Do Networked Workers Have More Control? The Implications of Teamwork, Telework, ICTs, and Social...

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Article

Do Networked Workers Have More Control? The Implications of Teamwork, Telework, ICTs, and Social Capital for Job Decision Latitude

Wenhong Chen1 and Steve McDonald2

AbstractThe shift toward “networked work” in the United States—spurred on by globalization, technological changes, and the reorganization of work activities—has important consequences for job quality that require further investigation. Using nationally representative data from the 2008 Networked Worker Survey, we examine how teamwork, telework, and information and communication technology use are associated with, and positively and significantly predict, job decision latitude (autonomy and skill development). The results imply that networked work helps enhance job decision latitude partly through greater network connectivity (social capital). Furthermore, the contribution of information and communication technology use to job decision latitude is contingent on its perceived benefits and on the organization of work into teams. These findings therefore help deepen our understanding of how the changing character of work affects worker control in contemporary workplaces.

Keywordsteamwork, telework, ICT use, social capital, communication overflow, job skills, autonomy

1University of Texas at Austin, TX, USA2North Carolina State University, Raleigh, NC, USA

Corresponding Author:Wenhong Chen, Department of Radio-TV-Film, Moody College of Communication, University of Texas at Austin, 1 University Station A0800, CMA 5.142, 2504 Whitis Ave Stop A0800, Austin, TX 78712-1067, USA. Email: [email protected]

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2 American Behavioral Scientist

Introduction

The organization of work practices in the United States has undergone a fundamental transformation during the last several decades. Organizations have shifted from tradi-tional mass production activities to increasingly flexible, team-based, and networked production activities (Castells, 2000; Cross & Parker, 2004; Heckscher & Adler, 2006; Knoke, 2001; Monge & Contractor, 2003). Information and communication technolo-gies (ICTs) have accelerated the pace of this transformation, allowing many types of work to be extended beyond the workplace and regular business hours, shifting the spatial and temporal boundaries between the public and private sphere (Rainie, Wellman, & Chen, 2012).

Researchers have long debated the consequences of workplace transformations and technological changes for job quality (e.g., Blauner, 1964; Braverman, 1975). Recent research focuses on aspects of work such as worker dignity (Hodson, 2001), intensifi-cation (Crowley, Tope, Chamberlain, & Hodson, 2010), job security (Kalleberg, 2011), and organizational commitment (Barley & Kunda, 2004). Our study focuses on how networked work within contemporary workplaces is associated with job decision lati-tude. Specifically, we examine three interrelated aspects of networked work: team-work, telework, and ICT use at work.

Work arrangements such as teamwork or telework have been widely adopted by organizations since the 1990s (Chudoba, Wynn, Lu, & Watson-Manheim, 2005; O’Leary, Mortensen, & Woolley, 2011), with almost two thirds (64%) of Americans working in teams (Rainie et al., 2012) and millions telecommuting at least occasion-ally (Mateyka, Rapino, & Landivar, 2012). Furthermore, the share of American work-ers who used the Internet at work reached 62% in 2008 (Madden & Jones, 2008).

However, the consequences of these changes for job decision latitude in the work-place remain unclear. Whereas some researchers have lauded the beneficial aspects of these kinds of changes, others have been less sanguine (e.g., Fonner & Roloff, 2010; Sewell, 1998). Unfortunately, few studies have conducted empirical analyses of how these features of contemporary work arrangements are associated with perceptions of job autonomy and skill development. Drawing on the organizational and digital inequality literature, this research contributes to the debate by evaluating a range of workplace conditions that are fundamental to observed workplace transformations in order to provide a deeper understanding of how the shift toward networked work is consequential for job quality. Specifically, we use nationally representative data from the Networked Worker Survey to examine how job decision latitude may vary by teamwork, telework, patterns and impact of ICT use, as well as social capital.

