7/29/2019 Noelle Chesley and Britta Johnson 2012_does Work-related Technology Use Influence Perceptions of Productivity a
1/38
RUNNING HEAD: TECHNOLOGY USE, PRODUCTIVITY AND DISTRESS
Does Work-Related Technology Use Influence Employee Perceptions of
Productivity and Distress?
Noelle Chesleya and Britta Johnsonb
University of Wisconsin-Milwaukee
Paper Prepared for Presentation at the First Inaugural Conference
of the Work Family Research Network, NY, NY, June 14th 16th, 2012.
a Direct correspondence to: Noelle Chesley, Assistant Professor, University of Wisconsin-Milwaukee, Department of Sociology, 2025 E. Newport Avenue, Milwaukee, WI 53221;414-229-2398 (phone); 414-229-4266 (fax);[email protected]
b Britta Johnson [4319 N. Alpine Ave, Shorewood, WI 53211; 414-517-4151 (phone);[email protected]]
mailto:[email protected]:[email protected]:[email protected]:[email protected]7/29/2019 Noelle Chesley and Britta Johnson 2012_does Work-related Technology Use Influence Perceptions of Productivity a
2/38
TECHNOLOGY USE, PRODUCTIVITY, AND DISTRESS 2
Abstract
Drawing on the Jobs Demands-Resources framework and 2008 survey data drawn
from a national sample of employees (PEW Networked Workers Study) , we test whether
work practices driven by Information and Communication Technology (ICT) use are linked
to higher levels of employee productivity and distress. We find that: 1) ICT-based work
extension, telecommuting, and network expansion are linked to higher levels of
productivity and distress; and 2) Using ICT to complete a range of general work tasks is
connected to higher productivity and lower distress levels. Overall, these findings
underscore thathowICT is used is more important than whetherit is used in
understanding its impact on employees.
Keywords: Information and Communication Technology (ICT) Use, Productivity, Distress,
Job-Demands-Resources model
7/29/2019 Noelle Chesley and Britta Johnson 2012_does Work-related Technology Use Influence Perceptions of Productivity a
3/38
TECHNOLOGY USE, PRODUCTIVITY, AND DISTRESS 3
Does Work-Related Technology Use Influence Employee Perceptions of
Productivity and Distress?
The majority of Americans use some type of Information and Communication
Technology (ICT) to perform their work (Bond, Thompson, Galinksy, & Prottas, 2002;
Madden & Jones, 2008). Research indicates that ICT has been incorporated into workplace
practices that are used to increase efficiencies and communication speeds, and that these
technologies have created an infrastructure that supports the global expansion of
organizations (Aneesh, 2006; Mamaghani, 2006). In spite of claims about the increased
efficiencies of technology use, or concerns about unintended consequences associated with
ICT use, we know relatively little about how work-related ICT use is influencing both
organizational and employee outcomes. How exactly is ICT used in workplaces? Does its
use support organizational goals, such as enhanced productivity? Are there any unintended
consequences associated with work-related ICT use for employees, such as increased
distress? The goal of this study is to address these questions by analyzing specific ICT-based
work practices to determine if these practices influence employee perceptions of
productivity and distress.
There is evidence that the adoption of technological innovations in the work place
has changed the organization of work in ways that could influence both employee
productivity levels and well-being. Previous research indicates that ICT broadens the
boundaries of where and when people can work (Bailey & Kurland, 2002; Bittman, Brown,
& Wajcman, 2009; Duxbury, Towers, Higgins, & Thomas, 2006; Hill Ferris, & Martinson,
2003; Kurland & Bailey, 1999; Murray & Rostis, 2007; Valcour & Hunter, 2005). The
blurring of work/non-work boundaries has been connected to increases in work-family
7/29/2019 Noelle Chesley and Britta Johnson 2012_does Work-related Technology Use Influence Perceptions of Productivity a
4/38
TECHNOLOGY USE, PRODUCTIVITY, AND DISTRESS 4
conflict (Glavin & Schieman, 2012) and may increase employee productivity and/or distress
(Chesley 2005). This should be especially true when ICT use occurs within organizational
cultures that expect high levels of employee engagement and performance (Murray &
Rostis, 2007; Towers, Duxbury, Higgins, & Thomas, 2006).
