American Communication = Journal

A publication of the American Communication=20 Association

Vol 9, Issue 4, = Winter=20 2007

Digg 3DDelicious=20 Del.icio.us 3DReddit=20 reddit =

Diffusion of the Concept that =E2=80=9Cthe Internet is Good" via = Television: How =E2=80=9CCNET Tech Briefs" Helped Shape American = Views about=20 the Internet

Philip J. Auter

Abstract

Diffusion Theory suggests that concepts such as =E2=80=9Cthe = Internet is good"=20 are diffused through society by opinion leaders, including local TV news = outlets. This study takes a look at an important time in the early = adoption of the Internet by U.S. society in general. In the 1990s, = the=20 Internet was used heavily by government and universities, but not as = much by the=20 public at large. Sixty-eight weeks of content analysis by students = in a=20 communication technology course revealed that the overwhelming = percentage of=20 stories was perceived as either positive (55.3%) or neutral = (31.7%). One=20 way analyses of variance revealed that stories with a negative tone = featured=20 significantly more people overall than positively oriented stories = =E2=80=93 F(3,334) =3D=20 3.568, p =3D .014. This appears to be a clear representation of = programming=20 designed to diffuse a concept that =E2=80=9Cthe Internet is good" to the = general public=20 and may have been one of the key elements to advancing Internet = use.

Keywords

Communication, CNET Tech Brief, diffusion of innovation, internet,=20 technology, television, TV, World Wide Web

Brief history of the Internet

Today, the Internet and the cyber economy in the United States have = risen to=20 meteoric heights, blazing through the much lamented but essentially = inoffensive=20 Millennium glitch, and ultimately the much more cataclysmic =E2=80=9CDot = Com Crash"=20 (Zakon). In just a few short years this revolutionary concept has = grown to=20 a third generation wireless web.

The history of the Internet and World Wide Web dates back to Cold War = tensions between the United States and the former Union of Soviet = Socialist=20 Republics when newly formed U. S. Advanced Research Projects = Agency (ARPA) worked with the RAND Corporation to create a = successful way to=20 communicate. Concepts like the decentralized network, which could = work=20 even if several nodes were down allowed computers to be linked by = telephone in=20 ways that were once unthinkable (Gaffin).=20 =20

In 1969 only connected four universities, UCLA, Stanford, University of = California at San=20 Bernadino, and University of Utah, = were=20 connected (Zakon). The Internet was born in 1983, disseminating = rapidly=20 through the early adopters of governmental and academic outlets. = The=20 National Science Foundation created NSFNET enabling linkage = of=20 mid-level nets that, in turn, connected universities, LANs, etc. Also in = 1986,=20 the first freenet =E2=80=94 a network designed to provide an Internet = link to=20 individuals with limited access to traditional avenues=E2=80=94was = formed in Cleveland,=20 Ohio and administered by Case Western University (Eddings).

=20

In 1989, Tim Berners-Lee created the World Wide Web while working at = CERN, the European Particle Physics = Laboratory=20 (Berners-Lee). The tool evolved Web pages, complete with = hypertext, into=20 rich, textured multimedia documents in the mid-1990s. = Mini-applications=20 such as hit-counters put power in web designer=E2=80=99s hands. Advanced = multimedia such=20 as Image maps (pictures that include several embedded hyper links), and = streamed=20 audio and video plug-ins, like Macromedia=E2=80=99= s Shockwave=20 Flash, now enhance the graphical interface with sound and=20 motion.

&n= bsp;=20

With the introduction of the graphically oriented Web, the growing = number of=20 commercial on-line services, and the dissemination of the net to wider = areas of=20 society, the definition of the Internet changed dramatically making=20 subscriptions and ads commonplace, shouldering the medium=E2=80=99s = funding=20 (Vincent). Commercialization of the Net (1990s) allowed for an = exponential=20 growth in for-profit and subsidized sites, but the Internet could not be = a=20 self-serving channel through which messages could be sent to attract = late=20 adopters because potential users often didn=E2=80=99t own computers or = subscribe to the=20 web. The opportunity arose for exposure through television.

