Abandoning innovations: network evidence on enterprise collaboration software
Jacob C. Fisher, Jonathon Cummings, and Yong-Mi Kim
Duke University
Introduction
Results
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We used data from an enterprise collaboration system at a large
technology company. The enterprise collaboration system allowed
employees to share documents, make blog posts, and post comments in
the context of online “communities.” We construct time-varying
measures of the number of active users that each person is connected
to by looking at when the intervals between people’s first posts and
their last posts in a given community overlap. The figure below shows
these timelines for a hypothetical community.
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Although the adoption of innovations has been the subject of much
research, few studies have examined when innovations are abandoned,
masking important variation in the number of total users. The figure
below shows the difference between cumulative and active users of the
technology in our study.
Data
Time
Although the number of cumulative users follows the traditional Sshaped diffusion curve, the number of active users begins declining just
as the S-curve begins to take off. Our study considers what influences
this decline.
Background
We hypothesize that people decide to abandon the innovation when
they perceive that it has less value for them. The innovation that we
consider is used for communication, and has significant network
externalities. That is, as the number of other users increases, the value
of the innovation increases, and vice versa.
A person who adopts here is
less likely to quit…
… than a person who adopts
here.
We develop time-varying measures of the value of the software to
each person, based on how many active users they are connected to,
and use those to predict abandoning.
Acknowledgements: this research was supported by NSF Award #OCI-1122286.
The orange line indicates the time between the focal person’s first
post to a community and his or her last post to a community, and the
gray lines indicate others’ first and last posts to the community, marking
their entries into and exits from the community. The dotted lines
indicate the steps to create the measure, moving from left to right.
Methods
We focused on both adoption and abandonment of the software.
We fit piecewise exponential, discrete-time survival models with a logit
link predicting the duration of time until someone uses the software
(adoption), or the duration of time between first use of the software
and last use of the software (abandonment). In addition, we fit
separate models for adoption and abandonment of open communities,
meaning communities that anyone could join, and restricted
communities, meaning communities where a moderator had to invite
new members.
We calculate the number of active users in the company overall, in
the same functional unit, and on the same supervisory team, as well as
whether or not the person’s immediate supervisor is an active user. For
the adoption models, we count any active users; for the abandonment
models, we count active users who are network neighbors using the
method above. Because we cannot measure passive use – viewing
posts without making posts – we use the 4-week moving average of the
number of active users. We standardized all of the non-binary variables
to make them comparable between models.
Each model controls for the time when the person first used the
software, the person’s gender, the organizational unit where the person
works (e.g., marketing, sales, etc.), the person’s education level, the
person’s employee type (i.e., whether he or she was a full-time
employee, a contractor, or a vendor), whether a workers was a mobile
or traditional worker (the former meaning that he or she primarily
works from home), the geographic region where he or she works, and
when the person was hired.
The results show first, that people are more likely to adopt the
software when more people who are close to them in the organization
are active users. The effect is largest for people using restricted
communities when their supervisor uses restricted communities.
Second, people are less likely to abandon when more people on the
same team are active users. There is no effect for members of the same
unit, and the effect for whether their supervisor is an active user is
much smaller. This suggests that supervisors drive adoption, while
peers drive continuation.
Future directions
As this work moves forward, we expect to investigate several
additional areas, including
• Identifying “causal” mechanisms
• Examining experimentation with the software
• Parsing out whether this is “value” or “authority” – when you use the
software because your boss uses it, are you using it because it’s more
useful, or because he or she told you to use it?
• Distinguishing categories of communities and types of use –
preliminary findings showed some differences in the activity level
(number of posts) and type (blog post, document, etc.) by category of
community (customer / business partner; product, solution, or
technology; etc.), but more analysis is needed.
We welcome suggestions for other directions to take!