Deepening our understanding of the role that ICT plays in shaping employee
outcomes requires more specific documentation of how employees use ICT at work and
linking such practices to organizational and employee outcomes. To accomplish this, we
draw on survey data collected by the PEW Internet and American Life Project (The 2008
Networked Workers Study(N = 1000)) from a nationally representative sample of workers.
This survey incorporates a range of measures tapping how ICT is used in workplaces, as
well as measures of employees perceived productivity and distress.
Four sets of literature inform this project. We draw first on Jobs Demands-Resources
theory and incorporate findings from the interdisciplinary technology studies literature to
better specify the potential role that ICT use may play in shaping work demands and
resources. Next we describe what we know about how ICTs are used in the workplace.
Finally, we review research that links work-related ICT use to variations in employee
productivity and distress.
BACKGROUND
The Job Demands-Resources (JD-R) theoretical model (Bakker & Demerouti, 2007;
Demerouti, Bakker, Nachreiner, & Schaufeli, 2001) provides one explanation linking
characteristics of the work environment, like ICT use, to employee outcomes. This model
proposes that job resources and demands are ongoing processes in the employment setting
and their interaction with each other is what determines employee health or distress. Job
7/29/2019 Noelle Chesley and Britta Johnson 2012_does Work-related Technology Use Influence Perceptions of Productivity a
5/38
TECHNOLOGY USE, PRODUCTIVITY, AND DISTRESS 5
resources can buffer the impact of job demands, or job demands can override the positive
effects of job resources.
The introduction of ICT into employment settings was intended to serve as a
resource for organizations and employees (Hill et al., 2003: Mamaghani, 2006; Valcour &
Hunter, 2005), and some research supports this premise. For example, ICT-supported
telecommuting helps some workers manage work-life balance (Duxbury et al., 2006; Hill et
al., 2003; Kurland & Bailey, 1999; Mamaghani, 2006), while ICT-based communication can
improve the ability of employees to share their ideas with coworkers (Mano & Mensch,
2010). H owever, as Wajcman (2008) points out, technological innovations often generate
unintended consequences and unanticipated (and often contradictory) effects (pg. 70).
Accordingly, there is evidence that ICT-based work practices can serve, not just as
resources, but may also promote greater work demands. Previous research has linked
work-related ICT use to an increase in the pacing of work (Chesley 2010a; Green 2004a,
2004b; Maume & Purcell, 2007), as well as a more fragmented work environment (Chesley
2010a; 2011) both of which are linked to increases in worker distress. Likewise, ICT-
facilitated work extension has been positively related to work-family conflict (Boswell &
Olson-Buchanan, 2007; Fenner & Renn, 2010).
The JD-R model theoretical model suggests that ICT use needs to be understood both
as a potential resource for employees, as well as a practice that may generate additional
demands. Thus, research that can better document the influence of ICT-based practices on
employee outcomes can not only help us understand how to maximize the benefits and
minimize the costs associated with these practices, but may also shed light on how specific
ICT-based practices operate as part of broad organizational systems.
