ENVIRONMENTAL, POPULATION, AND POLICY
FACTORS INFLUENCING TELEWORK
Scott Ross
Advisor: Clio Andris
Penn State University
Outline
o
o
o
o
o
o
o
Project Objectives
Why telework matters
Definition of telework
Literature review
Proposed methodology
Timeline
Anticipated results
Source: www.readytalk.com 2015
used with permission
Project Objectives
Determine the strength of spatial relationships for variables
correlated with telework
●
–
Education level
–
Population density
–
Commute time
–
ICT infrastructure
–Determine
if there is a tendency for states with policies
promoting telework to have higher rates of telework
Why Telework matters
●
Part of the growth of flexible work options designed to:
–
Retain and attract skilled workers (Forbes, 2013)
–
Reduce operating costs (Singh et al, 2012)
–
Increase access to work (Koeplinger, 2007)
–
Decrease traffic congestion and emissions (Khau 2012)
The following states have telework programs: NV, CA, AZ, VA, GA, WA,
others
●
Telework currently
●
Efforts to push telework globally, especially EU, China
●
Telework enhancement act 2010 (US fed Govt workers)
●
Data from global workplace analytics
Forbes – 30M work from home at least once a week, 3M work
exclusively at home, number may increase to 63% of workforce
●
World at work – 98% of surveyed organizations maintaining or
increasing telework options.
●
●
Definite challenges – especially engagement
●
Source: Global workplace Analytics. Used with permission.
Definitions of Teleworker
●
Definitions can be tricky and vary based on following factors:
–
Full-time or part time
–
Self employed or not
–
Number of days remote
–
Work from home, remote, telework center
–
Part of routine or ad-hoc
Different data sources have different qualifications for who is a
teleworker
●
Literature review
●
Education (Worldatwork 2014) others
●
Population density (Singh et al, 2012)
●
ICT availability (Tayyaran, Khan, 2003)
Computer ownership (Perez et al
2004)
–
Broadband availability (Koeplinger
2007)
–
–Commute
time (Singh et a, 2012)
Data Sources
●
ACS data (2010-2014) by zip code
–
Total employed workforce
–
educated population by degree level
–
average commute time
–
number of people who work from home
–
sector and industry
Data Sources continued
●
National broadband map
Telework jobs being
advertised online
●
Descriptions of different
state programs to encourage
telework
●
Source: National Broadband map
Proposed methodology
Collect/Prepare
Data
Correlate Data
Create Model
Identify Outliers
Evaluate potential
relationships with
state legislation
Proposed methodology
●
Collect telework data
●
Prepare and map data in arcGIS, calculate correlation coefficients
●
Display correlation by zip code
Identify areas with higher than national average rates of “work from
home”.
●
●
Correct for variation based on known correlations (develop a model)
●
Identify the impact of state regulations
●
Identify unusual clusters of telework
Timeline
●
January – Collect and Prepare all data
●
February – Correlate Data and Create a model
●
March – Validate Model, Identify Outliers
●
April – June Prepare for presentation
Anticipated results
Correlation coefficients and map of relationships for each variable
with telework rates
●
A best fit model that accounts for education, population density,
commute time, and ICT infrastructure
●
Identify if states where a positive deviation from the model exists
could be due to legislation.
●
Hypothesis: There will be a slight but measurable effect of favorable
telework legislation on the overall rates of telework in certain states.
●
Conferences
●
June 6-8 2016 Total Rewards Conference – San Diego
●
29 Mar- 2 Apr Association of American Geographers (AAG)
●
Possible conferences with Global workplace analytics,
Work Cited
Broadband Statistics. (n.d.). Retrieved December 15, 2015, from
http://www.broadbandmap.gov/analyze
●
Flickess, J. (2014). The 411 on Telework Technology. Retrieved
December 15, 2015, from
http://www.worldatwork.org/adimComment?id=74452
●
Lister, K. (2015, September 29). Latest Telecommuting Statistics |
Global Workplace Analytics. Retrieved December 15, 2015, from
http://globalworkplaceanalytics.com/telecommuting-statistics
●
Perez, M., Sanchez, A., Carnicer, P., & Jimenez, M. (2004). A Technology
Acceptance Model of Innovation Adoption: The case of Teleworking.
European Journal of Innovation Management, 7(4), 280-291.
doi:10.1108
●
Work Cited
Rapoza, K. (2013). One In Five Americans Work From Home, Numbers
Seen Rising Over 60%. (2013, February 18). Retrieved December 15,
2015, from http://www.forbes.com/sites/kenrapoza/2013/02/18/onein-five-americans-work-from-home-numbers-seen-rising-over-60/
●
Khau, J. (2012, September 17). The Rise of Telework and What it
Means. Retrieved December 15, 2015, from
http://www.newgeography.com/content/003082-the-rise-teleworkand-what-it-means
●
Koeplinger, N. (2007). Telecommuting Satisfaction, Lifestyle Choice and
Geography: Evidence from a Fortune 500 Firm. Retrieved December 16,
2015, from https://libres.uncg.edu/ir/uncg/f/umi-uncg-1392.pdf
●
Work Cited
Singh, P., Paleti, R., Jenkins, S., & Bhat, C. (2012, June 13). On Modeling
Telecommuting Behavior: Option, Choice, and Frequency. Retrieved
December 16, 2015, from
http://www.caee.utexas.edu/prof/bhat/ABSTRACTS/Telecommuting_P
aper_13June2012.pdf
●
Tayyaran, M., & Khan, A. (2003). The Effects of Telecommuting and
intelligent Transportation Systems on Urban Development. Journal of
Urban Technology, 10(2), 87-100. doi:10.1080
●