Literature Review

Job Decision Latitude

Job decision latitude is an integral part of job quality, a multidimensional construct that includes how workers get compensated as well as when, where, and how work gets done (Kalleberg, Reskin, & Hudson, 2000). In addition, it promotes a variety of



Chen and McDonald 3

cognitive, affective, and performance outcomes, as well as overall job satisfaction (Binnewies & Wörnlein, 2011; Bond & Bunce, 2003; Morgeson, Delaney-Klinger, & Hemingway, 2005; Thompson & Prottas, 2006). Job decision latitude has two dimen-sions: decision autonomy and skill utilization and development (Karasek, Baker, Marxer, Ahlbom, & Theorell, 1981). The former refers to the extent to which workers have the discretion and independence to decide when, where, and how their job is done (Borg & Kristensen, 2000; Halpern, 2005). The latter captures the extent to which workers can deploy and develop skills and expertise on the job (Fried, Hollenbeck, Slowik, Tiegs, & Ben-David, 1999). As noted above, relatively little is known about how networked work is associated with job decision latitude. Below, we discuss criti-cal aspects of networked work: teamwork, telework, ICT usage and impacts, as well as social capital and their implications for job decision latitude.

Teamwork

Many American firms have adopted team organized work activities, particularly since the 1980s (Knoke, 2001; Osterman, 2000). The majority of teamwork-related research assumes that people work in one team at a time, yet workers’ concurrent engagement in multiple teams is one of the major characteristics of the shift toward increasingly networked organizations (Maynard, Mathieu, Rapp, & Gilson, 2012; Rainie et al., 2012). Between 65% and 90% of knowledge workers work in multiple teams simulta-neously (O’Leary et al., 2011). For instance, more than 80% of Intel employees were found to work in teams, with 61% simultaneously working in three or more (Chudoba et al., 2005).

Teamwork is generally considered to enhance worker autonomy and discretion (Griffin, Patterson, & West, 2001; Hoegl & Gemuenden, 2001; Limerick, Cunnington, & Crowther, 2002). On the one hand, multiple team membership imposes competing demands on members’ attention and commitment, which can lead to information over-load, distress, and less than optimal performance at the individual, team, and organiza-tional levels (Maynard et al., 2012). On the other hand, multiple team membership helps workers gain diverse knowledge from multiple fronts, develop and deploy a variety of skills, build boundary-spanning networks, and enrich understanding of their work organizations (Edmondson, 2012), ultimately enhancing worker performance (Cummings & Haas, 2012). Thus,

Hypothesis 1: Multiple team membership is positively related to job decision latitude.

Telework

Telework and telecommuting, terms that are often used interchangeably, are work arrangements involving regularly working from home or offsite rather than entirely from a fixed, principal worksite (Hill, Hawkins, & Miller, 1996). Almost 1 out of 10 workers in the United States worked from home at least 1 day a week in 2010 (Mateyka




et al., 2012). Although early studies were inconsistent on the impacts of telework on job satisfaction and productivity (Bailey & Kurland, 2002), more recent empirical work finds that telework generally increases job satisfaction and performance (Bloom, Liang, Roberts, & Ying, 2013; Gajendran & Harrison, 2007; Hill et al., 1996).

However, telework often leads to role ambiguity and longer, often uncompensated, working hours (Bailey & Kurland, 2002). It also decreases support and feedback at work (Sardeshmukh, Sharma, & Golden, 2012) and often leads to feelings of isolation due to the lack of face-to-face communication with colleagues (Forester, 1989; Gajendran & Harrison, 2007; Morganson, Major, Oborn, Verive, & Heelan, 2010). The benefits and drawbacks of telework appear to depend on the types of employment. Male professionals and female pink-collar workers conduct most telework (Bailey & Kurland, 2002; Mateyka et al., 2012). Unsurprisingly, professional telework tends to be more beneficial than pink-collar telework (Stanworth, 1998; Sullivan & Lewis, 2001). Nonetheless, the literature is ambivalent on the extent to which telework is related to job decision latitude. As Wellman et al. (1996) point out:

Although much post-Fordist hype suggests that teleworking will liberate workers (e.g., Toffler 1980), research supports the neo-Fordist conclusion that managers retain high-level control of planning and resources but decentralize the execution of decisions and tasks. (p. 230)

Accordingly, the link between telework and job decision latitude requires further investigation. Thus,

Research Question 1: How is telework related to job decision latitude?

ICT Usage Patterns and Impacts

The growing use of ICTs at work has resulted in multifaceted and sometimes paradoxi-cal implications for work quality (Barley, Meyerson, & Grodal, 2011). On the one hand, frequent ICT use is related to increased workload, multitasking, accelerated pace of work, and negative perceptions about the work environment (Chesley, 2010; Kelly, Moen, & Tranby, 2011; Mark, Voida, & Cardello, 2012). The vast amount of information and communication from diverse sources and multiple channels is associ-ated with stress, alienation, depression, and work–family spillover (American Management Association, 2008; Berkowsky, 2013; Chesley, 2005; Eppler & Mengis, 2004; Towers, Duxbury, Higgins, & Thomas, 2006; Wellman et al., 1996).