7/29/2019 Noelle Chesley and Britta Johnson 2012_does Work-related Technology Use Influence Perceptions of Productivity a
6/38
TECHNOLOGY USE, PRODUCTIVITY, AND DISTRESS 6
ICT-Based Work Practices
Survey data from 2008 indicate that 62% of US employees use the Internet or email
at their workplace (Madden & Jones, 2008), Similar patterns have been identified in Canada
(Desjardins Financial Security, 2006). While previous research recognizes the key role of
ICT in organizations, past research often characterizes this use in very general terms, like
frequency of computer or Internet use. Our focus is not on very general forms of ICT use,
but on ICT use as it is embedded in a series of work practices that were not possible or
could not be performed on the same scale before the entrenchment of computers and
mobile communications (Chesley and Johnson, 2010b). Scholars have identified and
studied some work practices that are intimately connected to ICT use, such as ICT-
facilitated work extension (supplemental work done at home in addition to working
traditional work hours; Duxbury et al., 2006; Fenner & Renn, 2004, 2010; Towers et al.,
2006), ICT-supported teleworking (Bailey & Kurland, 2002; Hill et al, 2003; Kurland &
Bailey, 1999), ICT-facilitated social network expansion (Kennedy, Smith, Wells, & Wellman,
2008; Milliken & Dunn-Jensen, 2005), and ICT-based task completion (e.g. Email, instant
messaging, text messaging, etc.; Cutrell, Czerwinski, & Horvitz, 2001; Czerwinski, Cutrell, &
Horvitz, 2000; Mano & Mensch, 2010, Mark, Gudith, & Klocke, 2008).
The current literature lacks data on the incidence of some of the specific ICT-based
work practices just described (i.e. ICT-based task completion measures). When ICT-based
work practices are measured, inconsistent measurement strategies make using these
findings difficult to apply to broad populations. A 2005 US Department of Labor analysis
measured the amount of hours employees in their sample worked from home (i.e.
telecommuting) and performed unpaid supplemental work (i.e. work-extension). They find
7/29/2019 Noelle Chesley and Britta Johnson 2012_does Work-related Technology Use Influence Perceptions of Productivity a
7/38
TECHNOLOGY USE, PRODUCTIVITY, AND DISTRESS 7
that about 15% of respondents worked from home regularly (20.7 million) and 10.2 million
Americans reported that they performed job-related work at home without formal
compensation. Of these two groups, roughly 80% of these workers reported that they had
access to ICT devices that facilitated their work at home (US Department of Labor, 2005).
Limited previous research also provides some guidance on frequency of ICT use.
Job-type is associated with the level of work-related ICT use with almost three-fourths of
professionals, executives, or managers reporting use of the internet at work constantly or
several times a day (Madden & Jones, 2008). F urthermore, white-collar employees own
and use more gadgets such as laptops, PDAs, and mobile phones than other employees, and
tend to use them more frequently both in and out of the workplace (Madden & Jones, 2008).
Overall, more information is needed abouthowemployees use ICT at work, not what ICTs
they use, or how frequently they use specific devices.
ICT-Based Work Practices and Employee Productivity
Previous research does suggest a relationship among ICT adoption and broad
workplace productivity gains (Aral, Brynjolfsson, & Wu, 2006; Baily, 2004; Brynjolfsson &
Hitt, 2000; Stiroh, 2002) as well as organizational efficiency (Mamaghani,2006). However,
relatively little research to date has looked at how ICT use impacts individual levels of
employee productivity. ICT use provides employees with new ways to capture, organize,
analyze, and distribute information (ODriscoll, Biron, Cooper, 2009) which may help
increase individual productivity by creating more efficient means to complete work tasks.
In a 2001 Canadian survey, respondents linked ICT use to greater work productivity and
interest in their work (Duxbury & Higgins, 2001). Further, drawing on two regional samples
of U.S. employees, Chesley (2010a) found that frequency of ICT use (computer, email, and
7/29/2019 Noelle Chesley and Britta Johnson 2012_does Work-related Technology Use Influence Perceptions of Productivity a
8/38
TECHNOLOGY USE, PRODUCTIVITY, AND DISTRESS 8
cell phone) is positively associated with levels of perceived workplace effectiveness. Mano
and Mensch (2010) use a nationally representative sample of U.S. employed adults to
analyze the effects of email on employee perceptions of work effectiveness and found that
the amount of email sent and received is positively related to work performance. Finally,
Hill et al. (2003), found that telecommuters have higher levels of self-reported job
productivity than their colleagues in other work arrangements.
