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Subject: AEJ 96 NeuendoK CTM Understanding adopters of audio information services
From: Elliott Parker <[log in to unmask]>
Reply-To:AEJMC Conference Papers <[log in to unmask]>
Date:Mon, 23 Dec 1996 05:54:19 EST
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          Understanding Adopters of Audio Information Services
 
 
          by
 
 
          Kimberly A. Neuendorf, Ph.D.
 
          David Atkin, Ph.D
 
          Leo W. Jeffres, Ph.D.
 
 
          Department of Communication
          Cleveland State University
          Cleveland, OH  44115
 
          (216) 687-3994
 
 
 
          April 1, 1996
 
 
          Paper submitted for consideration for presentation to the
Communication Technology and Policy Division of the Association for Education in
Journalism and Mass Communication.
 
 
 
 
          Understanding Adopters of Audio Information Services
 
          Abstract
 
 
 
 
          The use of three newer phone-based audio innovations--1-900 numbers,
phone-based information services, and fax--was assessed in a probability survey
of urban respondents.  Support was found for the notion that social indicators
would be less important in the prediction of innovation use than would
attitudinal and communication variables.  Findings were interpreted in light of
diffusion of innovation theory and research on the adoption of new technologies.
 
 
 
 
           Key words: Audiotext, fax, 1-900 numbers, diffusion theory
          Adopters of Audio Services
 