The story of CNET

CNET = is a=20 non-typical combination of old and emerging media that operates a series = of=20 interwoven websites and also produces and syndicates a number of=20 technology-related TV shows, including the 30-minute TV Dot = Com, and=20 CNET Tech Briefs, a news package (=E2=80=9CCNET, Inc. = announces"; Serwer;=20 Zakon). The common theme in most CNET ventures is that they rate = and=20 review technology products and services (Bing). Although the = business=20 struggled financially for many years, it now appears to be extremely = profitable=20 (Serwer). The CNET family of programs and websites continues to = grow=20 including ventures into shopping sites such as Computer Shopper, and mySimon as well as XM Satellite = Radio (XM Satellite Radio).

Merrill Lynch consultant, Halsey Minor came up with the concept of a = business=20 that consisted of a cable TV channel and online content about technology = in=20 1992. Minor and partner Shelby Bonnie struggled to sell this = concept in an=20 essentially net-less world. It wasn=E2=80=99t until 1994 that = Minor was able to=20 persuade Paul Allen to give the company $5 million on the condition that = he was=20 able to sign a distribution contract for the TV show. CNET signed a deal with USA Networks that year to air = TV Dot=20 Com (=E2=80=9CCNET, Inc. announces," Serwer).

In June 1995, CNET ventured into = the=20 Internet with their CNET.com website and in 1996 the stock went = public. As=20 the Internet and its users continued to change, so did the focus of the=20 company. Although the website was originally designed to promote = TV=20 ventures, it appeared that the television programming was geared to = drive more=20 people to the CNET website (Serwer). Successive years saw the = development of a=20 series of interlinked websites including Download.com, News.com and the = Snap.com=20 portal. All totaled, CNET operates over 11 sites offering computer = information, news, shopping and search opportunities. CNET has = completed=20 agreements with a number of Internet service providers to make their = websites,=20 such as Download.com, the = ISPs=E2=80=99 default=20 links to software and information, thus increasing click-through = advertisements=20 (Bing, =E2=80=9CCNET, Inc. announces," Serwer).

But in order for CNET to = maintain its=20 strength in the area of online technology informer, analysts have = suggested that=20 it needed to increase =E2=80=9Cbrand awareness." At the time, the = CNET name may=20 not have been known by a large enough user-group, and competitors in = this arena=20 certainly existed =E2=80=94 including ZDNet (now=20 owned by CNET), PC World Online, = and Tech Web (Bing). Today CNET = produces a=20 number of technology TV shows including: The Web, The New=20 Edge, TV Dot Com, CNET Central, and the CNET = Tech=20 Briefs =E2=80=94 all focused on how new technology, especially the = Internet, affect=20 our day-to-day lives. The syndicated show TV Dot Com = reached over=20 1.2 million viewers in 1996, airing on 120 local television stations = (=E2=80=9CCNET,=20 Inc. announces," 1996). CNET Tech Briefs were provided = free to=20 stations that aired TV Dot Com. It would seem that these = programs, with=20 the apparent goal of motivating viewers to visit CNET=E2=80=99s = websites, would=20 certainly also aid in the process of diffusing the concept that = =E2=80=9Cthe Internet is=20 good."

Diffusion Theory

Diffusion theories, relatively young and not yet unified, attempt to = model=20 how new technologies and ideals move from conception, to development, = early=20 adoption, and finally widespread acceptance (Rogers). Many = perspectives,=20 hypotheses and theories reside under the =E2=80=9CDiffusion of = Innovation" umbrella=20 (Surry). All of these research tracks stem from initial work = performed by=20 Ryan and Gross in rural sociology.