Another way work-related ICT use can lead to productivity increases is by
facilitating practices that extend the workday. There is a growing body of research that has
studied this phenomenon (e.g. Boswell & Olson-Buchanan, 2007; Duxbury et al., 2006;
Fenner & Renn, 2010, Towers et al., 2006). For example, Duxbury et al. (2006) cite industry
data (BlackBerry and Intel) reporting that users of notebook computers at home work an
additional 5 hours per week, which results in an annual dollar benefit of $19,200 per
employee for the organization (p. 11). Towers et al. (2006) surveyed employees in a major
Canadian government department about their ICT use after hours and found a positive
relationship between frequency of ICT use outside of the office and total hours worked each
week.
Up for further debate within this literature is the argument about objective versus
subjective measures of productivitysome argue that subjective measures of
productivity/performance are not an accurate way to assess this research concept.
However, subjective measures of performance are used in research and are often
interpreted as being equivalent to objective measures (Wall, Michie, Patterson, & Wood,
2004). On a firm-level, there is evidence that subjective and objective measures of
performance are positively associated (Wall et al., 2004). Overall, previous research has
7/29/2019 Noelle Chesley and Britta Johnson 2012_does Work-related Technology Use Influence Perceptions of Productivity a
9/38
TECHNOLOGY USE, PRODUCTIVITY, AND DISTRESS 9
documented a positive association between work-related ICT use and higher productivity
using both subjective (e.g. self-perceptions) and objective (e.g. actual hours worked)
measures. One area that needs further attention is investigating whether different types of
ICT-based work practices result in different productivity outcomes. Does ICT Use
encourage productivity across the board, or do productivity gains require that ICTs are
used in specific ways to address particular types of work tasks?
ICT-Based Work Practices and Employee Distress
Work-related ICT use has been linked to employee distress (Chesley, 2005; Duxbury
& Higgins, 2001; Mano & Mesch, 2010; Murray & Rostis, 2007). In a 2006 survey of
Canadian workers, 54% of workers reported that ICT use maintained their existing level of
stress, while 29% reported that ICT use increased their level of stress (Desjardins Financial
Security, 2006). Similarly, Duxbury et al. (2006) reported that 45% of Canadian
respondents believed that ICT use had increased their stress levels. An under-studied area
of scholarship is how specific forms of ICT use (using email to communicate with clients and
co-workers, for example) might underlie specific employee outcomes, such as distress.
A limited number of studies have attempted to understand this in more detail. For
example, Murray & Rostis (2007) suggest that it is the constant communication delivered
by ICT and the inability to disconnect that is stressful for ICT users. Ferrer, Rosa, Abad, and
Fernandez-Montejo (2010), studied the effects that communication-oriented tasks
completed via computer versus in person had on cardiovascular response markers typically
associated with stress (systolic blood pressure and heart rate). They found that
respondents had greater cardiovascular responses when they completed tasks via computer
versus face-to-face, suggesting a link between ICT use and physiological stress markers for
7/29/2019 Noelle Chesley and Britta Johnson 2012_does Work-related Technology Use Influence Perceptions of Productivity a
10/38
TECHNOLOGY USE, PRODUCTIVITY, AND DISTRESS 10
some ICT-related tasks.
Some scholars argue that it is the ICT-facilitated intrusion of work into non-work
spaces and times which leads to negative effects on psychological well-being such as stress
and distress (Chesley, 2005, Duxbury et al., 2006; Major, Klein, & Erhart, 2002). Another
potential link between ICT use and levels of distress/stress in employees is how ICT use has
been shown to increase work hours for some ICT users (Boswell & Olson-Buchanan, 2007;
Duxbury et al., 2006; Fenner & Renn, 2010, Towers et al., 2006). This may be detrimental
for workers as previous research indicates that working longer hours can lead to increased
levels of perceived stress (Major et al., 2002) and clearly links higher stress levels to poor
physical and mental health (Thoits, 2010).