On the other hand, the presence of ICTs can increase workers’ perceptions of auton-omy and skill variety (Damarin, 2013; Long, 1993). ICTs can increase employment and career opportunities, allowing workers to be more mobile and providing them with greater latitude in terms of accessibility and availability (Rubery & Grimshaw, 2001; Wellman et al., 1996). Frequent Internet use is also associated with greater digi-tal skills (Zillien & Hargittai, 2009). Thus, the contrasting literature begs further investigation.



Chen and McDonald 5

Research Question 2: How are the usage patterns and impacts of ICTs at work related to job decision latitude?

Social Capital

Social capital, the access to resources embedded in social networks, can also poten-tially affect job decision latitude. The value of social capital on the labor market has been well documented: people with diverse, resource-rich networks are not only more likely to get jobs, but these will often be better-paid and more prestigious jobs, as they can access and mobilize information, influence, social credentials, and recognition via their network contacts (Lin, 2001; for a recent review, see McDonald, Gaddis, Trimble, & Hamm, 2013).

Workers with extensive connections have lower turnover (Castilla, 2005), are rewarded by higher compensation, faster promotion, and are credited for having better ideas (Burt, 2005). Having larger workplace communication networks is related to better job performance (Zhang & Venkatesh, 2013). Thus, organizations strategically recruit and deploy employees for their social capital as much as for their human capital (Lin, Zhang, Chen, Ao, & Song, 2009). Maintaining good interpersonal relationships at work is positively related to job decision latitude over time (Fried et al., 1999). Despite this evidence, the literature remains thin on how social capital is associated with job decision latitude. Therefore,

Research Question 3: Is social capital related to job decision latitude?

Summary

While much has been learned about how the networked organization of work activities influences job satisfaction and rewards, much remains unknown about the specific contribution of teamwork, telework, ICT usage, and social capital to job autonomy and skill development in contemporary workplaces.

Data and Method

The data used were from the Networked Worker Survey conducted by the Pew Internet and American Life Project in 2008. It was a nationally representative tele-phone survey of 2,134 adults in continental United States telephone households, including 1,000 adults who worked full-time or part-time when the survey was conducted and had a response rate of 24%. Sample weight was applied to match with the national distribution of gender, age, race, Hispanic origins, education, region, and population density (Madden & Jones, 2008). The analysis sample included 703 full- or part-time employed respondents after the listwise deletion of cases with missing values on the variables of interest. Table 1 reports the descrip-tive statistics.




The Dependent Variable: Job Decision Latitude

Each respondent was asked to use a 1- to 5-point scale (1 = strongly disagree to 5 = strongly agree) to evaluate the extent to which he or she was able to make work-related decisions and use skills at work, including (a) I have a lot to say about what happens in my job, (b) my job requires a high level of skill, (c) my job requires creativity, (d) my job requires abstract knowledge about the ideas behind my work, and (e) I have opportunities for advancement in my job. The items were adapted from the job deci-sion latitude scale (Karasek et al., 1981), with Cronbach’s alpha of .73. The sum score of the five items indicated job decision latitude, ranging from 5 to 25.

Independent Variables

Multiple team membership. Each respondent was asked the number of work groups he or she was a member of in the past month. The variable was continuous and capped at

Table 1. Descriptive Statistics (N = 703).

M or % SD Minimum Maximum

Job decision latitude 18.42 4.61 5 25Female 45% Age 39.96 12.20 18 81Marital status 65% Parent with children ≤18 45% White 72% Full-time employed 80% Self-employed 13% Education 3.11 1.16 1 5Income 4.66 2.12 1 8Manager 16% Organization size (large corporation = 1) 31% Occupation (professionals = 1) 23% Social capital 9.37 3.43 0 16Multiple team membership (square root) 1.23 0.99 0 3.32Onsite workers 55% Mixed workers 28% Home workers 17% E-mail 3.76 2.49 1 7Instant messaging 1.68 1.62 1 7Text message 1.54 1.31 1 7Social networking services 6% E-mail overflow 10.40 5.39 6 24Phone overflow 11.70 4.74 6 24Positive ICT impacts (square root) 3.27 0.60 2 4Negative ICT impacts 7.78 3.38 4 16

Note. ICT = information and communication technology.



