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Title: Abandoning innovations: network evidence on enterprise collaboration software
Authors: Jacob C. Fisher, Yong-Mi Kim, and Jonathon Cummings
Authors’ affiliation: Duke University
Abstract:
The diffusion of innovations is a central problem in the study of social networks. Although a
considerable amount of attention has been paid to when innovations are adopted, few studies
have considered the reverse process of when innovations are abandoned. We examine this
process among employees in a large technology company using a unique dataset on the use of an
enterprise collaboration system – an innovative software tool used to help employees collaborate
with one another. The data consider a bipartite network of over 49,000 employees connected by
over 26,000 communities, over a time period of around 4 years. Using timestamped data on
posts to the software, we construct a real-time measure of when an employee begins using the
software and ultimately abandons the software. We find that employees are more likely to stop
using the software when the software has lesser value for them. Value is measured as the
number of other users of the software. We consider value both locally – the number of other
users that someone is connected to – and globally – the number of users in the company as
whole. Our findings shed light on use in different areas of an organization can cascade into
widespread adoption or abandonment, and on the diffusion of innovations process as a whole.
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Introduction Abandoning innovations is an important and understudied component of the diffusion of
innovations. An extensive literature considers when and how innovations spread through a
connected group of people or organizations (Rogers 2003; Strang and Soule 1998). Few studies,
however, consider the reverse process – when and how people stop using an innovation that they
have previously adopted. This gap in the literature masks significant heterogeneity in the
diffusion of innovations process. Figure 1 illustrates this by showing the cumulative number of
users of an innovative piece of software in a company, along with the number of current users at
a given time. Although the number of cumulative users follows the traditional S-shaped
diffusion curve, reaching a total of approximately 50,000 users, the number of current users
suggests that use of the innovation is characterized by a changing set of members, whose total
number barely exceeds 20,000 at any time. This study asks what influences the abandoning
process driving these different trends.
[FIGURE 1 ABOUT HERE]
The few studies that examine abandonment processes suggest a reason for the
comparative lack of attention. While adoption of an innovation is a social process, abandonment
is a largely individual process, driven by personal preferences (Burns and Wholey 1993; Greve
2011; Rao, Greve, and Davis 2001; Terlaak and Gong 2008). In this conception, a person’s
belief about the value of an innovation drives both the adoption and abandonment of an
innovation. Prior to adopting the innovation, a person is uncertain about the benefits that the
innovation will provide for him or her. As a result, a potential adopter seeks additional
information about the value of the innovation, either from network connections through word of
mouth (Coleman, Katz, and Menzel 1966), or from other sources (Strang 2010), to reduce his or
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her uncertainty about the value of the innovation. Once someone adopts, however, he or she no
longer has to consult with others to determine the value of the innovation (cf. Terlaak and Gong
2008). The adopter can make a decision whether or not to abandon the innovation on the basis
of his or her own experience.
Drawing on the literature on network externalities (Shapiro and Varian 1999), we1
propose a mechanism for how social influences are incorporated into the decision to abandon an
innovation. We suggest that the value of an innovation used for communication – such as a fax
machine, email, or, in our study, a new software tool – is proportional to the number of other
users of the innovation. As more people use an innovation, the innovation has greater value for
each user, making each user less likely to abandon. Thus although the decision to discontinue
using the innovation is still made individually, it is informed by the decisions that others have
made. We note that the relationship between perceived value of the software and the number of
others who use the software may be moderated by homophily, or the tendency for people to
interact with similar others, in an organizational context (Kleinbaum, Stuart, and Tushman
2013). That is, it is not only the sheer number of others who use the software that influences its
continued use, but also how likely it is that a person will communicate with those people. We
examine organizational homophily in three areas: organizational unit, physical location, and
supervisory relationships. The differences introduced by how adopters value an innovation
provides an important mechanism to explain variation in who continues to use an innovation, and
who does not.
1 Jacob C. Fisher, Yong-Mi Kim, and Jonathon Cummings contributed to this study. Fisher designed the approach,
ran the statistical models, and wrote the study, Kim collected and pre-processed the data, and Cummings collected
the data and provided comments on the manuscript and analyses.
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We examine this process using detailed data on the use of a new software tool in a large
technology company. We analyze records of 1.2 million timestamped instances of employees
using the software. These records give us a fine-grained view of precisely when employees start
and stop using a new technology – in this case, the software. This approach represents a
methodological advance as well; rather than relying on matters of public record (Rao et al.
2001), sales of a technology (Greve 2011), or self-reports (Kremer et al. 2001) to determine
when someone abandoned an innovation, we can use the timestamped observations to infer the
time when each person in the company stopped using the software. This not only gives us a
highly precise measure of individual abandonments, but also allows us to infer when, for
example, a supervisor quit before a subordinate, or vice versa.
The individual nature of abandoning innovations Given the attention paid to social influences on the adoption of innovations, the apparent
absence of social influence on the reverse process, abandonment, is surprising. A long literature
has found that social influences help drive the diffusion of an innovation through a group of
people (Coleman et al. 1966; Rogers 2003; Valente and Rogers 1995), or a group of
organizations (Strang 2010; Strang and Soule 1998). Yet studies that look for social influence on
abandonments rarely find it. One firm’s abandonment of a practice may inform another firm’s
decision of whether or not to adopt (Greve 2011; Terlaak and Gong 2008), but in cases ranging
from security analysts covering firms (Rao et al. 2001) to firms abandoning matrix management
programs (Burns and Wholey 1993), others’ abandonment does not affect one’s own
abandonment of an innovative strategy.
Unlike adoption, however, people and firms decide whether to abandon an innovation
after they have direct experience with the practice (Gaba and Dokko 2015; Terlaak and Gong
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2008). Prior to adoption, the benefits of the practice to the potential adopter are unknown.
People and firms attempt to reduce this uncertainty by observing others’ behavior, or discussing
the benefits of the practice with prior adopters. Once they adopt the innovation, however, the
adopters’ direct experiences with the innovations informs their understandings of the
innovations’ benefits. When direct experience removes all uncertainty about the benefits of a
program, a person or a firm no longer needs to consult with others to decide whether to continue
using an innovation; they can simply draw on their experience. Thus adoptions can cascade
through a group of people because the people share information about the benefits of an
innovation, and abandonments do not cascade because people do not need information from
others to evaluate the benefits of the innovation. For certain innovations, the future benefits of
the innovation remain uncertain after adoption, and in these cases, studies find that innovators
respond to their peers’ behavior when deciding whether to abandon an innovation (Greve 1995).
In general, however, studies of abandonment suggest that abandoning an innovation, either at the
firm or at the individual level, is a decision made individually once the person or firm has
learned what benefits the innovation will provide.
This perspective assumes that innovations have a fixed value that people or firms
discover (or remain uncertain about) once they adopt the innovation. Not all innovations have
time-constant benefits, however. Innovations with network externalities may grow or shrink in
value as the number of other people or firms using the innovation increases or decreases. An
adopter may initially find that an innovation is worth continuing to use, only to change his or her
mind once the number of other users declines. Innovations with network externalities, therefore,
add another potential mechanism for social influence on abandonment: as others abandon, the
benefits to using the innovation decrease, leading to further abandonments.
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Network externalities as a source of value in innovations Network externalities occur when goods become more valuable as the number of others
using them rises (Shapiro and Varian 1999). Network externalities characterize situations where
there are demand-side economies of scale (Farrell and Saloner 1986) – that is, situations where
there are benefits to doing what others do. Most often, this refers to cases where standardization
is important, such as deciding the gauge of railroads (Shapiro and Varian 1999) or whether to
produce videocassettes in VHS or beta (Katz and Shapiro 1986), but the logic can be extended to
other situations as well. DiMaggio and Garip (2011), for example, point to chain migration as a
situation where following others’ actions is less costly than choosing a new action.
Network externalities are most prominent in communications technologies, where the
value of the technology is proportional to the number of connections that can be made with the
technology (Rohlfs 1974). The canonical example is the fax machine: owning a fax machine is
only valuable if others also own a fax machine. As more people own fax machines, the value of
owning a fax machine increases (or, at least, does not decrease). This relationship is often
formulated as Metcalfe’s Law, which states that if the value of communication technology for
each of the 𝑛 people is proportional to the number of other users, 𝑛 − 1, then the value of the
network as a whole is proportional to 𝑛 × (𝑛 − 1) = 𝑛2 − 𝑛 (Shapiro and Varian 1999).
Although the specific functional form of the increase in value has been debated (Zhang, Liu, and
Xu 2015), for the purposes of this study, it is sufficient to note that the benefits of using a
communication technology are non-decreasing with the number of other users.