Perhaps the seminal work in diffusion research has been done by = Everett=20 Rogers. His 1960 text on the subject is now in its fourth = edition. =20 In it, Rogers synthesizes findings from well over 3000 diffusion studies = to=20 generate a theoretical framework for diffusion of innovations. His = framework has four main components: the innovation itself, = communication=20 channels for disseminating the innovation=E2=80=99s message, time = involved for adoption=20 of the innovation, and the social system within which this all = occurs.

Surry and other writers have summarized the four most widely utilized = theories of diffusion that are discussed in great detail by = Rogers. =20 Individual Innovativeness Theory groups individuals into five categories = ranging=20 from innovators to laggards regarding innovation adoption. Earlier = stage=20 adopters become additions to the group of opinion leaders about a topic = =E2=80=93=20 additional voices to be heard in the promotion of an innovation by late = adopters=20 and laggards. Rate of Adoption Theory predicts that innovations = will be=20 adopted slowly until they hit a point of huge growth in = acceptance. If=20 this critical mass is not met, the adoption may not hold. The = Innovation=20 Decision Process Theory (Rogers) tracks the stages that a potential = adopter must=20 go through in order to adopt an innovation. Anyone who buys into = an=20 innovation must first gain knowledge of the innovation, then be = persuaded, make=20 a decision to adopt, implement the decision and finally confirm their = feelings=20 about the adoption of the innovation. Finally, Perceived = Attributes Theory=20 (Rogers) states that potential adopters look at trialability, = observability,=20 relative advantage, complexity, and compatibility to determine = innovation=20 adoption.

All of these theories of diffusion look at the adoption process from = slightly=20 different angles, but in each case, perceptions of the adopters (as well = as=20 their persuasiveness to others) are key to the development and growth of = the=20 adoption process. Rogers points out a paradox: innovation = acceptance can=20 remain concentrated in the hands of a small, wealthy, educated group = =E2=80=93 not=20 diffusing through society at all. This can be due to a number of = factors=20 including lack of economies of scale keeping the cost of adoption high, = limited=20 interest due to little perceived value of the innovation, and even = limited=20 awareness of the innovation=E2=80=99s existence.

Given these factors, it=E2=80=99s not hard to see how a new medium, = such as the=20 Internet once was, might have had a hard time breaking from the confines = of a=20 commercial-free, academic-government early adopter stage into early/late = majority acceptance. Recent trends have made it difficult for the=20 web-enthusiast to adopt new hardware and/or software upgrade because = they are=20 beyond the means of the users=E2=80=99 present computer. Like the = adopters of the=20 Net itself earlier in the decade, users face difficult decisions = regarding=20 adoption of innovations due to their limited ability to =E2=80=9Ctry = before you=20 buy."

In 2004, Li utilized Rogers=E2=80=99 diffusion approach to compare = differences in=20 adoption of cable TV shopping vs. Internet-based shopping in = Taiwan. In a=20 survey of over 1200 respondents, Li found that use of Internet-based = shopping=20 followed Rogers=E2=80=99 diffusion model. People considered to be = =E2=80=9Cinnovators" by=20 Rogers=E2=80=99 model were more likely to shop via the Internet than = were=20 =E2=80=9Claggards." =E2=80=9CPopularization" is a key effect that = Paul studied in the=20 diffusion of scientific concepts. These translations of science = into lay=20 terms are familiar to most TV news viewers in the form of science and = technology=20 segments regularly covered on cable and satellite news services such as = CNN and BBC. At every stage = popularization,=20 utilizing varying techniques, the idea is to =E2=80=9Cget the word = out."

In reviewing three case studies in the use of =E2=80=9Cindigenous = media" in the=20 innovation process in Africa and India, Henrich has pointed out that = utilization=20 of a familiar medium to spread a new message can increase awareness and = the=20 likelihood of acceptance. It is important to utilize regular = participants=20 in the familiar medium as opinion leaders regarding the innovative=20 concepts. Finally, it=E2=80=99s important to be aware of the = audience=E2=80=99s culture,=20 and needs and desires in order to effectively craft a message that will = persuade=20 them to adopt an innovation.

