PreviousNextTable of Contents7. SUMMARY AND CONCLUSIONS
In this chapter, we first summarize the key findings from the data collected through June 1995
(Section 7.1), then discuss additional analyses that could be performed on these and future data
(Section 7.2), and finally interpret the results to date (Section 7.3).
This report is an interim evaluation of telecommuting center use and its impacts on work
performance, job satisfaction, and travel behavior. To that end, four survey instruments were
developed to measure telecenter use and its effects at both RABO (Residential Area-Based Offices)
and non-RABO sites: an attitudinal survey, a travel diary, an attendance log, and an exit interview.
The survey and diary were administered to participants once before and once after the start of
telecommuting, the attendance log was used throughout the study period, and the interview was
conducted when participants left the program. The results of the complex evaluation process were
presented in five chapters: procedural issues in the evaluation process, attitudinal survey analysis,
analysis of telecommuting patterns, analysis of telecommuting retention, and travel characteristics.
Each of these chapters is summarized below.
The RABO Project not only provides information on the practice of telecommuting center use, it also
provides valuable lessons in the process of evaluating the use of telecommuting centers. The
procedural issues related in Chapter 2 dealt with contractual compliance, site usage measurement,
and modification of the survey process. Since telecommuting centers are a relatively new concept,
the lessons learned here will help later evaluation programs be more effective.
Changes to the contracts with site developers were necessitated by problems with data collection.
Some contracts did not directly tie the university to the administrators of the telecenter which
resulted in poor communication and inadequate survey response rates. At all centers, survey
response rates that were lower than desired led to modifications of the center funding policies.
Telecommuters were only counted in funding invoices if they had completed the required surveys.
This new policy also emphasized that, while participation itself was voluntary, survey completion
was a mandatory element of participation. Additional methods for improving survey collection
included shifting the duties of survey distribution and collection from the site administrator to the
Evaluation Manager for those centers who did not want to handle these activities.
A strict definition of telecenter occupancy was developed to ensure that the measured rate of
telecommuting reflected the goals of the study. The monthly site occupancy rate calculated to assess
compliance with contractual targets includes only the telecommuting occasions by project
participants that lasted at least four hours. However, there were other meaningful (in terms of travel
reduction) uses of the center that fell outside this narrow definition. As a result, uses by
telecommuters for any length of time were documented and evaluated. Further, most sites set up at
least some of their workstations for use by drop-in customers, and others specifically leased work
space to particular companies (neither of those types of center users participated in the evaluation).
Some participants who use the center as their primary place of business were included as project
participants even though their situation is quite different from the typical telecommuter from a large
organization. These participants may not have managers or co-workers to participate in surveys, may
not have travel reduction benefits, and during the demonstration period, may be unfairly subsidized
in relation to other similar businesses in terms of office space costs. It is recommended that a
screening process be used to give highest priority to those who are truly telecommuting while still
allowing other uses to continue at the site since a diversity of clients is a key to long-term operation.
Other issues involved in the evaluation process included changes to the survey procedure.
Originally, focus groups were to be used to identify any initial problems and concerns of both the
telecommuters and their managers with the use of telecommuting centers. However, problems with
timing and scheduling caused the focus groups to be dropped in favor of individual telephone
interviews. Additionally, the availability of a videoconferencing room at some telecommuting
centers led to the development of a usage log for these facilities. The information collected from the
videoconference logs will be summarized for the final report.
7.1.2 Attitudinal Survey Analysis
Similar attitudinal surveys were administered to three groups of participants (telecenter users, home-based telecommuters, and non-telecommuters) and their managers at two points in time (before the
start of telecenter use and approximately six months afterwards). These surveys collected data on
attitudes toward telecommuting, work characteristics, travel characteristics, and demographic
information. In the sections below, we respectively discuss results for the telecenter employee
survey, the manager survey for supervisors of telecenter users, and a comparison of employee and
manager responses.
7.1.2.1 Employee Survey Results
In this interim report, the description of the survey data is restricted to the telecenter users only.
Primarily, the data from the after-wave surveys is used to characterize center-based telecommuters
(sample size of 39). However, where appropriate, data from both before and after waves are utilized
to highlight changes related to the use of telecenters (a reduced sample size of 27). A summary of
the results from the six parts of the survey is provided below.
The section on demographics asked for general characteristics, such as age, income, and education.