Modern, web-based communication technologies provide an additional benefit for their
users: not only are they a method for communicating with others, but they are also a repository
for past communication. Unlike older technologies, web-based communication tools often save
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a record of what was said. Thus while the benefit to owning a telephone can only be
proportional to the number of current users of telephones, because a telephone owner cannot use
it to call someone who no longer uses a telephone, the benefit to using a technology that archives
the content of the communication could also increase with the amount of past content that has
been saved. Thus a web-based communication technology, like the enterprise collaboration
system examined in this study, could provide time-varying benefits to current users proportional
to either the number of other users they could communicate with using the technology, or the
amount of content previously created using the technology.2
Finally, a growing literature has begun to distinguish between direct, or general, network
externalities from local, or identity-specific, network externalities (DiMaggio and Cohen 2004;
Sundararajan 2008). A direct network effect means that the benefits created by an addition user
are the same – or do not vary systematically, at least – across the entire communication
technology. A local network effect, by contrast, suggest that the benefits created by an
additional user depend on that user’s relationship with the person perceiving the benefit. For
example, if a person is unlikely to communicate with the new user, the person derives little
benefit from that new user actively using the communication tool. If a person is very likely to
communicate with a new user, however, he or she may derive considerable benefit from that user
actively using the communication tool, and may feel that the benefits to using the software
decrease substantially when the other, nearby user abandons the technology.
2 As information stored by the software ages, however, it could become irrelevant or out of date. Rather than
providing additional benefits for users, storing additional irrelevant information could decrease the value of the
software by making it harder to find relevant information, a condition that Edmunds and Morris (2000) refer to as
information overload.
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These local network externalities may cause considerable inequality in the perceived
value of the software, which may lead to differential rates of abandoning the software. We focus
on local network externalities created by homophily, or the propensity for similar people to
connect to one another (McPherson, Smith-Lovin, and Cook 2001). Specifically, we focus on
the homophily that is induced by the constraints formal organizations place on individuals’
interactions through physical location, functional dependence, and the authority structure of the
organization
Sources of organizational homophily Using an innovation with network externalities provides greater benefits when the user is
more likely to be connected to other users. We focus on homophily as a source of likely
connections. Homophily is a nearly universal feature of social networks (McPherson and Smith-
Lovin 1987; McPherson et al. 2001); people who are similar on salient social characteristics are
more likely to be connected in settings ranging from universities (Kossinets and Watts 2009;
Wimmer and Lewis 2010), to schools (Goodreau, Kitts, and Morris 2009), voluntary
organizations (McPherson and Smith-Lovin 1987), and formal organizations (Kleinbaum et al.
2013). Homophily is generated by both choice and constraint. Choice, or preference, homophily
occurs because people choose to affiliate with others who share similar interests. Induced
homophily occurs people have constraints on their likelihood of encountering someone different.
We focus primarily on induced homophily created by the constraints that a formal organization
imposes on interactions.
Formal organizations constrain interactions through physical location and formal
structure (Kleinbaum et al. 2013). Both of these constraints limit the opportunities that
employees have to interact with each other. The formal structure of the organization dictates
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who employees must interact with to complete the tasks of their job. That formal structure
dictates communication patterns is true by design; as Allen (1977:211) notes, “The real goal of
formal organization is the structuring of communication patterns.” Formal structure divides
employees by business units, job functions, and quasi-formal organizational structures, such as
project teams (Galbraith 1973; Kleinbaum et al. 2013). Within each of these categories, we
expect that employees will be very likely to communicate, and will therefore benefit more from
using a communication tool if more people in the same business unit, job function, or quasi-
organizational structure (such as a project team) are available to communicate with. We expect,
therefore, that people will be less likely to abandon a communication tool when there are more
current users in the same part of the formal structure of the organization.
In particular, two aspects of the formal structure, functional dependence and authority,
are likely to be most important. First, employees who are functionally dependent on one another
– meaning that one needs to communicate with the other to complete tasks related to his or her
job – are more likely to need daily communication. As a result, we expect that employees will
derive greater value from using the software when other employees who they are functionally
dependent on also use the software. Second, employees likely have to communicate frequently
with the people who have authority over them – in this case, a supervisor. We expect that
supervisors have greater influence over the medium of communication, and therefore when
employees’ supervisors use the software more frequently, employees will derive greater benefits
from using the software.
Formal organizations also influence the physical location of their employees, which
limits their opportunities for interaction. Organizations provide their employees with physical
office space, and often require that the employees report to that office space for a minimum
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amount of time.3 By requiring employees to work in close proximity, the organization induces
homophily through propinquity (Blau and Schwartz 1984; Zipf 1949). Employees who work in
the same physical location are more likely to communicate simply because they are nearby and
the costs to interaction are low. Physical location often overlaps with position in the formal
structure; people who perform similar tasks, or who need to work together frequently, are often
co-located (Galbraith 1973). We expect, however, that employees who are co-located are likely
to interact frequently, even if they do not share similar job roles, by virtue of the shared space
they occupy. Therefore, even accounting for similarity of job roles, we expect that when more
people in the same physical location use a communication technology, users will perceive the
technology as more beneficial, and therefore will be less likely to abandon it.
We examine these expectations using data on the use of an innovative piece of
communication software among employees in a large technology company. The software
generated timestamps when people used the software, allowing us to consider the exact time
when someone made their first and last posts to the software. By considering when people made
their last posts to the software, we were able to measure when people abandoned an innovation.
The following section describes the study setting in greater detail.
Data: HighTech and HighTech Software To examine how network externalities influence abandonment of an innovation, we use
data on when employees in a large technology company used an innovative software tool –
specifically, a type of enterprise collaboration software. We will pseudonymously refer to the
company as HighTech, and the software as HighTech Software. HighTech developed HighTech
3 Increasingly, organizations are allowing their employees to work remotely, which limits the influence of physical
location.
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Software as a tool for communication, which employees could use to post documents, discussion
forums, and blog entries in online communities, with other employees. HighTech Software was
introduced as an alternative to other, existing forms of communication at HighTech; instead of
replacing email, phone calls, and other methods of regular communication, HighTech Software
was intended to allow employees an alternative method of communication that would allow them
to reach larger audiences.
HighTech Software posts were organized in online communities. Each post, document
uploaded, etc., was posted to a particular community. To post to, or read posts from, a
community, an employee had to join the community. Communities could be private, meaning
that employees had to request approval to join from a community moderator, or they could be
public, meaning that employees did not have to obtain approval to participate in the community.
Communities were organized around specific topics, which ranged from communities organized
for specific teams, to communities organized around shared social interests. Our analytic sample
uses data from 21,401 communities. We use shared community membership to construct a
bipartite network of employees connected by shared communities.
We consider abandonment among employees who were active users of HighTech
Software at any point during the study period. HighTech employees could use the software
actively or passively. Active use means that a person made posts to the software, while passive
use means that a person used the software to read posts made by others. We focus primarily on
active use, because our data contain information about when people posted to the software,
allowing us to measure active use directly. We do not have a timestamped measure of when
people viewed content in a community, and therefore we cannot measure duration of passive use.
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For our purposes, a person is an active user from the first time he or she posted something to a
HighTech Software community until the last time he or she posted something.
The study period ranges from February 2009, when HighTech introduced HighTech
Software for its employees, to August 2014, when HighTech announced the end of support for
HighTech Software. We take active users to mean anyone who posted content to the software at
least once in this time. During the study period, HighTech employed approximately 73,000 full-
time employees, distributed between 493 sites worldwide. Of those 73,000, 34,068 employees
used HighTech Software at least once. These 34,068 employee represent our analytic sample.
We exclude from our sample vendors, temporary employees, and contractors (approximately
53,000 people), as well as people who left the company during the study period.
Dependent variable: abandoning HighTech Software As a dependent variable, we use the length of time that an employee used of HighTech
Software. HighTech Software activity is timestamped, providing a rare opportunity to examine
not only adoption of the innovation, but also its continuing use. For example, using the
timestamps, people who start using HighTech Software early and continue throughout the study
period can be distinguished from people who start using HighTech Software early but stop
shortly thereafter. We focus primarily on the duration of use – that is, how long a person
continued using the software, given that they used it at least once. Using the HighTech Software
timestamps, the duration of a single person’s use can be calculated by subtracting the time of the
person’s first post from the time of the person’s last post.4
4 To perform arithmetic with time values, typically times are measured as the number of seconds after some arbitrary
date (cf. Grolemund et al. 2013). When two time values measured against the same arbitrary baseline are
subtracted, the baseline cancels, and the result is the number of seconds between the two posts, or the duration of
use.
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As Rogers (2003) notes, people may abandon innovations because the innovation has
been replaced by a preferable alternative. HighTech introduced a new alternative to HighTech
Software in 2014. This replacement was announced on May 1, 2014. Although HighTech
Software continued operating after the end of life announcement, we expect that people who
abandoned after the end of life announcement likely abandoned the software for a different
reason – namely, moving to the new software – than people who abandoned before the end of
life announcement. As such, we treat all people whose last post to the software was after April
1, 2014 as right censored, meaning that they are considered not to have abandoned the software.
