Translete Why People Engage in Supplemental Work

Why people engage in supplemental work: The role of
technology, response expectations, and communication
persistence
Summary
Supported by various collaboration technologies that allow communication from any
place or time, employees increasingly engage in technology-assisted supplemental
work (TASW). Challenges associated with managing work and nonwork time have
been further complicated by a global pandemic that has altered traditional work patterns
and locations. To date, studies applying a TASW framework have focused
mainly on individual uses of technology or connectivity behaviors and not considered
the potential team and social pressures underlying these processes. This study
provides clarity on the differences between technology use and TASW and sheds
light on the drivers of TASW in a work environment characterized by high connectivity
and diverse team structures. Specifically, we demonstrate how individual,
social, and material pressures concomitantly impact individual work practices in
a team context. Drawing on multisource and multilevel data provided by
443 employees nested in 122 teams, this study shows that individual collaboration
technology use and team-level response expectations are independently contributing
to TASW. Though the persistence of communication afforded by collaboration
technologies mitigates the impact of collaboration technology use on TASW, this
persistence is not found to impact the relationship between team-level response
expectations and TASW. We discuss how these findings inform our understanding
of TASW.
KEYWORDS
collaboration technologies, communication persistence, response expectations, team
structure, technology-assisted supplemental work
1 | INTRODUCTION
Employees around the world experienced an abrupt transition in their
work roles due to the COVID-19 pandemic (Vaziri et al., 2020). For
many workers, this involved a reconfiguration of the boundaries
between work and nonwork (Fisher et al., 2020) as workers began
teleworking almost overnight, in some cases for the first time
(Kramer & Kramer, 2020). These shifts likely increased challenges for
individual workers to manage work and nonwork time (Allen
et al., 2021) whereas pressures to work during evenings, nights, or on
weekends intensified. For instance, as work time becomes more
porous and individuals may attend to house chores, home schooling
or other social activities between work meetings, individuals may

choose to sacrifice sleep hours, nights, or early mornings to meet
work demands (Xiao et al., 2021). In addition, Chong et al. (2020)
noted that the interdependency of work may generate greater task
setbacks under conditions of forced telework during the pandemic,
which may require workers to engage in supplemental work to maintain
individual and team performance levels. However, technologyassisted
supplemental work (TASW) has been steadily on the rise over
the past decades and may contribute to a host of negative outcomes
(Eichberger et al., 2020) the ways individual, social, and technological
factors independently or conjointly affect these work practices are
largely unknown. Hence, the current study builds on the TASW
framework to examine how different factors present in contemporary
work environments may exert pressure to engage in TASW.
Information and communication technologies (ICTs) differ from
industrial or production technologies in that they not only facilitate
new work practices themselves, but in doing so produce information
that can be accessed by others and acted upon (Zuboff, 1988). Over
the past several decades, scholars have noted that as ICTs alter the
ways that information is presented and made available within organizations,
they support new structures and ways of organizing
(Barley, 1986; Volkoff & Strong, 2013; Zammuto et al., 2007). Specifically,
the use of ICTs can facilitate a variety of organizational changes
including altering advice networks (Leonardi, 2007), ways of learning
(Garud & Kumaraswamy, 2005), patterns of knowledge production
(Schultze & Boland, 2000) and individual work routines (Pentland &
Feldman, 2008). A particularly notable and influential aspect of contemporary
ICTs is that they afford workers greater forms of connectivity
with individuals, teams, and organizations (e.g., Nurmi &
Hinds, 2020; Wajcman & Rose, 2011). Through the use of mobile
phones, personal computers, and wireless networks, workers can
interact with each other without the constraints of time or need for
co-location. This malleability and ubiquity of ICTs also means that
employees increasingly work extended time outside the office—at
home beyond regular working hours, at night, or on weekends
(Dery & MacCormick, 2012; Fenner & Renn, 2010; Leonardi
et al., 2010; Wajcman & Rose, 2011).
For the present study, we are concerned with TASW defined as
distributed work practices performed after hours, often discretionary
and not covered by a formal contract or compensation, and accomplished
through ICTs such as laptops or other mobile devices
(Arlinghaus & Nachreiner, 2014; Fenner & Renn, 2004, 2010;
Ojala, 2011). Though certain work characteristics may impose
demands for extending one's connectivity to work or working during
nontraditional time periods, less is known about the particular
mechanisms—individual, social, and material—driving how and why
employees engage in productive work behavior, collaborate, and complete
substantial work tasks using these technologies outside of work
hours. Investigating how differing work conditions operate as pressures
underlying the performance of TASW makes several contributions
to organizational scholarship.
First, this work contributes to an understanding of how the
TASW framework operates in a work environment characterized by
high connectivity and diverse team structures. Previous studies
referencing the TASW framework typically operationalize TASW as
technology use after hours (e.g., Arlinghaus & Nachreiner, 2014;
Barber & Jenkins, 2014; Boswell & Olson-Buchanan, 2007; Chen &
Karahanna, 2014; Day et al., 2012; Derks & Bakker, 2014; Diaz
et al., 2012; Ohly & Latour, 2014; Olson-Buchanan & Boswell, 2006;
Park et al., 2011; Wajcman et al., 2010; Wright et al., 2014). Notably,
most of these studies implicitly reference TASW while actually capturing
the frequency (e.g., Boswell & Olson-Buchanan, 2007; Park
et al., 2011) extent (e.g., Diaz et al., 2012), duration (e.g., Wright
et al., 2014), or timing (Richardson & Benbunan-Fich, 2011) of ICT use
after hours, failing to address the extent to which these technologies
are used to actually perform work and complete work-related tasks
outside regular work hours (Fenner & Renn, 2010). Another concern
is that previous work mostly covers extended availability and connectivity
(Dery et al., 2014; Mazmanian et al., 2013; Thörel et al., 2020)
rather than supplemental work practices. Though escalating connectivity
to work is becoming increasingly common, contemporary work
may present connectivity demands (Nurmi & Hinds, 2020) that require
employees to go beyond merely signaling availability or monitoring
email messages (Thörel et al., 2020) and engage in more substantial
work tasks after hours (Gadeyne et al., 2018). Hence, although there
is a large body of literature referencing the TASW framework, the
links are primarily implicit, placing ICT use and TASW, or TASW and
extended availability or connectivity on equal footing. To provide conceptual
clarity, this study makes an important distinction between
these concepts that can contribute to theory regarding why individuals
engage in supplemental work and how particular aspects of ICT
use contribute to these behaviors.
Second, our investigation of the various mechanisms present in
the relationship between ICT use and TASW seeks to extend our
understanding of how organizational dynamics operate in a context of
interdependent work, and without the boundaries of time and location.
Hence, we examine a specific branch of ICTs—namely, collaboration
technologies. We use the term collaboration technology to refer
to a specific set of cloud-based software platforms aimed at
supporting collaboration and communication among participants as
well as facilitating information processing and accessibility (Dennis
et al., 2003; DeSanctis & Gallupe, 1987). Collaboration technologies
differ from other organizational technologies that offer possibilities
for ubiquitous connectivity among workers and teams (i.e., phones,
email) in that the (a) use of the technology is potentially visible over
time and accessible to third parties not initially relevant (Treem
et al., 2020) and (b) use of the technology can be related to an individual
task, be interdependent with the work of others, or move fluidly
between these states. Scholars have recognized that the materiality of
collaboration technologies that makes information visible in new ways
can alter modes of working. For instance, Leonardi (2007) studied the
use of a shared IT system by computer technicians and found that
the ability to view the activity of others led to changes in who
workers asked for task advice. Similarly, Dery et al. (2006) studied the
use of enterprise resource planning systems by bank managers. The
authors concluded that while the material nature of the technological
artifact required users to have specialized knowledge of how to enter

data, other nonmaterial factors including the time available to use and
learn the system contributed to limited-use practices. Though scholars
have considered ways that the materiality of collaboration technologies
might facilitate new forms of work within traditional work settings
and roles (e.g., Jasperson et al., 2005), less attention has been
paid to the extent to which interdependent work facilitated by collaboration
technologies might alter the boundaries of work itself and
contribute to TASW.
Third, by examining distinct aspects associated with the utilization
of collaboration technologies by workers embedded in teams, this
work examines the relative extent to which those differences are
driven by factors that are or are not under the control of the individual
worker. Scholarship on the role of collaboration technologies in
organizations has demonstrated that although they provide opportunities
for continuous connectivity, they are utilized by workers to
manage and regulate when and how they connect to work (Gibbs
et al., 2013). When this work takes place in an interdependent team
environment, workers may feel obligated to respond to communication
from other team members, and these obligations may erode some
of the control individuals have over work behaviors (Mazmanian
et al., 2005). Because collaboration technologies make communication
visible to other employees in ways that are different than other ICTs
offering connectivity (i.e., email and phone) they may create different
pressures regarding supplemental work (Leonardi & Vaast, 2017). By
focusing on the individual, social, and material aspects of work as
potentially competing or complementary in their relationship to
TASW, this work is consistent with calls to examine the ways technology
use in organizations presents possibilities for action, but is not
deterministic in its effects (Cecez-Kecmanovic et al., 2014; Leonardi &
Barley, 2008).
1.1 | Collaboration technology use and TASW
The notion that increased ICT use can escalate one's commitment or
connectivity to work is well documented in the literature (Dery &
MacCormick, 2012; Kolb et al., 2012; Matusik & Mickel, 2011;
Richardson & Benbunan-Fich, 2011; Wajcman & Rose, 2011; Wright
et al., 2014). Research suggests that workers whose activities require
continual coordination with colleagues, clients, or supervisors take a
predominantly positive attitude toward technology use, while
acknowledging that technologies may be accompanied by work activities
encroaching upon the private sphere (Cavazotte et al., 2014).
Employees seek to use technologies that will facilitate more efficient
collaboration and information flows across spatial and temporal
boundaries, but are wary of the communication that invariably accompanies
this level of connectivity.
Organizations are increasingly operating across geographical
borders (and time zones) and are implementing new collaboration technologies
creating demands for TASW (Golden & Raghuram, 2010;
Nurmi & Hinds, 2020; Piszczek, 2017; Thörel et al., 2020). Employees
have been found to reciprocate the distribution of mobile technology
by using these technologies to extend their connectivity (Richardson &
Benbunan-Fich, 2011). Conversely, when these technologies are used
frequently and intensely, employees will have more possibilities to
engage in supplemental work, as there are no technological barriers
preventing these practices (Venkatesh & Vitalari, 1992). The use of
ICTs is sometimes referred to as the electronic leash, tethering
employees to work (Büchler et al., 2020; Richardson & Thompson,
2012; Schlachter et al., 2018) and encroaching into individuals' personal
lives and nonwork time (Schlachter et al., 2018).