Overall, previous theory and research clearly support a connection between work-
related ICT use and employee perceptions of productivity and distress. There is limited
evidence linking specific ICT devices/platforms and frequency of use to both employee
productivity or distress (e.g. Bittman et al., 2009; Czerwinski et al., 2000; Mano & Mesch,
2010). Other research focuses on use of a range of devices or applications on user
perceptions and work outcomes (e.g. Boswell & Olson-Buchanan, 2007; Chesley, 2010a;
Murray & Rostis, 2007). However, we are not aware of research that examines how a series
of workplace practices, like using ICT to manage contacts, schedule meetings, or work
outside the office, influence measures of employee productivity and distress. Using the JD-R
framework as a guide, the current project will further clarify the relationship between ICT-
based work practices and individual employee outcomes. The following hypotheses are
informed by previous research and testing them will allow us to confirm and further
specify the relationships, if any, among ICT-based work practices and employee
7/29/2019 Noelle Chesley and Britta Johnson 2012_does Work-related Technology Use Influence Perceptions of Productivity a
11/38
TECHNOLOGY USE, PRODUCTIVITY, AND DISTRESS 11
productivity and distress:
H1: Employees that are engaged in ICT-based work practices will report higher
levels of productivity compared to those who do not engage in these practices,
controlling for other factors.
H2: Employees that are engaged in ICT-based work practices will report higher
levels of distress compared to those who do not engage in these practices,
controlling for other factors.
METHOD
Data
This study uses survey data from the 2008 Networked Workers survey conducted by
the PEW Internet and American Life Project. The focus of the survey was to determine the
amount, type, and influence of technology use on a nationally representative sample of US
workers. This is an ideal dataset for examining the role that ICT-based work practices play
in influencing levels of productivity and distress in employed US adults because of the
breadth of ICT related topics it covers and because the data are drawn from a recent and
nationally representative sample of US working adults. All individuals interviewed for this
survey (n = 1000) self-identified as full-time or part-time workers. ICT use was defined as
computer, internet, email, instant messaging, or cell phone use. Our analytic sample
includes all respondents who indicated that they use some form of ICT and excludes cases
with missing data from the analysis, resulting in an analytic sample of 712 workers.
The response rate for this survey was 24%. Recent research suggests that there is
not a consistent relationship between response rates and non-response bias. A recent
7/29/2019 Noelle Chesley and Britta Johnson 2012_does Work-related Technology Use Influence Perceptions of Productivity a
12/38
TECHNOLOGY USE, PRODUCTIVITY, AND DISTRESS 12
meta-analysis (Groves 2006) suggests that data drawn from probability-based samples
with measures of key auxiliary variables (particularly gender and urban/rural address) on
respondents and non-respondents are best positioned to deal with issues of response bias.
Our data are drawn from a probability-based sample with a clear sampling frame and PEW
used auxiliary variables (including gender and region) to create a weight to correct for
differential patterns of non-response. In our analyses we use this weight to correct for non-
response bias and Taylors linearization method is used to incorporate a design effect into
the standard error estimates (following recommendations from PEW). Survey commands
in STATA 11.2 (e.g. svy: logistic) were used to produce our estimates.
Measures
Dependent Variables. Perceived employee productivity is measured through a single
question that asks respondents, How much, if at all, have technologies such as the internet,
email, cell phones, instant messaging improved your ability to do your job? The responses
range from 1 (a lot) to 4 (not at all). This variable is recoded as a dichotomous variable
where 1 indicates improvement in productivity (some, or a lot) and 0 indicates little to
no increase in productivity. Perceived employee distress is measured through a single item
which asks How much, if at all, have technologies such as the internet, email, cell phones,
instant messaging increased stress in your job? The responses ranged from 1 (a lot) to 4
(not at all). This variable is recoded as a dichotomous variable where 1 indicates an
increase in distress (some, or a lot) and 0 indicates little or no increase in distress.
