Diffusion Along the Borderlands: p. 1
Diffusion in the Borderland:
A Study of the Implementation of Broadband
Connectivity in an Ecological Reserve
Tracking Number: ICA-4-10488
Kimberly Mann Bruch (Student)
School of Communication–San Diego State University
San Diego Supercomputer Center
9500 Gilman Drive, La Jolla, CA 92093-0505
858-822-0977 – kbruch@ucsd.edu
Peter A. Andersen
School of Communication–San Diego State University
5500 Campanile Drive, San Diego, CA 92182-4561
peterand@mail.sdsu.edu
Brian Spitzberg
School of Communication–San Diego State University
5500 Campanile Drive, San Diego, CA 92182-4561
spitz@mail.sdsu.edu
Paper submitted for competitive evaluation for presentation at the
International Communication Association Conference,
San Diego, May 2003
Diffusion Along the Borderlands: p. 2
Diffusion in the Borderland:
A Study of the Implementation of Broadband
Connectivity in an Ecological Reserve
ABSTRACT
This study investigates the communication and diffusion of a broadband
telecommunications infrastructure (the High Performance Wireless Research and Education
Network - HPWREN). Recently deployed sensors and cameras allow researchers at wildlife
reserves throughout the world to conduct studies and receive sensor and camera data via the
Internet. Based on Rogers’ (1995) diffusion of innovations theoretical framework, field
researchers’ perceptions of network connectivity, communication channels, and use of the
network were assessed. Results supported eight of nine hypotheses related to network
attributes (relative advantage, compatibility, complexity), communication channels, and
network use. Regression analysis of diffusion communication supported the importance of
relative advantage and complexity but not perceived compatibility. Regression analysis of all
variables, with adoption as the dependent variable, showed perceived compatibility,
perceived complexity, and diffusion communication all significantly predict HPWREN
adoption. Results are discussed in terms of the vital role played by communication during the
innovation development, implementation, and use stages.
Diffusion Along the Borderlands: p. 3
Diffusion in the Borderland: A Study of the Implementation of Broadband
Connectivity in an Ecological Reserve
Along the borderlands in the extreme southwest of the United States lies the Santa
Margarita Ecological Reserve, part of a pristine network of wildlife reserves located among
the most populous regions of southern California. Located in San Diego and Riverside
counties this 4500-acre ecological reserve is the site for our study of the diffusion of a
National Science Foundation (NSF)-funded high technology broadband information system
called the High Performance Wireless Research and Education Network (HPWREN). This
study examines the diffusion of the HPWREN system among field researchers at the reserve.
THE CONTEXT
The NSF recently proposed that Congress provide federal funding for the
development of a National Ecological Observatories Network (NEON), which first equips
ecological reserves throughout the country with computerized instrumentation (e.g., remote
sensors) and then networks the reserves together (Dalton, 2000). This broadband
telecommunications network would allow for simultaneous transmission of signals (e.g.,
voice, data, video). In turn, both local and national ecologists can easily share field data
collections with one another and also communicate their findings via observatory museums
with targeted publics such as students, the general public, government agencies, and
policymakers. Efforts such as NEON seek to bridge the gap between the scientific
community, the general public, and policymakers.
Scholars such as Valente and Rogers (1995), as well as Dearing, Meyer, and
Kazmierczak (1994), frequently discuss social change and policy research in relation to
technology and scientific innovations. However, a review of more than 100 diffusion studies
reveals dearth of data on diffusion in the scientific community and no quantitative study
regarding the impact of technology upon ecological field research has been conducted. Many
researchers focus their efforts on technology implementation, its use, and adoption among
individuals and organizations (Papa, 1990; Rubinyi, 1989). These studies, however, do not
Diffusion Along the Borderlands: p. 4
include specific insights into new communication technologies among scientists or along the
borderlands between ecologists, students in the field of environmental science, applicable
landowners, government agencies, policymakers, and the general public.
Social science research that assesses the current methods of communication among
ecological field researchers, their perceived attitudes toward technology, and the impact of
broadband connectivity upon communication methods and ecological field research will
provide the broader community with a better understanding of how NSF-funded high
technology projects such as the NEON project might positively impact environmental issues
faced by the United States. Creation of high technology systems such as NEON and
HPWREN do not guarantee their success; it is through the process of diffusion of innovations
that humans come to understand, use and benefit from new technologies.
Diffusion of Innovations: Perceived Attributes, Communication, and Adoption
The diffusion of innovations, according to Rogers (1995), is a ubiquitous human
process whereby ideas, processes, and technologies are spread throughout a social system.
He further defines diffusion of innovations as a process involving four elements: (a) one or
more social systems, (b) an innovation, (c) one or more communication channels, and (d)
time. As mentioned, this study examines attributes of a new technological innovation among
field station scientists.
Vital to the adoption of any innovation are five specific attributes: (a) relative
advantage, (b) compatibility, (c) complexity, (d) trialability, and (e) observability (Rogers,
1995). Relative advantage refers to the degree that an innovation appears to be better than its
preceding idea, practice, or object. Relative advantage is typically expressed as the degree to
which an innovation benefits, profits, or generates prestige for the individual or the overall
social system. While the benefits of an innovation may outweigh its costs for one social
system, this may not be the case for other social systems; for example, some individuals
place more value on financial profitability while others may consider other attributes, such as
overall societal benefit, more important.
Diffusion Along the Borderlands: p. 5
Similarly, compatibility is the innovation’s level of consistency with previous
experiences, existing ideals, and potential adopters’ needs (Rogers, 1995). Compatibility
encompasses the degree to which an innovation fits with the culture of a social system; for
instance, an innovation developed for one culture may not necessarily be compatible with
another the belief system and needs of another culture. Meanwhile, complexity is how
difficult or easy an innovation appears to an individual (Rogers, 1995). While some
innovations are easy to understand at first glimpse, others are more complicated and require
thorough explanation.
Finally, trialability refers to the degree that an individual can test an innovation
within a limited basis while observability is the visibility level of the innovation–how much
or little others observe the results of an innovation (Rogers, 1995). Related to compatibility,
trialability involves individuals actually trying out an innovation to better understand if and
how it will work for their particular social systems. Likewise, observability allows an
individual to see how an innovation works out for others and hear about the experiences of an
innovation from others. Though trialability and observability are important attributes, the
proposed model specifically focuses on relative advantage, compatibility, and complexity
measures.