Employees may feel the obligation to utilize the available options
technologies offer and engage in more substantive supplemental work
behaviors when collaboration technologies are used across spatial and
temporal boundaries. Collaboration technologies, such as Google
Workspace or Microsoft 365, include various applications that enable
distributed coworkers to share files, edit documents individually or
collectively, and collaborate synchronously through video and conference
calls. These technologies are typically aimed at collaboration and
productivity and, as such, require more attention or effort from their
users (Robey et al., 2000) than technologies that are more focused on
facilitating connectivity (Gadeyne et al., 2018). Focusing on collaboration
technologies foregrounds the potential interdependence among
organizational members when completing tasks. In work contexts
mere connectivity may prove to be inadequate as collaboration technologies
use may—implicitly or explicitly—require more commitment,
contributions, or engagement from users, leading to TASW. Hence,
we hypothesize the following:
Hypothesis 1. Collaboration technology use is positively related
to TASW.
1.2 | Response expectations and TASW
Many studies have argued that employers and employees may
develop responsiveness expectations that shape how technologies
are used and may escalate employees' connectivity to work (Derks
et al., 2015; Leonardi et al., 2010; Mazmanian et al., 2013). The shared
expectations about responsiveness within a team may mold a social
norm regarding connectivity after hours (Derks et al., 2015). Hence, at
a team-level response expectations may normalize into a social pressure
to remain available and accountable to others after hours. More
broadly, questions of if, when, and how to connect to work are situated
in the age-old antinomy of individual and technical agency versus
normative social pressures (Leonardi & Barley, 2010;
Orlikowski, 1992). Thus, it can be argued that both the material features
(casu quo; technical systems; Leung & Wang, 2015) as well as
the social practices and expectations (casu quo; social systems;
Leung & Wang, 2015) in workplaces play a role in employees' decisions
to engage in TASW. These team-level response expectations,
such as whether other team members or supervisors expect a
response to work-related messages during nonwork hours could be
an important driver in TASW (Fenner & Renn, 2010). Specifically,
workers will look at the behaviors and communication of influential
van ZOONEN ET AL. 869
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peers and organizational members to gain insights into appropriate
ways that ICTs should be used within a context (Fulk, 1993; Schmitz &
Fulk, 1991). Important referents that influence employees' behaviors
and choices tend to be (a) people with whom employees frequently
communicate, (b) those in similar roles, and (c) those who occupy a
high(er)-status position (Boh & Wong, 2015; Friedkin, 1993; Shah
et al., 2006). Prior research showed that managers and team members
are two dominant social relationships that influence how employees
perceive their work environment (Tierney, 1999). Hence, supervisors
and team members are two key referent groups that can potentially
influence expectations about responsiveness.
Team-level response expectations refer to the shared beliefs
regarding appropriate levels of responsiveness within the team. Such
shared expectations or norms may exert a powerful form of social
control and direct individual behavior within social groups
(Barker, 1993; Eby & Dobbins, 1997; Taggar & Ellis, 2007). Several
scholars have directed attention to how individual-level cognitions
about response expectations may contribute to an environment that
expects workers to be constantly connected to work (e.g., Derks &
Bakker, 2014; Dery & MacCormick, 2012; Mazmanian et al., 2013).
Others have noted that social norms and expectations serve as important
predictors of higher levels of connectivity behaviors after hours
(e.g., Adkins & Premeaux, 2014; Gadeyne et al., 2018; Mazmanian
et al., 2013; Thörel et al., 2020). This previous work demonstrates the
need to distinguish between social norms that contribute to states of
connectivity and availability, and social norms around ICT use that
results in substantive work practices that constitute TASW. We argue
that shared expectations at a team level are particularly influential in
predicting individuals' work behaviors within the team. This means
that employees in teams with high shared response expectations may
signal their proficiency within the team by responding to these expectations
(Paczkowski & Kuruzovich, 2016) through technology-assisted
supplemental work practices.
In addition, response expectations are particularly acute at the
team level, where employees often have a shorter response cycle to
other team members, and the pace of responsiveness is reciprocal
(Tyler & Tang, 2003). Employees may strategically manage a certain
type of image of being responsive, as this can enhance one's reputation
as a caring and sensitive colleague (Barley et al., 2011) and proficient
coworker (Paczkowski & Kuruzovich, 2016). Conversely,
nonresponsiveness is often interpreted with negative attributes,
such as incompetence and lack of commitment (Sarker &
Sahay, 2004). Team-level response expectations may be so strong
that team member's unavailability outside office hours is accepted
only when it is collectively agreed on (Perlow & Porter, 2009).
Indeed, Fenner and Renn (2004, 2010) suggested that organizations
that promote quick responses may coerce employees to remain connected
and engage in supplemental work after work hours. Hence,
we hypothesize that team-level response expectations are associated
with TASW.
Hypothesis 2. Team-level response expectations are positively
related to TASW.
1.3 | Interplay between collaboration technology
use and response expectations
In line with recent theorizing on connectivity demands, we acknowledge
that workers have some agency in when and how to connect to
work (Nurmi & Hinds, 2020). These choices are highly interwoven
with the social (organizational) context (Schlachter et al., 2018)—here
team-level response expectations. Studies have articulated that being
responsive to others is a social practice that may escalate to a norm
among coworkers in a team, leading to escalating engagement and
cycles of increased responsiveness (Mazmanian et al., 2013). Hence,
the interplay between individual technology use and response expectations
leads to a redefinition of work practices and work roles. Building
on these findings, one might argue that the individual use of
collaboration technologies may escalate into TASW practices, especially
when these uses are embedded in social contexts characterized
by high shared response expectations.
Nurmi and Hinds (2020) argued that workers may face
connectivity-related demands (i.e., pressures) but also retain some
agency in their decisions about how to respond—for example, engage
in more frequent communication, after-hour communication, and/or
site visits to dispersed colleagues. Derks et al. (2015) found that
smartphone use increases work–life interferences especially for
employees who felt strong responsiveness norms. They articulate that
employees not only learn from the behaviors of their colleagues
that are reflected in these norms but they also mimic their colleagues'
behaviors regarding smartphone use after work. In the context of collaborative
tools, these norms may exacerbate the extent to which
these collaboration technologies are used to engage in supplemental
work. Gadeyne et al. (2018) demonstrated that laptop and PC use led
to more time- and strain-based work-to-life conflict than smartphone
use because connecting through these technologies required a higher
level of attention and focus than connecting through a smartphone. In
light of the discussion above, collaboration technology use may
increase TASW, especially when such usage is embedded in a social
context characterized by high shared response expectations among
coworkers and supervisors. Hence, we hypothesize the following:
Hypothesis 3. Team-level response expectations moderate the positive
relationship between collaboration technology use and
TASW such that the relationship becomes stronger as response
expectations increase.
1.4 | Perceived persistence of communication
and TASW
Collaboration technology may facilitate work in dispersed teams,
where team members work across disparate locations and times
(Ellison et al., 2015; Martins et al., 2004). These technologies often
have distinct features that present opportunities for employees to
take action, communicate, and access information in ways that would
be difficult or impossible with other technologies. As such, we
870 van ZOONEN ET AL.
recognize that not only do team members act by using these collaboration
technologies, these technologies also play an active role in
keeping communication available to users. Specifically, organizational
realities may be shaped by the interplay of human agency and the
materiality of technical artifacts, as technologies may constrain or
afford—but not determine—the possibility of achieving new goals and
routines (Leonardi, 2011; Orlikowski, 2000). For instance, collaboration
technologies can materially support the persistence of communication
across time and space, allowing workers to interact with that
communication in myriad ways. In turn, workers can then make decisions
as to how, if at all, to take action in an environment where technology
makes communication available, visible, and persistent.
Materially, persistence describes the relative permanence or
ephemerality of communication and as such relates to key characteristics
of collaboration technologies such as archiving, durability,
recordability, and reviewability (Fox & McEwan, 2017). Hence, persistence
may help employees as it allows them to access, save, capture,
replicate, and recirculate communication long after the message or
signal is originally produced. Because communication is persistent,
content can potentially be reused and reanalyzed over time. In practice,
the ability of a technology to support persistence does not at all
determine that communication will be available to others over time,
or that users of a technology will recognize or perceive possibilities
for persistence. Instead, persistence can be viewed as a possibility of
technology use, and not an inherent feature of a technology nor a
binary outcome (Evans et al., 2016). In analyzing individuals' activities
in online spaces, Mynatt et al. (1998) note that persistence can help
“support a wide range of user interaction and collaborative activity”
(p. 210). Workers may view persistence as supportive of knowledge
sharing across space and time and as facilitating the growth of available
content in organizations (Treem & Leonardi, 2013).
Therefore, perceived persistence could moderate the effects of
collaboration technology use and TASW, as members might take into
account the ability to go back and look at the communication record
without the need for synchronous collaboration with team members
working at different location or in different time zones. Especially in
dispersed teams, with workers collaborating across geographical distance
and time zones, the persistence of communication allows individuals
to go back to content and contribute at times that may not
require them to work at night or during the weekends (Erhardt
et al., 2016). For example, a team member who is starting the workday
can go back and look at the communication between other team
members who may have already ended their workday without the
need of interrupting each other outside office hours. This way, team
members in different time zones could perform their parts of the collaborative
work at their discretion by tracking the needed information
from the durable communication records. Thus, we hypothesize the
following:
Hypothesis 4. The perceived persistence of communication moderates
the positive relationship between collaboration technology
use and TASW such that the relationship becomes weaker
as perceived persistence of communication increases.
Technological advances, and in particular the growth of collaboration
technologies, have made it easier for people to get work done
remotely. However, the ability to work anywhere, anytime often morphs
into an expectation to work everywhere, all the time (von Bergen
& Bressler, 2019). We argue that the perceived persistence of
communication shapes anticipated interaction among team members
and supplemental work. Specifically, in team work, the perceived persistence
of communication could help to mitigate the effects of
response expectations on TASW. Perceived persistence of communication
could moderate these effects because it provides workers with
greater agency and discretion to revisit interactions and contribute at
a later time, allowing workers to delay their responses (Erhardt
et al., 2016). Similarly, employees working in dispersed teams may feel
pressure to work after hours due to a lack of overlapping office hours
with their colleagues (Espinosa & Carmel, 2003), especially if high
shared response expectations prevail within the team. However, when
perceived persistence of communication is higher team members are
provided with more leeway in deciding when and where to contribute
despite the team-level response expectations, reducing the need to
perform TASW. Hence, we hypothesize the following:
Hypothesis 5. Perceived persistence of communication moderates
the positive relationship between team-level response expectations
and TASW such that the relationship becomes weaker
as perceived persistence of communication increases.
