Competitor Neglect and Technology Adoption
Estimating chances of success is an important part of strategy.
Competitor Neglect is a known cognitive phenomenon where human agents systematically ignore the effects of competition while forecasting success. This can be fairly common and consequential.
As Joe Roth, the ex-chairman of Walt Disney Studios, once explained, “this is why so many big budget movies open on the same weekend at the box office” – despite being a counterproductive strategy for each of them.1
Despite its importance, competition is often excluded even from formal strategic estimations, let alone casual assumptions. This affects a variety of markets where people make strategic judgements – including consumer facing technology markets.
A well-known concept in technology go-to-market strategy is Technology Adoption – a process difficult to model, as it estimates adoption preparedness for new technology in a given population2.
A model known as the Technology Acceptance Model 2 (TAM) emphasizes five factors influencing individuals’ acceptance of new technology –
- Perceived usefulness
- Perceived ease of use
- Users’ attitude toward the technology
- Behavioral intentions
- Actual usage behavior
The TAM and other informal estimates neglect the inclusion of competitor effects as an important factor in their estimation of how the market will respond to their technology, leading to serious blind spots.
“Let’s have that cool feature”
As an example of this phenomenon, recent user research conducted at Vectorform found that one of the main factors users consider in downloading and consequently keeping an app on their phones is the ratio of perceived usefulness to memory space cost. Both of these factors are directly influenced by competition in the market.
Facilitated by research and design over time, competition in the app market has increased consumer expectations for usability. It has also reduced the available space for apps, as memory size upgrades comparatively increase at a much slower rate – typically with expensive device upgrades for newer model phones.
User interviews indicated that usefulness is determined by frequency of use or value per app launch (depending on the app type). Additionally, the app size acceptance threshold varied by users’ phone memory size. Users expressed their need to strategize app usage due to both memory space limitations and an overabundance of useful apps on the market.
These needs may work in conjunction with limitation in app usage time, which may have reached a threshold according to Nielsen research cited by TechCrunch.
“..there are hints of people possibly approaching a limit to how much they might use them (apps): despite the rise in app numbers, the amount of time that people are spending in apps has remained essentially flat: collectively, they are being used for 39 minutes per day today, compared to 37 minutes in 2011.”
Additionally, the ComScore 2016 Mobile App Report notes that 63% of those 39 minutes are spent on users’ one or two favorite apps, and almost 90% of them are spent on users’ top 5 apps, leaving roughly 10% for the rest.
Contrast this insight with an estimate of success solely focused on how many features your app will have.
As app technology continues to support business ideas, it is important to think of adoption factors that define users’ acceptance of new technology.
Our takeaways for companies investing in new apps for their audience are:
- Avoid focusing solely on the feature-richness of your app idea in estimating its adoption and success – it may take the app closer to your user acceptance threshold.
- Be mindful of the ratio of usefulness to app-size during all stages of planning.
- Obtain an understanding of user acceptance of the usefulness-to-size ratio for your target user segment.
- Emphasize UX research throughout the research, discovery, design, and production phases to help find potential unknown critical factors.
- Camerer C. F., Lovallo D. Overconfidence and excess entry: An experimental approach. Amer. Econom. Rev.(1999) 89:306–318.
- Straub, E. T. (2009). Understanding technology adoption: Theory and future directions for informal learning. Review of educational research, 79, 625-649.
- Comscore 2016 Mobile App Report
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