Key Independent Variables: ICT-Based Work Practices. ICT-facilitated Work Extension
is measured using two index variables that tap work extension due to email or phone use.
7/29/2019 Noelle Chesley and Britta Johnson 2012_does Work-related Technology Use Influence Perceptions of Productivity a
13/38
TECHNOLOGY USE, PRODUCTIVITY, AND DISTRESS 13
Respondents were asked: Do you check your work-related email on: 1) weekends; 2)
vacations; 3) before work; 4) after work; 5) when you are sick; 6) when running errands?
Responses range from 1 (often), to 4 (never). Responses were recoded so that higher
numbers indicated greater work extension and summed together to form an index (6 = no
email-related work extension; 24 = high email work extension). Respondents were also
asked an identical set of questions about phone use. A general index of work extension
combines all measures of email and phone use outside of work (12 = low work extension;
48 = high work extension).
Measures ofICT-Based Telecommuting are based on responses to two questions:
Has using email changed the amount of time you spend: 1) specifically working from
home, and 2) working at places other than the office and at home? Responses for both
questions are: 1) yes, increased; 2) yes, decreased; 3) no, has not changed the amount of
time I spend doing these things. These variables were recoded so that respondents who do
not use work email are grouped in the third comparison category (no change).
ICT-Based Network Expansion is measured using responses to a single item: How
much, if at all, have technologies such as the Internet, email, cell phones, instant messaging
expanded the number of people you communicate with? Responses are: 1) not at all; 2)
only a little; 3) some; 4) a lot. This variable was recoded so that higher numbers
correspond to greater levels of network expansion (1= no network expansion to 4 = a lot of
network expansion).
A last set of variables captures the role of ICT use in effectively accomplishing a
series of specific workplace tasksglobally referred to as ICT-Based Task Completion.
Respondents were asked: Which is the most effective way to handle the following
7/29/2019 Noelle Chesley and Britta Johnson 2012_does Work-related Technology Use Influence Perceptions of Productivity a
14/38
TECHNOLOGY USE, PRODUCTIVITY, AND DISTRESS 14
situations: 1) arrange meetings/appointments; 2) edit/review documents; 3) ask questions
about work issues; 4) deal with sensitive issues; 5) bring a problem to your supervisor?
From this, we created a series of five binary variables that capture whether a task (e.g.,
arranging meetings) is best accomplished in person (0) versus using ICT (1).
Control Variables. A number of other characteristics are likely to influence employee
perceptions of productivity and distress. Gender, age, race, and level of education are
factors known to influence job conditions at work. We control for gender (1 = female), age
(measured in years), and race/ethnicity. Although respondents were originally classified
into four racial/ethnic categories (white, black, Hispanic, other), small cell sizes require us
to collapse race into a dichotomy (0 = other, 1 = non-Hispanic white). Education is also
controlled and measured categorically; respondents fall into one of four categories: 1) less
than high school; 2) high school graduate; 3) some college; 4) college or better.
In addition, we control for several characteristics associated with a respondents
employment that may have an impact on the dependent variables. To measure hours
worked, respondents were asked, How many hours do you work in a typical week? The
responses ranged from 1 to 61 (PEW coded everyone working more than sixty hours per
week as 61, thus this variable is truncated). The income measure asks, What was your
wage last year (2007) before taxes? The original variable had eight response categories,
several with small cell sizes. As a result, we recoded this item into a four-category variable:
1) Less than $30,000; 2) $31,000 to $49,000; 3) $50,000 to $74,000; 4) $75,000 or more.
The income variable had a fairly large number of missing cases (103 or about 10%).
Multivariate analyses of the patterns of missing income data indicate that age (p < .001) and
education level (p < .05) are positively related to missing income reports (analyses not
7/29/2019 Noelle Chesley and Britta Johnson 2012_does Work-related Technology Use Influence Perceptions of Productivity a
15/38
TECHNOLOGY USE, PRODUCTIVITY, AND DISTRESS 15
shown).