Chen and McDonald 7

11 or more. As the distribution of this variable was skewed, its square root term was used.

Telework. Each respondent was asked to use a 1- to 5-point scale (1 = never to 5 = daily or almost daily) to report the frequency of working from home. Three binary variables were constructed: (a) home worker was coded as 1 if the respondent worked from home every day or almost every day, (b) onsite worker was coded as 1 if the respon-dent never worked from home, and (c) mixed worker was coded as 1 if the respondent worked from both home and at the worksite (see Mateyka et al., 2012).

ICT usage patterns in the workplace. Each respondent was asked to use a 1- to 7-point scale (1 = never to 7 = constantly) to report while at work, how often he or she (a) checked work e-mail, (b) sent instant messaging to colleagues, (c) sent text mes-sage to colleagues, and (d) communicated with colleagues using social networking services (SNSs) like MySpace or Facebook. As only 6% respondents communicated with colleagues using SNSs while at work, SNSs use was recoded as binary (1 = yes).

ICT usage patterns beyond the workplace—Communication overflow. Each respondent was asked to use a 1- to 4-point scale (1 = never to 4 = often) to report how frequently they checked work-related e-mails or made/received work-related telephone calls in six situ-ations beyond the workplace: (a) on weekends, (b) on vacations, (c) before going to work for the day, (d) after leaving work for the day, (e) when the respondent was on sick leave, and (f) on the go (e.g., during commuting or shopping). Two sum scores, e-mail overflow and phone overflow use, were constructed, with each ranging from 6 to 24.

ICT impacts. Respondents were asked to use a 1- to 4-point scale (1 = not at all to 4 = a lot) to evaluate eight types of ICT impacts on their work. A factor analysis suggested a solution of two factors, the positive and negative ICT impacts, respectively. The fac-tor of the positive ICT impacts included four items: productivity (ability to do one’s job), flexibility (flexibility in hours worked), collaboration (ability to share ideas), and connection (expanded communication). The Cronbach’s alpha was .80. The sum score of these four items measured the positive ICT impacts, ranging from 4 to 16. As the distribution of this variable was skewed, its square root term was used. The factor of the negative impacts included four items: increased demand for long hours, increased job stress, stickiness (harder to forget about work), and distraction (harder to focus at work). The Cronbach’s alpha was .75. The sum score of these four items measured the negative ICT impacts, ranging from 4 to 16.

Social capital. The position generator was used to map the respondents’ social capital via a list of high- and low-status occupations, indicating their access to a wide range of resources (Lin, 2001). Social capital was measured by the sum score of occupations in which the respondent knew someone, ranging from 0 to 16.

Interactions. As ICT use facilitates teamwork and telework and often has a positive association with social capital (Chen & Wellman, 2009; Chudoba et al., 2005;




Steinfield, DiMicco, Ellison, & Lampe, 2009), we constructed mean-centered interac-tion terms of teamwork and telework with ICT use/impacts. Only significant interac-tion effects were reported.

Worker and job characteristics. Several individual and job characteristics known to be related to job quality were controlled. Binary variables included gender (1 = female), marital status (1 = married or lived with partner), parent with children (1 = having children aged 18 or younger), race (1 = non-Hispanic White), types of employment (1 = full-time and 1 = self-employment), job authority (1 = manager), organization size (1 = large corporation), and occupation (1 = professional). Age was measured in years. Education included 1 = less than high school, 2 = high school, 3 = some college, to 4 = college or higher. Income was measured by a 1- to 8-point scale ranging from 1 = “less than $10,000” to 8 = “$100,000 or more.”

Results

To examine the hypotheses and research questions, ordinary least square regressions were conducted. VIF (variance inflation factor) and tolerance (1/VIF) values sug-gested that multicollinearity was not a concern. The results were robust as known confounds were controlled (see Table 2).

Model 1 focused on basic worker and job characteristics. Results showed that women, full-time employees, higher paid employees, and managers reported greater levels of job decision latitude. The next two models introduced social capital and mul-tiple team membership into the models, both of which were positively and signifi-cantly related to job decision latitude. Once teamwork was controlled, the coefficient for social capital became non-significant. Models 4 and 5 took into account telework and showed that compared with onsite workers, both mixed and home teleworkers reported higher levels of job decision latitude.