Specifically, the diffusion of innovations theory indicates that social systems and
individuals develop innovations when members perceive high relative advantage and
compatibility coupled with low complexity toward discussed innovations (Rogers, 1995). In
other words, members of a social system are less likely to develop innovations if incentives
are not prominent, complexity is high, and there is little relative advantage and compatibility;
whereas members are more likely to adopt an innovation if incentives are prominent,
complexity is low, and perceived relative advantage and compatibility is high. Recently,
Leung and Wei (2000) found that cellular telephones have a high level of compatibility and
relative advantage among individuals, particularly businesspersons, in need of immediate
access to mobile communication. Although this finding is no surprise, their research also
Diffusion Along the Borderlands: p. 6
indicated that cell phones provide certain individuals with emotional gratifications, as the
technology is a mechanism for immediate communication with distant family members and
friends; this fits the profile of high relative advantage and compatibility as well. Meanwhile,
cell phones typically have low complexity; therefore, cell phones provide an innovation that
has been cleverly developed for a society that is becoming more mobile and technologically
advanced, yet still needs a method to communicate with family, friends, acquaintances, and
colleagues. Hence, Leung and Wei found high levels of relative advantage and compatibility
among cell phone adopters.
If perceived attributes during the innovation development phase are associated with
adoption individuals should seek information about the innovation through various
communication channels. For instance Rice and Shook (1990) found that positive perceptions
of an innovations attributes was associated with increased communication. Given positive
perceptions about an innovation we suggest that communication will not only increase, it will
be more favorable regarding the innovation. Hence,
H1: As scores within the HPWREN Relative Advantage Index at time one (T1)
increase, favorable multichannelled diffusion communication at time two (T2)
increases.
H2: As scores within the HPWREN Compatibility Index increase (T1), favorable
multichannelled diffusion communication (T2) increases.
H3: As scores within the HPWREN Complexity Index (T1) increase, favorable
multichannelled diffusion communication (T2) decreases.
In turn, favorable communication should be related to positive attitudes toward the
innovation at a later time. Specifically, perceived complexity decreases as social system
members speak more positively and individuals receive favorable mass media exposure
between the innovation development and innovation implementation phases of diffusion.
Likewise, perceived relative advantage and compatibility increases as positive diffusion
communication channels increase. For instance, Valente and Saba (1998) found that mass
Diffusion Along the Borderlands: p. 7
media communication channels are positively related to information awareness stages in the
diffusion process while interpersonal communication channels (referred to as “personal
networks” by Valente and Saba) are positively related to behavior change. That is, if a person
has a broad personal network and communicates positively about an innovation, the person
likely perceives the relative advantage and compatibility of an innovation in a more favorable
light; if interpersonal communication channels of an individual are primarily negative, the
person likely perceives the innovation’s relative advantage and compatibility in a more
negative light. On the other hand, “individuals who lack personal contact with many users of
an innovation may turn to the mass media for information about new ideas and products” (p.
116). Hence it is likely that positive communication is associated with more positive
attributes toward key elements of an innovation.
H4: As positive diffusion communication channels at time 2 (T2) increase, the
degree of perceived complexity (T2) decreases.
H5: As positive diffusion communication channels (T2) increase, the degree of
perceived relative advantage (T2) increases.
H6: As positive diffusion communication channels (T2) increase, the degree of
perceived compatibility (T2) increases.
The proposed model suggests that diffusion communication coupled with continued
perceptions eventually lead to the adoption decision: an individual either adopts or rejects the
innovation (see Figure 1).
As previously discussed, Rogers (1995) found that positive relationships exist
between relative advantage and adoption as well as compatibility and adoption. Meanwhile,
Rogers illustrated a negative relationship between complexity and adoption rates. Additional
diffusion studies confirm the relationships posited by Rogers. For instance, Rubinyi (1989)
examined an office setting originally unfamiliar with computerized systems and their efforts
to computerize all internal office functions. After one year, more than 90% of the groups did
not have the majority of their network functions up and running. However, by the end of the
Diffusion Along the Borderlands: p. 8
two-year study, most of the groups were networking data between their own group and other
groups. On the other hand, computer use in general did not have the same slow adoption rate.
Rubinyi attributed the slow networking adoption rate to its complexity–compared with
simply using computers, networking takes more coordination with others and more technical
savvy than simply operating a single computer that is not networked to others.
Likewise, Manross and Rice (1986) found that technical savvy, or lack thereof, also
played a large role in the adoption rate of an intelligent telephone in an organization.
Although their study did not find a correlation between overall attitudes and usage of the
phone’s specific functions, Manross and Rice discovered a difference between the
relationship among those users at differing organizational levels (e.g., managers versus
administrative assistants), their attitudes, and adoption decisions. Further, Kleinman (2000)
found that the flexibility of CMC provided high relative advantage among women scientists
and engineers who joined a 24-hour on-line forum, which allowed them to share their
research with peers and mentors. Consistent with this research we posit that:
Diffusion Along the Borderlands: p. 9
H7: Subjects who score high within the HPWREN Relative Advantage Index at time
2 (T2) are more likely to use the network connectivity than those who score
low.
H8: Subjects who score high within the HPWREN Compatibility Index (T2) are
more likely to use the network connectivity than those who score low.
H9: Subjects who score high within the HPWREN Complexity Index (T2) are less
likely to use the network connectivity than those who score low.
METHODS
Participants
The participants were field scientists, ranging from evolutionary biologists to wildfire
management researchers, affiliated with Field Stations Program of a major Southwestern
University that oversees the Santa Margarita Ecological Reserve. Thirty-seven of the 40
eligible field scientists participated in the study (a 92.5 % participation rate). The respondents
were 81% male and 19% female, 54% are faculty members from southern California
universities, 41% are staff at academic, military, and research institutions and agencies and
5% are graduate students at the aforementioned University.
Setting
Spanning approximately 4500 acres with elevation ranges from 150 to 2300 feet, the
Santa Margarita Ecological Reserve is nestled between the Santa Ana Mountains in the
northeast portion of San Diego County. Home to one of the last free-flowing streams in
southern California, topography is rugged and complex with low hills and intervening
drainages. In Fall 2001, HPWREN researchers began development and implementation of a
broadband telecommunications infrastructure and reserve field scientists now have the
capability of sending and receiving data at a speed of 45 megabits per second (Figure 2).
Diffusion Along the Borderlands: p. 10
More than 50 research projects are being conducted at reserve - including a wide
array of studies, such as (a) threatened and endangered species; (b) water quality and public
health; (c) agriculture; (d) global change; (e) fundamental ecology, geology and geography;
and (f) environmental engineering. The newly established high-speed data connection,
however, prompts additional researchers to utilize technology as a means of data collection
and transmission. For instance, a weather station (Figure 3) represents only one example of
the many sensors being implemented throughout the reserve.