2 | METHOD
2.1 | Procedure and participants
The participants for this research were recruited from a global company,
which we call Freight Inc., that offers products and services to
improve trade and cargo flows. Freight Inc. has locations in over a
hundred countries employing approximately 12 000 employees
worldwide. Employees that worked in office roles (rather than field
roles) and were part of a team of at least three members were eligible
to participate. Teams were relatively stable, as team roles, leadership,
and purpose, were well-defined (Wageman et al., 2012). Specifically,
international teams at Freight Inc. offer cargo handling solutions to
their clients ranging from logistics planning and operations to automation
solutions for cargo flows. These global cargo flow solutions
require constant coordination and collaborations within and across
global and local teams and organizational sites. The nature of the
company's global operations creates opportunities for workers to collaborate
and provide solutions to problems outside regular work times
at their respective locations, for instance, because clients, managers,
or team members are located elsewhere. In our discussions with the
liaisons at Freight Inc., it became apparent that employees were often
connected to their coworkers outside work hours. These interactions
involved both brief communication through email about work-related
matters, and more substantial contributions and completion of tasks
through collaboration technologies. At the time of this study, Google
Workspace (formerly G Suite) was the primary collaboration technology
used within the teams and organization. As such, Google
Workspace was the focus of our inquiry into the practical implications
of collaboration technology use. Google Workspace offers cloudbased
productivity and collaboration tools that include functionalities
related to filesharing and online (video)conferencing. Google
Workspace offers filesharing options (Google Drive) similar to Microsoft
Office365 and online conferencing tools (Google Meet) similar to
those offered in MS Teams for instance. Similar to Google Workspace,
various alternative services, whether offered by Microsoft, IBM, or
other smaller competitors, aim to facilitate collaborative work in a
fluid online environment by allowing users to share, edit, and store
documents independent of time and place and afford possibilities for
online communication and meetings.
As such, Google Workspace integrates several applications that
were particularly relevant to team collaboration—that is, Google Drive
and Google Meet. When working on cargo solutions for their clients,
Freight Inc. employees rely on Google Drive to share information,
expertise, and edit and collaborate on spreadsheets and documents.
Google Drive also offers a repository of company data relevant to
their daily operations, facilitating anytime, anywhere access to information.
Google Meet is primarily used to coordinate tasks and have
virtual (audio and/or video) meetings with team members, for instance
to explore cargo handling solutions between different (global) sites.
Ultimately, the applications offered through Google Workspace are
aimed at helping its users to keep their workflows organized.
In this study, we draw on multilevel data from multiple sources.
Individual and team-level responses were gathered from team members
through an online survey, and the company's human resource
department provided demographics and background data. Questionnaires
were administered in English. Employees participated on a voluntary
basis and did not receive any compensation. They were
contacted through email, and questionnaires were collected during a
3-week time period.
In total, 443 participants nested in 122 teams completed the
questionnaire. In total, there are 1075 teams with at least three team
members; hence, the team-level response rate is 11.35%, the withinteam
response rate of participating teams was 40%. Finally, at the
individual level, 443 of the 5310 eligible employees participated
(8.3%). The average team size was four, ranging from three to 10 members
in each team. Of these 122 teams, 67 teams were composed of
co-located members, and 55 teams consisted of dispersed members,
of which 25 teams had members working in different time zones. At
the individual level, we examined whether nonresponse was due to
any individual employee characteristics using data provided by the
human resources department. Independent samples T tests demonstrate
that participants were slightly older than nonrespondents
(M(respondents) = 42.74, SD = 10.26; M(nonresponse) = 41.31, SD = 10.72;
t = _2.836, p = .005). Respondents did not differ from nonrespondents
in terms of organizational tenure (M(respondents) = 7.02,
SD = 8.32; M(nonresponse) = 7.79, SD = 8.67; t = 1.816, p = .069); average
weekly work hours (M(respondents) = 39.29, SD = 3.83; M(nonresponse)
= 39.48, SD = 9.45; t = 0.411, p = .681); or gender
(χ2 = 0.244, p = .621), respondents were mostly male (77.4%). These
results indicate that nonresponse is not likely to be a result of these
demographic or employment characteristics.
2.2 | Measures
2.2.1 | Dependent variables
Technology-assisted supplemental work (TASW) was measured using a
four-item scale adopted from Fenner and Renn (2010). All items were
rated with a five-point scale ranging from 1 (never) to 5 (always [every
day]). Participants were instructed to indicate how frequently they
engaged in supplemental work in an average week (e.g., “When I fall
behind in my work during the day, I work hard at home at night or on
weekends to get caught up by using my smartphone or computer”;
see Appendix A for all items). The scale statistics are α = .91,
M = 2.60, SD = 1.12.
2.2.2 | Independent variables
2.2.3 | Individual-level measures
Collaboration technology use was measured using a seven-item scale
assessing several work practices that are commonly performed
through these technologies at the organization. The scale items were
self-constructed for the purpose of this study and similar to formative
measures previously used by Bala et al. (2017). A selected group of
workers at the research site provided an expert review to assess the
extent to which these items adequately captured the most common
work practices. Respondents were asked to indicate how frequently
(1 [weekly or less] to 6 [multiple times per hour]) they performed these
behaviors using collaboration technologies (i.e., Google Workspace).
Sample items include “I use Google Drive to share files” The scale statistics
are α = .88, M = 3.02, SD = 0.82.
Perceived persistence of communication was measured using a
four-item scale by Fox and McEwan (2017). Persistence refers to the
permanence or ephemerality of communication (Fox &
McEwan, 2017, p. 303). As per the authors' suggestion we changed
the wording “this channel” (p. 303) in the original items to collaboration
technologies to reflect the focus of this study. Respondents were
asked to what extent information shared through ICTs remained available.
Responses to statements could vary between 1 (strongly
disagree) and 5 (strongly agree). A sample item was “The collaboration
technologies we use at [organization] keep a record of communication
that I can go back to and look at.” In line with Rice et al. (2017), perceived
persistence is conceptualized as individual-level perception.
The across-team variance in perceived persistence is σ2 u0 =.098 and
the within-team variance is σ2e
=.728, suggesting that the shared variance
at group level (ICC[1]) is 11.9%. This indicates that perceived
persistence did indeed differ among workers overall and within teams.
The scale statistics are α=.94, M=3.68, SD=0.91.
2.2.4 | Team-level measures

Response expectations were measured adopting the six-items published
by Derks et al. (2015, p. 163) assessing response expectations of
supervisors and coworkers. Respondents were prompted to consider
the statements as they relate to their work team. We adopted the
measures by Derks et al. based on a direct consensus model of composition,
rather than a referent-shift model. The direct-consensus
composition model suggests that the average level of response expectations
is considered to be an adequate representation of the
response expectations of the group as a whole (van Mierlo et al.,
2009). Wallace et al. (2016) suggest that aggregation choices are
dependent on the conceptual and empirical justification. Conceptually,
when there is consensus and response expectations are shared by
other members of the team, the aggregate composes a construct at
the team level (e.g., response norms). Hence, although the referent
measured was “I” or “My supervisor,” the referent of interest was the
team (i.e., collective response expectations within the team). Thus,
response expectations represent a social norm about responsiveness
that are understood by members of a group and guide or constrain
(social) behavior without the force of policy (Cialdini & Trost, 1998).
At the team-level response expectations include the shared expectations
valued others—that is, referents, here supervisors and team
colleagues—have of responsiveness within their team.
Empirically, a direct consensus method is particularly appropriate
for the study of norms formation because it reflects a process of interaction,
which is the basis of the norm construction (Taggar &
Ellis, 2007). Individuals may have difficulties acting as reliable informants
about the group as a whole (van Mierlo et al., 2009). However,
within-group homogeneity is a valid assumption as expectations
derived from interactions with dispersed supervisors and colleagues
are often shared within teams. Third, as indicated below, coefficients
of agreement support aggregation of individual-level data to the team
level. As such, the direct consensus model is deemed an appropriate
composition model for response expectations (Chan, 1998).
Respondents were asked to indicate their agreement or disagreement
with regard to expectation about responsiveness in their role
within their team. Respondents were prompted to consider the context
of their work teams and direct supervisor; “Please consider the
interactions with other members of your team and your team supervisor
when responding to the following statements.” Sample items were
“My supervisor expects me to respond to work-related messages during
my free time” and “When I send a message to colleagues during
the weekend, most colleagues respond the same day.” Response categories
ranged from 1 (strongly disagree) to 5 (strongly agree). The scale
statistics are α = .86, M = 2.20, SD = 0.94.
A prerequisite to conducting multilevel analysis is demonstrating
that higher level predictors share substantial within-group variance.
Intraclass correlation (ICC[1]), is used to assess proportional consistency
of the total variance that can be explained by team membership,
indicating that the proportion of the variability in individual team
member's ratings could be attributed to team membership. ICC(2) provides
an estimate for reliability of group means, based on mean
squares from one-way ANOVA. For this study, the ICC(1) for response
expectations was 0.28. The shared variance among team members
was significant (F = 2,532 p < .001). In addition, the ICC(2) for
response expectations was 0.86, exceeding the 0.7 value recommended
by Klein and Kozlowski (2000).
2.2.5 | Controls
Although not the main focus of our analysis, we believe that teamlevel
parameters—for example, team size and team dispersion—and
individual characteristics of work—for example, working hours—
related to structural and spatio-temporal context may operate as confounding
factors in the relationships underlying the extent to which
workers engage in TASW. Human resource data allow us to control
for quasi-material features of the team contexts (Barley et al., 2011;
Olson & Olson, 2000) that can play an important role in how workers
extend their efforts beyond regular work hours in global
organizations.
2.2.6 | Individual-level controls
Team leader location. In (globally) dispersed teams, the location of the
team leader may also play a role as team members working at a distance
(compared with their team leader) may feel excluded and “out
of the loop” (O'Leary & Cummings, 2007). Thus, members working
remotely from team leaders may have a stronger need to show their
presence and contribution by engaging in TASW. Subsequently, for
each member, human resource data was used to determine whether
the supervisor was co-located (0) or working in a remote location (1).
Finally, work hours and tenure are included as TASW is closely associated
with employees' time management and ability to organize their
work; actual work hours and organizational tenure may impact TASW
(Fenner & Renn, 2010). Work hours and organizational tenure for all
respondents was provided by human resources data in hours per
week and total number of years, respectively.
2.2.7 | Team-level controls
Time-zone differences. When working across time zones, other team
members or leaders may take remote employee's TASW for granted
as synchronous collaboration within the team is sometimes needed,
even though real-time problem solving in global teams often
decreases as time differences increase (O'Leary & Cummings, 2007).
At the same time, remote members may experience higher pressure
for responsiveness outside office hours as time differences exacerbate
the lag in response time in distributed teams (Sarker & Sahay, 2004).
Based on the geographical location of each team member in a team,
the maximum time difference within each team was calculated. The
average time difference within dispersed teams was 3.64 h, ranging
from 1 h (Helsinki, Finland and Milan, Italy) to 12.5 h (between team
members in Chennai, India, and Oakland, USA). Co-location of team
members, referring to spatial distance, can also play a role in how
much employees engage in TASW. Spatial distance has been shown
to have several effects on collaboration (see, e.g., Cummings
et al., 2009). Teams of which the constituent members were
completely co-located were coded 0, and teams with members that
were not co-located were coded 1. Finally, team size can be of importance,
as in larger teams coordination of work may become more complex
and make extensions of work outside regular hours more likely.