There are slightly more female telecenter users than male ones, and nearly half the sample is between
the ages of 35 to 44 years. The average household size of three persons is consistent with the fact
that more than half of the respondents have children under 16 years of age. Vehicle availability is
high among the respondents with 2.3 vehicles per household and 1.4 vehicles per worker. The
telecenter users are highly educated: about 30% have had additional schooling after college.
Additionally, many of the participants have high incomes (about 70% have annual household
incomes greater than $55,000).
Job characteristics varied among the center-based telecommuters. Slightly over half of the sample
hold professional/technical positions which are usually easily adapted to telecommuting, and as a
whole, they are experienced in their field with an average length of time in the profession of 10.2
years. Flextime schedules are popular among the telecenter users (used by nearly 65%). Finally, the
respondents spend a good portion of their workday working independently (47%) or remotely (18%),
both of which are good indicators of positions with telecommutable tasks.
The responses from the attitudinal sections on job performance and satisfaction and work
environment characteristics show primarily positive results. There is little change in performance
or satisfaction characteristics between survey waves suggesting that working from a telecommuting
center does not drastically change these factors. The only significant change was a slight drop in the
perceived opinion of the supervisor on the telecommuter's ability to meet deadlines. The ratings on
the statements about work characteristics also remained primarily the same between survey
measurements. Distractions at the telecenter were slightly more of a problem than originally
envisioned, but the average response on the after survey is still to disagree with the statement that
distractions were a problem. Finally, the most important work characteristics to the respondents are
working effectively, having needed equipment, and having work judged by the results.
The survey also measured the amount of telecommuting the telecenter users had done, are currently
doing, and plan to do in the future. The average experience with telecommuting from a center was
about one year at the time of the after survey, and about half also had experience with home-based
telecommuting. On the other hand, about 40% of the respondents did not have the option to
telecommute from home which indicates that centers may help spread the transportation and other
benefits of telecommuting to a larger segment of the workforce. With the time saved by
telecommuting, the respondents most often spend time with family or friends, get more sleep, and/or
relax by themselves.
When distributing their work time for the ideal situation, the respondents preferred to work from the
regular workplace and the telecenter about equal amounts, 40% to 45% of their time (each) on
average. However, they actually reported telecommuting only about 30% of the time even though
their jobs were suitable for telecommuting for about 40% of the time, on average (see Section 4.4.2
for actual telecommuting frequency based on attendance log data). The respondents predicted
greater frequencies of future center-based telecommuting than current levels (38%), but that expected
frequency was substantially lower than was reported on the before survey (50%). In addition, the
results from the choice, preference, and expectation of telecommuting indicate that combined home
and center telecommuting appears to be a popular option.
In the section on travel, the commute to the regular workplace was reported as 44.2 miles in length,
while the commute length to the telecommuting center was given as 7.3 miles, on average. The
resulting average commute travel savings by using the center instead of going to the main office for
the after survey respondents is 36.9 miles. Despite the reduction in travel, the majority of travel to
the telecenter is on freeways, suggesting that the centers are far from the average participant's
residence. This is especially true of the respondents from non-RABO centers who have longer
commutes than RABO telecommuters, on average, to both the regular workplace (53.1 vs. 39.1
miles) and the telecommuting center (9.1 vs. 6.5 miles). Additionally, telecenter use was not found
to have much effect on residential relocation decisions in this short time frame.
The tabulations of the attitudinal surveys provide a good characterization of the telecommuting
center users. However, the survey data will also be used to model the decision to adopt
telecommuting. Further analysis will be presented in the final report and/or in subsequent studies.
7.1.2.2 Manager Survey Results
This section summarizes the survey results from 28 supervisors of center-based telecommuters. On
the whole, the respondents reported an optimistic and positive attitude toward telecommuting. The
analysis showed clearly that supervisors' opinions of the performance of their employees did not
diminish with the introduction of telecommuting.
Characteristics dealing with the workplace atmosphere (such as motivation, professional appearance,
and distractions) were considered to be similar at both the regular workplace and the telecenter.
Areas in which the center was perceived less positively than the regular workplace concerned the
supervisor-employee relationship (such as communication, availability, professional interaction, and
administrative burden) as well as security of information and property. However, mean ratings for
the telecenter on these characteristics were all neutral or better, indicating that the disadvantage is
relative, not absolute. These attitudes seem to be generic to telecommuting in general since they
tended to be even less favorable for home-based telecommuting.
