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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.


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.


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 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:


(0.195) (0.194) (0.194) (0.195) (0.195) (0.194) (0.194)

Education: graduate degree (reference:


(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)


(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)


(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)



(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)


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)


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)


(0.019) (0.019) (0.019) (0.019) (0.019) (0.019) (0.019)



(0.925) (0.911) (0.913) (0.916) (0.924) (0.922) (0.915)



(0.195) (0.195) (0.194) (0.195) (0.195) (0.195) (0.195)



(0.079) (0.079) (0.079) (0.079) (0.079) (0.079) (0.079)

38


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)


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)


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)


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)


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)



(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)


(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)


(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)


(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)


(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)


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)


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)


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)



(0.904) (0.895) (0.912) (0.907) (0.908) (0.908) (0.895)



(0.194) (0.196) (0.192) (0.195) (0.195) (0.195) (0.195)

43



(0.079) (0.080) (0.079) (0.079) (0.079) (0.079) (0.080)


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)


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)


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)


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)


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


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)


(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)



(0.096) (0.096) (0.096) (0.096) (0.096) (0.096) (0.096)



(0.097) (0.097) (0.097) (0.097) (0.097) (0.097) (0.097)



(0.099) (0.099) (0.099) (0.099) (0.099) (0.099) (0.099)


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)


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)


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)


(0.019) (0.019) (0.019) (0.019) (0.019) (0.019) (0.019)



(0.912) (0.913) (0.913) (0.914) (0.912) (0.913) (0.910)



(0.195) (0.194) (0.194) (0.195) (0.194) (0.195) (0.194)



(0.079) (0.079) (0.079) (0.079) (0.079) (0.079) (0.079)

48


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)


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)


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)


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)


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)



(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)


(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)



(0.096) (0.096) (0.096) (0.096) (0.096) (0.096) (0.096)



(0.097) (0.097) (0.097) (0.097) (0.097) (0.097) (0.097)



(0.100) (0.099) (0.099) (0.099) (0.100) (0.100) (0.100)


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)


(0.097) (0.097) (0.097) (0.097) (0.097)


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)


(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)


(0.096) (0.096) (0.096) (0.096) (0.096)


(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).