Diffusion Along the Borderlands: p. 11
The reserve is being equipped with additional sensors that provide researchers with
high-resolution time series measurements of physical, chemical, and biological variables
found not only in the air, but also in the watershed. Systems that capture both audio and
video are also being deployed in the gorge of the reserve so that scientists can remotely
monitor habitats (Figure 4). Consequently, ecoinformatics development such as this falls
right in line with the NSF’s recently proposed National Ecological Observatories Network
(NEON). With the goal of equipping ecological reserves around the country with
computerized instrumentation infrastructures, NEON would also network all of the reserves
together via broadband telecommunications.
As mentioned in the introduction of this paper, the proposed NEON project would
allow U.S. ecologists to easily share field data collections with one another and communicate
their findings with their students, government agencies, private landowners, and the general
public.
Diffusion Along the Borderlands: p. 12
Procedures
Permission to collect participant data regarding broadband connectivity use, diffusion
communication, and relative advantage, compatibility, and complexity was granted to
researchers, including the author, affiliated with the NSF-funded High Performance Wireless
Research and Education Network (HPWREN) by Human Subjects Committees at two major
Southern California Universities. Pre-connectivity data were collected from HPWREN
researchers in December 2001 while broadband connectivity via HPWREN was being
established at the reserve; post-connectivity data were collected in March 2002. Telephone
interviews were used to collect diffusion communication, relative advantage, compatibility,
and complexity data and email surveys were used to collect HPWREN usage data.
Specifically, the variables were measured using two sets of questions: the pre-test
(see Appendix A) was conducted prior to HPWREN connectivity while the post-test (see
Appendix B) was administered after connectivity was established. Pre-test survey questions
concerned perceived attributes and were administered at time one. The pre-test survey
Diffusion Along the Borderlands: p. 13
consisted of 24 items related to perceived relative advantage, compatibility, and complexity;
this telephone survey was administered during the innovation development stage.
Subsequently, post-test surveys consisted of 27 items and were conducted in two phases. The
first 21 questions (time two) examined continued perceptions (relative advantage,
compatibility and complexity) while the final six questions (time three) measured adoption
decision. The survey questions were developed based upon prior research conducted by
Rogers (1995), Valente (1996), Rice (1994), Rice and Gattiker (2001), Spitzberg (2002), and
Reagan (1989).
In addition to quantitative data collection specific to the hypotheses posited in this
thesis, qualitative data were also recorded. This information provided insight regarding the
specific real-time data that adopters plan to measure, their use of specialized sensors, needed
technical support, and HPWREN training that users consider beneficial. Additionally,
qualitative data allowed for a better understanding of the non-adopters’ decisions to reject the
innovation.
Variables
Using the aforementioned diffusion of innovations theoretical framework (Rogers,
1995), the dependent variable of this research was the adoption of the innovation: broadband
connectivity. The independent variables consisted of perceived attributes (relative advantage,
compatibility, and complexity) as well as diffusion communication–though both
interpersonal and mass media channels.
Perceived Attributes and Continued Perceptions
Perceived attributes, which were measured during pre-test surveys, refer to the
attitudes during the innovation development. Continued perceptions, which were measured
during the post-test surveys, refer to the attitudes during the innovation implementation and
use. Because the field scientists were not heavily involved with the development process,
however, a significant difference between perceptions at time one and time two surveys was
not anticipated. On the other hand, comparison of pre-connectivity perceptions with post-
Diffusion Along the Borderlands: p. 14
connectivity perceptions proves interesting for HPWREN researchers as they examine the
evolution of understanding among broadband telecommunications users and provide the most
efficient infrastructure and training materials. Specifically, several indexes were used to
measure relative advantage, compatibility, and complexity.
Relative Advantage. To measure relative advantage, five items were employed: the
extent to which HPWREN (a) improves the field scientist’s research endeavors; (b) improves
teaching efforts; (c) allows for more efficient information dissemination; (d) benefits
outweigh costs; and (e) has more advantages than disadvantages. Cronbach reliability
estimates indicated that the relative advantage measure was reliable (T1 = .81; T2 = .84).
Compatibility. Five items were employed that ascertained HPWREN’s compatibility
with the participant’s (a) current technology; (b) level of network training; (c) research
needs; (d) methods of data collection; and (e) teaching curricula. Cronbach reliability
estimates indicated the five-item version of the compatibility index was moderately reliable
(T1= .73; T2 = .71).
Complexity. Similarly, five measured complexity by determining whether or not the
participant (a) has questions about HPWREN; (b) perceives the network as difficult to use;
(c) must acquire technical assistance to use HPWREN; (d) prefers person-to-person technical
assistance; and (e) is not very knowledgeable about networking technology. Cronbach
reliability estimates indicated that substantial increase in reliability for the complexity
measure was achieved when one item was removed from the index, so the item measuring
preference for person-to-person technical assistance was suggested for deletion from the
complexity index resulting in a four-item version of the complexity index which was
moderately reliable (T1 = .70; T2 = .74).
Diffusion Communication
Diffusion communication encompasses both interpersonal and mass media
communication channels. Scholars measure interpersonal communication channels using
Likert-scale questions similar to those in this study (e.g., Rice, 1991; Valente & Saba, 1998).
Diffusion Along the Borderlands: p. 15
To measure interpersonal communication channels specific to innovation perceptions, the
participants were asked about the extent to which information they received about HPWREN
was favorable: (a) within their workplace and (b) outside of their workplace. To measure
mass media communication channels specific to innovation perceptions, the participants
were asked about the extent to which they received favorable information about HPWREN:
(a) in print media; (b) broadcast media; and (c) electronic media (see Appendix B). The
highest level of reliability was attained by combining these measures to form a single
diffusion communication measure ( = .68).
Adoption Decision
In order to better understand the adoption decision of participants, they were asked
about their (a) current use of the network; (b) perceived use in their future work; (c)
perceived future use for research endeavors; (d) perceived future use for teaching efforts; and
(e) perceived future use for overall purposes. These variables were measured using standard
attitude scales on part and future frequency of use that were administered using electronic
mail surveys (see Appendix B, questions 22-26). Cronbach reliability analysis indicated that
the five measures for adoption decision were all reliable ( = .88).
RESULTS
A path model (Figure 1) was developed to provide an omnibus test of the hypotheses.
Given the size of the sample, true multivariate path analysis was considered inappropriate.
Instead, bivariate correlations and multiple regression were used to test the model.