Other job and organizational characteristics are also known to influence
productivity and distress experiences. We control for job type using a categorical measure
based on the question, What type of work do you do? with possible responses: 1)
Professional; 2) Manager/Executive/Business Owner; 3) Clerical/Office/Sales; 4) Service
Work; 5) Skilled Trades/Semi-Skilled Trades/Other. Due to small cell sizes, we combined
business owner into the Manager/Executive category and semi-skilled trades, and
other into the skilled trades category. We also control for self-employment using a
binary measure (1=self-employed; 0=otherwise). Finally, we control for type of
organization/employer. There are seven response options: 1) large corporation; 2)
medium-size company; 3) small business; 4) federal/state/local government; 5) school or
educational institution; 6) non-profit; 7) other. This variable was reduced to four
categories: 1) Large Private Sector (large corporation); 2) Medium Private Sector (medium-
sized company); 3) Small Private Sector (small business); 4) Other (federal/state/local
government, school/educational institution, non-profit, other).
The remaining variables draw on job characteristics that measure employee
perceptions of job satisfaction, autonomy, advancement opportunities, and complexity. To
measurejob satisfaction, respondents were asked, How satisfied are you with your job?
The responses ranged from 1 (completely satisfied) to 4 (completely dissatisfied). This was
recoded as a binary variable to reflect those that are generally satisfied (1) and those that are
generally dissatisfied (0). Job Autonomyis measured with a single item which asks, I have a
lot to say about what happens in my job. Responses range from 1 (strongly disagree) to 5
(strongly agree). Job Advancementis also measured using a single item. Respondents were
7/29/2019 Noelle Chesley and Britta Johnson 2012_does Work-related Technology Use Influence Perceptions of Productivity a
16/38
TECHNOLOGY USE, PRODUCTIVITY, AND DISTRESS 16
asked I have opportunities for advancement in my job. Responses range from 1 (strongly
disagree) to 5 (strongly agree). Response scales for both Job Autonomy and Job
Advancement were reversed so higher numbers correspond to higher levels of agreement.
Job Complexityis measured using responses to four items: 1) My job requires a high level of
skill 2) My job requires creativity 3) My job requires that I do the same thing over and
over and 4) My job requires abstract knowledge about the ideas behind my work The
responses for these questions range from 1 (strongly disagree) to 5 (strongly agree). The
variables were recoded so that higher numbers correspond to higher levels of agreement
(except for the question that asks, My job requires me to do the same thing over and
over). The final measure is an index that sums across these four items (4 = low job
complexity to 20 = high job complexity).
Analytic Strategy
We use bivariate logistic regression to analyze the relationships among ICT-based
work practices, perceived productivity, and distress. In order to fully capture the influence
of each ICT-based work practice on the dependent variables, we estimate a series of models
that test the influence of each ICT-based work practice on productivity or distress after
accounting for all control variables. These results are summarized in Tables 2 & 3.
RESULTS
Table 1 provides descriptive statistics for the analytic sample. About a third of these
workers report an increase in distress that they believe is connected to ICT Use, while more
than two-thirds report that their productivity has increased as a result of their ICT Use.
Thus, a sense that ICT use is connected to productivity is more prevalent than the sense
7/29/2019 Noelle Chesley and Britta Johnson 2012_does Work-related Technology Use Influence Perceptions of Productivity a
17/38
TECHNOLOGY USE, PRODUCTIVITY, AND DISTRESS 17
that use is stressful in this national sample of workers. We can also get a sense of how
prevalent particular ICT-Based work practices are in a recent cross-section of the
workforce. The average work extension score is about 22, which suggests that using email
and phones to access work outside of standard times and places (e.g. while sick or on
vacation) is ubiquitous. Indeed, only 13% of our sample reports never using the phone or
email to check in with work on weekends, before or after work, etc. Most contemporary
workers appear to use technology to communicate with work in places that used to be
outside of the reach of work (like running errands).
