form of impact: intellectual impact.
These are the clever interaction
techniques, enlightening study
results, novel sensing hardware,
and other tasty morsels we reveal or
invent that potentially create and
shape new computing experiences.
The greatest opportunity to
have HCI intellectual impact is
in the very earliest stages of a
product category. This is when
the intellectual landscape is wide
open, and questions and problems
abound. Everything is new and there
is no doctrine. This is when HCI
innovators can come in, establish
user needs, shape attitudes, define
features, invent new lingo and
methods, and fill capability voids
with new enabling technologies. As
with all innovation-driven pursuits,
the rate and impact of contributions
start high and naturally taper off
as the subfield or product category
matures. James Utterback and
William Abernathy described this
“innovation dynamic” back in 1975
[ 2] (Figure 2).
Unfortunately, I believe this
gentle innovation roll-off that
occurs synchronously with product
maturation is inaccurate for HCI.
The real curve is much uglier and
tells the story of a dilemma we as
innovators in HCI must face. To be
frank, I derive this thesis not from
extensive research or interviews,
but rather from my own views and
experiences as an academic who’s
had some luck in transferring
technologies to industry, and also
as an entrepreneur with my startup
Qeexo [ 3], which has software on
more than 150 million smartphones
to date.
THE DILEMMA
First off, we need to extend our
timeline, considering the period
of work before embryonic activity,
when the concept is a mere glint
in an HCI community’s eyes. This
almost always predates commercial
activity, as borne out in Brad
Myers’s 1998 Interactions article “A
Brief History of Human Computer
Interaction Technology” [ 4]. This
holds true for today’s emerging HCI
areas, like virtual and augmented
reality (first researched in the late
1960s), voice-driven interfaces (also
1960s), and the Internet of Things
and proliferation. Money is made!
Eventually, as the technology and
market mature, improvement
and adoption slow and there are
diminishing returns. The product
ages, and may even die, but for the
sake of simplicity we’ll focus on the
earlier periods.
Figure 1 is a reasonable
generalization of a complex
process—it’s still being taught
today. In such graphs, the axes are
important to note, as they elucidate
the values of the chart’s designer.
In this case, I’ve chosen a popular
formulation that plots time against
market size, but you’ll also find
variants that use product sales,
revenue, number of problems solved,
technical maturity, and so on. These
are all important metrics, but they
also almost exclusively view the
process of innovation through a
business or technical lens.
For many HCI innovators, impact
is a professional and personal
goal. Impact, of course, is also an
imprecise word. Affecting millions
of lives (happiness, health, etc.)
through an app or feature that
you developed is without doubt
impactful. This type of impact
largely follows that S-curve, as more
users correlates with more influence.
But many in HCI, especially
academics, also seek a very specific
EMBRYONIC
• Prototypes
• Iterative research
• Community Building
GROWTH
• Commercial feasibility
• Product launch
• Proliferation of features
MATURITY
• Dominant design
• Maintaining markets
• Incremental improvement
• Commoditization
• Consolidation
• Possible death
M
ar
ket
Siz
e
Time
Figure 1. Technology Lifecycle S-Curve, adapted from [ 1].
EMBRYONIC
• Prototypes
• Iterative research
• Community Building
GROWTH
• Commercial feasibility
• Product launch
• Proliferation of features
MATURITY
• Dominant design
• Maintaining markets
• Incremental improvement
• Commoditization
• Consolidation
• Possible death
Ra
t
e
o
f
Inn
ova
ti
on
(a
nd
Po
te
nt
ia
l
fo
r
In
te
l
le
ct
ua
l
I
mpa
ct)
Time
Figure 2. Innovation rate over time, adapted from [ 2], overlaid approximately onto our earlier
technology lifecycle epochs.