Noelle Chesley and Britta Johnson 2012_does Work-related Technology Use Influence Perceptions of...

7/29/2019 Noelle Chesley and Britta Johnson 2012_does Work-related Technology Use Influence Perceptions of Productivity a

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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]


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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


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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


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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


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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.


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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


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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


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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


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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


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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


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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


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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.


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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


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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


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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


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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


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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


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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


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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

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