Only the use of SNSs was significantly related to job decision latitude (see Model 6). The frequency of phone overflow, but not that of e-mail overflow, was significantly associated with job decision latitude (see Model 7). Positive ICT impacts were signifi-cantly related to greater and negative ICT impacts with less job decision latitude (see Model 8). Model 9 reported an interaction effect between the positive ICT impacts and multiple team membership on job decision latitude. The relation between the positive ICT impacts and job decision latitude was weaker among workers who were involved in a greater number of teams.

To summarize, Hypothesis 1, which hypothesized a positive relation between mul-tiple team membership and job decision latitude, was supported. As to Research Question 1, compared with onsite workers, teleworkers, and especially home workers, had greater job decision latitude. However, when communication overflow was con-trolled, there were no significant differences in job decision latitude among onsite, mixed, and home workers.

Regarding Research Question 2, SNSs use at work, but not e-mail, instant messag-ing, or text message use, was significantly related to job decision latitude. However,



9

Tab

le 2

. O

rdin

ary

Leas

t Sq

uare

Reg

ress

ion

of Jo

b D

ecis

ion

Latit

ude.

Mod

el 1

Mod

el 2

Mod

el 3

Mod

el 4

Mod

el 5

Mod

el 6

Mod

el 7

Mod

el 8

Mod

el 9

Fem

ale

0.67

† (0

.39)

0.61

(0

.40)

0.66

† (0

.38)

0.53

(0

.39)

0.60

(0

.38)

0.64

† (0

.38)

0.72

† (0

.37)

0.65

† (0

.37)

0.67

† (0

.37)

Age

−0.

00

(0.0

2)−

0.01

(0

.02)

−0.

01

(0.0

2)−

0.01

(0

.02)

−0.

01

(0.0

2)−

0.01

(0

.02)

−0.

00

(0.0

2)−

0.00

(0

.02)

−0.

00

(0.0

2)M

arita

l sta

tus

0.26

(0

.45)

0.20

(0

.46)

0.33

(0

.44)

0.18

(0

.45)

0.29

(0

.44)

0.36

(0

.44)

0.42

(0

.44)

0.26

(0

.43)

0.28

(0

.43)

Pare

nt w

ith c

hild

ren ≤1

80.

56

(0.4

1)0.

54

(0.4

2)0.

38

(0.4

1)0.

50

(0.4

1)0.

36

(0.4

0)0.

40

(0.4

0)0.

33

(0.4

0)0.

29

(0.4

0)0.

28

(0.4

0)W

hite

0.03

(0

.46)

0.08

(0

.46)

0.18

(0

.45)

0.12

(0

.45)

0.21

(0

.44)

0.32

(0

.45)

0.32

(0

.45)

0.30

(0

.44)

0.34

(0

.43)

Full-

time

empl

oyed

1.31

* (0

.60)

1.33

* (0

.61)

1.29

* (0

.59)

1.37

* (0

.59)

1.31

* (0

.58)

1.42

* (0

.57)

1.48

* (0

.58)

1.41

* (0

.57)

1.36

* (0

.56)

Self-

empl

oyed

1.61

**

(0.6

1)1.

57*

(0.6

1)1.

75**

(0

.57)

1.13

† (0

.63)

1.41

* (0

.60)

1.37

* (0

.60)

1.13

† (0

.63)

1.02

(0

.63)

0.97

(0

.62)

Educ

atio

n0.

16

(0.2

2)0.

11

(0.2

2)−

0.04

(0

.22)

0.01

(0

.22)

−0.

10

(0.2

1)−

0.08

(0

.21)

−0.

05

(0.2

1)−

0.10

(0

.21)

−0.

08

(0.2

1)In

com

e0.

32*

(0.1

3)0.

30*

(0.1

3)0.

18

(0.1

3)0.

25†

(0.1

3)0.

15

(0.1

3)0.

13

(0.1

3)0.

11

(0.1

3)0.

12

(0.1

3)0.

13

(0.1

3)M

anag

er1.

80**

* (0

.48)

1.71

***

(0.4

7)1.

37**

(0

.47)

1.50

**

(0.4

8)1.

26**

(0

.49)

1.29

**

(0.4

8)1.

09*

(0.4

9)1.

14*

(0.4

8)1.

17*

(0.4

8)O

rgan

izat

ion

size

(la

rge

corp

orat

ion

= 1

)0.