Independent variables: value of the software The key independent variable is how much value a person perceives that the software has
when he or she uses it. Since HighTech Software is a software tool for communication, people
perceive value in the software based on the number of other people they could use the software
to communicate with (Shapiro and Varian 1999). HighTech Software maintains a record of past
communications in the communities where they were posted. Documents that were posted stay
posted to the community, as do forum discussions or blog posts. This represents a distinct
possible source of value: a person could view HighTech Software as more valuable because he
or she has the opportunity to communicate with more people in the future, or because he or she
can view more information that has already been added. Therefore, we consider both the number
of current users and the number of posts made. Both of these measures are constructed as time-
varying variables. The number of current users during the interval [𝑡, 𝑡 + Δ𝑡) is the number of
users whose first post occurred before 𝑡 + Δ𝑡 and whose last post occurred after 𝑡 + Δ𝑡. The
number of posts in the same interval is the cumulative number of posts created before 𝑡 + Δ𝑡.
We divide the possible set of communication alters – as well as the posts they made – along two
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dimensions: where they are in the organization, and whether a person is already connected to
them.
Organizational position
To determine the amount of value that a potential communication partner would add, we
consider where that potential communication partner is in the organization relative to the focal
person. We consider relative distance with respect to three aspects of the organizational
structure: the physical locations of office buildings, the functional units of the organization, and
the supervisory structure of the organization. Table 1 provides descriptive statistics for these
measures.
[TABLE 1 ABOUT HERE]
First, we consider the number of current users who work in the same building. These
people are the ones with whom a person is most likely to have met physically in person (Blau
1977; McPherson 1983, 2004), and therefore are the ones with whom a person is most likely to
want to communicate. For robustness, we also consider the number of current users who work in
the same UN region (i.e., Africa, Asia, Europe, Latin America, Northern America, and Oceania).
Second, we consider the number of current users who are members of the same
functional unit. Functional units include groups of employees who all work on similar tasks
(e.g., software engineering, marketing, sales, etc.). Employees are more likely to want to
communicate with others working on the same topics, either to coordinate their work activities or
to share best practices.
Third, we consider the number of current users who are members of the same supervisory
team. Supervisory teams are subsets of functional units that include all of the people who work
for the same supervisor. Employees who work for the same supervisor are likely to need to
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coordinate their activities. More people who work for the same supervisor use HighTech
Software likely indicates that the team relies more heavily on HighTech Software for its
communications, giving it additional value for a current user.
Fourth, we consider whether or not a person’s supervisor is a current user of the software.
Supervisors have power over their employees, and they can require that their employees use
HighTech Software. In our framework, a supervisor using the software represent a special case
of a network effect. When a person’s supervisor uses the software, we expect that the software
gains additional value above and beyond the increase in value caused by adding another current
user because the supervisor sets the norms, in many cases, for how the employee uses the
software.
Network position
The value added by an additional user may also depend on whether or not a person is
directly connected to another user. We construct a similar set of time-varying variables
indicating how many current users and how many posts a person is connected to at a given time
(neighboring users and neighboring posts, respectively). We measure the number of current
users that a person is connected to by calculating the number of current users in communities
where the person is also a current user. We measure the number of posts that a person is
connected to by counting the cumulative number of posts in communities where the person has
posted at least once. We divide the number of neighboring users and neighboring posts along the
same organizational divisions: users/posts from the same office building, from the same unit,
from the same supervisory team, and made by one’s supervisor.
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Distant users
Part of the purpose of HighTech Software was to improve communication among
members of the organization who might not otherwise have an opportunity to talk to one another.
As such, users may derive value from the number of distant users, or posts made by distant users,
rather than the number of proximate users. We measure the number of distant users as the
number of current users in a different office building, a different organizational unit, or a
different supervisory team who a person is connected to. We measure the number of posts by
distant users similarly as the number of posts made by people in a different building, unit, or
team, that a person is connected to. We focus on users and posts that a person is connected to
because posts that (1) a person is not connected to, and (2) are made by someone that the person
is unlikely to encounter in real life are effectively invisible to that person.
Control variables We include control variables for several important organizational and individual factors
that we expect might influence abandoning the software. The controls include 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. We measure the time
that someone first used the software as the number of weeks after the software was introduced,
and we measure the date that a person was hired similarly, as the number of number of weeks
prior to the introduction of HighTech Software. For people who were hired before the
introduction of HighTech Software, this measure is negative; for people who were hired after,
the measure is positive.
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Major releases
Both social and technical factors influence when people use or abandon a new software
tool. HighTech Software had five major releases, a pre-release, and four major releases, each
marking a significant change in the technical value of the software. Technical factors changed
between major releases5 of HighTech Software; each time a new version was introduced, the
technical capabilities of the software changed noticeably. We control for technical factors by
including a dummy variable indicating which major release was available in a given person-
week.
Analytical strategy We model the data using a piecewise exponential discrete time survival model (Allison
2014), with one week time intervals. We chose to make discrete time intervals one week long:
for theoretical and practical reasons. Theoretically, we expect that people do not process the
number of other users or posts instantaneously. Rather, we expect that when a user becomes
inactive, another user does not realize that he or she has become inactive until the inactive user
has not posted for some time. Visual inspection of the median frequency of posts (appendix
figure 2) suggests that users typically post every two weeks. Therefore, a user would not realize
that another user is inactive until at least two weeks have elapsed. We therefore choose one
week as our time interval as a conservative measure. Practically, choosing a one week time
interval leaves us with an analytical dataset of approximately 1.7 million person-weeks of
observations. Measuring the time interval at the person-day would yield approximately 12.6
5 For clarity, I will use the terms “major release” and “version” synonymously. Technically, a version is a subset of
a major release – all major releases represent new versions, but not all version changes are major releases.
However, since we do not consider minor version changes in this study, we can refer to major releases as versions
without a loss of information.
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million person-days of observation, which would introduce significant challenges for data
storage and analysis. Given the median frequency of posts, we expect that we lose little by
binning our data weekly.
The piecewise exponential survival model treats the log of the baseline hazard as constant
within given time intervals.6 Visual inspection of the logged baseline hazard of abandoning over
time (appendix figure 2) showed a spike in the hazard of abandoning in the first week, followed
by a constant hazard of abandoning over time. The spike in the first week occurs when people
experiment with the software, posting once or twice, and then not using the software again. To
control for this, we include an indicator variable for the first week. This defines two intervals
where the log hazard is constant: during the first week, and after the first week. We fit the
piecewise exponential survival model using a logit link.
Results
Number of current users Table 2 shows survival models of abandonment predicted by the number of current users
of HighTech Software. Current users are divided into current users in the same unit, on the same
supervisory team, in the same region, in the same office building, and in the company as whole.
A series of binary variables indicates whether the person’s supervisor had never used the
software, was a current user of the software, or had abandoned the software before the current
6 Appendix figure 2 shows the log hazard plotted against the five other possible specifications we considered:
exponential (log hazard is constant over time), Gompertz (log hazard changes linearly with time), Weibull (log
hazard changes linearly with the log of time), a specification where the log hazard changes with time squared, and
the fully general Cox specification (log hazard is constant within each time point). Of these, the piecewise
exponential provided the best parsimonious fit; by design, the Cox model fits the data perfectly, but the number of
additional parameters estimated, combined with the sparsity of cases with survival times greater than 200 weeks,
would have led to unnecessarily inefficient estimates.
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week. For comparability, each of the variables that count the number of current users have been
standardized.
[TABLE 2 ABOUT HERE]
The models in table 2 show an effect primarily for the number of current users who are
members of the same supervisory team. Model 2 shows that as the number of users on the same
supervisory team increases, the log odds of a user abandoning the software decrease by
approximately 23.1%. Model 3 shows that people who have supervisors who currently use the
software are 0.705 times as likely to abandon the software as people whose supervisors have
never used the software, and people whose supervisors have abandoned the software are 1.05
times as likely to abandon the software as people whose supervisors never used the software.
Similar effects do not appear for current users in other areas. Models 1, 4, and 5 show that more
current users in the same unit, office building, or region has no effect on abandoning the
software, while model 6 shows that when there are more current users in the company as a
whole, people were more likely to abandon the software. These effects remain relatively similar
when taken together, as in model 7. Overall, Table 2 shows minimal network externalities
arising from more current users, other than users on the same supervisory team.
Number of posts [TABLE 3 ABOUT HERE]
Table 3 shows the effect of the number of posts on the odds of abandoning. While the
number of users captures the number of possible communication partners that someone could
have, the number of posts captures the amount of content that someone could access using the
software. Similarly to the models in table 2, the values for number of posts have been
standardized for comparability.
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Table 3 shows primarily positive effects of the number of posts on the odds of
abandoning HighTech Software. Models 2 and 3 show similar effects to the models in table 2.