Nearly all of the managers (93%) indicated having a positive attitude toward telecommuting in
general, and 82% rated their level of satisfaction with center-based telecommuting as high or very
high. (However, a selection bias must be noted, as managers who were dissatisfied with
telecommuting would be less likely to have lasted long enough to complete an after survey. As
indicated in Section 5.3, supervisor-related concerns were cited by 5 of the 20 employees from whom
reasons for quitting could be obtained as important reasons for quitting telecommuting). Six
potential advantages were viewed by managers to be at least moderately significant following the
introduction of center-based telecommuting: improved employee retention, improved ability to
recruit employees, increased productivity, compliance with environmental regulation, improved
employee relations, and (marginally) reduced absenteeism. However, from 11% to 21% of the
managers reported "no opinion" on the four following potential advantages of telecommuting:
improved ability to recruit employees, reduced health costs, compliance with environmental
regulations, and improved disaster response capability. This suggests the need to raise awareness
of the potential benefits of telecommuting in these areas.
It is an important result that the perceived advantages of telecommuting are those for which the
benefit is difficult to quantify (customer service and productivity), while telecommuting is not
perceived to offer advantages on "hard" money items such as office space and parking costs. This
will continue to make center-based telecommuting difficult to justify in purely economic terms.
Indeed, while 39% of the respondents indicated that the organization was likely to (continue to) offer
center-based telecommuting, an equal proportion cited reduced costs, the ability to quantify the
benefits, and increased manager acceptance as factors that needed to change before the organization
would be likely to offer center-based telecommuting.
About half of the managers expected that more of the organization's workforce would be
telecommuting from a center in the future. However, from one-sixth to one-third of the
organizations themselves did not have official opinions on various potential advantages of
telecommuting according to the respondents. When opinions by the management levels above the
supervisors were expressed, they tended to be less positive than those of the supervisors. Indeed,
it appears that some managers are supporting telecommuting for their staff in the face of actively
negative attitudes on the part of upper management. This suggests the need for upper-level
management to have increased exposure to the benefits of telecommuting.
Although the employees performed well at the telecenter or even better than they did at the regular
workplace in some aspects, managers still preferred telecommuting to be a part-time alternative for
their employees. Very few managers expected their employees to be telecommuting from the center
full-time. The managers' average ideal distribution of work time for their employees included nearly
64% at the regular workplace and 29% for center-based telecommuting. The current and the
expected future telecommuting frequencies of 30% (which is equivalent to 1.5 days per week) are
consistent with the managers' ideal work time distribution. However, in the managers' perception,
the appropriate telecommuting frequency for their employees was more constrained by job suitability
(32% of work time on average) than by the managers' willingness (37%). In any case, the managers
still feel that the regular workplace is the primary work location, to be used three or more days out
of the work week.
Home-based telecommuting was not perceived as positively as center-based telecommuting with
respect to job suitability and permitted frequency, although the self-selection bias of the sample must
be taken into account in interpreting this result. The managers were willing for the employees to
telecommute nearly three times as often from the center as from home. Also, some mixture of center
and home-based telecommuting was considered ideal by nearly one-third of the managers.
This expectation of part-time telecommuting may act to inhibit the adoption of telecommuting
centers. If employees are only using the center one or two days per week, there may be little
opportunity for their space at the regular workplace to be used for other purposes. If an organization
must continue to offer the same amount of space at the regular workplace as before, plus pay rent
on space at the telecenter, other telecommuting advantages will have to be that much stronger to
compensate for the added cost.
7.1.2.3 Employee-Manager Comparison
The comparison of similar questions from the employee and manager surveys is constrained by the
limited sample sizes in the interim data set. The data compared in this section are overall group
means rather than matched employee-manager results. Consequently, the differences in means may
be due to the mismatch between groups rather than to the differences between employees and their
particular managers.
Not surprisingly, the preferred amount of telecommuting differs between employees and managers.
On average, employees would ideally work less of their time at the regular workplace (44.6%) and
more at the telecommuting center (41.5%) than managers would prefer them to (63% and 29% at the
regular workplace and telecenter, respectively). Working at the main office and the telecenter proved
to be the most preferred combination of workplaces for both groups. Importantly, employees
believed that the nature of the job allowed for about 40% telecommuting from a center, while
managers said only 32% of time was suitable, on average. Although some of the telecommuting
frequency averages are similar for employees and managers, the managers select lower
telecommuting amounts when there are substantial differences between the two.