Conversely, in smaller teams, it may be easier to accommodate individual
needs, and awareness of others' overtime and schedules are
more obvious (Bowers et al., 2000). Team size was measured simply
as the number of employees that constitute the team.
2.3 | Confirmatory factor analysis
A multilevel confirmatory factor analysis for the four-factor model
was conducted indicating excellent model fit: χ2 (177) = 369.46;
CFI = 0.97; TLI = 0.96; SRMR = 0.06 and RMSEA = 0.050. The
latent variables were scaled by fixing the factor loadings of the first
indicator for each latent variable, error terms were modeled independently,
and cross-loadings were not estimated. Note that a one-factor
solution for collaboration technology use and response expectations
indicated significantly better model fit compared with the two-factor
solutions splitting Google Meet and Drive use and supervisor and
coworker expectations (Δχ2 = 41.21, p < .001). For response expectations,
the items load strong on the single factor at the between level,
ranging from .75 to .99. Consistent with our assumption that response
expectations operate at the team level of analysis, the factor loadings
of the items at the within level, ranging from .42 to .85, are not as
strong as the between factor loadings. Overall, convergent and discriminant
validity was established. Convergent validity was examined
by evaluating the average variance extracted (AVE) of each construct
against its correlation with other constructs in the model. Notably, the
AVE, which are all above the threshold of .50, ranging from .52 to .79.
Correlations ranged from .00 to .43. All constructs demonstrate convergent
validity. Discriminant validity was established by evaluating
the maximum shared variance (MSV), ranging between .08 and .18,
against the square root of the AVE, ranging between .72 and .89. All
constructs demonstrate good discriminant validity as the MSV is
lower than the AVE.
2.4 | Strategy of analysis
The nested data structure with individual responses at level
1 (N = 443) nested within teams at level 2 (N = 122) was analyzed
using hierarchical linear modeling. Prior to hypotheses testing
individual-level predictor, collaboration technology use, and the moderator,
persistence of communication, were centered to the individual
mean. The team-level predictor, response norms, was centered around
the grand mean (Bauer & Curran, 2005). Curve estimations
demonstrated that the relationships in the model were sufficiently
linear. To test our hypotheses, we start with the null model and subsequently
estimate a sequence of increasingly complex models adding
level 1 and level 2 predictors as well as (cross-level) interactions and
controls. Overall, the analysis focused on understanding the factors
that explain three sources of variance at two levels (a) lower level
direct effects, that is, individual-level factors; (b) cross-level direct
effects, that is, team-level factors; and (c) cross-level interactions, that
is, cross-level factors that explain variance across-group slopes.
3 | RESULTS
3.1 | Descriptive statistics and control variables
Correlations and descriptive statistics are provided in Table 1. In order
to examine the proportion of variance that is attributed to different
levels of analysis, we tested the null model for TASW. The results indicate
that the mean for TASW was 2.60 (γ00, t = 38.97, p < .001). The
model also demonstrates significant variance component at the team
level (σ2 u0 =0.264 χ2=241.47, df=121, p < .001). The across-team
variance in individual TASW is .264 and the within-team variance is
.983. As shown in Table 2, the ICC=.212, which means that team differences
account for about 21.2% of the variability TASW. Hence,
these results provide evidence for a nested data structure that
requires a multilevel modeling analytical approach.
Model 4 in Table 2 examines controls for team structures and
individual work characteristics (Δ _ 2x log = 12.61, Δdf = 6
p = .050). Specifically, the findings indicate that team-level controls,
team-member dispersion (γ = .095, SE = .141, t = 0.673, p = .502),
and team size (γ = _.041, SE = .044, t = _0.940, p = .351) are not
significantly related to TASW. The results further indicate that timezone
differences are associated with TASW (γ = .070, SE = .025,
t = 2.788, p = .006), suggesting that the larger the time-zone difference
between team members, the more they engage in TASW. Finally,
individual-level controls; supervisor dispersion (γ = .023, SE = .136,
t = 0.170, p = .865), organizational tenure (γ = .004, SE = .006,
t = 0.614, p = .539), and working hours (γ = _.008, SE = .013,
t = _0.616, p = .538) are not associated with TASW.
3.2 | Hypothesis testing
Figure 1 depicts the results of our hypothesized model. Model 1 in
Table 2 demonstrates the effects of individual-level predictors on
TASW, this model includes the main effects of communication technology
use and persistence of communication. The model including
the individual-level predictors showed significant improvement over
the Null Model (Δ _ 2x log = 18.27, Δdf = 2 p < .001). The estimate
for collaboration technology use (γ = .316, SE = .080, t = 3.961,
p < .001) was statistically significant and positive, supporting Hypothesis
1. Persistence of communication was not significantly related to
TASW (γ = .053, SE = .065, t = 0.829, p = .408).
TABLE 1 Means, standard deviations, and correlations for all study variables
Variable M (SD) 1 2 3 4 5 6 7 8 9
Level 1 variables
1. TASW 2.60 (1.12) .91 — — — — — —
2. Collaboration technology use 3.02 (0.82) .23* .88 — — — — —
3. Persistence of communication 3.68 (0.98) .12* .31* .94 — — — —
Level 2 variables
4. Response expectations 2.20 (0.94) .32* .04 _.01 .86 — — —
Controls
5. Co-location (spatial distance) 0.45 (0.50) .16* .10* .01 .20* — — — — —
6. Time-zone difference 3.64 (2.43) .19* .13* .18* .06 .39* — — — —
7. Team size 3.95 (1.42) _.03 _.14* _.08 .03 _.02 .09* — — —
8. Team leader location 0.28 (0.45) .12* .08 .03 .13* .60* .30 .01 — —
9. Working hours p/w 39.29 (3.83) .04 .03 .04 .18* .01 _.01 .09* .11* —
10. Organizational tenure 7.02 (8.32) .01 _.18* _.12* _.01 .09* _.04 .08 _.08 .03
Note: Values in bold on the diagonal are reliability coefficients (alpha), values above .80 are considered indicative of good reliability.
*Significant correlations are flagged p < .05.
TABLE 2 Multilevel results for technology-assisted supplemental work
Technology-assisted supplemental work
Level and variable Null model
Model 1 L1
predictors
Model 2 L2
predictors
Model 3 (crosslevel)
interactions
Model 4 with
controls
Level 1 predictors
Intercept 2.601 (0.067)*** 2.601 (0.067)*** 2.604 (0.057)*** 2.631 (0.059)*** 3.031 (0.559)***
Collaboration technology use 0.316 (0.080)*** 0.316 (0.080)*** 0.304 (0.080)*** 0.308 (0.080)***
Persistence of communication 0.053 (0.065) 0.053 (0.064) 0.057 (0.065) 0.059 (0.065)
Level 2 predictors
Response expectations 0.554 (0.089)*** 0.556 (0.089)*** 0.537 (0.086)***
Interaction terms
Collaboration technology use x
persistence of communication
_0.273 (0.102)** _0.277 (0.101)**
Response
expectations _ persistence of
communication
0.099 (0.105) 0.098 (0.106)
Response
expectations _ collaboration
technology use
0.076 (0.126) 0.072 (0.127)
Controls
Co-location (spatial distance) 0.095 (0.141)
Time-zone difference 0.070 (0.025)**
Team size _0.041 (0.044)
Team leader location 0.023 (0.136)
Weekly work hours _0.008 (0.013)
Organizational tenure 0.004 (0.006)
Variance components
Variance (L1) individuals 0.983 0.929 0.921 0.901 0.905
Variance (L2) teams 0.264 0.278 0.157 0.158 0.111
(Continues)
Subsequently, in Model 2, response expectations were added to
the model again demonstrating significant model improvement
(Δ _ 2x log = 34.05, Δdf = 1 p < .001). The results showed (see
Table 2) that response expectations exhibit a strong positive effect on
TASW (γ = .554, SE = .089, t = 6.216, p < .001). This finding supports
Hypothesis 2. Subsequently, Model 3 adds the hypothesized interaction
terms, demonstrating a significant model improvement compared
with the model without the interactions (Δ _ 2x log = 8.57, Δdf = 3
p = .036). There was no significant interaction between collaboration
technology use and response expectations (γ = .076, SE = .126,
t = 0.602, p = .547), suggesting that higher response expectations
within teams do not affect the relationship between individual technology
use and TASW. Hence, Hypothesis 3 was not supported.
Hypothesis 4 reflects the assumption that persistence of communication
moderates the impact of communication technology use on
TASW, such that this relationship is weaker when persistence of communication
is higher. The results further demonstrate that persistence
of communication moderates the effects of collaboration technology
use on TASW (γ = _.273, SE = .102, t = _2.693, p = .007). The
interaction was probed using the Johnson–Neyman technique
(Bauer & Curran, 2005). Figure 2 plots the conditional effect (dashed
line) of collaboration technology use on TASW across the distribution
of persistence of communication, including a 95% confidence interval
(gray area around dashed line). The confidence interval indicates that
collaboration technology use is negatively related to TASW when the
mean-centered value of persistence of communication is below 0.40
(i.e., 4.08 in raw values). This suggests that when persistence of communication
is low, collaboration technology use is positively associated
with TASW. In contrast, at high levels of persistence (above
0.40), there is no significant relationship between collaboration technology
use and TASW (Figure 2). In other words, the positive relationship
between collaboration technology use and TASW is weakened as
the level of persistence increases. These findings support the argument
that persistence of communication and collaboration technology
use provide employees more agency over their work, allowing the
possibility to address work demands at their discretion, whether at
nights, weekends or, probably preferably, during regular work hours
(in the near future, for instance). When communication persistence is
TABLE 2 (Continued)
Technology-assisted supplemental work
Level and variable Null model
Model 1 L1
predictors
Model 2 L2
predictors
Model 3 (crosslevel)
interactions
Model 4 with
controls
ICC 0.212
R2 18.52%
Model information
AIC 1337.37 1323.10 1291.05 1288.48 1287.87
_2 log likelihood (FIML) 1331.37 1313.10 1279.05 1270.48 1257.87
Number of parameters 3 5 6 9 15
Note: L1 = Level 1; L2 = Level 2. Values in parentheses are standard errors; t statistics are computed as ratio of each regression coefficient divided by
its
standard error (reported in text).
Abbreviations: FIML, full information maximum likelihood estimation; ICC, intraclass correlation.
***Significance levels at p < .001. **Significance levels at p < .01.
FIGURE 1 Multilevel regression model of the antecedents of technology-assisted supplemental work (TASW). Unstandardized structural
regression weights are shown. Significance is flagged; ***p < .001, **p < .01
higher, workers are less likely to engage in supplemental work practices
than when communication persistence is lower in teams where
collaboration technology use is high. These findings support
Hypothesis 4.