More posts made by members of the supervisory team in general, and by the person’s supervisor
in particular, are associated with lower log odds of abandoning the software. Models 1, 5, and 6,
however, show that as the number of posts by people in the same unit, region, or in the company
overall increase, the log odds of abandoning the software increase. The effects are sizable; a one
standard deviation change in the number of posts made by users from the same unit, region, or in
the company overall is associated with a 36.1%, 32.0%, or 82.2% increase in abandoning the
software. When included in the same model, however, only the coefficient for number of posts
made by members of the same supervisory team, by supervisors, and by employees of the
company as a whole remain significant. This suggests that the effect of additional posts by
members of the same supervisory team likely does provide additional value for users, while only
additional posts by users of the company overall reduce value for users.
These effects, however, are for users and posts that a person is not connected to. In
HighTech Software, to communicate with another user, or to view a post, a person had to be a
member of the same community as that person or the community where the post was added.
Therefore the number of total users or the number of total posts may not accurately capture the
network externalities created by additional users or additional content, because the person could
not necessarily use HighTech Software to communicate with them. As such, tables 4 and 5 show
the effects of changes in the number of users or the number of posts that someone was connected
to (i.e., the number of current users or number of cumulative posts in their network
neighborhood).
21
Number of current neighbors [TABLE 4 ABOUT HERE]
Table 4 shows piecewise exponential survival models of the number of current neighbors
predicting abandonment of HighTech Social. Since people could not communicate with people
who were not members of the same community, additional users in the same unit, region, or
team might not create network externalities unless they were connected to the user. We examine
this by looking at the number of active users in the communities where a person was also an
active user. Table 4 divides these active neighbors into the number of neighbors in the same
unit, supervisory team, office building, region, or in the company overall. A binary variable
indicates whether or not a person’s supervisor was their neighbor.7 As in the previous tables, the
counts of current neighbors are standardized to facilitate comparisons.
Table 4 shows strong negative effects for the number of current neighbors on the log
odds of abandoning the software, across all organizational divisions. Being connected to more
active users in the same unit, on the same supervisory team, in the same office building, in the
same global region, or in the company as a whole, as well as being connected to one’s
supervisor, all are associated with lower log odds of abandoning HighTech Software. Including
all of these effects in the same model, however, indicates that the only independent effects are
for number of people in the same supervisory team, for being connected to one’s supervisor, and
for people in the company as whole. This suggests that the network externalities created by
additional users are primarily tied to being connected to people that a person is likely to work
with, or to more people generally.
[TABLE 5 ABOUT HERE]
7 For people who did not have a supervisor, this indicator variable is set to 0.
22
Table 5 shows mixed effects for the number of posts that a person is connected to.
Models 1 and 5 show that additional posts by members of the same unit or global region in
communities that a person has posted to increase the log odds of abandoning the HighTech
Software. Models 2 and 3, however, show that more posts by members of the same supervisory
team, or more posts by one’s supervisor, decrease someone’s odds of abandoning the software.
When included in the same model, the effect for number of posts by users in the same global
region becomes insignificant, and the number of posts in the company overall becomes
significantly negatively associated with abandoning the software. Thus being connected to more
posts by members of a person’s supervisory team – including the person’s supervisor – increases
the chances that the person will continue to use the software, while being connected to more
posts from one’s unit decreases the chances that the person will continue to use the software.
Tables 4 and 5 show homophilous connections. People may feel that the software is
more valuable when there is a greater possibility of heterophilous ties, or a larger amount of
heterophilous content. For example, a person might use email, telephones, or face-to-face
communication to interact with people in the same building, but might rely on HighTech
Software to communicate with people in other office buildings. Tables 6 and 7 explore that
possibility, by considering the number of current neighbors and posts made by people in
different areas of the organization.
Heterophilous ties [TABLE 6 ABOUT HERE]
Table 6 shows strong negative effects of the number of current users in different areas of
the organization on the log odds of abandoning the software. Being connected to more active
users in different units, supervisory teams, office buildings, and regions are all associated with
23
lower log odds of abandoning the software.8 The effects are similar in size – a one standard
deviation increase in the number of connections in each different area is associated with a
roughly 30% lower chance of abandoning the software.
[TABLE 7 ABOUT HERE]
Table 7 shows minimal effects of the number of posts from different areas of the
organization. Models 1 through 4 suggest that there is no effect of the number of posts made by
people in different units, supervisory teams, office buildings, and regions on the log odds of
abandoning the software. This suggests that, while past recorded content may continue to be
valuable for people who are on the same team, only current, possible connections increase the
value of continuing to use the software.
[FIGURE 2 ABOUT HERE]
Figure 2 shows a coefficient plot summarizing the final model from each table. Different
colored coefficients represent coefficients from different models. For example, the dark blue
coefficient for company overall represents the coefficient from model 7 in Table 3 for the
number of users in the company overall, while the light blue coefficient for company overall
represents the coefficient from model 7 from Table 4 for number of posts by users in the
company overall. The figure allows easier comparison of the fully controlled models. From the
figure, the largest negative effects are whether a person’s supervisor is a neighbor, and the
number of people on the team who are neighbors. The figure also shows smaller, but consistent,
8 When combined in a single model, the effects of the number of current neighbors in other buildings or regions
disappear, and the effect of the number of current neighbors become large and inversely related – a common
occurrence among highly correlated predictors in linear models. The number of current neighbors in different unit
and in different supervisory teams are correlated at 0.99. As such, we disregard the coefficients from the combined
model.
24
negative effects for the number of users who are in the same region, the number of users who are
on the same team, and whether a person’s supervisor is a current user, regardless of whether
those people are neighbors.9
Discussion Although considerable attention has been paid to when innovations diffuse, far less
attention has focused on when innovations are abandoned. We address this gap by considering
when employees of a large technology company stopped using an innovative piece of software.
The software recorded the timestamps for each of the employees’ interactions, giving us an
unusually fine-grained look at when an innovation was abandoned. We suggest that network
externalities, meaning the benefits generated by more people using the software, influence a
user’s decision whether to continue using the software or to abandon the software. These
network externalities could vary depending on the probability that two people would
communicate. In more homophilous ties, the two people might be more likely to communicate,
meaning that having an additional user in the same unit, same supervisory team, same office
building, or same region might make continuing to use the software more worthwhile than
another user anywhere in the company.
Our findings show a significant network effect for primarily for people on the same
supervisory team. As more people on the same supervisory team –and, in particular, when a
person’s supervisor – use the software, a person is less likely to abandon the software. This
suggests that abandonment of the software is driven by the day to day requirements of work.
When a team decides to use the software for communication, members of the team are more
9 We disregard the highly collinear effects of users on other teams and other units in the heterophilous ties panel of
the figure, as before.
25
likely to continue using it, presumably to coordinate their activities with other team members.
We did not observe a similar effect for members of the same unit, or for people in the same
office building or global region, with whom an employee would be less likely to communicate.
The influence of network externalities on abandonments, therefore, seems to be primarily local.
We also found consistent evidence that more users and more posts in the company as a
whole increased the chance that a person would abandon the software. This finding is at odds
with our expected mechanism – we expected that the value of the software would be
nondecreasing in the number of users. This anomalous finding has 2 possible explanations.
First, it could be capturing some unmeasured component of time since introduction of HighTech
Software. As the software had been in existence longer, more employees had begun using the
software, and more employees abandoned the software as well. Although the piecewise
exponential survival models that we used only considered people who were at risk at a given
time period, and we included a control for the time when a person started using the software,
there may have been a relationship with a different functional form that these controls did not
capture. Second, the effect could have captured the amount of cognitive processing needed to
use the software. As more posts were available on the software, people would have to search
through more information to find the information that they wanted. As such, increasing the
number of posts available would increase the costs to using the software, making people more
likely to abandon its use.
The mechanism that we propose, network externalities, introduces an alternative process
for how social influences could change when people abandon an innovation. Previous studies on
abandoning innovations found mixed results for whether people or firms were influenced by
others when they abandoned an innovation. Many of the studies found that people or firms were
26
influenced by others when they adopted an innovation, but were not influenced by others when
they abandoned it. These studies proposed that prior to adoption, people sought information
about the benefits of the innovation from friends and neighbors, but once they adopted the
innovation, their direct experience with the innovation could provide all the necessary
information. We add to that explanation by considering innovations whose value could vary
with time, and with the number of neighbors who use it. Our mechanism suggests that after
adoption, people reevaluate whether they should continue using the innovation based on the
benefits that it offers. The benefits that the innovation offers, in turn, depends on the number of
others who are using the innovation. This process, therefore, leads to apparent social influences
in the abandonment of an innovation.