[Table 1 AboutHere]
The majority of respondents report that ICT use has not changed the amount of time
they spend telecommuting. Less than a fifth of the workforce (18%) reports an increase in
the time they spend using technology to facilitate working at home or someplace else.
Thus, increases in ICT-based telecommuting (from home or other locations) are not the
norm. Respondents do agree that ICT use is influencing the size of their work-related
networks. When asked to rank their network expansion on a scale of 1 (not at all) to 4 (a
lot), the mean response is 3, which suggests that most workers have seen an increase in the
scope of their work related communications.
Finally, the measures for ICT-facilitated task completion indicate a strong
preference to use ICT to complete specific work tasks. For example, 52% of respondents
use ICT (email, phone, IM, or text message) to arrange meetings or appointments (versus
14% who prefer to do these things in person and 34% who do not use ICT at work), and
46% use ICT to edit or review documents (versus 21% in person and 33% who do not use
ICT) . However, some work tasks are still primarily attended to in person, including: asking
7/29/2019 Noelle Chesley and Britta Johnson 2012_does Work-related Technology Use Influence Perceptions of Productivity a
18/38
TECHNOLOGY USE, PRODUCTIVITY, AND DISTRESS 18
questions about work issues (45%), dealing with sensitive issues (59%), and bringing up a
problem to your supervisor (54%).
Demographically, just under half of the respondents are female (47%), and the
average employee age is 40. The majority of the sample is white (77%) and fairly well
educated; 41% of respondents have a college degree or better. The average number of
hours worked per week is 41 and the largest income grouping is those that make less than
$30,000 per year (35%), followed closely by those who make $30,000 to under $50,000
(27%). The two most common job type categories were skilled/semi-skilled/other (31%)
and professional (22%). The majority of the respondents are employed by someone else
(88%) versus 12% who report being self-employed. The sample largely works in the
private sector (74%) compared to 26% that work in other sectors.
Job satisfaction, job autonomy, and job complexity measures were all fairly high for
this sample. For example, 90% of respondents reported they were completely or mostly
satisfied with their job. The mean for job autonomy is 3.7 (on a five point scale) and the
mean for job advancement opportunities is 3.4 (on a 5 point scale). Average Job complexity
is 14, which suggest many respondents are in jobs that require greater levels of creativity
and skill.
ICT-Based Work Practices, Distress, and Productivity
The influence of ICT-based work practices on distress and productivity are
documented in Table 2. The first set of models tests the influence of phone and email based
work-extension on employee self-reports of increased distress and productivity. The
results illustrate that both phone and email use can extend work outside traditional work
boundaries in ways that are associated with increased distress and productivity. For each
7/29/2019 Noelle Chesley and Britta Johnson 2012_does Work-related Technology Use Influence Perceptions of Productivity a
19/38
TECHNOLOGY USE, PRODUCTIVITY, AND DISTRESS 19
one-unit increase in work-extension, perceived distress increases about 5% (p < 0.001)
and perceived productivity increases about 9% (p < 0.001).
[Table 2 AboutHere]
We also tested the influence of telecommuting on employees reported distress and
productivity levels. After controlling for a number of demographic and work-related factors,
the models suggest that increased ICT-facilitated commuting (from home or another
location) is positively linked to perceived distress and productivity levels. Here, employees
experiencing an increase in time spent telecommuting are 124% more likely to report an
increase in distress when compared with employees who report no change in telecommuting
time. Increases in telecommuting are also associated with perceived productivity increases.
Here employees reporting a telecommuting increase are also 589% more likely to report an
increase in productivity. ICT-facilitated network expansion is also associated with increases
in both distress and productivity. Employees experiencing a one-unit increase in network
expansion are 43% more likely to report increased distress related to ICT use (p < 0.001)
and 119% more likely to report productivity gains related to work-based ICT use (p