evan Sinar & Tiffany Poeppelman
Twitter: @EvanSinar & @TRPoeppelman
Atkinson, R. D., & Wu, J. (2017). False alarmism: Technological disruption and the U.S. labor market, 1850-2015. Information Technology and Innovation Foundation. Retrieved from https://www.itif.org/publications/2017/05/08/false-alarmism-technological-disruption-and-us-labor-market-1850-2015
Gartner Hype Cycle (2017). Research methodologies. Retrieved August 26, 2017 from http://www.gartner.com/technology/research/methodologies/hype-cycle.jsp
Grand, J. A., Rogelberg, S. G., Allen, T. D., Landis, R. S., Reynolds, D. H., Scott, J. C., Tonidandel, S., & Truxillo, D. M. (in press). A systems-based approach to fostering robust science in industrial-organizational psychology, Industrial and Organizational Psychology: Perspectives on Science and Practice, 11(1).
Panetta, K. (2017). Top trends in the gartner hype cycle for emerging technologies, 2017. Retrieved from http://www.gartner.com/smarterwithgartner/top-trends-in-the-gartner-hype-cycle-for-emerging-technologies-2017
Knowledge@Wharton (2017, July 14). The future of artificial intelligence: Why the hype has outrun reality. Retrieved from http://knowledge.wharton.upenn.edu/article/dont-believe-hype-ai-driven-world-still-long-way-off/