We focus primarily on a tool for communication, because network externalities have
been most clearly documented in communication technologies. The mechanism we propose may
also operate in other settings with network externalities, however. For example, if an innovative
practice gains legitimacy from more firms using it, then a firm will be less likely to abandon the
practice when more other firms are also still using the practice. When other firms begin to
abandon the practice, the practice would lose legitimacy and would provide fewer benefits to
firms, leading to more abandonments. Thus our mechanism could also be used to explain
cascades of abandonments, as people and firms evaluate the benefits of the innovations they
adopt on an ongoing basis.
Our study suffers from several limitations. First, we only consider employees adopting a
communication tool at a single, large technology company. The results presented here may not
generalize to other settings, other types of innovation, or other time periods. Second, we only
consider active use of the software. For someone to actively use the software, they must
27
continue to post information to a community. We do not capture passive use of the software,
where employees could use the software to read posts by others, but do not post themselves,
because the software only recorded posts, not page views. Cessation of active use may differ
from cessation of passive use. For example, someone might be more likely to actively post to
communities for their supervisory team, and may be less likely to post to global communities.
This discrepancy would also explain our null findings for the effect of number of current users in
the office building or unit on the chances of abandoning the software. People may continue to be
more likely to passively use the software when more people from their unit or office building are
users, but this would not be captured by our measures.
In spite of these limitations, this approach represents an important step forward for the
literature on the diffusion of innovations. Adoption of innovations is an important process, but it
is only half of the puzzle. To understand how innovations spread through a community, we must
also consider when they are abandoned. Our approach suggests that an integrated framework,
centered on beliefs about the value of the innovation, could be constructed to explain when
people begin and end using an innovation. Moreover, considering the network externalities of an
innovation may also point to how abandonments, like adoptions, could cascade through a group
of people.
28
Figure 1: Cumulative and current users by time
29
Table 1: descriptive statistics
Statistic N Mean St. Dev. Min Max
# of current users in the same unit 1,714,115 2,003.698 1,174.737 1 3,624
# of current users on the same supervisory team 1,714,115 3.881 3.305 1 43
# of current users in the same office building 1,714,115 666.505 987.079 1 3,090
# of current users in the same global region 1,714,115 4,766.506 2,921.081 1 8,628
# of current users in the company as whole 1,714,115 11,354.630 3,798.329 1 14,725
# of posts by neighbors in the company as whole 1,714,115 3,656.424 7,466.801 0 95,849
# of posts by neighbors in the same unit 1,714,115 908.687 1,766.268 0 27,300
# of posts by neighbors in the same office building 1,714,115 400.443 1,320.912 0 24,319
# of posts by neighbors in the same region 1,714,115 1,897.865 4,287.165 0 46,735
# of posts by neighbors on the same supervisory team 1,714,115 30.501 203.335 0 10,412
# of posts by neighbors by supervisor 1,714,115 3.132 20.796 0 919
# of posts in the company as whole 1,714,115 306,496.400 180,518.500 2 628,642
# of posts in the same region 1,714,115 130,153.000 107,039.200 1 368,497
# of posts in the same office building 1,714,115 19,637.440 33,622.980 1 141,566
# of posts by supervisor 1,714,115 11.641 41.006 0 1,026
# of posts by members of the same supervisory team 1,714,115 107.636 279.359 1 10,710
# of posts by people in the same unit 1,714,115 51,122.590 40,123.600 1 161,018
# of neighboring posts by people in different units 1,714,115 2,747.737 6,153.748 0 87,678
# of neighboring posts by people in different teams 1,714,115 3,625.923 7,444.427 0 94,648
30
# of neighboring posts by people in different buildings 1,714,115 3,255.981 6,814.337 0 93,699
# of neighboring posts by people in different regions 1,714,115 1,758.559 4,282.838 0 74,346
# of current neighbors 1,714,115 65.631 203.943 0 2,596
# of current neighbors in the same unit 1,714,115 14.483 33.266 0 421
# of current neighbors in the same region 1,714,115 26.892 83.691 0 1,086
# of current neighbors on the same team 1,714,115 2.157 3.002 0 49
# of current neighbors in the same office building 1,714,115 7.845 28.628 0 502
(Binary variable) Indicator for “is supervisor a neighbor?” 1,714,115 0.102 0.303 0 1
# of current neighbors in different units 1,714,115 51.148 179.309 0 2,369
# of current neighbors in different office buildings 1,714,115 57.786 187.277 0 2,539
# of neighbors in different global regions 1,714,115 38.739 135.567 0 2,152
# of neighbors on different supervisory teams 1,714,115 63.475 202.802 0 2,549
(Binary variable) Indicator for female 1,714,115 0.241 0.428 0 1
(Binary variable) Indicator for mobile worker 1,625,868 0.642 0.479 0 1
Hire week (measured as number of weeks before software
was introduced 1,714,115 173.521 265.523
-
285.143 1,006.857
Employee level (1 = low, 12 = high) 1,714,115 1.295 0.690 1 12
31
Table 2: Piecewise exponential survival model of abandonment by number of current users
Dependent variable:
Abandoning HighTech Software
(1) (2) (3) (4) (5) (6) (7)
Intercept -7.052*** -7.173*** -6.999*** -7.061*** -7.059*** -6.852*** -6.727***
(0.098) (0.097) (0.097) (0.097) (0.102) (0.122) (0.126)
# of users in the same unit (4 week
moving average) 0.006 -0.001
(0.027) (0.032)
# of users in the same supervisory team
(4 week moving average) -0.263*** -0.232***
(0.011) (0.011)
Supervisor currently using the software
(reference: Supervisor never used the
software)
-0.349*** -0.259***
(0.019) (0.019)
Supervisor abandoned the software
(reference: Supervisor never used the
software)
0.054** 0.087***
(0.020) (0.020)
32
# of users in the same office building (4
week moving average) -0.007 -0.012
(0.009) (0.009)
# of users in the same global region (4
week moving average) -0.003 -0.116*
(0.034) (0.046)
# of users in the company overall (4 week
moving average) 0.075** 0.188***
(0.027) (0.040)
Time of first use (weeks after
introduction) 0.007*** 0.007*** 0.007*** 0.007*** 0.007*** 0.007*** 0.006***
(0.0002) (0.0002) (0.0002) (0.0002) (0.0002) (0.0002) (0.0002)
Quit in first week (reference: did not quit
in first week) 3.674*** 3.646*** 3.665*** 3.673*** 3.673*** 3.678*** 3.655***
(0.020) (0.020) (0.020) (0.020) (0.020) (0.020) (0.021)
Female (reference: male) -0.087*** -0.095*** -0.088*** -0.086*** -0.087*** -0.087*** -0.093***
(0.019) (0.019) (0.019) (0.019) (0.019) (0.019) (0.019)
Education: no college (reference:
Bachelor's degree) 1.103 0.893 0.986 1.098 1.102 1.103 0.818
(0.914) (0.907) (0.907) (0.913) (0.914) (0.915) (0.899)
33
Education: Associate's degree (reference:
Bachelor's degree) 0.038 0.080 0.017 0.037 0.037 0.039 0.056
(0.195) (0.194) (0.194) (0.195) (0.195) (0.194) (0.194)
Education: graduate degree (reference:
Bachelor's degree) 0.035 0.045 0.045 0.036 0.034 0.035 0.054
(0.079) (0.079) (0.079) (0.079) (0.079) (0.079) (0.079)
Mobile worker (reference: traditional
worker) -0.034 -0.032 -0.034 -0.034 -0.034 -0.034 -0.033
(0.018) (0.018) (0.018) (0.018) (0.018) (0.018) (0.018)
Region: Africa (reference: North
America) 0.206 0.204 0.173 0.198 0.199 0.206 -0.121
(0.116) (0.116) (0.116) (0.116) (0.144) (0.116) (0.163)
Region: Oceania (reference: North
America) -0.090 -0.075 -0.111* -0.097 -0.097 -0.091 -0.384**
(0.053) (0.053) (0.053) (0.054) (0.097) (0.053) (0.121)
Region: Asia (reference: North America) 0.050* 0.061** 0.035 0.045* 0.045 0.050* -0.169*
(0.020) (0.020) (0.020) (0.021) (0.065) (0.020) (0.084)
Region: Europe (reference: North
America) -0.091*** -0.066* -0.071** -0.098*** -0.095 -0.091*** -0.258**
(0.026) (0.026) (0.026) (0.028) (0.063) (0.026) (0.080)
34
Region: Latin America (reference: North
America) 0.135** 0.170*** 0.164*** 0.131** 0.129 0.135** -0.100
(0.048) (0.048) (0.048) (0.048) (0.094) (0.048) (0.119)
Employee's tenure (weeks before
HighTech Software) 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001
(0.00003) (0.00003) (0.00003) (0.00003) (0.00003) (0.00003) (0.00003)
Employee's level 0.079*** 0.062*** 0.066*** 0.080*** 0.079*** 0.079*** 0.054***
(0.012) (0.012) (0.012) (0.012) (0.012) (0.012) (0.012)
Current version: major release 1
(reference: pre-release) 0.039 0.103 0.073 0.043 0.042 -0.018 0.051
(0.100) (0.099) (0.099) (0.100) (0.102) (0.102) (0.102)
Current version: major release 2
(reference: pre-release) 0.496*** 0.646*** 0.578*** 0.507*** 0.507*** 0.331** 0.435***
(0.100) (0.096) (0.096) (0.096) (0.109) (0.115) (0.116)
Current version: major release 3
(reference: pre-release) 1.039*** 1.243*** 1.133*** 1.056*** 1.055*** 0.798*** 0.889***
(0.107) (0.097) (0.097) (0.097) (0.119) (0.133) (0.134)
Current version: major release 4
(reference: pre-release) 1.721*** 1.920*** 1.780*** 1.735*** 1.735*** 1.514*** 1.592***
(0.106) (0.099) (0.099) (0.099) (0.116) (0.126) (0.128)
35
Functional unit fixed-effects? Yes Yes Yes Yes Yes Yes Yes
Observations 1,490,528 1,490,528 1,490,528 1,490,528 1,490,528 1,490,528 1,490,528
Log Likelihood -83,335.980 -82,974.790 -83,096.350 -83,335.720 -83,336.000 -83,332.250 -82,813.460
Akaike Inf. Crit. 166,740.000 166,017.600 166,262.700 166,739.400 166,740.000 166,732.500 165,706.900
Note: *p<0.05; **p<0.01; ***p<0.001
36
Table 3: Piecewise exponential survival model of abandonments by number of posts created
Dependent variable:
event
(1) (2) (3) (4) (5) (6) (7)
Intercept -6.987*** -7.073*** -7.076*** -7.051*** -6.867*** -6.056*** -6.036***
(0.097) (0.097) (0.097) (0.097) (0.098) (0.105) (0.108)
# posts from users in the same unit 0.308*** 0.015
(0.019) (0.025)
# posts from users in the same
supervisory team -0.130*** -0.138***
(0.014) (0.015)
# posts by supervisor -0.126*** -0.120***
(0.013) (0.013)
# posts from users in the same office
building 0.007 -0.016
(0.008) (0.009)
# posts from users in the same global
region 0.278*** -0.007
37
(0.020) (0.025)
# posts from users in the company overall 0.600*** 0.641***
(0.024) (0.035)
Time of first use (weeks after
introduction) 0.006*** 0.007*** 0.007*** 0.007*** 0.006*** 0.006*** 0.005***
(0.0002) (0.0002) (0.0002) (0.0002) (0.0002) (0.0002) (0.0002)
Quit in first week (reference: did not quit
in first week) 3.719*** 3.666*** 3.669*** 3.673*** 3.698*** 3.775*** 3.770***
(0.021) (0.020) (0.020) (0.020) (0.020) (0.021) (0.021)
Female (reference: male) -0.087*** -0.083*** -0.090*** -0.088*** -0.087*** -0.088*** -0.084***
(0.019) (0.019) (0.019) (0.019) (0.019) (0.019) (0.019)
Education: no college (reference:
Bachelor's degree) 1.261 1.068 1.083 1.105 1.196 1.230 1.183
(0.925) (0.911) (0.913) (0.916) (0.924) (0.922) (0.915)
Education: Associate's degree (reference:
Bachelor's degree) 0.045 0.044 0.035 0.037 0.043 0.021 0.026
(0.195) (0.195) (0.194) (0.195) (0.195) (0.195) (0.195)
Education: graduate degree (reference:
Bachelor's degree) 0.034 0.041 0.037 0.033 0.042 0.034 0.046
(0.079) (0.079) (0.079) (0.079) (0.079) (0.079) (0.079)
38
Mobile worker (reference: traditional
worker) -0.035* -0.034 -0.031 -0.034 -0.032 -0.033 -0.029
(0.018) (0.018) (0.018) (0.018) (0.018) (0.018) (0.018)
Region: Africa (reference: North
America) 0.214 0.197 0.195 0.214 0.835*** 0.209 0.156
(0.116) (0.116) (0.116) (0.116) (0.125) (0.116) (0.130)
Region: Oceania (reference: North
America) -0.093 -0.078 -0.098 -0.084 0.481*** -0.090 -0.113
(0.053) (0.053) (0.053) (0.054) (0.068) (0.053) (0.075)
Region: Asia (reference: North America) 0.046* 0.055** 0.046* 0.057** 0.529*** 0.051* 0.025
(0.020) (0.020) (0.020) (0.021) (0.040) (0.020) (0.048)
Region: Europe (reference: North
America) -0.088*** -0.082** -0.076** -0.083** 0.326*** -0.090*** -0.095*
(0.026) (0.026) (0.026) (0.028) (0.040) (0.026) (0.046)
Region: Latin America (reference: North
America) 0.142** 0.139** 0.149** 0.141** 0.694*** 0.140** 0.131
(0.048) (0.048) (0.048) (0.048) (0.063) (0.048) (0.070)
Employee's tenure (weeks before
HighTech Software) 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001
(0.00003) (0.00003) (0.00003) (0.00003) (0.00003) (0.00003) (0.00003)
39
Employee's level 0.079*** 0.074*** 0.073*** 0.079*** 0.080*** 0.081*** 0.071***
(0.012) (0.012) (0.012) (0.012) (0.012) (0.012) (0.012)
Current version: major release 1
(reference: pre-release) 0.066 0.066 0.054 0.039 -0.020 -0.017 0.023
(0.100) (0.099) (0.099) (0.099) (0.100) (0.100) (0.100)
Current version: major release 2
(reference: pre-release) 0.443*** 0.549*** 0.529*** 0.500*** 0.307** 0.129 0.185
(0.096) (0.096) (0.096) (0.096) (0.097) (0.097) (0.098)
Current version: major release 3
(reference: pre-release) 0.747*** 1.124*** 1.092*** 1.043*** 0.656*** 0.013 0.069
(0.099) (0.097) (0.097) (0.097) (0.101) (0.106) (0.107)
Current version: major release 4
(reference: pre-release) 1.172*** 1.833*** 1.789*** 1.722*** 1.154*** 0.097 0.154
(0.105) (0.099) (0.099) (0.099) (0.107) (0.119) (0.119)
Functional unit fixed-effects? Yes Yes Yes Yes Yes Yes Yes
Observations 1,490,528 1,490,528 1,490,528 1,490,528 1,490,528 1,490,528 1,490,528
Log Likelihood -83,206.960 -83,282.180 -83,272.280 -83,335.610 -83,242.080 -83,025.980 -82,886.030
Akaike Inf. Crit. 166,481.900 166,632.400 166,612.600 166,739.200 166,552.200 166,120.000 165,850.100
40
Note: *p<0.05; **p<0.01; ***p<0.001
41
Table 4: Piecewise exponential survival model of abandoning by number of current neighbors
Dependent variable:
event
(1) (2) (3) (4) (5) (6) (7)
Intercept -7.108*** -7.409*** -7.002*** -7.128*** -7.094*** -7.096*** -7.381***
(0.097) (0.097) (0.097) (0.097) (0.097) (0.097) (0.097)
# of neighbors in the same unit (4 week
moving average) -0.569*** 0.033
(0.018) (0.024)
# of neighbors in the same supervisory
team (4 week moving average) -1.143*** -1.092***
(0.018) (0.020)
Supervisor is current neighbor (reference:
Supervisor is not current neighbor) -1.187*** -0.439***
(0.042) (0.044)
# of neighbors in the same office building
(4 week moving average) -0.688*** 0.023
(0.034) (0.023)