How could the innovators and early adopters reach the majority with = their=20 message that =E2=80=9Cthe Internet is good"? The answer suggested = by Henrich is=20 media and their messages =E2=80=93 spread by television. These = would be used, as=20 noted by Paul to popularize scientific concepts to a larger = broader-based=20 audience. The purpose of this study was to look at one 5-day/week=20 syndicated news feature on new technology, the CNET Tech Brief. Based = on a=20 content analysis of the program scripts, the researcher attempted to = discover=20 whether or not this type of programming might help diffuse the concept = that =E2=80=9Cthe=20 Internet is good."

Research Questions

In order to speculate on the potential diffusive value of the CNET Tech Brief stories, it = is=20 important to deconstruct them, reviewing their content to determine the=20 predominant types of stories featured as well as the kinds of people who = discuss=20 the product or service highlighted. These content analysis issues = lead to=20 the first series of research questions:

What is the predominant direction or tone of these technology = features?=20
What is the predominant topic of these technology features?=20
Do the segments primarily feature products or services that are=20 speculative (on the drawing board), new, upgrades to what is already=20 available, or do they simply make viewers aware of existing products = and=20 services?=20
How many people and what type are predominantly featured in these = stories?=20
What is the average breakdown of interviewees in these features? = Diffusion=20 research suggests that the audience may accept message points of view = if the=20 sender is perceived as credible. The CNET packages, taken as a whole, may = be=20 considered the messenger, but they speak with a variety of = voices. =20 Comparing the overall tone of the story =E2=80=93 as well as the = number and types of=20 voices in these messages =E2=80=93 to the topic featured, is the basis = of the final=20 research questions:=20
What is the relationship between the tone of the story and the = topic=20 featured?=20
What is the relationship between the tone of the story and the = =E2=80=9Cnewness"=20 of the product or service featured?=20
What is the relationship between the tone of the story and the = types of=20 people interviewed?=20
What is the relationship between the =E2=80=9Cnewness" of the = product or service=20 featured and the type of people interviewed?

Method

The Tech Brief Scripts

CNET Tech Briefs are = short=20 feature packages usually running about two minutes long and dealing with = technology topics. They have been produced since 1996 and are = often=20 offered for free to TV stations that purchase TV Dot Com, a = 30-minute=20 technology show created by the same producers. Most Tech = Briefs=20 are repackaged segments from TV Dot Com. From 1996 to 1997, = CNET=20 shipped stations three Tech Briefs per week and in 1997, they = began=20 sending five. Stations typically run the packages as a daily = technology=20 segment in one of their local newscasts.

The Database

The database for this study consisted of 68 weeks of Tech = Brief=20 scripts for the period from January 6, 1997 through July 8, 1998 = =E2=80=93 approximately=20 a year and a half. (Eight weeks of scripts were not available = =E2=80=94 the weeks=20 beginning on: 12/30/96, 4/7/97, 4/13/97, 12/22/97, 1/5/98, 2/2/98, = 2/9/98, and=20 2/16/98.) Scripts were obtained from a television station that = airs CNET Tech Briefs. = Permission was=20 also obtained from CNET to utilize this database for this study. Each = week=E2=80=99s=20 material consisted of five scripts for a total of 339 scripted Tech=20 Brief segments. Scripted material included a suggested local = anchor=20 introduction and story tag, scripted reporter copy, transcription of = dialog from=20 interview subjects, suggested informational graphics and total run time = of the=20 package.