Conversely, Hypothesis 5 suggested that persistence of communication
would moderate the effect of response expectations on
TASW. As demonstrated in the slopes as outcomes model (Model 3)
in Table 2, team-level response expectations did not interact with
individual-level persistence of communication (γ = .099, SE = .105,
t = 0.941, p = .347). Therefore, the findings lack support for
Hypothesis 5. Finally, the model explains 6.23% of the variance at
Level 1 and 57.95% of variance at Level 2, yielding a total explained
variance of 18.52%.
4 | DISCUSSION
4.1 | Theoretical implications
This study examines various concomitant pressures underlying the
performance of TASW. In doing so we make several contributions to
the understanding of what drives supplemental work in an environment
of high connectivity, interdependent tasks, and diverse team
structures—a context increasingly found in contemporary
organizations.
First, the findings give rise to reconsider the importance of TASW
in the context of an increasingly boundary-less work environment.
They do so especially against the backdrop of a global health pandemic
and the associated remote work mandates that have almost
overnight retired the “nine-to-five commuter” in favor of the “24/7
always available” worker. It might be tempting to abandon the idea
that employees have designated times to engage with work-related
issues (i.e., work hours) and down-time in which workers recover from
work (i.e., nonwork hours). However, in contrast, we believe that what
constitutes work and nonwork time and by extension when and why
workers engage in supplemental work practices is now more important
than ever. Research has shown that employees increasingly experience
greater difficulties in managing work and nonwork times (Wang
et al., 2021), for instance as (forced) remote work leads to greater task
setbacks (Chong et al., 2020), or as they, willingly or compulsory, sacrifice
nonwork time to engage in supplemental work (Xiao et al., 2021).
Hence, it is important to highlight the need for time off, recovery from
work, sleep, and the overall benefits of time in daily life that is distinctly
recognized as nonwork. In a working paper, DeFilippis
et al. (2020) report that the average workday span increased by 8.2%
during a single lockdown period. The authors concluded that the average
workday span of an employee was higher in every week following
the lockdown than any week in the 8 weeks prior to the lockdown.
Our findings contribute to addressing contemporary challenges in
organizational behavior by identifying important mechanisms that
contribute to this increase in workday span, and in identifying drivers
of these processes also suggest potential interventions. Specifically,
we demonstrate that interdependent work in teams supported by collaboration
technology use increases supplemental work whereas persistence
of communication can, only at high levels, mitigate this
impact. In addition, when high response expectations are shared
within a team, employees may reciprocate by engaging in supplemental
work, and this happens independently from individual collaboration
technology use or perceptions of persistence. These findings indicate
that increased use of collaboration technologies, which we might
expect in an environment of distributed work, is not deterministic of
increased TASW, and that workers' engagement with the affordances
of these technologies (viz., persistence) may limit TASW. Moreover,
organizations or teams wishing to limit potential increases in TASW
can make response expectations more explicit to reduce this potential
pressure on workers.
Conceptually, in terms of what this means for our understanding
of supplemental work, it is important to acknowledge that what constitutes
after-hour work remains highly relevant, especially in times
where employees may feel pressured to encroach upon their own
FIGURE 2 Johnson–Neyman (J–N)
interaction plot for persistence and
collaboration technology use on technologyassisted
supplemental work (TASW). The
vertical line next to the Y-axis indicates the
region of significance marker
nonwork time. For instance, flexibility is often constrained by task
demands, local clients, fixed deadlines, static workflows, and the
demands and characteristics of employees' social lives. Hence, our
position is that supplemental work is highly dependent on the perceptions
and individual practices of workers, rather than what we would
traditionally view as office hours (e.g., 8:00 AM–6:00 PM: Nurmi &
Hinds, 2020). The results of this study are not restricted to a standard
or uniform idea of work and nonwork time and therefore offer a more
ecologically valid representation of what drives TASW across a variety
of diverse organizational and team contexts.
In addition, this study brings some conceptual clarity to the study
of connectivity and supplemental work by explicitly recognizing the
distinctions between technology use and TASW, and between TASW
and connectivity. This is important as previous research adopting a
TASW framework has often conflated technology use after hours,
work-related extensions of availability, and connectivity, without
articulating the conceptual and empirical differences between these
uses and practices. Because connectivity demands are an inherent
feature of contemporary (global) work (Nurmi & Hinds, 2020), it is critical
to articulate the nature of these demands in terms of distinct
technological and social pressures.
We demonstrate that individual technology use and team-level
response expectations represent two important drivers of TASW that
operate independently. Specifically, the findings suggest that technology
use may present a pressure to engage in TASW by affording the
possibility to do so, while team-level response expectations operate
as a (independent) social pressure to engage in TASW. The absence of
cross-level interactions between these pressures appear to counter
earlier work that argued that the flexibility of technology and
response expectations are intertwined and contribute to a spiral of
escalating engagement in work by organizational members
(Mazmanian et al., 2013). Recognizing that these drivers can operate
independently pushes back against deterministic views that having
more technology available anytime or anywhere will inevitably result
in TASW, while at the same time recognizing that shared team-level
expectations, and individual technological uses may operate more in
parallel rather than intertwined as previously assumed. Moreover, by
examining TASW among individuals operating in diverse team structures,
these findings are robust across teams with various levels of
dispersion and demonstrate that the pressures underlying TASW are
not driven by distance among workers. However, it should be noted
that when teams work across various time zones, we do find a small
incremental increase in TASW. Overall, the findings highlight the relevance
of recognizing TASW as emerging from multiple individual,
social, and material pressures that are likely to be present across a
variety of work contexts.
The significant influence of team-level effects on TASW demonstrates
the need to examine organizational ICTs as enacted through
multilevel processes (Bélanger et al., 2013). The results indicate the
importance of the perceived obligation of team members in understanding
the likelihood of an individual engaging in TASW. Specifically,
response expectations have a strong positive effect on the performance
of TASW. Work in teams creates a form of interdependence
such that the ability of any single team member to complete work
tasks is dependent on the work of others. Therefore, individual team
members may feel pressure to engage in TASW to avoid holding up
work for the entire team. Examining TASW in a context of collaborative
team work highlights the different types of obligations and pressures
workers face when considering supplemental work. Specifically,
we demonstrate that shared response expectations at the team level
represent a pressure that shapes individual work practices such that
employees engage in supplemental work to conform with the expectations
within the team.
With the idea of a traditional work environment morphing, and
opportunities for work no longer encumbered by time and space,
determining drivers of supplemental work are more important than
ever. Although it may be tempting to succumb to rhetoric that available
connectivity and collaboration technologies make supplemental
work fait accompli, the results indicate that employees can play an
active role in managing pressures associated with the use of
collaboration technologies. For instance, the results indicate that
when collaboration technology use was associated with high persistence
of communication, the effect on TASW was lessened. This suggests
that workers are adjusting behaviors in conjunction with the
ways collaboration technology alter communication possibilities. Previous
examinations of the interplay between materiality and human
agency in the context of technology implementation have highlighted
the ways workers altered behaviors to adjust to the material limitations
of new technologies (e.g., Leonardi, 2011). The findings of this
work indicate the opposite; the materiality of the collaboration technologies
changed routines as workers were afforded more agency
over how to complete work. Though the ability of collaboration
technologies to make information more available across time and
space is often viewed as a key driver of TASW (Duxbury et al., 1996;
Venkatesh & Vitalari, 1992), the results regarding persistence of communication
challenge this assumption and demonstrate the need to
examine the material features of technology and ways it might support
more flexibility in work. This suggests that collaboration technologies
may present different forms of agency and ways of working
relative to communication technologies that do not focus on
interdependent action or are concerned with availability or connectivity
(Dery et al., 2014).
Broadly, this work contributes to a growing line of organizational
scholarship on the ways flexible and robust ICTs might alter ways of
working (e.g., Leonardi, 2007; Zammuto et al., 2007), by demonstrating
the ways interdependency and expanded boundaries of use associated
with collaboration technologies pose new challenges.
Specifically, as individuals perceive persistence and response expectations
differently, they may approach TASW in a manner that matches
with their individual work goals. In noting the different ways
employees might perceive and appropriate the materiality of IT in
organizations, Griffith et al. (2019) develop the concept of systems
savvy to refer to “individual's capacity to see the interdependence of
technological and social systems and to construct synergies between
them” (p. 491). The findings of this work are consistent with the idea
that individuals vary in how they identify and manage the technical
and social aspects of workplace technology and that this has consequences
for individuals' choices regarding work behaviors. Specifically,
the findings suggest that materiality of technology interacts with collaboration
technology use shaping individual work practices, but that
the impact of team-level response expectations on TASW is unaffected
by these material affordances. Furthermore, this study indicates
that considerations of systems savvy should extend beyond
traditional work contexts to consider TASW. Collectively, these findings
indicate the need to consider the ways multiple forms and
sources of agency, including the materiality of technology and team
organization, interact with individual agency to support or even
encourage different ways of working (Boudreau & Robey, 2005). By
considering the antecedents to the role of technology in work efforts
that extend beyond traditional organizational structures, this work
adds to our understanding of how organizational ICTs can shape the
boundaries (or lack thereof) individuals enact between work and other
activities (Winter et al., 2014).
4.2 | Practical implications
The findings are highly relevant in an era in which work is increasingly
characterized by dispersed work practices and hyper-connectivity
through (organizational) ICTs. Escalating engagement, which leads
employees to work everywhere all the time, is one of the most prominent
challenges in contemporary forms of organizing. The findings of
this study add to our understanding of these challenges by demonstrating
what individual and team-level characteristics contribute to
such extended work practices, providing organizations, managers, and
practitioners with concrete insights that may steer organizational
interventions. First, the findings demonstrate a strong positive effect
of team-level response expectations on TASW. Clearly the shared
expectation of needing to be responsive at all times leads workers to
extend their work practices to nights and weekends. From that perspective,
many organizations may follow countries such as France
(El Khomri law: Martin, 2017) and Germany (anti stress law:
Stuart, 2014) that have either laws in place, or proposed, to regulate
employees' connectivity after-hours. At the very least, such debates
provide a clear step to cut employees' technological “leashes” by signaling
that constant connectivity should not be the norm and workers
have a right to detach from work (von Bergen & Bressler, 2019). Similarly,
several companies have set their internal servers not to route
email to individual accounts after hours, a popular example being
Volkswagen (Haridy, 2018). Organizations have an important role in
clarifying expectations, and there is an onus on managers and
employees to better manage and negotiate collective expectations
about constant communication (Becker et al., 2018).
Second, the findings confirm the assumption that the use of collaboration
technologies is associated with higher levels of TASW, but
this relationship is less strong when persistence of communication is
high rather than low. Hence, the findings suggest that especially at
high levels of persistence, collaboration technology use may not
increase individual TASW. For organizations implementing these
technologies and developers who build them, these findings are interesting
as they may inform design decisions. This means that organizations
and developers should pay specific attention to how their
technologies may afford a continuous and persistent stream of information
and communication that employees may use at their
discretion.