28

(0.4

3)0.

29

(0.4

2)0.

14

(0.4

1)0.

38

(0.4

2)0.

22

(0.4

0)0.

20

(0.4

0)0.

32

(0.3

9)0.

30

(0.3

9)0.

37

(0.3

9)O

ccup

atio

n (p

rofe

ssio

nals

= 1

)0.

22

(0.6

0)0.

24

(0.6

1)0.

31

(0.5

9)0.

17

(0.6

0)0.

25

(0.5

9)0.

26

(0.5

8)0.

37

(0.5

7)0.

42

(0.5

7)0.

39

(0.5

7)So

cial

cap

ital

0.14

* (0

.06)

0.09

(0

.06)

0.13

* (0

.06)

0.09

(0

.06)

0.09

(0

.06)

0.06

(0

.07)

0.04

(0

.06)

0.04

(0

.06)

Mul

tiple

tea

m m

embe

rshi

p1.

12**

* (0

.20)

1.02

***

(0.2

1)1.

00**

* (0

.21)

0.92

***

(0.2

1)0.

90**

* (0

.21)

0.93

***

(0.2

1)

(con

tinue

d)



10

Mod

el 1

Mod

el 2

Mod

el 3

Mod

el 4

Mod

el 5

Mod

el 6

Mod

el 7

Mod

el 8

Mod

el 9

Tel

ewor

k (R

ef =

ons

ite w

orke

rs)

Mix

ed w

orke

rs1.

40**

(0

.48)

0.96

* (0

.48)

0.95

* (0

.48)

0.59

(0

.46)

0.50

(0

.46)

0.44

(0

.46)

H

ome

wor

kers

1.49

**

(0.5

5)1.

13*

(0.5

4)1.

04†

(0.5

5)0.

61

(0.5

5)0.

66

(0.5

5)0.

64

(0.5

5)Em

ail

−0.

05

(0.0

9)−

0.06

(0

.09)

−0.

13

(0.0

9)−

0.13

(0

.09)

IM0.

11

(0.1

2)0.

10

(0.1

2)0.

08

(0.1

2)0.

10

(0.1

2)T

ext

mes

sage

0.01

(0

.14)

−0.

01

(0.1

4)−

0.03

(0

.14)

−0.

03

(0.1

4)SN

Ss1.

63*

(0.7

7)1.

46†

(0.7

8)1.

38†

(0.7

7)1.

31†

(0.7

6)Em

ail o

verf

low

0.01

(0

.05)

0.01

(0

.05)

0.02

(0

.05)

Phon

e ov

erflo

w0.

12*

(0.0

6)0.

13*

(0.0

6)0.

13*

(0.0

6)Po

sitiv

e IC

T im

pact

s1.

20**

(0

.44)

0.97

* (0

.43)

Neg

ativ

e IC

T im

pact

s−

0.14

* (0

.06)

−0.

14*

(0.0

6)Po

sitiv

e IC

T im

pact

s ×

Tea

mw

ork

−0.

64*

(0.3

2)C

onst

ant

14.1

0***

(1

.03)

13.2

5***

(1

.10)

13.4

7***

(1

.06)

13.4

0***

(1

.10)

13.5

6***

(1

.06)

13.1

5***

(1

.14)

12.0

0***

(1

.17)

9.96

***

(1.5

1)10

.48*

**

(1.4

7)A

djus

ted

R20.

11**

*0.

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Chen and McDonald 11

when communication overflow was controlled, SNSs use was only marginally signifi-cant. As to ICT usage patterns beyond the workplace, phone overflow, but not e-mail overflow, was significantly related to job decision latitude. Furthermore, there was a positive relation between positive ICT impacts and job decision latitude and a negative relation between negative ICT impacts and job decision latitude. Concerning Research Question 3, results suggested a positive relation between social capital and job deci-sion latitude, which however lost statistical significance once multiple team member-ship was controlled.

Discussion and Conclusion

Despite the rapid diffusion of ICTs, teamwork, and telework in many organizations in the last several decades and a growing literature on the rise of networked work (Castells, 2000; McAfee 2010; Monge & Contractor, 2003; Rainie et al., 2012), few studies have systematically examined the implications of networked work for job quality.