Hypothesis Tests
Eight of the nine posited hypotheses were supported using bivariate correlations
(Figure 5). Hypothesis one, which predicted that as scores within the HPWREN Relative
Advantage Index (T1) increase, positive diffusion communication channels (T2) increase,
was supported (r = .56, p < .001, r2 = .31). Likewise, hypothesis two, which stated as scores
within the HPWREN Compatibility Index (T1) increase, positive diffusion communication
channels (T2) increase, was also supported (r = .52, p < .001, r2 = .27). Similarly, as the
Diffusion Along the Borderlands: p. 16
initial perceived complexity of HPWREN increased among study participants, positive
diffusion communication channels decreased and hypothesis three, which stated as scores
within the HPWREN Complexity Index (T1) increase, positive diffusion communication
channels (T2) decrease, was also supported (r = -.44, p = .007, r2 = .19).
Hypothesis four, which stated as positive diffusion communication channels (T2)
decreases, the degree of perceived complexity (T2) decreases, was supported (r = -.52, p =
.001, r2 = .27). Likewise, hypothesis five, predicting that as positive diffusion communication
channels (T2) increase, perceived relative advantage (T2) increases, was supported (r = .57, p
< .001, r2 = .33). Additionally, hypothesis six, which anticipated that as positive diffusion
communication channels (T2) increase the degree of perceived compatibility (T2) increases,
was also supported (r = .48, p = .003, r2 = .23).
Finally, as the continued perceptions regarding relative advantage and compatibility
of HPWREN increased among study participants, innovation acceptance increased. That is,
as stated in hypotheses seven and eight, subjects who score high within the HPWREN
Relative Advantage Index (T2) are more likely to use the network connectivity (T3) than
those who score low (r = .68, p < .001, r2 = .46) and subjects who score high within the
HPWREN Compatibility Index (T2) are more likely to use the network connectivity (T3)
than those who score low (r = .55, p < .001, r2 = .30). However, as the continued perceptions
regarding complexity of HPWREN increased, innovation acceptance did not decrease.
Therefore, hypotheses one through eight were supported, while hypothesis nine,
which predicted that subjects who score high within the HPWREN Complexity Index (T2)
are less likely to use the network connectivity (T3) than those who score low was not
supported (r = -.31, p = .066).
Diffusion Along the Borderlands: p. 17
Diffusion Along the Borderlands: p. 18
As an exploratory analysis, paired samples t-tests were performed to detect
differences in the means for relative advantage, compatibility, and complexity. The t-tests
showed no overall valence shift between perceived attributes during the innovation
development stage and continued perceptions during the innovation implementation and use
stage.
Multiple Regression
Again, given the small sample, it was considered inappropriate to conduct a complete
multivariate path analysis or structural equation modeling. Therefore, to further test the
analytic model for direct effects and mediator variables, hierarchical multiple regression was
performed. The procedure was to enter groups of variables in the model first, and then the
next, and so forth. The sequence was then altered systematically. This procedure permitted an
interpretation of moderation or mediation among the variables (Baron & Kenny, 1986).
Then, the best fitting model given these results was assembled. Recognizing that there is
substantial room for interpretation in such a procedure, the resulting model (Figure 6)
indicated that initial perceptions of attributes (relative advantage and complexity at time one)
impact diffusion communication related to the innovation (HPWREN). Further, initial
perceptions of attributes (compatibility and complexity at time one) and diffusion
communication predict innovation adoption. However, continued perceptions of attributes
(relative advantage, compatibility, and complexity at time two) were not shown to be
significant predictors of innovation adoption. These quantitative results, as well as qualitative
results, are explicated in the discussion section of this paper.
DISCUSSION
Results using bivariate correlations provided support for eight of the nine posited
hypotheses. In the discussion we present some qualitative data that provides additional
insight regarding why these hypotheses were supported. Finally additional analysis using
multiple regression provided a more complete test of the entire diffusion model.
Diffusion Along the Borderlands: p. 19
Hypotheses One and Two
Strong positive relationships between both relative advantage and compatability with
positive diffusion communication channels suggest that these initial positive predispositions
lead to the seeking of positive information about the innovation. These two variables, which
each explain 31% and 27% of the variance respectively in positive diffusion communication,
are exemplified by the following qualitative data collected during T1 interviews:
“Basically, we have a couple of people with dip nets that examine fish spawn.
What we do is hike up the stream, take multiple samples, and count the number of
fish - determining species and other things. We then type our field notes directly
into a PDA and we actually hope to implement field records into an Internet
database soon. Yes, real-time digital field notes would be terrific. I admire SDSU
for pursuing this, as I find it entirely appropriate.” -Fish ecologist
“I’ve been talking with faculty around the country about using real-time data in
their classrooms and feedback has been very positive.” -Biology professor
Hypothesis Three
Results showed that complexity is strongly and negatively related to positive
communication about the innovation with complexity explaining about 27% of the variance
in positive diffusion communication. These results support the notion that complexity make
potential adopters fail to seek information or find negative messages about an innovation.
These relationships can best be better understood by the following qualitative data collected
during T1 interviews.
“Monitoring via sensors and equipment takes maintenance, which can be difficult
to implement without causing disturbance...I did hear that this network was in the
plan for the reserve, but don’t know anything about it.” -Golden eagle researcher
“The automated collection of biological data is very hard...Broadband is
wonderful to have there, but the biggest question is how do we automate
biological sensing? Field biological collection tools are not yet to the point to
fully utilize this type of technology. I just haven’t really heard much about the
technical aspects of this network, so I can’t really say that much right now.”
-Mammologist
“The USGS has streamflow data available online and it is organized really well
with a good interface. It is simple, easy to use...Organization of data can impact
Diffusion Along the Borderlands: p. 20
the usefulness of the data...Ease of access will control the usage of this data, too.”
-Watershed researcher
Hypothesis Four
Results of the present study showed that as positive diffusion communication
channels increase, the degree of perceived complexity substantially decreases. Over one
quarter of the variance in reduced complexity is a function of positive diffusion
communication. Alternatively, this hypothesis also suggests that negative communication
increases the perceived complexity. Qualitative data typify this relationship.