Methodology

A codebook was developed so that coders could look at each script on = four=20 separate dimensions: direction/tone of story, topic of = story,=20 type of technology highlighted, and person(s) = interviewed=20 about the topic (See Appendix A and B). Coders read the entire = story about=20 a technology or service and then decided if the tone of the presentation = was=20 =E2=80=9Cpositive," =E2=80=9Cnegative," =E2=80=9Cneutral," or = =E2=80=9Cmixed" and whether the primary topic=20 highlighted was =E2=80=9Chardware," =E2=80=9Csoftware," = =E2=80=9CInternet," or something else. =20 They also determined if the technology/service discussed was = =E2=80=9Cnew," =E2=80=9Cexisting,"=20 =E2=80=9Cupgraded," or =E2=80=9Cspeculative." Finally, coders = checked how many times=20 =E2=80=9Cinternal experts," =E2=80=9Cexternal experts," =E2=80=9Cusers," = and =E2=80=9Cnon-users" were=20 highlighted in each script. (see Appendix A.)

Eight students in an undergraduate communication technology class = analyzed 55=20 to 60 days (11 - 12 weeks) of scripts each. One coder = double-checked 55=20 days (11 weeks =E2=80=94 from the week beginning on 03/16/98 through the = week beginning=20 068/98) worth of scripts in order for intercoder reliability analysis to = be=20 performed. Data was scanned for analysis in SPSS PC.

Results

Intercoder Reliability Checks

The size of the subset of data dual-coded (n =3D 110) did = not allow=20 for a valid measure of the association of responses between the two = coders on=20 three of the variables: =E2=80=9Cdirection/tone of story," = =E2=80=9Ctopic of the story," and=20 =E2=80=9Ctype of technology (newness) highlighted." It is also = important to note=20 that one of the stories reviewed by =E2=80=9CCoder A" was missing these = values. =20 Frequency analyses of the two coders=E2=80=99 responses offers some = basic=20 interpretations of the reliability of the findings, however.

Direction/Tone of Story

Both Coder A and B suggested that the overwhelming majority of = stories were=20 positive (A: n =3D 33; 61.1%; B: n =3D 41, = 74.5%) and that=20 there were almost no negative stories (A: n =3D 4; 7.4%; = B:=20 n =3D 3, 5.5%). While this lends some support to the = notion that=20 tone has been reliably interpreted across coders, it is very important = to note=20 that there were discrepancies in the number of neutral and mixed = stories, as=20 well as the total number of stories considered positive (See Figure = 1).

Figure 1: Intercoder Comparison of Story Tone

=20

Topic of the Story

Coders seemed to have more trouble with this item =E2=80=93 perhaps = because hardware,=20 software and the Internet are so interrelated. While both Coders A = and Be=20 felt that the majority of the stories dealt with the Internet (A:=20 n =3D 28; 51.9%; B: n =3D 33, 60.0%), they = deviated on their=20 interpretation of what the other stories were about (See Figure 2).

Figure 2: Intercoder Comparison of Story Topic

=20

Type of Technology Highlighted

Although the coders had some difficulty discerning the topic of some = Tech=20 Brief segments, they appeared to be strongly in agreement about the = =E2=80=9Cnewness" of=20 the product or service being featured. Both coders considered the=20 overwhelming number of stories to be about existing items (A: = n =3D=20 43; 79.6%; B: n =3D 35, 63.6%), and were also in agreement = about the=20 number of stories that fell into the other categories (See Figure = 3).

Figure 3: Intercoder Comparison of Technology = Highlighted

=20

Number and Types of People Featured

Although the sample was too small for cross-tabulation, the = researcher was=20 able to perform T-test analyses to determine whether or not there were = any=20 significant differences in Coder A and B=E2=80=99s determination of the = amount of=20 various types of people featured in the average Tech Brief=20 segment. Independent analyses suggest that there was no = significant=20 difference in the number of types of people that Coders A and B saw in = the=20 stories (See Table 1).

Table 1: Intercoder Comparison of Number and Types of People=20 Featured*

=20

*(Total n =3D 110. Coder A: n =3D = 55. Coder=20 B: n =3D 55)

Basic Content

In order to breakdown the structure of the Tech Brief = segments, a=20 series of descriptive analyses were performed. Before cases with missing = values=20 were excluded, the sample consisted of 339 stories.