Responses for similar job performance and satisfaction questions and the results of analyses of
variance for similar work environment characteristics questions were mostly similar for both study
groups. Surprisingly, on some job performance factors, employees rated themselves lower than the
managers did. The three job satisfaction factors that had sizeable differences were resource
availability, client demands, and supervisor appreciation. Employees were less satisfied with the
first two job factors and more satisfied on the last factor than the managers were.
7.1.3 Analysis of Telecommuting Patterns
Chapter 4 describes a study of the telecommuting patterns of center-based telecommuters, taken
primarily from information compiled from the attendance logs at the telecenters. This analysis
identifies patterns of telecommuting duration and frequency, and increases our understanding of
telecommuter working behavior on telecommuting days. The study analyzes telecommuting patterns
both at the aggregate (site) level and the disaggregate (individual) level. Both analyses are consistent
with each other and complementary.
For most of the telecenters, a usage rate of between 10% and 20% was maintained. Though the
usage rates fluctuated, overall growth is apparent. As of the June 30, 1995 cutoff date for this
interim report, the RABO telecenters had been open an average of 9.1 months, with a minimum of
2.5 months and a maximum of a little more than 20 months. The two non-RABO telecenters have
been operating for much longer, an average of 3.1 years.
At RABO sites, the average telecommuting frequency was 25%, or 1¼ days per week. More than
half of the telecommuters telecommuted less than one day per week on average, and 22%
telecommuted 1 to 2 days per week. The non-RABO telecommuters telecommuted less frequently
than those who were at RABO sites; the average was 17.2%, with about 75% of non-RABO
telecenter users telecommuting less than one day per week.
Attrition at the telecenters was relatively high, with 50% of all telecommuters quitting within the
first nine months. Although little comparative data are available, this appears to be higher than for
home-based programs. Reasons for quitting telecommuting are analyzed in Chapter 5. But in any
case, the frequency and distribution of telecommuting are crucial factors to consider in any forecast
of levels and impacts of telecommuting. Of the 92 RABO participants who telecommuted often
enough to analyze, half telecommuted for at least 8 months, and more than 25% telecommuted for
at least one year. At non-RABO sites, 50% of the 130 telecommuters analyzed telecommuted for
at least 9 months, and 25% telecommuted for at least 2 years. There is no significant difference in
telecommuting duration between RABO and non-RABO sites.
A majority of telecommuters (53%) worked at the telecenters for at least 6 hours on average on their
telecommuting days. The most common telecommuting pattern was to work entirely at the
telecenter. Approximately 22% of the telecommuters at RABO sites telecommuted with this pattern
on all of their telecommuting occasions, and an additional 23% did so at least 80% of the time. At
least 34% usually worked at more than one work location, including 8% who always did. The
second most common workplace combination was telecenter/other work location (i.e., other than
home or the regular workplace). Contrary to expectation, center- and home-based telecommuting
are not often combined on the same day; patterns involving these two locations occurred only 17%
of the time at RABO sites.
Driving alone was the dominant transportation mode used by the telecommuters in commuting to
the center. About 46% of the RABO telecommuters drove alone to the center on all of their
telecommuting occasions. More than two-thirds drove alone to the center very frequently (more than
75% of their occasions).
Chapter 5 explored the attrition of telecommuters in the RABO Project. First, the characteristics of
the respondents who quit were compared with the characteristics of those who stayed with the
program. Second, the motivations of the quitters to leave the program were described. Third, the
duration and frequency of telecommuting among both the stayers and quitters was investigated. In
order to conduct this analysis, a particular sample of project participants was identified.
The responses to the before-wave surveys were used to find differences between 22 stayers and 24
quitters. According to employment type, administrative workers were more likely to quit (5 of the
7) and sales workers were more likely to remain in the program (5 of the 6). Surprisingly, quitters
spent more time working remotely (more suited for telecommuting) than stayers. However, this
result may explain why quitters preferred to spend more time working from home (23.5%) than
stayers did (12.2%). Although job performance and satisfaction did not differ significantly between
the two groups, certain work environment characteristics were significantly different. Quitters were
more likely to worry about distractions at the telecenter and to consider the need for essential
equipment to be important. Unfortunately, the findings from the survey data cannot point to the
motivation for quitting, especially since the survey was administered prior to the experience with
telecenter use.