“It would be nice to keep everyone up to date about the network - to keep all in
touch with available data...Keep in touch with the Supercomputer Center and see
how we can best incorporate multiple databases, because that is what they do
there and would help us know more about the network.” -Biology professor
Hypotheses Five and Six
Results showed that as positive diffusion communication channels increase, the
degree of perceived relative advantage and compatibility increases. Positive communication
explains about 1/3 of the variance in relative advantage and nearly a quarter of the variance
in compatibility. The following qualitative data collected during T2 interviews with
respondents provides additional data about this process:
“I just received your packet of information about the network and am now taking
a class out to SMER...I hope to show them some of this technology out in the
field.” -Biology professor
“The network has just been explained to me by SMER staff and I would love to
use this sometime in the future. Before when you talked to me, I just didn’t know
enough about it, but now I may try to work out some research project around the
technology in the future.” -Mammologist
These results also reveal that respondents who have negative, or simply a lack of,
diffusion communication channels will not have a positive perception regarding relative
advantage and compatibility. Instead, these respondents are likely to have a negative or no
opinion about the network’s relative advantage and compatibility with their work:
Diffusion Along the Borderlands: p. 21
“I am still not sure about HPWREN, but that is probably because I have not heard
about it as I am somewhat disjunct from the school as an adjunct faculty
member.” -Adjunct biology professor
Hypotheses Seven and Eight
Perhaps the most important findings for diffusion theory were that subjects who
perceive HPWREN had high relative advantage and compatibility were more likely to use it
than those who score low. Relative advantage explained 46% of the variance and
compatibility explained 30% of the variance in adoption.
The qualitative data reveal some of the nuances of this process:
“I will soon be recording sounds from owls, as there may be a correlation between
moon phase and weather. Real-time weather data streamed via this network and
correlated with owl data and moon phase data will allow me to measure the
spacing between calls and see if there are relationships between the sounds,
weather, and moon phases...Yes, I will use this network.” -Biologist
“We now have our moss research at the James Reserve online and it’s great...We
want to set up more of these real-time stations...We need more experiments to
gauge results...Yes, we will use HPWREN at SMER.” -Fungus and moss
researcher
“This is a fantastic program and I’m anxious to see how this changes both
research and teaching in profound ways. I’d also be interested in seeing the use of
SGI Visual Area Networks with HPWREN, as I am already using both networks.”
-Geology professor
Likewise, respondents who score lower within the relative advantage and
compatibility indices are less likely to adopt the technological innovation. Further qualitative
results, such as the following comment from T2 interviews, support this hypothesis.
“My biggest concern is not the technology, but HPWREN is just not going to
really aid me with my data collection. This would require radio collars on the
animals, which to my knowledge is not easily available at this time. Although
there are passive integrative transponders that allow for the animal to be injected
with a tag under the skin and then records their actions as they move through
underground hoop antennas, this technique requires hoop antennas everywhere which causes a trampling issue. There are also radio transmitters that you can put
in an animal...this is used largely in quail research...the animal transmits a signal
Diffusion Along the Borderlands: p. 22
to a nearby radio tower. However, this technique is rarely used due to challenges
like automatic triangulation. If auto-triangulation was in place at SMER, we could
use this, but until then, it is just not feasible for my particular research agenda.” Mammologist
Hypothesis Nine
The last hypothesis predicted that subjects scoring higher on the complexity index at
time 2 would be less likely to use the network connectivity than those who score low on the
complexity index. Hypothesis nine was not supported. Though results were in the
hypothesized direction in that subjects who thought HPWREN was highly complex, were
less likely to use the system than subjects who believed it was fairly simple the relationship.
However, the relationship was not statistically significant.
Because simplicity typically attracts more users to a technological innovation than
complexity (Rogers, 1995), this finding was somewhat surprising. However, it should be
noted that the current sample consists of highly educated field scientists who are possibly less
intimidated by “high-tech” innovations than less educated adopters. Further, multiple
regression results contradicted the bivariate correlations and showed that perceived
complexity (T1) has an impact upon both diffusion communication (T2) and adoption (T3);
see Figure 6 for details.
Diffusion Along the Borderlands: p. 23
Diffusion Along the Borderlands: p. 24
Evaluating the Overall Model
Simultaneous multiple regression analysis of diffusion communication with relative
advantage, compatibility, and complexity as predictors resulted in significance for both
relative advantage (r = .55, p = .02, r2 = .30) and complexity (r = .36, p = .029, r2 = .13). That
is, diffusion communication (T2) is significantly affected by perceived relative advantage
(T1) and perceived complexity (T1); however, multiple regression results indicated that
perceived compatibility is not a significant predictor of diffusion communication (see Figure
6). However, when adoption is viewed as a dependent variable for the time 1 variables of
relative advantage, compatibility, complexity, and the time 2 variable of diffusion
communication, compatibility reappears to play a role in predicting adoption (r = .45, p =
.04, r2 = 20) along with perceived complexity (r = .37, p = .04, r2 = .14) and diffusion
communication (r = .33, p = .04, r2 = .11). However, relative advantage at time 1 falls out of
the picture. Whereas relative advantage at time 2 is strongly related to adoption at time 2 (r =
.68, p < .001, r2 = .46), at time 1 its influence appears to be mediated by other factors.
Perhaps the most important lesson learned during this study was the vital role played
by communication during the innovation development, implementation, and use stages. The
interview process alone allowed many field scientists at the reserve to consider an innovation
(HPWREN) that they might have otherwise never heard about. Further, the study showed
that even though some respondents are not technologically savvy, they are willing to adopt
technological innovations - provided perceived attributes are favorable.
Although a few respondents already use PDAs, the majority of respondents use a
manual field notebook for recording data collection findings. They then transfer the
information to their office or laboratory computer. However, qualitative data, such as the
following, indicates consideration of new electronic field devices:
“It depends on where I am going, as most of the time it is easier to carry a
notebook then a computer in remote areas. I am thinking about getting a PDA
though.” -Hydrologist
Diffusion Along the Borderlands: p. 25
Again, this type of insight shows that if the innovation proves to be compatible with
their current data collection procedures, the field researcher is likely to utilize it - as long as
they receive information about it via their communication channels.
Limitations of the Study
Even though this study proved to be quite successful, at least three limitations are
apparent: (a) only one reserve was examined, (b) only one innovation was studied, and (c)
the sample was relatively small. In order to compare the results of the Santa Margarita
Ecological Reserve study participants with another sample, broadband connectivity must first
be established at an additional reserve. This would allow for further analysis of the
hypotheses. Consequently, HPWREN researchers are currently planning to connect the
nearby Sky Oaks Field Station to the network; this will provide an optimal comparative
sample set.
The current study’s limitation of only one innovation was also somewhat problematic.
When only generalizing to the adoption of HPWREN, the diffusion of innovations theoretical
framework works perfectly and results are impressive; however, if generalizing to overall
innovation diffusion, this study is somewhat problematic as only one innovation was
examined - rather than multiple innovations. Therefore, because the research examines only
one innovation, HPWREN, the results cannot be generalized to all technological innovations
(e.g., data-loggers and PDAs) related to field science. Perhaps another study that examines
the attitudes, communication, and adoptions of data-loggers and PDAs among field scientists
would also prove useful for the NSF and additional entities.