RQ 1: The first research question addressed the tone = of the=20 Tech Brief segments. Of the 338 valid cases, frequency analysis = revealed that the overwhelming percentage was perceived as positive = (n=20 =3D 187, 55.3%). The next largest category of stories (n = =3D 107,=20 31.7%) was neutral and less than 10% of the stories were interpreted by = the=20 coders as negatively slanted (n =3D 31, 9.2%) or as a mixture = of positive=20 and negative perspectives (n =3D 13, 3.8%).

RQ 2: The second task was to determine the breakdown = of the=20 types of stories covered. Of the 337 valid cases, frequency = analysis=20 showed the majority of the stories (n =3D 162, 48%) were = determined by=20 the coders to be about the Internet. The second largest grouping = of=20 stories was =E2=80=9Cother" =E2=80=93 the unidentifiable (n =3D = 103, 30.6%). Only a=20 small number of stories were determined by the coders to be about = computer=20 hardware (n =3D 34, 10.1%) or software (n =3D 36, = 10.7%).

RQ 3: Concerning =E2=80=9Cnewness," frequency analysis = revealed that=20 the study participants coded 62% (n =3D 209) of the stories as = existing=20 technologies or services. The next most common type of story was = about new=20 technologies (n =3D 74, 22.0%). Upgrades (n =3D = 35, 10.4%)=20 and speculative (n =3D 19, 5.6%) products and services were a = negligible=20 percentage of the total sample.

RQ 4: About half of the Tech Brief segments = featured=20 either two (n =3D 86, 25.4%) or three (n =3D 88, = 26.0%) people=20 other than the reporter. Stories that featured no people other than the = reporter=20 were less common (n =3D 60, 17.7%). In the minority were = one=20 (n =3D 54, 15.9%) or four (n =3D 35, 10.3%) people in = addition to=20 the reporter, although some featured five or more people (n =3D = 16,=20 4.7%). The mean number of people featured was 2.12.

RQ 5 and 6: Statistics included the overall quantity = of people=20 and a breakdown by type. There was an average of 1.11 internal = experts in=20 each story, .51 external experts, .43 product/service users, and less = than zero=20 non-users (n =3D 339). Internal Experts received the = lion=E2=80=99s share=20 of airtime (52.4%) while external experts (24.1%) and users (20.8%) = split=20 the rest. Non-users, on average, received almost no airtime.

Relationships

RQ 7 and 8: Research questions seven and eight dealt with = the=20 relationship between the tone of the story and the topic of the segment = =E2=80=93=20 including its newness. While tone and segment topic was = un-related,=20 Chi-squared results suggest that tone and type (newness) are related, = but a=20 measure of association suggests that this relationship is very weak at = best=20 (chi-square =3D 27.48, p =3D .001; lambda =3D .01, p =3D .438).

RQ 9: Another important consideration was how the tone = of the=20 story was related to the number and types of people featured. = One-way=20 analysis of variance suggested significant relationships between the = tone of a=20 story and the total number of people interviewed as well as the number = of=20 external experts (those unaffiliated with the company that profits from = that=20 product or service) and the number of non-users featured (See Table = 2.) =20 Negative stories (n =3D 31, M =3D 2.87) featured more = individuals=20 interviewed overall than positive stories (n =3D 187, M =3D = 1.979). They also=20 featured significantly more external experts (n =3D 31, = M =3D=20 1.097) than positive stories (n =3D 187, M =3D = .428). =20 Additionally, negative stories featured more non-users (n =3D = 31,=20 M =3D .2903) than did positive stories (n =3D 187, = M =3D=20 < 0.00).