42
# of neighbors in the same global region
(4 week moving average) -0.446*** -0.013
(0.020) (0.034)
# of neighbors in the company overall (4
week moving average) -0.366*** -0.086**
(0.016) (0.033)
Time of first use (weeks after
introduction) 0.006*** 0.006*** 0.007*** 0.006*** 0.006*** 0.006*** 0.006***
(0.0002) (0.0002) (0.0002) (0.0002) (0.0002) (0.0002) (0.0002)
Quit in first week (reference: did not quit
in first week) 3.663*** 3.602*** 3.663*** 3.663*** 3.675*** 3.677*** 3.607***
(0.020) (0.021) (0.020) (0.020) (0.020) (0.020) (0.021)
Female (reference: male) -0.080*** -0.035 -0.084*** -0.075*** -0.080*** -0.080*** -0.036
(0.019) (0.019) (0.019) (0.019) (0.019) (0.019) (0.019)
Education: no college (reference:
Bachelor's degree) 0.948 0.653 1.046 1.010 1.004 1.036 0.648
(0.904) (0.895) (0.912) (0.907) (0.908) (0.908) (0.895)
Education: Associate's degree (reference:
Bachelor's degree) 0.073 0.177 0.047 0.055 0.053 0.045 0.171
(0.194) (0.196) (0.192) (0.195) (0.195) (0.195) (0.195)
43
Education: graduate degree (reference:
Bachelor's degree) 0.049 0.047 0.050 0.066 0.046 0.042 0.052
(0.079) (0.080) (0.079) (0.079) (0.079) (0.079) (0.080)
Mobile worker (reference: traditional
worker) -0.042* -0.048** -0.027 -0.041* -0.038* -0.040* -0.047**
(0.018) (0.018) (0.018) (0.018) (0.018) (0.018) (0.018)
Region: Africa (reference: North
America) 0.172 0.104 0.178 0.147 0.124 0.181 0.095
(0.116) (0.116) (0.116) (0.116) (0.116) (0.116) (0.116)
Region: Oceania (reference: North
America) -0.100 -0.054 -0.092 -0.124* -0.170** -0.098 -0.056
(0.054) (0.054) (0.053) (0.053) (0.053) (0.053) (0.054)
Region: Asia (reference: North America) 0.028 0.080*** 0.050* 0.040* -0.016 0.031 0.075***
(0.020) (0.020) (0.020) (0.020) (0.020) (0.020) (0.021)
Region: Europe (reference: North
America) -0.090*** -0.028 -0.069** -0.123*** -0.140*** -0.093*** -0.027
(0.026) (0.027) (0.026) (0.026) (0.026) (0.026) (0.027)
Region: Latin America (reference: North
America) 0.155** 0.309*** 0.195*** 0.160*** 0.079 0.120* 0.311***
(0.048) (0.049) (0.048) (0.048) (0.048) (0.048) (0.049)
44
Employee's tenure (weeks before
HighTech Software) 0.00004 -0.00002 0.00004 0.00005 0.00004 0.00004 -0.00002
(0.00003) (0.00003) (0.00003) (0.00003) (0.00003) (0.00003) (0.00003)
Employee's level 0.068*** 0.038** 0.070*** 0.074*** 0.071*** 0.072*** 0.036**
(0.012) (0.012) (0.012) (0.012) (0.012) (0.012) (0.012)
Current version: major release 1
(reference: pre-release) 0.068 0.159 0.031 0.063 0.078 0.077 0.155
(0.100) (0.100) (0.100) (0.100) (0.100) (0.100) (0.100)
Current version: major release 2
(reference: pre-release) 0.557*** 0.642*** 0.508*** 0.536*** 0.557*** 0.554*** 0.642***
(0.096) (0.096) (0.096) (0.096) (0.096) (0.096) (0.096)
Current version: major release 3
(reference: pre-release) 1.153*** 1.225*** 1.067*** 1.106*** 1.141*** 1.140*** 1.233***
(0.097) (0.097) (0.097) (0.097) (0.097) (0.097) (0.097)
Current version: major release 4
(reference: pre-release) 1.841*** 1.980*** 1.759*** 1.790*** 1.821*** 1.815*** 1.986***
(0.099) (0.099) (0.099) (0.099) (0.099) (0.099) (0.099)
Functional unit fixed-effects? Yes Yes Yes Yes Yes Yes Yes
Observations 1,490,528 1,490,528 1,490,528 1,490,528 1,490,528 1,490,528 1,490,528
45
Log Likelihood -82,529.980 -80,175.530 -82,764.140 -82,885.710 -82,905.430 -82,942.500 -80,111.440
Akaike Inf. Crit. 165,128.000 160,419.100 165,596.300 165,839.400 165,878.900 165,953.000 160,300.900
Note: *p<0.05; **p<0.01; ***p<0.001
46
Table 5: Piecewise exponential survival model of abandonment by number of posts in neighborhood
Dependent variable:
event
(1) (2) (3) (4) (5) (6) (7)
Intercept -7.063*** -7.056*** -7.063*** -7.056*** -7.059*** -7.056*** -7.083***
(0.097) (0.097) (0.097) (0.097) (0.097) (0.097) (0.097)
# posts from users in the same unit 0.029*** 0.074***
(0.008) (0.014)
# posts from users in the same
supervisory team -0.084*** -0.072***
(0.018) (0.017)
# posts by supervisor -0.121*** -0.113***
(0.018) (0.018)
# posts from users in the same office
building 0.007 -0.002
(0.008) (0.011)
# posts from users in the same global
region 0.021* 0.034
47
(0.009) (0.019)
# posts from users in the company overall 0.011 -0.067***
(0.008) (0.018)
Time of first use (weeks after
introduction) 0.007*** 0.007*** 0.007*** 0.007*** 0.007*** 0.007*** 0.007***
(0.0002) (0.0002) (0.0002) (0.0002) (0.0002) (0.0002) (0.0002)
Quit in first week (reference: did not quit
in first week) 3.673*** 3.670*** 3.670*** 3.673*** 3.672*** 3.673*** 3.668***
(0.020) (0.020) (0.020) (0.020) (0.020) (0.020) (0.020)
Female (reference: male) -0.088*** -0.086*** -0.088*** -0.087*** -0.088*** -0.087*** -0.089***
(0.019) (0.019) (0.019) (0.019) (0.019) (0.019) (0.019)
Education: no college (reference:
Bachelor's degree) 1.087 1.095 1.098 1.103 1.082 1.093 1.079
(0.912) (0.913) (0.913) (0.914) (0.912) (0.913) (0.910)
Education: Associate's degree (reference:
Bachelor's degree) 0.039 0.036 0.038 0.037 0.036 0.037 0.043
(0.195) (0.194) (0.194) (0.195) (0.194) (0.195) (0.194)
Education: graduate degree (reference:
Bachelor's degree) 0.037 0.033 0.037 0.034 0.034 0.035 0.042
(0.079) (0.079) (0.079) (0.079) (0.079) (0.079) (0.079)
48
Mobile worker (reference: traditional
worker) -0.033 -0.035 -0.032 -0.034 -0.034 -0.034 -0.030
(0.018) (0.018) (0.018) (0.018) (0.018) (0.018) (0.018)
Region: Africa (reference: North
America) 0.208 0.203 0.201 0.208 0.217 0.207 0.213
(0.116) (0.116) (0.116) (0.116) (0.116) (0.116) (0.116)
Region: Oceania (reference: North
America) -0.091 -0.087 -0.091 -0.088 -0.078 -0.090 -0.069
(0.053) (0.053) (0.053) (0.053) (0.054) (0.053) (0.054)
Region: Asia (reference: North America) 0.054** 0.054** 0.051* 0.052* 0.060** 0.052* 0.070***
(0.020) (0.020) (0.020) (0.020) (0.020) (0.020) (0.021)
Region: Europe (reference: North
America) -0.090*** -0.089*** -0.083** -0.089*** -0.082** -0.091*** -0.067*
(0.026) (0.026) (0.026) (0.027) (0.027) (0.026) (0.027)
Region: Latin America (reference: North
America) 0.138** 0.137** 0.139** 0.137** 0.147** 0.137** 0.156**
(0.048) (0.048) (0.048) (0.048) (0.048) (0.048) (0.049)
Employee's tenure (weeks before
HighTech Software) 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.00005
(0.00003) (0.00003) (0.00003) (0.00003) (0.00003) (0.00003) (0.00003)
49
Employee's level 0.081*** 0.076*** 0.077*** 0.079*** 0.080*** 0.080*** 0.076***
(0.012) (0.012) (0.012) (0.012) (0.012) (0.012) (0.012)
Current version: major release 1
(reference: pre-release) 0.027 0.047 0.052 0.038 0.031 0.036 0.041
(0.100) (0.099) (0.099) (0.100) (0.100) (0.100) (0.100)
Current version: major release 2
(reference: pre-release) 0.480*** 0.514*** 0.523*** 0.499*** 0.486*** 0.494*** 0.502***
(0.096) (0.096) (0.096) (0.096) (0.096) (0.096) (0.096)
Current version: major release 3
(reference: pre-release) 1.013*** 1.068*** 1.079*** 1.044*** 1.024*** 1.036*** 1.049***
(0.097) (0.097) (0.097) (0.097) (0.097) (0.097) (0.097)
Current version: major release 4
(reference: pre-release) 1.680*** 1.758*** 1.771*** 1.722*** 1.695*** 1.711*** 1.732***
(0.100) (0.099) (0.099) (0.099) (0.100) (0.100) (0.100)
Functional unit fixed-effects? Yes Yes Yes Yes Yes Yes Yes
Observations 1,490,528 1,490,528 1,490,528 1,490,528 1,490,528 1,490,528 1,490,528