The exit interview captured the reasons for quitting. The most important reason given was that
respondents changed position within the company or their assigned tasks changed (25%). Thus,
external corporate downsizing and reorganization were the most likely causes of quitting. Other
important reasons include the supervisor requiring or encouraging the respondent to quit (21%) and
leaving the company (13%). The reason for quitting is unknown for four individuals (17%).
The attendance log data showed that telecommuting duration and frequency varied widely within
each group. The quitters who actually used the telecenter at least twice (16 of the 24 quitters)
telecommuted an average of 7.2 months before leaving the program. For comparison, the stayers
had telecommuted for an average of 10.7 months at the time of the final data entry. Quitters with
some telecenter experience generally telecommute less often than stayers (24% vs. 30%, or 1¼ vs.
1½ days per week). This difference is reflected in the distribution of telecommuting frequency,
where 9 quitters (56%) telecommuted less than one day per week while 13 stayers (54%)
telecommuted one day per week or more. Perhaps quitters did not telecommute often enough to
make the changes to their work schedule worthwhile.
In Chapter 6 the travel characteristics of the respondents were studied. Four main travel indicators,
namely the number of trips, PMT (person-miles traveled), VMT (vehicle-miles traveled), and mode
choice distributions, were studied. Two main sets of comparisons were made: the first between the
control group of non-telecommuters and telecenter users on non-telecommuting days and the other
between telecommuting and non-telecommuting days for telecenter users.
The control group was found to have significantly different travel characteristics than the
telecommuting group, with the latter making fewer trips (4.3 compared to 5.9) but traveling larger
distances (90.9 average weekday PMT compared to 47.9 miles for the control group), on average.
The differences in PMT and VMT could be attributed to differences in commute distances, and the
difference in number of trips could be linked to the fact that telecenter users are left with a
significantly smaller amount of time for discretionary activities in view of their long commutes to
the regular workplace.
Since the non-telecommuter group is not as comparable to the telecenter users as would be desired,
we focus on the comparison between telecommuting days and non-telecommuting days for telecenter
users. Comparing telecommuting days and non-telecommuting days, one finds that while the
average number of trips are almost the same, PMT and VMT values are significantly different, with
the average weekday distance traveled by all modes decreasing by more than 74% on telecommuting
days. Also, telecommuters on telecommuting days showed a reduction of nearly 52% in PMT when
compared with the controls. Thus, these results point to considerable savings in travel on
telecommuting days, not just against telecommuters' own extreme baseline, but against a more
normal employee's travel behavior on an average workday.
Next, the distribution of trips with respect to time of day and purpose was explored. Significant
differences were found between the temporal distributions of trips on telecommuting days and non-telecommuting days. Comparisons of telecommuters' non-telecommuting days with the control
group, however, showed no significant differences. The distributions exhibited an interesting
ordering of trip start times with telecenter users on non-telecommuting days starting the earliest,
followed by the control group, and then telecenter users on telecommuting days (because of their
significantly shorter commute distances). The temporal distribution for telecenter users on
telecommuting days also showed a significant lunch time peak. Comparisons of the distribution of
trip purposes indicated that the distributions for the control group and the telecenter users on non-telecommuting days were statistically different while those on telecommuting and non-telecommuting days were even more significantly different. The differences arose due to (1) a
significantly higher number of return home and eat meal trips, (2) a higher proportion of shopping
and social/recreation trips, and (3) an absence of change mode trips on telecommuting days. This
last factor corroborates the conclusions drawn by comparing the travel indicators for non-telecommuters and telecenter users on telecommuting days (see Section 6.2.3) and the discussion
on mode choice (Section 6.3), and implies that a smaller variety of modes are used on telecommuting
days, with most of the trips made while driving alone.
To study the trip chaining behavior of the respondents, the average number of links in a home-home
chain were compared for telecommuting and non-telecommuting days. The comparison revealed
a significantly higher number of links on non-telecommuting days. This could be attributed to the
long commute distances on such days. As the average number of trips is almost the same for the two
sets of days, one could hypothesize that on telecommuting days the respondents make a larger
number of home-to-home cycles involving a smaller number of links.
A comparison of commute and non-commute travel on telecommuting and non-telecommuting days
showed that there is a drastic reduction in the commute PMT (by 66.9 miles) and VMT (by 41.0
miles) on telecommuting days. Also, the non-commute PMT decreases almost a mile on
telecommuting days. However, non-commute VMT actually increases by two and a half miles on
telecommuting days. Therefore, there is a decrease in the non-vehicular, non-commute travel
occurring on telecommuting days.