Table 2: Oneway ANOVAs of Tone of Story by Types of People=20 Interviewed

=20

Table 3: Oneway ANOVAs of =E2=80=9CNewness" of Story by Types of = People=20 Interviewed

=20

Discussion

On the whole, CNET Tech = Briefs=20 from 1997-1998 appear to have been informative, predominantly positively = slanted=20 stories about the Internet and World Wide Web. Whether designed to = intentionally or not, they also appear to have been excellent opinion = leader=20 messages that may have helped the majority to see the value in adopting = the=20 innovations of the Internet. Stories, especially the = positively-oriented=20 ones, were filled with internal experts and users =E2=80=93 early = adopters of various=20 technologies that became technology opinion leaders in their praise for = the=20 innovation. The message in its entirety allowed viewers to become = more=20 knowledgeable about the technology, be persuaded to buy into the = innovation, and=20 even to see users =E2=80=93 who had already implemented the decision to = adopt =E2=80=93 confirm=20 their pro-innovation feelings. Finally, as structure, the Tech = Brief stories allowed viewers to discern the trialability (and = parasocially=20 try the technology by viewing the others experiences); observe the = innovation;=20 and determine its advantages, complexity and compatibility.

Viewers who watched the CNET = Tech=20 Briefs within their local newscast five days a week saw repeated = messages=20 featuring various specific innovations, but as a whole painting a = picture of a=20 =E2=80=9Cgood" Internet =E2=80=93 an innovation that was relatively easy = and infinitely useful=20 to the viewer. While each individual segment might carry some = weight with=20 the viewers, taken as a whole, the message could have been a very strong = suggestion to the majority of Americans that adoption of this technology = would=20 be beneficial to their lives. And viewing these segments within = the=20 context of a local newscast, hosted by anchors that the viewers = =E2=80=9Cknow" and=20 trust, would only enhance the effect.

CNET Tech Briefs appear = to have=20 been a very effective way to drive traffic to their own website = =E2=80=93 a forerunner=20 to how almost all TV news and entertainment programs promote their own=20 sites. However, in its attempt to provide entertaining, = informative=20 content regarding the Internet and other technologies, it may have had a = more=20 powerful effect. CNET Tech Briefs =E2=80=93 and other TV = programs like it=20 =E2=80=93 could very well have been a primary catalyst in helping = Internet adoption=20 reach critical mass and thus usher in a communication revolution.

Works Cited

Bing, Michelle. =E2=80=9CCNET =E2=80=94=20 Computer News You Can Use," Online: The Leading Magazine for = Information=20 Professionals. September 1998: 90.

Berners-Lee, Tim. =E2=80=9CBio," World = Wide Web=20 Consortium, Feb. 19, 2005 Cambridge, MA, USA. 19 July = 2004.

Cailliau, Robert. =E2=80=9CA Little = History." World Wide=20 Web Consortium. 1995. June 17, 2004

=E2=80=9CCNET, Inc. Announces=20 Second Quarter Financial Results; Company Shows 72% Increase in Revenue = Over=20 Second Quarter." PR Newswire. 17 October 1996. June 17, = 2004

Eddings, John. How the Internet = Works. 2nd=20 ed. Emeryville, CA: Ziff-Davis, 1996.

Gaffin, Adam. =E2=80=9CEFF=E2=80=99s=20 Guide to the Internet, V. 3.21.=E2=80=9D EFF. 1999. 19 July = 2004.

Henrich, Daniel. =E2=80=9COramedia and = Diffusion of=20 Innovation: A Review of Three Case Studies in Participatory Media=20 Development." PHD Colliquilum. Regent University. Virginia Beach, = VA. 13=20 June 1996.

Li, ShuChu S. =E2=80=9CExamining = the Factors=20 that Influence the Intentions to Adopt Internet Shopping and Cable = Television=20 Shopping in Taiwan." New Media & Society 6.2 (2004):=20 173-193.

Paul, Danette. =E2=80=9CSpreading = Chaos: The=20 Role of Popularizations in the Diffusion of Scientific Ideas." = Written=20 Communication 21.1 (2004): 32-68.