Finally, the sample size was also somewhat limiting for the study. Although the
sample consisted of nearly all (37 of 40) field scientists affiliated with the Santa Margarita
Ecological Reserve, this small number of participants is somewhat problematic. Again, an
additional study that examines Sky Oaks scientists would help address the issue.
Diffusion Along the Borderlands: p. 26
Future Implications
This research not only quantitatively describes the attitudes of the field researchers
toward broadband connectivity, but also outlines a model for a longitudinal future study.
From the analytical research standpoint, the described objectives are important, as additional
ecological reserves may be added to the NSF-funded HPWREN and diffusion of this
technology is better understood by determining the perceived attributes and utility of
broadband connectivity among ecological field researchers–as oftentimes these perceptions
are discussed in qualitative manners, but never truly quantified. Additionally, this study
provides a propositional model that may be used for future longitudinal studies involving the
diffusion of technological innovations among particular social systems.
Perhaps even more importantly, the study has the potential for providing quantitative
information to an array of publics regarding the impact of broadband connectivity on
ecological research. These findings further allow policymakers, such as those charged with
NSF proposals like NEON, to better understand how such environmental monitoring projects
might positively impact the future of ecological monitoring and the overall environmental
research agenda in the United States.
Because our society often relates environmental protection with technological
innovations (Miller & Garnsey, 2000), it is important that scholars continue to research the
diffusion process and publish their findings in applicable journals so that the information is
widely disseminated. Further, diffusion studies that better explain the benefits to the
environment via particular technological innovations should be a focus of mass media
channels so that the general public is also aware of important environmental issues and how
their attitudes and behaviors impact the environment.
Additionally, NEON researchers may be able to learn from the Dutch science shops,
which have existed for more than 30 years (Farkas, 1999), and better match the agenda of
environmental research with that of the greater community by providing not only information
Diffusion Along the Borderlands: p. 27
about the issues and concerns at hand, but immediate ways that individuals can participate in
science and environmental protection initiatives.
Further, initiatives like NEON also allow societal members other than those with the
most education and financial resources to access information that is typically reserved for
citizens that are the best-educated and most wealthy (Miller & Garnsey, 2000). That is, once
NEON sites are in place, common citizens can virtually tour ecological observatories around
the country, ranging from the mountaintops of Colorado’s front range to the swamplands of
the Mississippi delta. NEON will not only be a resource that allows scientists to better
communicate with one another and thereby promote interdisciplinary collaborations, but the
network has the potential of serving as a national treasure to be shared by every citizen.
Diffusion Along the Borderlands: p. 28
REFERENCES
Baron, R. M., & Kenny, D. A. (1986). The moderator-mediator variable distinction in social
psychological research: Conceptual, strategic, and statistical considerations. Journal of
Personality and Social Psychology, 51, 1173-1182.
Dalton, R. (2000). NEON to shed light on environment research. Nature, 404, 216.
Dearing, J. W., Meyer, G., & Kazmierczak, J. (1994). Portraying the new: Communication
between university innovators and potential users. Science Communication, 16, 11-42.
Farkas, N. (1999). Dutch science shops: Matching community needs with university R&D.
Science Studies, 2, 256-273.
Kleinman, S. S. (2000). Social identification in a computer-mediated group for women in
science and engineering. Science Communication, 21, 344-366.
Leung, L., & Wei, R. (2000). More than just talk on the move: Uses and gratifications of the
cellular phone. Journalism and Mass Communication Quarterly, 77, 308-320.
Manross, G. G., & Rice, R. E. (1986). Don’t hang up: Organizational diffusion of the
intelligent telephone. North Holland Information & Management, 10, 161-175.
Miller, D., & Garnsey, E. (2000). Entrepreneurs and technology diffusion: How diffusion
research can benefit from a greater understanding of entrepreneurship. Technology in
Society, 22, 445-465.
Papa, M. J. (1990). Communication network patterns and employee performance with new
technology. Communication Research, 17, 344-368.
Reagan, J. (1989). New technologies and news use: Adopters vs. nonadopters. Journalism
Quarterly, 66, 871-877.
Rice, R. E. (1991). Organic organizations and centralized units: Use, contexts, and outcomes
of word processing. In J. Morell & M. Fleischer (Eds.), Advances in implementation
and impact of computer systems (pp. 111-141.). New York: JAI.
Rice, R. E. (1994). Network analysis and computer-mediated communication systems. In S.
Wasserman & J. Galaskiewicz (Eds.) Advances in social network analysis (pp. 167203). Newbury Park, CA: Sage.
Rice, R. E., & Gattiker, V. E. (2001). New media and organizational structuring. In F. Jablin
& L. Putman (Eds.), The new handbook of organizational communication: Advances in
theory, research, and methods (pp. 544-581). Thousand Oaks, CA: Sage.
Diffusion Along the Borderlands: p. 29
Rice, R. E., & Shook, D. E. (1990). Voice messaging, coordination, and communication. In
J. Galegher, R. Kraut, & C. Egido (Eds.), Intellectual teamwork: Social and technology
foundation of cooperative work (pp. 327-350). Hillsdale, NJ: Lawrence Erlbaum.
Rogers, E. M. (1995). Diffusion of innovations (4th ed.). New York: Free Press.
Rubinyi, R. M. (1989). Computers and community: The organizational impact. Journal of
Communication, 39, 110-123.
Spitzberg, B. (2002). Preliminary Development of a Model and Measure of Computer
Mediated Communication (CMC) Competence. Unpublished manuscript, San Diego
State University.
Valente, T. W. (1996). Social network thresholds in the diffusion of innovations. Social
Networks, 18, 69-89.
Valente, T. W., & Rogers, E. M. (1995). The origins and development of the diffusion of
innovations paradigm as an example of scientific growth. Science Communication, 16,
242-273.
Valente, T. W., & Saba, W. P. (1998). Mass media and interpersonal influence in a
reproductive health communication campaign in Bolivia. Communication Research, 25,
96-124.
Diffusion Along the Borderlands: p. 30
APPENDIX A
PRE-TEST SURVEY QUESTIONS
Diffusion Along the Borderlands: p. 31
TELEPHONE QUESTIONNAIRE SCRIPT
<>
indicates note to interviewer only - not to be read aloud to subject.
<1. Enter phone number as ID variable.>
<In the case of missing data, indicate 9 in the applicable edge coding section.>
Hi. I’m a graduate student and am following up on an email that I sent you a few days ago
regarding an NSF-funded research project that is providing Santa Margarita Ecological Reserve and
the Sky Oaks Field Station with high-speed network connectivity.