Though the average numbers of trips on both non-telecommuting days and telecommuting days are
almost equal, the distribution of trips between commute and non-commute purposes differs. On
telecommuting days, there is a statistically significant increase of 0.5 commute trips. Also, there is
a decrease of 0.6 non-commute trips on telecommuting days, though the difference is not statistically
significant.
Next, the commute mode choice distributions for the study groups were analyzed. The travel diary
and attitudinal survey data show that there is a substantial difference between the commute mode
choices of telecenter users on telecommuting days and non-telecommuting days. The percentage of
drive-alone trips is substantially higher and the percentages of transit and rideshare trips are
substantially lower on telecommuting than on non-telecommuting days. Also, a before and after
comparison of the commute mode splits reported in the attitudinal survey revealed that
telecommuting has not affected the commute mode choices of the respondents on non-telecommuting days.
Finally, to obtain a better understanding of the overall process, the aggregate values of the travel
indicators were studied. This was done by weighting the travel indicators by the corresponding
telecommuting frequency. The aggregate figures indicate that at current frequencies of
telecommuting (18.2% on average, or approximately once in five days), the telecenter users travel
significantly larger distances: a composite weekday average of 73.1 miles compared to 49.1 miles
for the control group. Two factors contribute to the difference in aggregate travel between telecenter
users and the controls: (1) the non-telecommuting day PMT (VMT) for the telecenter users is
considerably larger than that for the controls, and (2) the level of telecommuting is not high enough
to counter this difference. But, while the telecenter users still travel more than the control group
members, they would have had an average PMT of 90.9 miles had they not been telecommuting.
Telecommuting from a center reduced their total weekday travel by nearly 19%. Two factors
contribute to the difference in aggregate travel between telecenter users and control group members:
(1) the PMT (and VMT) on non-telecommuting days for the telecenter users is considerably larger
than that for control group members, and (2) the level of telecommuting is not high enough to
counter this difference.
It could be hypothesized that commute distance is an important factor in the preference to
telecommute and that a self-selection bias occurred in the selection of the study groups, with
respondents who lived farther away opting to be in the telecommuting group. However, the
possibility that commute distance might have been a criterion used by the employers in selecting
respondents for the telecommuting group (thus generating an unintentional bias) and the fact that at
least three-quarters of the non-telecommuting group expressed a desire to telecommute suggest that
commute length is not the only motivation in a preference to telecommute. If telecommuting is
primarily attractive to long-distance commuters, considerable per capita reductions in travel will
result though only in a particular market segment. Conversely, if the appeal is more universal, the
reductions in travel per capita are not likely to be as high as in the sample but would apply to a larger
segment of the workforce.
As an interim evaluation report, this document imparts the results of the analysis of only a portion
of the data to be collected under the RABO project evaluation. The additional data that has been
collected since June 30, 1995, along with the data collected previously, will be analyzed for the final
report and in additional studies as funding permits. The combined data set will allow both the
confirmation (or revision) of the findings presented in this report as well as the ability to conduct
new analyses. The high-quality and multi-faceted data set provided by this study is expected to yield
new insights into telecommuting for some time to come.
All analyses described in this report could benefit from additional data. Comparisons of before and
after changes to employee and manager characteristics may become significant given a sufficient
sample size. Additional months of attendance log data can be used to look into the patterns relating
to the frequency and duration of telecenter use and the aggregate utilization of sites that have
increased operational longevity and marketing experience. One can also learn more about the
reasons for quitting from the additional participants who quit. More completed travel diaries can be
used to better pinpoint the changes to travel behavior. In fact, the evaluation of the full data set may
reverse some conclusions based on the interim analysis if a significant amount of different data is
collected.
A recent addition to the evaluation was the development of a videoconferencing sign-in log (Section
2.4.2). This survey instrument was put into use after June 30, 1995, so no results are yet available
for the interim report. However, analysis of the logs will be able to show the frequency of
videoconference use and will qualitatively indicate possible travel savings based on whether the
videoconference substituted for a lengthy trip to an in-person meeting.
Besides replication of the analyses reported here with a larger data set, new analyses could also be
conducted. Some potential analyses of interest are described below and are classified by the survey
instrument on which they are based.