Log Likelihood -83,330.200 -83,320.790 -83,303.240 -83,335.680 -83,333.010 -83,335.180 -83,276.210
Akaike Inf. Crit. 166,728.400 166,709.600 166,674.500 166,739.400 166,734.000 166,738.400 166,630.400
50
Note: *p<0.05; **p<0.01; ***p<0.001
51
Table 6: Piecewise exponential survival models of abandonment by number of current neighbors in
different areas
Dependent variable:
event
(1) (2) (3) (4) (5)
Intercept -7.089*** -7.092*** -7.091*** -7.090*** -7.082***
(0.097) (0.097) (0.097) (0.097) (0.097)
# current neighbors in different units (4 week moving average) -0.305*** 2.324***
(0.015) (0.132)
# current neighbors in different supervisory teams (4 week moving
average) -0.342*** -2.591***
(0.016) (0.190)
# current neighbors in different office buildings (4 week moving
average) -0.337*** -0.167
(0.016) (0.158)
# current neighbors in different global regions (4 week moving
average) -0.311*** 0.113
(0.015) (0.060)
52
Time of first use (weeks after introduction) 0.006*** 0.006*** 0.006*** 0.006*** 0.006***
(0.0002) (0.0002) (0.0002) (0.0002) (0.0002)
Quit in first week (reference: did not quit in first week) 3.679*** 3.678*** 3.678*** 3.678*** 3.671***
(0.020) (0.020) (0.020) (0.020) (0.020)
Female (reference: male) -0.080*** -0.080*** -0.081*** -0.081*** -0.082***
(0.019) (0.019) (0.019) (0.019) (0.019)
Education: no college (reference: Bachelor's degree) 1.054 1.041 1.043 1.060 0.988
(0.909) (0.908) (0.908) (0.909) (0.907)
Education: Associate's degree (reference: Bachelor's degree) 0.041 0.043 0.043 0.040 0.055
(0.195) (0.195) (0.195) (0.195) (0.194)
Education: graduate degree (reference: Bachelor's degree) 0.041 0.042 0.038 0.039 0.046
(0.079) (0.079) (0.079) (0.079) (0.079)
Mobile worker (reference: traditional worker) -0.039* -0.040* -0.040* -0.040* -0.040*
(0.018) (0.018) (0.018) (0.018) (0.018)
Region: Africa (reference: North America) 0.184 0.182 0.186 0.208 0.175
(0.116) (0.116) (0.116) (0.116) (0.116)
Region: Oceania (reference: North America) -0.097 -0.098 -0.095 -0.070 -0.113*
(0.053) (0.053) (0.053) (0.054) (0.054)
53
Region: Asia (reference: North America) 0.033 0.031 0.032 0.053** 0.021
(0.020) (0.020) (0.020) (0.020) (0.020)
Region: Europe (reference: North America) -0.092*** -0.093*** -0.089*** -0.072** -0.100***
(0.026) (0.026) (0.026) (0.026) (0.027)
Region: Latin America (reference: North America) 0.118* 0.119* 0.117* 0.136** 0.131**
(0.048) (0.048) (0.048) (0.048) (0.048)
Employee's tenure (weeks before HighTech Software) 0.00004 0.00004 0.00004 0.00004 0.00005
(0.00003) (0.00003) (0.00003) (0.00003) (0.00003)
Employee's level 0.073*** 0.072*** 0.072*** 0.073*** 0.073***
(0.012) (0.012) (0.012) (0.012) (0.012)
Current version: major release 1 (reference: pre-release) 0.075 0.076 0.076 0.071 0.063
(0.100) (0.100) (0.100) (0.100) (0.100)
Current version: major release 2 (reference: pre-release) 0.548*** 0.552*** 0.551*** 0.544*** 0.549***
(0.096) (0.096) (0.096) (0.096) (0.096)
Current version: major release 3 (reference: pre-release) 1.128*** 1.136*** 1.136*** 1.125*** 1.138***
(0.097) (0.097) (0.097) (0.097) (0.097)
Current version: major release 4 (reference: pre-release) 1.802*** 1.811*** 1.811*** 1.799*** 1.818***
54
(0.099) (0.099) (0.099) (0.099) (0.099)
Functional unit fixed-effects? Yes Yes Yes Yes Yes
Observations 1,490,528 1,490,528 1,490,528 1,490,528 1,490,528
Log Likelihood -83,038.200 -82,976.240 -82,982.590 -83,028.440 -82,784.500
Akaike Inf. Crit. 166,144.400 166,020.500 166,033.200 166,124.900 165,643.000
Note: *p<0.05; **p<0.01; ***p<0.001
55
Table 7: Piecewise exponential survival model of abandonments by number of posts by neighbors in
different areas of the organization
Dependent variable:
event
(1) (2) (3) (4) (5)
Intercept -7.056*** -7.057*** -7.057*** -7.056*** -7.059***
(0.097) (0.097) (0.097) (0.097) (0.097)
# posts from neighbors in different units 0.005 -0.204***
(0.008) (0.048)
# posts from neighbors in different supervisory teams 0.011 0.244***
(0.008) (0.070)
# posts from neighbors in different office buildings 0.010 0.004
(0.008) (0.056)
# posts from neighbors in different global regions -0.001
(0.008)
Time of first use (weeks after introduction) -0.00001
(0.00000)
56
Quit in first week (reference: did not quit in first week) 0.007*** 0.007*** 0.007*** 0.007*** 0.007***
(0.0002) (0.0002) (0.0002) (0.0002) (0.0002)
Female (reference: male) 3.673*** 3.673*** 3.673*** 3.673*** 3.673***
(0.020) (0.020) (0.020) (0.020) (0.020)
Education: no college (reference: Bachelor's degree) -0.087*** -0.087*** -0.087*** -0.087*** -0.088***
(0.019) (0.019) (0.019) (0.019) (0.019)
Education: Associate's degree (reference: Bachelor's degree) 1.097 1.092 1.092 1.102 1.090
(0.914) (0.913) (0.913) (0.914) (0.912)
Education: graduate degree (reference: Bachelor's degree) 0.037 0.037 0.037 0.037 0.042
(0.195) (0.195) (0.195) (0.195) (0.194)
Mobile worker (reference: traditional worker) 0.034 0.034 0.035 0.034 0.039
(0.079) (0.079) (0.079) (0.079) (0.079)
Region: Africa (reference: North America) -0.034 -0.034 -0.034 -0.034 -0.033
(0.018) (0.018) (0.018) (0.018) (0.018)
Region: Oceania (reference: North America) 0.206 0.207 0.207 0.206 0.220
(0.116) (0.116) (0.116) (0.116) (0.116)
Region: Asia (reference: North America) -0.090 -0.090 -0.091 -0.090 -0.070
(0.053) (0.053) (0.053) (0.054) (0.054)
57
Region: Europe (reference: North America) 0.051* 0.052* 0.051* 0.050* 0.067**
(0.020) (0.020) (0.020) (0.020) (0.021)
Region: Latin America (reference: North America) -0.091*** -0.091*** -0.091*** -0.091*** -0.075**
(0.026) (0.026) (0.026) (0.027) (0.027)
Employee's tenure (weeks before HighTech Software) 0.136** 0.137** 0.137** 0.136** 0.151**
(0.048) (0.048) (0.048) (0.048) (0.049)
Employee's level 0.0001 0.0001 0.0001 0.0001 0.0001
(0.00003) (0.00003) (0.00003) (0.00003) (0.00003)
Current version: major release 1 (reference: pre-release) 0.079*** 0.080*** 0.080*** 0.079*** 0.081***
(0.012) (0.012) (0.012) (0.012) (0.012)
Current version: major release 2 (reference: pre-release) 0.038 0.035 0.036 0.041 0.027
(0.100) (0.100) (0.100) (0.100) (0.100)
Current version: major release 3 (reference: pre-release) 0.499*** 0.494*** 0.495*** 0.503*** 0.479***
(0.096) (0.096) (0.096) (0.096) (0.096)
Current version: major release 4 (reference: pre-release) 1.043*** 1.035*** 1.037*** 1.050*** 1.014***
(0.097) (0.097) (0.097) (0.097) (0.097)
major.releaseMR4 1.721*** 1.709*** 1.712*** 1.731*** 1.683***
58
(0.100) (0.100) (0.100) (0.099) (0.100)
Functional unit fixed-effects? Yes Yes Yes Yes Yes
Observations 1,490,528 1,490,528 1,490,528 1,490,528 1,490,528
Log Likelihood -83,335.810 -83,335.040 -83,335.230 -83,336.000 -83,322.320
Akaike Inf. Crit. 166,739.600 166,738.100 166,738.500 166,740.000 166,718.600
Note: *p<0.05; **p<0.01; ***p<0.001
59
Figure 2: Coefficient plots summarizing each of the full models
60
Appendix figure 1: Plot of several hazard estimates by time
Notes: black point ranges indicate estimates from the completely general specification, which models the hazard using one indicator
variable for each value of week after adoption. This approach for selection a function to represent the baseline hazard follows the
approach in Singer and Willett (2003).