I’m calling to ask you a few questions about the impact of the network upon your research
and your teaching, and which future networking schemes could best benefit your work.
Basically, the two Field Stations are currently being connected to a high-speed network called
HPWREN, which is funded by the National Science Foundation. The speed of the network is around
45 Megabits per second and will allow researchers like you to transmit large amounts of real-time
information from the field station to the campus database where it can be readily accessed by
researchers and students through a website. An in-field station network of automated data acquisition
towers are also being developed; these towers are intended to assist researchers like you with data
collection methods.
As mentioned in my email, I’d like to ask you a few questions about the impact of this
network connectivity upon your research.
2. What will you and your team need to utilize the network? (Answer yes to as many of these as
applicable). Do you need...
a. computer equipment
b. specialized sensors
c. technical assistance/support (e.g., technician)
d. training
e. other: ______________________________
3. What are your sources of data for your field experiments? (Again, answer yes to as many of these
as applicable). Do you use...
a. soil samples from plots
b.climate data from tower
c. other: ________________________________
4. What are your current datalogging methods? Do you...
a. write in field book, data forms, or maps, and later type into computer
(computer brand/model: _______________)
b. type directly into PDA from field
(PDA brand/model:___________________)
c. laptop computer
(laptop brand/model:___________________)
d. other: ______________________________
5. Does your research...
a. Always
b. Sometimes
c. Seldom or
d. Never
...require continuous measurements (continuous = measurements that are taken more than once per
day)?
Diffusion Along the Borderlands: p. 32
6. What is your sample interval (how often)?
7. Do you...
a. Always
b. Sometimes
c. Seldom or
d. Never
...use specific sensors (e.g., cameras, meteorological data, soil probes, etc)?
8. Describe the sensors that you use: past, present, and hopefully future (i.e., what types of data could
be useful for you in the future)?
Okay, that sounds really interesting. I’ll make sure and relay that information to Sedra and HansWerner. Now, I have a few more questions for you. If I were putting words into your mouth about the
following statements, would you strongly agree, agree, have no opinion, disagree, or strongly
disagree....
9. HPWREN will help me improve my research endeavors by providing me with better access to realtime data than my current system. Do you...
a. Strongly agree (5)
b. Agree (4)
c. Have no opinion (3)
d. Disagree, or (2)
e. Strongly disagree (1)
...with that statement?
10. HPWREN will help me improve my teaching efforts by providing me with improved access to
real-time data in the classroom setting.
a. Strongly agree (5)
b. Agree (4)
c. Have no opinion (3)
d. Disagree, or (2)
e. Strongly disagree (1)
11. HPWREN will allow me to disseminate research information to colleagues and applicable publics
in a more efficient manner.
a. Strongly agree (5)
b. Agree (4)
c. Have no opinion (3)
d. Disagree, or (2)
e. Strongly disagree (1)
12. All things considered, HPWREN benefits outweigh costs.
a. Strongly agree (5)
b. Agree (4)
c. Have no opinion (3)
d. Disagree, or (2)
e. Strongly disagree (1)
Diffusion Along the Borderlands: p. 33
13. Overall, HPWREN has more advantages than disadvantages.
a. Strongly agree (5)
b. Agree (4)
c. Have no opinion (3)
d. Disagree, or (2)
e. Strongly disagree (1)
14. HPWREN would be very compatible with my current technology.
a. Strongly agree (5)
b. Agree (4)
c. Have no opinion (3)
d. Disagree, or (2)
e. Strongly disagree (1)
15. I will need little specialized training to use HPWREN technology.
a. Strongly agree (5)
b. Agree (4)
c. Have no opinion (3)
d. Disagree, or (2)
e. Strongly disagree (1)
16. HPWREN meets several technological needs of my research project.
a. Strongly agree (5)
b. Agree (4)
c. Have no opinion (3)
d. Disagree, or (2)
e. Strongly disagree (1)
17. I will be able to use HPWREN for research purposes, without changing the structure of my field
data collection procedures.
a. Strongly agree (5)
b. Agree (4)
c. Have no opinion (3)
d. Disagree, or (2)
e. Strongly disagree (1)
18. I will be able to use HPWREN for teaching purposes, without significantly changing the structure
of my course curricula.
a. Strongly agree (5)
b. Agree (4)
c. Have no opinion (3)
d. Disagree, or (2)
e. Strongly disagree (1)
19. I have questions about the basic concepts of HPWREN and how real-time field data collection
and transmission works.
a. Strongly agree (5)
b. Agree (4)
c. Have no opinion (3)
d. Disagree, or (2)
e. Strongly disagree (1)
Diffusion Along the Borderlands: p. 34
20. HPWREN will be somewhat difficult for my staff and me to use for data collection and
transmission.
a. Strongly agree (5)
b. Agree (4)
c. Have no opinion (3)
d. Disagree, or (2)
e. Strongly disagree (1)
21. I will have to acquire the help of a technical person to help me with the use of HPWREN.
a. Strongly agree (5)
b. Agree (4)
c. Have no opinion (3)
d. Disagree, or (2)
e. Strongly disagree (1)
22. When I have a problem with a technological device, I prefer person-to-person technical
assistance.
a. Strongly agree (5)
b. Agree (4)
c. Have no opinion (3)
d. Disagree, or (2)
e. Strongly disagree (1)
23. I am not very knowledgeable about technology and do not currently feel comfortable using
HPWREN.
a. Strongly agree (5)
b. Agree (4)
c. Have no opinion (3)
d. Disagree, or (2)
e. Strongly disagree (1)
Okay, I just have one final question for you...
24. In the context of measuring the impact of broadband connectivity upon your field research, can
you think of other things you would like to communicate that I should have asked you about? Or, do
you have any comments that you would like to add for my study?
Once the network connectivity is in place, I will be contacting you for a post-test evaluation. I really
appreciate your time. Thank you.
Diffusion Along the Borderlands: p. 35
APPENDIX B
POST-TEST SURVEY QUESTIONS
Diffusion Along the Borderlands: p. 36
TELEPHONE QUESTIONNAIRE SCRIPT
< > indicates note to interviewer only - not to be read aloud to subject.
<Enter phone number as ID variable.>
<In the case of missing data, indicate 9 in the applicable edge coding section.>
Hi. I’m a graduate student and am following up with the additional survey that I mentioned to you
regarding an NSF-funded research project that is providing Santa Margarita Ecological Reserve and
the Sky Oaks Field Station with high-speed network connectivity. I’m calling to ask you a few more
questions about the impact of the network upon your research and your teaching, and which future
networking schemes could best benefit your work. As you know, the Santa Margarita Ecological
Reserve has been connected to a high-speed network called HPWREN, which is funded by the
National Science Foundation. The speed of the network is around 45 Megabits per second and allows
researchers like you to transmit large amounts of real-time information from the field station to the
campus database where it can be readily accessed by researchers and students through a website. An
in-field station network of automated data acquisition towers continue to be developed; these towers
are intended to assist researchers like you with data collection methods. Just like the last time we
talked, I have a few questions about the impact of this network connectivity upon your research.