4.3 | Limitations and future directions
As with any study, several limitations need to be acknowledged in
light of the findings presented here. First, this study relies on crosssectional
survey data collected from 433 employees spread over
122 teams. Although the study included secondary data sources, the
lack of longitudinal data limits our ability to make strong (causal)
claims about the hypothesized relationships in our model, or include
temporality in our modeling. Second, this study considered the effects
of team structure and response expectations. Although team structure
largely failed to uniquely contribute to explain variance in individuallevel
TASW, response expectations were found to have positive
effects. This suggests that other social dynamics at a group level may
also contribute to explain variance in TASW. For instance, psychological
and broader organizational climates characterized by promoting
workaholism and competitiveness may also contribute to TASW
(Fenner & Renn, 2004, 2010), as well as team and organizational level
work–life boundary preferences and initiatives (Kossek et al., 2010).
Hence, future studies may consider a broader model that also
accounts for organizational climate and work–life issues. Third, TASW
covers voluntary supplemental work that is not covered by a formal
contract; however, supplemental work can also be performed as part
of an agreement with other team members or supervisors. Although,
not necessarily covered in a formal contract, supplemental work as
part of an agreement may render some mechanisms related to agency
and control more or less relevant. Future research may further
develop our understanding of TASW by explicating a more nuanced
framework for supplemental work.
Finally, the current study is situated in a global company that
relies on Google Workspace to facilitate collaboration. The use of different
features forms the overall perception of platform use, but to
account for the different types of use and variability in effects, a
broader set of features could be explored. Hence, focusing on Google
Workspace and operationalizing feature level use of this technology
may limit our ability to extrapolate to collaboration technologies more
broadly. Future studies may seek to examine these mechanisms with
other (cloud-based) collaboration technologies, across organizations,
and types of work. Despite some limitations, this study advances our
understanding of TASW by demonstrating how individual, social, and
material pressures contribute to TASW.
ACKNOWLEDGMENTS
The authors would like to thank Kerk F. Kee, William C. Barley, and
Wouter de Nooy for their helpful comments in developing this
manuscript.
ORCID
Ward van Zoonen https://orcid.org/0000-0002-8531-8784
Anu Sivunen https://orcid.org/0000-0001-7068-2260
Jeffrey W. Treem https://orcid.org/0000-0003-3269-5559
REFERENCES
Adkins, C. L., & Premeaux, S. A. (2014). The use of communication technology
to manage work–home boundaries. Journal of Behavioral and
Applied Management, 15(2), 82–100.
Allen, T. D., Merlo, K., Lawrence, R. C., Slutsky, J., & Gray, C. E. (2021).
Boundary management and work–nonwork balance while working
from home. Applied Psychology, 70(1), 60–84. https://doi.org/10.
1111/apps.12300
Arlinghaus, A., & Nachreiner, F. (2014). Health effects of
supplemental work from home in the European Union. Chronobiology
International, 31(10), 1100–1107. https://doi.org/10.3109/07420528.
2014.957297
Bala, H., Massey, A. P., & Montoya, M. M. (2017). The effects of process
orientations on collaboration technology use and outcomes in product
development. Journal of Management Information Systems, 34(2),
520–559. https://doi.org/10.1080/07421222.2017.1334494
Barber, L. K., & Jenkins, J. S. (2014). Creating technological boundaries to
protect bedtime: Examining work–home boundary management, psychological
detachment and sleep. Stress and Health, 30(3), 259–264.
https://doi.org/10.1002/smi.2536
Barker, J. R. (1993). Tightening the iron cage: Concertive control in selfmanaging
teams. Administrative Science Quarterly, 38(3), 408–437.
https://doi.org/10.2307/2393374
Barley, S. R. (1986). Technology as an occasion for structuring: Evidence
from observations of CT scanners and the social order of radiology
departments. Administrative Science Quarterly, 31(1), 78–108. https://
doi.org/10.2307/2392767
Barley, S. R., Meyerson, D. E., & Grodal, S. (2011). E-mail as a source and
symbol of stress. Organization Science, 22(4), 887–906. https://doi.
org/10.1287/orsc.1100.0573
Bauer, D. J., & Curran, P. J. (2005). Probing interactions in fixed and multilevel
regression: Inferential and graphical techniques. Multivariate
Behavioral Research, 40(3), 373–400. https://doi.org/10.1207/
s15327906mbr4003_5
Becker, W. J., Belkin, L., & Tuskey, S. (2018). Killing me softly: Electronic
communications monitoring and employee and spouse well-being. In
Academy of Management Proceedings (Vol. 2018, No. 1, p. 12574).
Briarcliff Manor, NY 10510: Academy of Management. https://doi.
org/10.5465/AMBPP.2018.121
Bélanger, F., Watson-Manheim, M. B., & Swan, B. R. (2013). A multi-level
socio-technical systems telecommuting framework. Behaviour & Information
Technology, 32(12), 1257–1279. https://doi.org/10.1080/
0144929X.2012.705894
Boh, W. F., & Wong, S.-S. (2015). Managers versus co-workers as referents:
Comparing social influence effects on within- and outsidesubsidiary
knowledge sharing. Organizational Behavior and Human
Decision Processes, 126, 1–17. https://doi.org/10.1016/j.obhdp.2014.
09.008
Boswell, W. R., & Olson-Buchanan, J. B. (2007). The use of communication
technologies after hours: The role of work attitudes and work–life
conflict. Journal of Management, 33(4), 592–610. https://doi.org/10.
1177/0149206307302552
Boudreau, M.-C., & Robey, D. (2005). Enacting integrated information
technology: A human agency perspective. Organization Science, 16(1),
3–18. https://doi.org/10.1287/orsc.1040.0103
Bowers, C. A., Pharmer, J. A., & Salas, E. (2000). When member homogeneity
is needed in work teams: A meta-analysis. Small Group Research, 31
(3), 305–327. https://doi.org/10.1177/104649640003100303
Büchler, N., ter Hoeven, C. L., & van Zoonen, W. (2020). Understanding
constant connectivity to work: How and for whom is constant
connectivity related to employee well-being? Information and
Organization, 30(3), 100302. https://doi.org/10.1016/j.infoandorg.
2020.100302
Cavazotte, F., Heloisa Lemos, A., & Villadsen, K. (2014). Corporate smart
phones: Professionals' conscious engagement in escalating work connectivity.
New Technology, Work and Employment, 29(1), 72–87.
https://doi.org/10.1111/ntwe.12022
Cecez-Kecmanovic, D., Galliers, R. D., Henfridsson, O., Newell, S., &
Vidgen, R. (2014). The sociomateriality of information systems current
status, future directions. MIS Quarterly, 38(3), 809–830. https://doi.
org/10.2307/26634999
Chan, D. (1998). Functional relations among constructs in the same content
domain at different levels of analysis: A typology of compositional
models. Journal of Applied Psychology, 83, 234–246. https://doi.org/
10.1037/0021-9010.83.2.234
Chen, A., & Karahanna, E. (2014). Boundaryless technology: Understanding
the effects of technology-mediated interruptions across the boundaries
between work and personal life. AIS Transactions on Human–
Computer Interaction, 6(2), 16–36. https://doi.org/10.17705/1thci.
00059
Chong, S., Huang, Y., & Chang, C. H. D. (2020). Supporting interdependent
telework employees: A moderated-mediation model linking daily
COVID-19 task setbacks to next-day work withdrawal. Journal of
Applied Psychology, 105(12), 1408–1422. https://doi.org/10.1037/
apl0000843
Cialdini, R. B., & Trost, M. R. (1998). Social influence: Social norms, conformity
and compliance. In Gilbert, D. T., Fiske, S. T., & Lindzey, G. (Eds.),
The Handbook of Social Psychology (pp. 151–192). New York: McGraw-
Hill.
Cummings, J. N., Espinosa, J. A., & Pickering, C. K. (2009). Crossing spatial
and temporal boundaries in globally distributed projects: A relational
model of coordination delay. Information Systems Research, 20(3), 420–
439. https://doi.org/10.1287/isre.1090.0239
Day, A., Paquet, S., Scott, N., & Hambley, L. (2012). Perceived information
and communication technology (ICT) demands on employee outcomes:
The moderating effect of organizational ICT support. Journal of
Occupational Health Psychology, 17(4), 473–491. https://doi.org/10.
1037/a0029837
DeFilippis, E., Impink, S., Singell, M., Polzer, J. T., & Sadun, R. (2020). Collaborating
during coronavirus: The impact of COVID-19 on the nature
of work. Harvard Business School Organizational Behavior Unit Working
Paper, (21-006), 21-006. Available at SSRN: https://ssrn.com/
abstract=3665883
Dennis, A. R., Venkatesh, V., & Ramesh, V. (2003). Adoption of collaboration
technologies: Integrating technology acceptance and
collaboration technology research. Working papers on information
systems, 3(8), 3–8. http://aisel.aisnet.org/sprouts_all/43
Derks, D., & Bakker, A. B. (2014). Smartphone use, work–home interference,
and burnout: A diary study on the role of recovery. Applied Psychology,
63(3), 411–440. https://doi.org/10.1111/j.1464-0597.2012.
00530.x
Derks, D., van Duin, D., Tims, M., & Bakker, A. B. (2015). Smartphone use
and work–home interference: The moderating role of social norms and
employee work engagement. Journal of Occupational and Organizational
Psychology, 88(1), 155–177. https://doi.org/10.1111/joop.
12083
Dery, K., Hall, R., & Wailes, N. (2006). ERPs as ‘technologies-in-practice’:
Social construction, materiality and the role of organisational factors.
New Technology, Work and Employment, 21(3), 229–241. https://doi.
org/10.1111/j.1468-005X.2006.00177.x
Dery, K., Kolb, D., & MacCormick, J. (2014). Working with connective flow:
How smartphone use is evolving in practice. European Journal of Information
Systems, 23(5), 558–570. https://doi.org/10.1057/ejis.2014.13
Dery, K., & MacCormick, J. (2012). Managing mobile technology: The shift
from mobility to connectivity. MIS Quarterly Executive, 11(4), 159–173.
DeSanctis, G., & Gallupe, R. B. (1987). A foundation for the study of group
decision support systems. Management Science, 33(5), 589–609.
https://doi.org/10.1287/mnsc.33.5.589
Diaz, I., Chiaburu, D. S., Zimmerman, R. D., & Boswell, W. R. (2012). Communication
technology: Pros and cons of constant connection to work.
Journal of Vocational Behavior, 80(2), 500–508.