Rogers, Everett M. Diffusion= =20 of Innovations, 4th ed. New York: The Free Press, = 1995.

Ryan, Bryce, and Neil C. Gross. =E2=80=9CThe=20 Diffusion of Hybrid Seed Corn in Two Iowa Communities." = Rural=20 Sociology, 8 (1943): 15-24.

Serwer, Andy. =E2=80=9CCNET:=20 Revenge of the Preppies." Fortune 21 June 1999: = 112-114.

Surry, Daniel W. =E2=80=9CDiffusion=20 Theory and Instructional Technology.=E2=80=9D Annual meeting of the = Association for=20 Educational Communications and Technology Conference. Albuquerque, NM. = February=20 1997.

Vincent,=20 Christopher R. (1995) =E2=80=9CA Cultural Transition:The = Commercialization of the=20 Internet." Swissnet. 25 October 1995, Massachusetts Institute of Technology, = Boston.=20 May 7, 2004 http://swissnet.ai.mit.edu/6095/student-papers/fall95-p= apers/vincent-culture.html

XM = Satellite Radio.=20 =E2=80=9CXM=20 Satellite Radio Dedicates Channel to Technology News Leader = CNET.=E2=80=9D XM=20 Satellite Radio. June 21 1999. 19 July 2004.

Zakon, Robert H. =E2=80=9CHobbes=E2=80=99 = Internet Timeline=20 v8.0=E2=80=9D Zakon.org. 1 Jan = 2005. 19 July=20 2004.

Appendix A: Script Coding Instruction Sheet

Please follow these instructions carefully before filling in your=20 answers. Each script requires an answer=20 sheet. Areas to be filled in are numbered 1-15. = Instructions=20 for each numbered segment are as follows:

1. Script #. Fill in the number of the script in the space = provided,=20 located in the top left of your answer sheet.

2. Coder #. Fill in the number you have been assigned in the = space=20 provided, located in the top right of your answer sheet.

3. Direction/Tone. Read the entire = script=20 carefully. Decide whether the overall message is positive, = negative,=20 neutral or a mix. Mark your answer in the space provided, located = in the=20 top right of your answer sheet.

4. Topic. Indicate whether the script deals with technology that = can be=20 classified primarily as hardware, software or Internet = applications. For=20 technology that does not fit any of these categories, mark = =E2=80=9Cother."

5. Type. Mark the answer that most closely describes the = technology=20 being discussed. Note that there is only = one=20 response to this question.

6 - 15. Person interviewed. For each script, at least one = person=20 is interviewed, usually two or more. Numbers apply to each = response to the=20 interviewer=E2=80=99s questions. For each response, mark the = appropriate=20 information about the person responding, whether it is the same person = or a=20 different person who is being interviewed. =E2=80=9CInternal = expert" means someone=20 who is an expert on the topic and works for or is a representative of = the=20 company or institution developing or marketing the technology. = =E2=80=9CExternal=20 expert" means someone who is an expert on the topic and is not = affiliated with=20 the company or institution developing or marketing the technology.

When you have finished marking your answers, turn the answer sheet = and script=20 together face down, aside from your remaining scripts. Use a new = answer=20 sheet for each script, then place the finished questionnaire, along with = its=20 accompanying script, on top of the last answer sheet and script. = Continue=20 until you have finished all the scripts assigned to you, then give the = stack of=20 completed information to your instructor. If you have questions, = please=20 raise your hand and an instructor or graduate assistant will help = you. =20 Thank you for your time and effort!

About the Authors

Philip J. Auter is an Associate Professor in the Dept. of = Communication at=20 the University of Louisiana at Lafayette. Correspondence: Dept. of = Communication=20 at the University of Louisiana at Lafayette, Burke-Hawthorne 101, P.O. = Box=20 43650, Lafayette, LA 70504. Email: http://www.acjournal.org/holdings/vol9/winter/articles/auter@l= ouisiana.edu=20 Web: http://www.auter.tv/.