1. First of all, did you receive the packet of information that I sent you via the mail last week?
a. Yes (2)
b. No (1)
Okay, now, here are a few more questions that I have for you. They will seem familiar, because I
asked you most of these questions just a couple of months ago. If I were putting words into your
mouth about the following statements, would you strongly agree, agree, have no opinion, disagree, or
strongly disagree....
2. HPWREN will help me improve my research endeavors by providing me with better access to realtime data than my current system. Do you...
a. Strongly agree (5)
b. Agree (4)
c. Have no opinion (3)
d. Disagree, or (2)
e. Strongly disagree (1)
...with that statement?
3. HPWREN will help me improve my teaching efforts by providing me with improved access to
real-time data in the classroom setting.
a. Strongly agree (5)
b. Agree (4)
c. Have no opinion (3)
d. Disagree, or (2)
e. Strongly disagree (1)
4. HPWREN will allow me to disseminate research information to colleagues and applicable publics
in a more efficient manner.
a. Strongly agree (5)
b. Agree (4)
c. Have no opinion (3)
d. Disagree, or (2)
e. Strongly disagree (1)
Diffusion Along the Borderlands: p. 37
5. All things considered, HPWREN benefits outweigh the costs.
a. Strongly agree (5)
b. Agree (4)
c. Have no opinion (3)
d. Disagree, or (2)
e. Strongly disagree (1)
6. Overall, HPWREN has more advantages than disadvantages.
a. Strongly agree (5)
b. Agree (4)
c. Have no opinion (3)
d. Disagree, or (2)
e. Strongly disagree (1)
7. HPWREN would be very compatible with my current technology.
a. Strongly agree (5)
b. Agree (4)
c. Have no opinion (3)
d. Disagree, or (2)
e. Strongly disagree (1)
8. I will need little specialized training to use HPWREN technology.
a. Strongly agree (5)
b. Agree (4)
c. Have no opinion (3)
d. Disagree, or (2)
e. Strongly disagree (1)
9. HPWREN meets several technological needs of my research project.
a. Strongly agree (5)
b. Agree (4)
c. Have no opinion (3)
d. Disagree, or (2)
e. Strongly disagree (1)
10. I will be able to use HPWREN for research purposes, without changing the structure of my field
data collection procedures.
a. Strongly agree (5)
b. Agree (4)
c. Have no opinion (3)
d. Disagree, or (2)
e. Strongly disagree (1)
11. I will be able to use HPWREN for teaching purposes, without significantly changing the structure
of my course curricula.
a. Strongly agree (5)
b. Agree (4)
c. Have no opinion (3)
d. Disagree, or (2)
e. Strongly disagree (1)
Diffusion Along the Borderlands: p. 38
12. I have questions about the basic concepts of HPWREN and how real-time field data collection
and transmission works.
a. Strongly agree (5)
b. Agree (4)
c. Have no opinion (3)
d. Disagree, or (2)
e. Strongly disagree (1)
13. HPWREN will be somewhat difficult for my staff and me to use for data collection and
transmission.
a. Strongly agree (5)
b. Agree (4)
c. Have no opinion (3)
d. Disagree, or (2)
e. Strongly disagree (1)
14. I will have to acquire the help of a technical person to help me with the use of HPWREN.
a. Strongly agree (5)
b. Agree (4)
c. Have no opinion (3)
d. Disagree, or (2)
e. Strongly disagree (1)
15. When I have a problem with a technological device, I prefer person-to-person technical
assistance.
a. Strongly agree (5)
b. Agree (4)
c. Have no opinion (3)
d. Disagree, or (2)
e. Strongly disagree (1)
16. I am not very knowledgeable about technology and do not currently feel comfortable using
HPWREN.
a. Strongly agree (5)
b. Agree (4)
c. Have no opinion (3)
d. Disagree, or (2)
e. Strongly disagree (1)
17. The information that I have received and heard about HPWREN from colleagues within my
workplace has been favorable.
a. Strongly agree (5)
b. Agree (4)
c. Have no opinion (3)
d. Disagree, or (2)
e. Strongly disagree (1)
18. The information that I have received about HPWREN from acquaintances outside of my
workplace has been favorable.
a. Strongly agree (5)
b. Agree (4)
Diffusion Along the Borderlands: p. 39
c. Have no opinion (3)
d. Disagree, or (2)
e. Strongly disagree (1)
19. The information that I have received and heard about HPWREN from print media
(e.g., newspapers, magazines) has been favorable.
a. Strongly agree (5)
b. Agree (4)
c. Have no opinion (3)
d. Disagree, or (2)
e. Strongly disagree (1)
20. The information that I have received and heard about HPWREN from broadcast media
(e.g., television, radio) has been favorable.
a. Strongly agree (5)
b. Agree (4)
c. Have no opinion (3)
d. Disagree, or (2)
e. Strongly disagree (1)
21. The information that I have received and heard about HPWREN from electronic media
(e.g., websites, electronic newsletters) has been favorable.
a. Strongly agree (5)
b. Agree (4)
c. Have no opinion (3)
d. Disagree, or (2)
e. Strongly disagree (1)
22. Are you currently using HPWREN?
a. Yes (2)
b. No (1)
23. Will you use HPWREN in your future work?
a. Yes - Definitely (5)
b. Yes - Probably - (4)
c. Unsure (3)
d. No - Probably (2)
e. No - Definitely (1)
24. I am likely to use HPWREN in my research in the coming year.
a. Strongly agree (5)
b. Agree (4)
c. Have no opinion (3)
d. Disagree (2)
e. Strongly disagree (1)
25. I am likely to use HPWREN in my teaching in the coming year.
a. Strongly agree (5)
b. Agree (4)
c. Have no opinion (3)
Diffusion Along the Borderlands: p. 40
d. Disagree (2)
e. Strongly disagree (1)
26. I am likely to use HPWREN in some way in my work in the coming year.
a. Strongly agree (5)
b. Agree (4)
c. Have no opinion (3)
d. Disagree (2)
e. Strongly disagree (1)
Okay, I just have one final question for you...
27. In the context of measuring the impact of broadband connectivity upon your field research, can
you think of other things you would like to communicate that I should have asked you about? Or, do
you have any comments that you would like to add for my study?