Duxbury, L. E., Higgins, C. A., & Thomas, D. R. (1996). Work and family
environments and the adoption of computer-supported supplemental
work-at-home. Journal of Vocational Behavior, 49(1), 1–23. https://doi.
org/10.1006/jvbe.1996.0030
Eby, L. T., & Dobbins, G. H. (1997). Collectivistic orientation in teams: An
individual and group-level analysis. Journal of Organizational Behavior,
18(3), 275–295. https://doi.org/10.1002/(SICI)1099-1379(199705)
18:3<275::AID-JOB796>3.0.CO;2-C
Eichberger, C., Derks, D., & Zacher, H. (2020). Technology-assisted supplemental
work, psychological detachment, and employee well-being: A
daily diary study. German Journal of Human Resource Management.
https://doi.org/10.1177/2397002220968188
Ellison, N. B., Gibbs, J. L., & Weber, M. S. (2015). The use of enterprise
social network sites for sharing knowledge in distributed organizations:
The role of organizational affordances. American Behavioral Scientist,
59(1), 103–123. https://doi.org/10.1177/0002764214540510
Erhardt, N., Gibbs, J., Martin-Rios, C., & Sherblom, J. (2016). Exploring
affordances of email for team learning over time. Small Group Research,
47(3), 243–278. https://doi.org/10.1177/1046496416635823
Espinosa, J. A., & Carmel, E. (2003). The impact of time separation on coordination
in global software teams: A conceptual foundation. Software
Process: Improvement and Practice, 8(4), 249–266. https://doi.org/10.
1002/spip.185
Evans, S. K., Pearce, K. E., Vitak, J., & Treem, J. W. (2016). Explicating
affordances: A conceptual framework for understanding affordances
in communication research. Journal of Computer-Mediated Communication,
22(1), 35–52.
Fenner, G. H., & Renn, R. W. (2004). Technology-assisted supplemental
work: Construct definition and a research framework. Human Resource
Management, 43(2–3), 179–200. https://doi.org/10.1002/hrm.20014
Fenner, G. H., & Renn, R. W. (2010). Technology-assisted supplemental
work and work-to-family conflict: The role of instrumentality beliefs,
organizational expectations and time management. Human Relations,
63(1), 63–82. https://doi.org/10.1177/0018726709351064
Fisher, J., Languilaire, J.-C., Lawthom, R., Nieuwenhuis, R., Petts, R. J.,
Runswick-Cole, K., & Yerkes, M. A. (2020). Community, work, and family
in times of COVID-19. Community, Work & Family, 23, 247–252.
https://doi.org/10.1080/13668803.2020.1756568
Fox, J., & McEwan, B. (2017). Distinguishing technologies for social interaction:
The perceived social affordances of communication channels
scale. Communication Monographs, 84(3), 298–318. https://doi.org/10.
1080/03637751.2017.1332418
Friedkin, N. E. (1993). Structural bases of interpersonal influence in groups:
A longitudinal case study. American Sociological Review, 58(6),
861–872. https://doi.org/10.2307/2095955
Fulk, J. (1993). Social construction of communication technology. The
Academy of Management Journal, 36(5), 921–950.
Gadeyne, N., Verbruggen, M., Delanoeije, J., & De Cooman, R. (2018). All
wired, all tired? Work-related ICT-use outside work hours and workto-
home conflict: The role of integration preference, integration norms
and work demands. Journal of Vocational Behavior, 107, 86–99.
https://doi.org/10.1016/j.jvb.2018.03.008
Garud, R., & Kumaraswamy, A. (2005). Vicious and virtuous circles in the
management of knowledge: The case of infosys technologies. MIS
Quarterly, 29(1), 9–33. https://doi.org/10.2307/25148666
Gibbs, J. L., Rozaidi, N. A., & Eisenberg, J. (2013). Overcoming the
“ideology of openness”: Probing the affordances of social media for
organizational knowledge sharing. Journal of Computer-Mediated Communication,
19(1), 102–120. https://doi.org/10.1111/jcc4.12034
Golden, T. D., & Raghuram, S. (2010). Teleworker knowledge sharing and
the role of altered relational and technological interactions. Journal of
Organizational Behavior, 31(8), 1061–1085. https://doi.org/10.1002/
job.652
Griffith, T. L., Sawyer, J. E., & Poole, M. S. (2019). Systems savvy: Practical
intelligence for transformation of sociotechnical systems. Group Decision
and Negotiation, 28(3), 475–499. https://doi.org/10.1007/
s10726-019-09619-4
Haridy, R. (2018). The right to disconnect: The new laws banning afterhours
work emails. https://newatlas.com/right-to-disconnect-afterhours-
work-emails/55879/
Jasperson, J., Carter, P. E., & Zmud, R. W. (2005). A comprehensive conceptualization
of postadoptive behaviors associated with information
technology enabled work systems. MIS Quarterly, 29, 525–557.
https://doi.org/10.2307/25148694
Klein, K. J., & Kozlowski, S. W. (2000). From micro to meso: Critical steps
in conceptualizing and conducting multilevel research. Organizational
Research Methods, 3(3), 211–236. https://doi.org/10.1177/
109442810033001
Kolb, D. G., Caza, A., & Collins, P. D. (2012). States of connectivity: New
questions and new directions. Organization Studies, 33(2), 267–273.
https://doi.org/10.1177/0170840611431653
Kossek, E., Lewis, S., & Hammer, L. B. (2010). Work–life initiatives and
organizational change: Overcoming mixed messages to move from the
margin to the mainstream. Human Relations, 63(1), 3–19. https://doi.
org/10.1177/0018726709352385
Kramer, A., & Kramer, K. Z. (2020). The potential impact of the COVID-19
pandemic on occupational status, work from home, and occupational
mobility. Journal of Vocational Behavior, 119, 103442. https://doi.org/
10.1016/j.jvb.2020.103442
Leonardi, P. M. (2007). Activating the informational capabilities of information
technology for organizational change. Organization Science, 18(5),
813–831. https://doi.org/10.1287/orsc.1070.0284
Leonardi, P. M. (2011). When flexible routines meet flexible technologies:
Affordance, constraint, and the imbrication of human and material
agencies. MIS Quarterly, 35(1), 147–167. https://doi.org/10.2307/
23043493
Leonardi, P. M., & Barley, S. R. (2008). Materiality and change: Challenges
to building better theory about technology and organizing. Information
and Organization, 18, 159–176. https://doi.org/10.1016/j.infoandorg.
2008.03.001
Leonardi, P. M., & Barley, S. R. (2010). What's under construction here?
Social action, materiality, and power in constructivist studies of technology
and organizing. The Academy of Management Annals, 4(1), 1–
51. https://doi.org/10.5465/19416521003654160
Leonardi, P. M., Treem, J. W., & Jackson, M. H. (2010). The connectivity
paradox: Using technology to both decrease and increase perceptions
of distance in distributed work arrangements. Journal of Applied Communication
Research, 38(1), 85–105. https://doi.org/10.1080/
00909880903483599
Leonardi, P. M., & Vaast, E. (2017). Social media and their affordances for
organizing: A review and agenda for research. Academy of Management
Annals, 11(1), 150–188. https://doi.org/10.5465/annals.2015.0144
Leung, K., & Wang, J. (2015). Social processes and team creativity in
multicultural teams: A socio-technical framework. Journal of
Organizational Behavior, 36(7), 1008–1025. https://doi.org/10.1002/
job.2021
Martin, C. (2017). The right to disconnect: A new right for French employment.
https://www.internationallaborlaw.com/2017/02/02/the-rightto-
disconnect-a-new-right-for-french-employees/
Martins, L. L., Gilson, L. L., & Maynard, M. T. (2004). Virtual teams: What
do we know and where do we go from here? Journal of Management,
30(6), 805–835. https://doi.org/10.1016/j.jm.2004.05.002
Matusik, S. F., & Mickel, A. E. (2011). Embracing or embattled by converged
mobile devices? Users' experiences with a contemporary
connectivity technology. Human Relations, 64(8), 1001–1030. https://
doi.org/10.1177/0018726711405552
Mazmanian, M., Orlikowski, W. J., & Yates, J. (2013). The autonomy paradox:
The implications of mobile email devices for knowledge professionals.
Organization Science, 24(5), 1337–1357. https://doi.org/10.
1287/orsc.1120.0806
Mazmanian, M. A., Orlikowski, W. J., & Yates, J. (2005). Crackberries: The
social implications of ubiquitous wireless e-mail devices. In Designing
ubiquitous information environments: Socio-technical issues and challenges
(pp. 337–343). Boston, MA: Springer.
Mynatt, E. D., O'Day, V. L., Adler, A., & Ito, M. (1998). Network communities:
Something old, something new, something borrowed …. Computer
Supported Cooperative Work, 7(1–2), 123–156. https://doi.org/10.
1023/a:1008688205872
Nurmi, N., & Hinds, P. (2020). Work design for global professionals: Connectivity
demands, connectivity behaviors, and their effects on psychological
and behavioral outcomes. Organization Studies, (online first),
4(12), 1697–1724. https://doi.org/10.1177/0170840620937885
Ohly, S., & Latour, A. (2014). Work-related smartphone use and well-being
in the evening. Journal of Personnel Psychology, 13(4), 174–183.
https://doi.org/10.1027/1866-5888/a000114
Ojala, S. (2011). Supplemental work at home among Finnish wage earners:
Involuntary overtime or taking the advantage of flexibility? Nordic
Journal of Working Life Studies, 1(2), 77–97. https://doi.org/10.19154/
njwls.v1i2.2346
O'Leary, M. B., & Cummings, J. N. (2007). The spatial, temporal, and configurational
characteristics of geographic dispersion in teams. MIS
Quarterly, 31(3), 433–452. https://doi.org/10.2307/25148802
Olson, G. M., & Olson, J. S. (2000). Distance matters. Human
Computer Interaction, 15(2), 139–178. https://doi.org/10.1207/
S15327051HCI1523_4
Olson-Buchanan, J. B., & Boswell, W. R. (2006). Blurring boundaries: Correlates
of integration and segmentation between work and nonwork.
Journal of Vocational Behavior, 68(3), 432–445. https://doi.org/10.
1016/j.jvb.2005.10.006
Orlikowski, W. J. (1992). The duality of technology: Rethinking the
concept of technology in organizations. Organization Science, 3(3),
398–427. https://doi.org/10.1287/orsc.3.3.398
Orlikowski, W. J. (2000). Using technology and constituting structures: A
practice lens for studying technology in organizations. Organization
Science, 11(4), 404–428. https://doi.org/10.1287/orsc.11.4.404.14600
Paczkowski, W. F., & Kuruzovich, J. (2016). Checking email in the bathroom:
Monitoring email responsiveness behavior in the workplace.
American Journal of Management, 16(2), 23–39.
Park, Y., Fritz, C., & Jex, S. M. (2011). Relationships between work–home
segmentation and psychological detachment from work: The role of
communication technology use at home. Journal of Occupational
Health Psychology, 16(4), 457–467. https://doi.org/10.1037/
a0023594
Pentland, B. T., & Feldman, M. S. (2008). Designing routines: On the folly
of designing artifacts, while hoping for patterns of action. Information
and Organization, 18(4), 235–250. https://doi.org/10.1016/j.
infoandorg.2008.08.001
Perlow, L. A., & Porter, J. L. (2009). Making time off predictable—and
required. Harvard Business Review, 87(10), 102–109.