This study not only offers insights on how American workers have become net-worked workers but also how networked work affects job decision latitude. Drawing on the nationally representative Networked Worker Survey, this research offers a unique vantage point to understand how work arrangements, including teamwork and telework, ICT use and impacts, as well as social capital are related to workers’ job decision latitude, an important indicator of job quality.

First, multiple team membership has critical implications for job quality. The greater the number of teams a worker is involved in, the higher the levels of job deci-sion latitude. Even though bureaucratic structures are still in effect, multiple team memberships may complement traditional hierarchies and enable workers more con-trol. While teleworkers, and especially home workers, have greater job decision lati-tude than onsite workers, the difference becomes statistically non-significant once communication overflow is controlled. The results suggest that teamwork, rather than telework, is more relevant to job quality.

Second, the results highlight the implications of ICT usage and its positive and negative impacts on job quality. ICT use at work and beyond offers workers opportuni-ties to have more job decision latitude. Both SNSs use at work and phone overflow from work to other life domains are significantly related to greater job decision lati-tude. However, the more negative ICT impacts workers experience, the lower their levels of job decision latitude. By contrast, the more positive ICT impacts workers experience, the higher their levels of job decision latitude. Nonetheless, it seems that the implications of positive ICT impacts on job decision latitude are attenuated among workers who engaged with a greater number of teams. This finding implies that orga-nizations and workers need to be aware of possible negative implications of multiple team membership.

Third, social capital is related to job decision latitude but the relationship becomes non-significant once multiple team membership is controlled.

In addition, managers have greater job decision latitude than subordinates. Full-time workers have greater job decision latitude than part-time. Self-employed have




greater job decision latitude than employees but the difference becomes non-signifi-cant when ICT impacts on work are controlled. Better-paid workers also have greater job decision latitude but the relationship becomes non-significant when ICT usage patterns at work are controlled. By and large, education, age, race, and family status are not significant to job decision latitude. This research also does not identify any significant differences in job decision latitude by organization size or occupation.

The research has several limitations. First, the data are cross-sectional. Longitudinal data are needed to specify the causal direction. For instance, telework can be a result and a cause of autonomy (Schieman & Glavin, 2011). Second, while the Networked Worker Survey measured the intensity or frequency of teamwork and telework, it did not have team-level questions such as whether, and the extent to which, workers were engaged in globally dispersed or virtual teams, or their teams’ composition in terms of demographics and skillsets. Third, the survey also had limited information at the orga-nizational level, especially questions on the extent to which the organization the respondent worked for had become a networked organization. Such questions are important as job quality may vary by organizational structure and culture (Hoegl & Gemuenden, 2001). Thus, future research needs to capture richer data on various aspects of teamwork and telework at the team and organizational levels. Fourth, due to space limitation, this research focused on job decision latitude. Future research needs to expand to other aspects of job quality. Fifth, future research needs to pay more attention to the mechanisms that link work arrangements as well as ICT use and impacts with job quality.

Despite these caveats, this research makes a significant contribution to the emerging literature on networked work. It systematically examines three important aspects of net-worked work: multiple team membership, telework, and ICT usage patterns and impacts as well as their implications for job decision latitude as one of the major indicators of job quality. The findings have practical implications for job and organizational design. A growing number of workers are trapped in precarious jobs and become vulnerable to poor working conditions. Although organizations need to be aware of the negative impact of ICTs and multiple team membership, technology and work arrangements can be used to give workers more latitude for work-related discretion and skill utilization.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

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Author Biographies

Wenhong Chen is an assistant professor at the Department of Radio-TV-Film at the University of Texas, Austin. Her research has been focused on the social implications of digital media and communication technologies. She has published more than 20 articles, including publications in Social Networks, Human Communication Research, the Journal of the Association for Information Science and Technology, New Media and Society, Journal of Computer-Mediated Communication, Management and Organization Review, and Entrepreneurship Theory & Practice. She is the guest editor for Information, Communication & Society on the special issue, “The Internet in Chinese Societies.”

Steve McDonald is an associate professor of sociology at North Carolina State University. His research examines inequality in access to social capital across the life course. Specific areas of investigation include (a) social networks and job finding, (b) informal mentoring and occupa-tional attainment, (c) online network relations and employment outcomes. He has published over 20 research articles, including publications in the American Journal of Sociology, Social Networks, Social Forces, Social Problems, and Gender & Society. He is also the guest editor for Volume 24 of the Research in the Sociology of Work series on “Networks, Work, and Inequality.”



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