7.2.1 Attitudinal Survey and Sign-in Log Data
Additional studies of the attitudes of employees and managers toward telecommuting are also
possible. The three dimensions of the survey plan (before and after; employee and manager; and
telecenter user, home-based telecommuter, and non-telecommuter) allow for a number of
comparisons across groups. First, direct comparisons between the attitudes and characteristics of
telecenter users and each control group would provide useful insights into the type of individual who
wants to telecommute from a center as opposed to from home or not at all. Second, each employee
could be matched with his or her manager to compare responses to telecommuting attitudes and work
characteristics. Third, comparisons of before and after telecommuting can be performed as
conducted in the evaluation reported here. For the last two comparisons, the control groups can be
used to control for background changes in the workplace in order to isolate differences between
employees and managers and between before and after telecommuting.
Importantly, this data set provides for the modeling of telecommuting preference and choice.
Although preference modeling has been performed using the interim before data (Stanek, 1995),
further studies of both telecommuting preference and choice can be conducted using the full before
and after data sets. These models can be used to identify key factors in the decision-making process
and to predict the future amount of telecommuting by the workforce. In particular, the after data can
be used to build binary and multinomial models of choice and frequency. Furthermore, analyzing
the before and after data together may offer a rare opportunity to calibrate a prospective expression
of preference against the actually chosen telecommuting frequency. In addition, all models
mentioned above can be applied both to the employee's decision to telecommute and to the manager's
decision to have the employee telecommute.
As a precursor to the telecommuting choice models, factor analysis is used to reduce the responses
on the job satisfaction and workplace attitudinal questions to their underlying perceptual dimensions.
Scores on these dimensions or factors are then used as explanatory variables in preference and choice
models. The factor analysis procedure can also be used to compare perceptual structures between
employees and managers as well as to detect changes in these structures after the start of
telecommuting.
The sign-in logs provide data for developing, for the first time, models to predict telecommuting
duration as a function of hypothesized explanatory variables from the attitudinal surveys. In
addition, the attendance logs provide a supplementary source of telecommuting frequency data for
choice models.
The travel diaries provide a rich source of information about the transportation impacts of
telecommuting. Perhaps most importantly, the data provide an opportunity to conduct an emissions
analysis of telecommuting center use similar to the study by Henderson and Mokhtarian (1996) of
the much smaller sample of telecenter users in the Puget Sound telecommuting project. One would
expect the emissions analysis to show reductions in the pollutants most closely tied to VMT,
particulate matter and nitrogen oxides, to be commensurate with the VMT reductions reported in
Section 7.1.5. Since the number of trips did not change, if the number of cold and hot starts remains
approximately the same, then there will be little change to carbon monoxide and hydrocarbon
emissions. However, these pollutants also have a component that is a function of VMT, so some
benefit to air quality for those components would be realized. On the other hand, if the reduced trip
chaining observed to occur on telecommuting days means an increase in the number of cold starts,
these pollutants would be adversely affected. Conducting a rigorous emissions analysis would be
important to determining the net impact of these counteracting factors.
In an attempt to measure the effects of telecenter use on household travel, travel diaries were
administered to all members of the telecenter user households who were sixteen years of age or
older. Using this additional data, an analysis of travel at the household level can be performed to
examine whether reductions in travel by the telecommuter are partially compensated for by increases
in travel on the part of household members. Also, the emissions analysis would be more rigorous
if all uses of a household vehicle were accounted for, thus allowing each particular trip to be more
accurately classified as either a hot or cold start. Although household member data is generally less
complete, it may be possible in the larger final data set to identify a subsample with complete data
that is large enough to analyze.
The travel diary data also allow for a spatial analysis of the travel impacts of telecenter use. Such
an analysis would analyze the extent to which new locations are visited after telecommuting and the
spatial orientation of those locations relative to home, the telecenter, and the regular workplace
(similar to the study by Saxena and Mokhtarian (1997) for home-based telecommuting). An
interesting difference from the previous study is the introduction of the telecommuting center as a
frequently-visited destination. This may lead to the identification of new destinations near the
center, which has implications for the local economic development impacts of telecenters.
Overall, the interim experience with telecommuting centers has been positive, with some indicators that require continued monitoring. Employee reactions to center-based telecommuting have been favorable, and no adverse impacts on productivity and job satisfaction were measured. There may be a selection bias in these results as these data were obtained only for employees remaining in the program. However, reasons for leaving the program are discussed below. On average, telecenter users preferred to work from the regular workplace and the telecommuting center for approximately equal amounts. In particular, about 31% of the telecenter users also preferred to work at home for some part of the work week; however, according to current practice, home- and center-based telecommuting are seldom undertaken on the same day.