Piszczek, M. M. (2017). Boundary control and controlled boundaries: Organizational
expectations for technology use at the work–family interface.
Journal of Organizational Behavior, 38(4), 592–611. https://doi.
org/10.1002/job.2153
Rice, R. E., Evans, S. K., Pearce, K. E., Sivunen, A., Vitak, J., & Treem, J. W.
(2017). Organizational media affordances: Operationalization and
associations with media use. Journal of Communication, 67(1),
106–130. https://doi.org/10.1111/jcom.12273
Richardson, K., & Benbunan-Fich, R. (2011). Examining the antecedents of
work connectivity behavior during non-work time. Information and
Organization, 21(3), 142–160. https://doi.org/10.1016/j.infoandorg.
2011.06.002
Richardson, K. M., & Thompson, C. A. (2012). High tech tethers and workfamily
conflict: A conservation of resources approach. Engineering Management
Research, 1(1), 29–43. https://doi.org/10.5539/emr.v1n1p29
Robey, D., Boudreau, M. C., & Rose, G. M. (2000). Information technology
and organizational learning: A review and assessment of research.
Accounting, Management and Information Technologies, 10(2), 125–155.
https://doi.org/10.1016/S0959-8022(99)00017-X
Sarker, S., & Sahay, S. (2004). Implications of space and time for distributed
work: An interpretive study of US–Norwegian systems development
teams. European Journal of Information Systems, 13(1), 3–20. https://
doi.org/10.1057/palgrave.ejis.3000485
Schlachter, S., McDowall, A., Cropley, M., & Inceoglu, I. (2018). Voluntary
work-related technology use during non-work time: A narrative synthesis
of empirical research and research agenda. International Journal
of Management Reviews, 20(4), 825–846. https://doi.org/10.1111/
ijmr.12165
Schmitz, J., & Fulk, J. (1991). Organizational colleagues, media richness,
and electronic mail: A test of the social influence model of technology
use. Communication Research, 18(4), 487–523. https://doi.org/10.
1177/009365091018004003
Schultze, U., & Boland, R. J. (2000). Knowledge management technology
and the reproduction of knowledge work practices. The Journal of Strategic
Information Systems, 9, 193–212. https://doi.org/10.1016/
S0963-8687(00)00043-3
Shah, P. P., Dirks, K. T., & Chervany, N. (2006). The multiple pathways of
high performing groups: The interaction of social networks and group
processes. Journal of Organizational Behavior, 27(3), 299–317. https://
doi.org/10.1002/job.368
Stuart, K. (2014). German minister calls for anti-stress law ban on emails
out of office hours. https://www.theguardian.com/technology/2014/
aug/29/germany-anti-stress-law-ban-on-emails-out-of-office-hours
Taggar, S., & Ellis, R. (2007). The role of leaders in shaping formal team
norms. The Leadership Quarterly, 18(2), 105–120. https://doi.org/10.
1016/j.leaqua.2007.01.002
Thörel, E., Pauls, N., & Göritz, A. S. (2020). Are the effects of work-related
extended availability the same for everyone? Journal of Work and
Organizational Psychology, 36(2), 147–156.
Tierney, P. (1999). Work relations as a precursor to a psychological climate
for change: The role of work group supervisors and peers. Journal of
Organizational Change Management, 12(2), 120–134. https://doi.org/
10.1108/09534819910263668
Treem, J. W., & Leonardi, P. M. (2013). Social media use in organizations:
Exploring the affordances of visibility, editability, persistence, and
association. Annals of the International Communication Association, 36
(1), 143–189. https://doi.org/10.1080/23808985.2013.11679130
Treem, J. W., Leonardi, P. M., & van den Hooff, B. (2020). Computermediated
communication in the age of communication visibility. Journal
of Computer-Mediated Communication, 25(1), 44–59. https://doi.
org/10.1093/jcmc/zmz024
Tyler, J. R., & Tang, J. C. (2003). When can I expect an email response? A
study of rhythms in email usage. In ECSCW 2003 (pp. 239–258).
Dordrecht: Springer. https://doi.org/10.1007/978-94-010-0068-0_13
van Mierlo, H., Vermunt, J. K., & Rutte, C. G. (2009). Composing group-
Level constructs from individual-level survey data. Organizational
Research Methods, 12(2), 368–392. https://doi.org/10.1177/
1094428107309322
Vaziri, H., Casper, W. J., Wayne, J. H., & Matthews, R. A. (2020). Changes
to the work–family interface during the COVID-19 pandemic: Examining
predictors and implications using latent transition analysis. Journal
of Applied Psychology, 105(12), 1073–1087. https://doi.org/10.1037/
apl0000819
Venkatesh, A., & Vitalari, N. P. (1992). An emerging distributed work
arrangement: An investigation of computer-based supplemental
work at home. Management Science, 38(12), 1687–1706. https://doi.
org/10.1287/mnsc.38.12.1687
Volkoff, O., & Strong, D. M. (2013). Critical realism and affordances: Theorizing
it-associated organizational change processes. MIS Quarterly, 37
(3), 819–834. https://doi.org/10.25300/MISQ/2013/37.3.07
von Bergen, C. W., & Bressler, M. S. (2019). Work, non-work boundaries
and the right to disconnect. The Journal of Applied Business and Economics,
21(2), 51–69.
Wageman, R., Gardner, H., & Mortensen, M. (2012). The changing ecology
of teams: New directions for teams research. Journal of Organizational
Behavior, 33(3), 301–315. https://doi.org/10.1002/job.1775
Wajcman, J., & Rose, E. (2011). Constant connectivity: Rethinking interruptions
at work. Organization Studies, 32(7), 941–961. https://doi.
org/10.1177/0170840611410829
Wajcman, J., Rose, E., Brown, J. E., & Bittman, M. (2010). Enacting virtual
connections between work and home. Journal of Sociology, 46(3),
257–275. https://doi.org/10.1177/1440783310365583
Wallace, J. C., Edwards, B. D., Paul, J., Burke, M., Christian, M., & Eissa, G.
(2016). Change the referent? A meta-analytic investigation of
direct and referent-shift consensus models for organizational climate.
Journal of Management, 42(4), 838–861. https://doi.org/10.1177/
0149206313484520
Wang, B., Liu, Y., Qian, J., & Parker, S. K. (2021). Achieving effective
remote working during the COVID-19 pandemic: A work design perspective.
Applied Psychology, 70(1), 16–59. https://doi.org/10.1111/
apps.12290
Winter, S., Berente, N., Howison, J., & Butler, B. (2014). Beyond the organizational
‘container’: Conceptualizing 21st century sociotechnical
work. Information and Organization, 24(4), 250–269. https://doi.org/
10.1016/j.infoandorg.2014.10.003
Wright, K. B., Abendschein, B., Wombacher, K., O'Connor, M.,
Hoffman, M., Dempsey, M., Krull, C., Dewes, A., & Shelton, A. (2014).
Work-related communication technology use outside of regular work
hours and work life conflict: The influence of communication technologies
on perceived work life conflict, burnout, job satisfaction, and
turnover intentions. Management Communication Quarterly, 28(4),
507–530. https://doi.org/10.1177/0893318914533332
Xiao, Y., Becerik-Gerber, B., Lucas, G., & Roll, S. C. (2021). Impacts of
working from home during COVID-19 pandemic on physical and mental
well-being of office workstation users. Journal of Occupational and
Environmental Medicine, 63(3), 181–190. https://doi.org/10.1097/
JOM.0000000000002097
Zammuto, R. F., Griffith, T. L., Majchrzak, A., Dougherty, D. J., & Faraj, S.
(2007). Information technology and the changing fabric of organization.
Organization Science, 18(5), 749–762. https://doi.org/10.1287/
orsc.1070.0307
Zuboff, S. (1988). In the age of the smart machine. New York, NY: Basic
Books.
AUTHOR BIOGRAPHIES
Ward van Zoonen (PhD, University of Amsterdam) is an assistant
professor at the Amsterdam School of Communication Research
(ASCoR), University of Amsterdam. His research interests include
the relationship between information and communication technology
use and work outcomes. His work specifically explores
connectivity and communication visibility in global organizational
contexts.
Anu Sivunen (PhD, University of Jyväskylä) is a professor of communication
in the Department of Language and Communication
Studies at University of Jyväskylä, Finland. Her research focuses
on communication processes in global teams and in other distributed
work arrangements, workspaces, and the affordances of
organizational communication technologies. Her work has
appeared in journals from a variety of disciplines, such as the Journal
of Communication, Journal of Computer-Mediated Communication,
Journal of the Association for Information Systems, Human
Relations, and Small Group Research.
Jeffrey W. Treem (PhD, Northwestern University) is an associate
professor of communication studies at the University of Texas at
Austin where he is part of the Organizational Communication &
Technology group. His research interests include the relationship
between technology use and social perceptions of expertise, primarily
in organizational contexts. Specifically, his work explores
how the material affordances of communication technologies
affect attributions of knowledge.
How to cite this article: van Zoonen, W., Sivunen, A., &
Treem, J. W. (2021). Why people engage in supplemental
work: The role of technology, response expectations, and
communication persistence. Journal of Organizational Behavior,
42(7), 867–884. https://doi.org/10.1002/job.2538
APPENDIX A.
Study scales
Collaboration technology use
• I use [collaboration technology] to share files.
• I use [collaboration technology] to edit files.
• I use [collaboration technology] to collaborate in shared
documents.
• I use [collaboration technology] to store files.
• I use [collaboration technology] for videoconferencing.
• I use [collaboration technology] to chat with coworkers.
• I use [collaboration technology] for audio conferencing.
Persistence of communication (Fox & McEwan, 2017)
• The collaboration technologies we use at [organization] keep a
record of communication that I can go back and look at.
• I can retrieve past messages that been sent through collaboration
technologies.
• Collaboration technologies used at [organization] keep a record of
communication that can last long after the initial communication.
• Communication through collaboration technologies exists long
after the initial interaction is finished.
Response expectations (Derks et al., 2015)
• My supervisor expects me to respond to work-related messages
during my free time after work.
• When I don't answer to messages in my free time, my supervisor
clearly shows that he/she does not appreciate it.
• I feel that I have to respond to messages from my supervisor
immediately also during leisure time.
• I often receive messages from my colleagues during the weekend.
• When I send a message to colleagues during the weekend, most
colleagues react the same day.
• If I do not respond to messages from my colleagues, my position in
the group is threatened.
Technology-assisted supplemental work (Fenner & Renn, 2010)
• When I fall behind in my work during the day, I work hard at home
at night or on weekends to get caught up by using my smartphone
or computer.
• I perform job-related tasks at home at night or on weekends using
my smartphone or computer.
• I feel my smartphone or computer is helpful in enabling me to work
at home at night or on weekend.
• When there is an urgent issue or deadline at work, I tend to perform
work-related tasks at home during the night or on weekends
using my smartphone or computer.
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2.2.4 | Team-level measures

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