The transportation impacts of center-based telecommuting were complex. On the less desirable side,
there was an increase in drive-alone trips and a decrease in trip chaining on telecommuting days.
Most commuting to the telecenter took place by driving alone, despite efforts to locate centers
sufficiently close to residential areas that walking and biking would be attractive commute modes.
Interestingly, there was a small increase (of 0.5, significant at a = 0.02) in the number of commute
trips made on telecommuting days, apparently due to telecommuters making trips home for lunch
and returning to the center in the afternoon. On the positive side, however, telecommuting did not
adversely affect commute mode choices on non-telecommuting days. And most importantly, the
number of person-miles traveled (PMT) decreased by an average of nearly 74% on telecommuting
days, while the total number of trips made remained constant.
To place the PMT reduction in the proper perspective, it is important to realize two things. First, the
reduction represents a comparison between travel on non-telecommuting weekdays and
telecommuting weekdays for center-based telecommuters. Thus, the overall impact on travel will
be a function of the frequency of telecommuting. When travel indicators on telecommuting and non-telecommuting days were weighted by the average frequency with which each type of day occurs,
an average reduction of 31% in total weekday travel of telecenter users was found.
Second, the telecommuters in this sample lived farther from work, and hence had a much greater
average non-telecommuting day PMT, than the non-telecommuting control group members (90.9
vs. 47.9 miles). Although on telecommuting days the telecommuters traveled less than the control
group, in the aggregate (telecommuting and non-telecommuting days combined) they still traveled
more. If, in the future, telecommuting continues to be adopted primarily by long-distance
commuters, the per capita reductions in travel will be considerable, but this change will be achieved
by a limited segment of the market. If, on the other hand, the adoption of telecommuting is more
universal, the per capita reductions in travel will be smaller, albeit achieved by a wider segment of
the market. In either case, the specific reductions measured in this study will not be representative
of the impacts for the population as a whole.
On the organizational side, managers of telecenter users were generally supportive, with 93% having
a positive attitude toward telecommuting in general, and 82% rating their level of satisfaction with
center-based telecommuting as high or very high. (However, a selection bias must be noted since
managers who were dissatisfied with telecommuting would be less likely to remain in the program
long enough to complete an after survey). Opinions of upper management tended to be more neutral
according to the immediate supervisors of telecommuters. The perceived advantages of
telecommuting were those for which the benefit is difficult to quantify (customer service and
productivity), while telecommuting is not perceived to offer advantages on "hard" money items, such
as office space and parking costs. This will continue to make center-based telecommuting difficult
to justify in purely economic terms. Indeed, while 39% of the manager respondents indicated that
the organization was likely to offer center-based telecommuting to its staff, an equal proportion cited
lowering the cost, being able to quantify the benefits, and increased manager acceptance as factors
that needed to change before the organization would be likely to offer center-based telecommuting.
Managers continued to view the regular workplace as the primary work location for their employees,
to be used for at least three days per week on average. This expectation of part-time telecommuting
may act to inhibit the adoption of the center-based form, as there will be little opportunity for the
organization to re-use the telecommuter's space in the regular workplace.
Average site occupancies ranged between 10 and 20% of available workspace days, with a generally
upward trend. The 10 RABO sites with sufficient attendance log data to be included in this report
had been open a minimum of 2.5 months and a maximum of 20 months (average 9.1 months)
through June 1995. It will be important to examine how site occupancy changes with an additional
year of operation (July 1995 to June 1996). For those who used the centers at least twice,
telecommuting frequencies averaged 25% (1¼ days per week) at RABO sites and 17% at non-RABO
sites.
Attrition at the telecenters was relatively high: 50% of all telecommuters quit within the first nine
months. Although little comparative data are available, this appears to be higher than home-based
programs. Results of exit interviews, conducted with the 24 participants who quit after this program
began and who could be reached, suggest that primary reasons for quitting relate to changes in job
circumstances (25%) and to supervisor's desires (21%) rather than to employee dissatisfaction with
telecommuting. Nevertheless, the frequency and duration of telecommuting are crucial factors to
consider in any forecast of levels and impacts of telecommuting.
In summary, while transportation and other impacts are unequivocally positive on net for those who telecommute on the days they are telecommuting and for the duration of their telecommuting experience, concerns remain about high attrition among telecenter users and about the perceived cost-effectiveness of center-based telecommuting to organizations.
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