 
             UNDERSTANDING ADOPTERS OF AUDIO INFORMATION SERVICES
              While the telephone is commonly taken for granted as a low
technology, culturally-integrated voice medium, recent advances in telematics
make it ideally situated as an electronic pathway for new information and
entertainment services.  The Telecommunication Act's deregulatory strictures,
including the removal of media crossownership barriers, should facilitate a
sweeping transformation in the industry that can enable telephony to emerge as a
dominant player in the electronic media environment (H.R. 1555, 1996).  In
addition, the phone industry continues to offer "intelligent" services (e.g.,
automated office systems) that blur traditional distinctions between
conventional communication media and their high-tech counterparts.
                Thus, the telephone--long associated with person-to-person
communication--now can operate in a manner more characteristic of mass media, as
a sender of mediated information and entertainment.  For that reason, it's
important to consider audience uses for a wide range of information services,
including those delivered via telephone.  Since the phone companies have yet to
introduce their own Internet services, and only about 10% of Americans subscribe
to computer-based online services, we consider audience users of more
conventional telephone adjuncts.  In particular, this paper profiles audience
users and utilities for three widespread applications delivered via
telephone--audiotext, 1-900 services, and fax machines.
          Background
              Mass media phone services rely heavily on "media stimulated" users
for their audiences, since their advertisements or promotions in print and
broadcast media are often the only means potential users have of learning of
them.  LaRose and Atkin (1992, p. 414) provide the following profile of the $1
billion audiotext industry[1]:
               About two-thirds of the content accessed through national
               900 numbers is entertainment, another 15 percent is live
conversation (on
               so-called Group Access Bridging or "GAB lines"), 10 percent is
polling and
               the rest consists of news and information programs, promotions
and
               user-supported customer service lines. . . There are also
audiotext
               services that allow their callers to learn about the activities
of their
               favorite entertainers or to take part in trivia contests, to
confess their
               sins or to hear confessions recorded by others, or to talk to the
undead.
             Other work (Glascock & LaRose, 1992) suggests that
sexually-oriented services remain prominent, having survived a legal challenge
(Sable Communications, Inc. v. FCC, 1989), although industry leader Carlin
Communications filed for bankruptcy during the early 1990s.  As those authors
suggest, the fact that 60% of respondents in a national survey reported using
audiotext suggests that it may be the first widely used interactive mass medium.
              Despite often being categorized alongside audiotext, 1-900 service
is distinctive in that callers can "vote" in media-stimulated plebiscite by
pressing buttons on their keypad.   The three major long distance carriers in
the U.S.--AT&T, MCI and GTE--have all implemented interactive 1-900 services
that allow users to retrieve information, complete transactions or answer
questions by using their touch-tone phones.  The media's increasing use of
unscientific "self-selected listener opinion surveys," or SLOPs, has attracted
heavy criticism from social scientists (Gollin, 1992).  Such surveys are
susceptible to "rigging" by blocks of organized voters, multiple voting, and
demographically skewed respondent pools (Atkin & LaRose, 1994a).[2]
               Although little work focuses exclusively on adoption of fax
services, adoption of that technology has been related to 1-900 use and cable
subscription (Atkin, 1995).  Dobos (1988) also found adoption of fax related to
use of other new telecom-munication technologies in the office (e.g.,
teleconferencing).  Given the interrelationships among these media adoption
patterns, it's useful to examine parallel work with other media.
              Diffusion of innovations research suggests that earlier adopters
of new technology tend to be upscale, as was the case with pioneer videotext
services (Ettema, 1989).[3]  Despite that, adopters of 1-900 phone services were
found to be lower in S.E.S. (Atkin & LaRose, 1994a; LaRose & Atkin, 1992).  This
led the authors to conclude these phone-based services were inexpensive
substitutes for those who are not skilled in accessing other two-way polling
alternatives (e.g., online services).
               Aside from that, demographic differences between users and
nonusers of audiotext and 1-900 polling were few, as adoption was more
powerfully predicted by technology uses.  These findings are consistent with
research on uses of other media approaching the "flat" part of their diffusion
curve--such as cable--given that differences between adopters and nonadopters
level over time (Dutton, Rogers, & Jun, 1987a; Rogers, 1995; Sparkes & Kang,
1986).  Reagan (1987, 1991), for instance, found that adoption of most
telecommunication technologies studied was more powerfully predicted by (1) use
of other such technologies and (2) attitudes toward them.  Based on that work,
we hypothesize that:
          H1:   Adoption of audiotext, 1-900 polls and services, and fax
               service will be more powerfully explained by technology use
variables than
               demographics.
               In comparing these technologies, it is important to consider how
people integrate new information technologies into existing patterns of
behavior.  This may be due to compatibility between innovations and existing
social norms or patterns of behavior (Rogers, 1995).  An alternate approach
suggests that new technologies are most likely to be adopted if they are
functionally similar to existing ones.  In the case of two of the three audio
technologies studied here, the delivery system is not simply "compatible" with
older established technologies, it is the century-old technology of telephony.
And, "adoption" of audiotext and 1-900 services operationally consists of
nothing more than "picking up the phone and dialing"--something virtually all
Americans are accustomed to doing from childhood.
              Given the technology linkages noted above, it's useful to draw
from traditions in diffusion research focusing on user needs met by technology.
Researchers (LaRose & Atkin, 1992) found some support for the proposition that
people are likely to adopt information technologies that are functionally
similar to others that they already use.  In that study, use of local audiotext
information services was related to information technologies--videotext, ATMs
and 800 numbers and telephone answering machines--which all share the function
of providing information on demand to the user.
               Thus, we see a merging of conventional, entertainment-oriented
mass media, including broadcast television, and more information-oriented
point-to-point media, such as telephony.
          This blurring of media definitions has been hastened by recent
governmental actions to relax cross-media ownership and content delivery
restrictions, as well as the continued development of fiber optics and
Integrated Digital Services Networks (ISDNs).  These actions should widen the
field of phone information providers in the years to come.
              As of 1995, 94% of all U.S. households had a telephone and fewer
than 10% of them subscribed to some sort of videotext service (Lewis, 1995).
Thus, while past work (LaRose & Atkin, 1992) documents the importance of
technology compatibility, the question remains as to what types of functional
similarity are important.  As those authors suggest, this may be a function of
compatibility with existing products.  That is, since they involve fairly
"low-tech" applications of a highly familiar telephone medium, audiotext
services are easier to adopt than, say, videotext (which entails the virtual
requirement of computer access).  Clearly, audiotext does not involve the
specialized business-oriented database services associated with the failed
videotext services, where some degree of computer literacy is typically
required.
              Most audiotext studies suggest that technology needs and uses are
far more explanatory of adoption than demographics (e.g., O'Keefe & Sulanowski,
1992).  However, researchers (Atkin, 1995; LaRose & Atkin, 1992) found
inconsistences among technology-based predictors of audiotext, which may stem
from the different user groups associated with each technology.  Those
researchers concluded that the "technology cluster" concept of adoption provides
a better fit than general innovator profiles.  For instance, audiotext appeals
to those interested in using information services primarily for convenience, as
users are also likely to use electronic mail.
              Atkin (1995) found that 1-900 polling and use of fax machines
occupy a similar dimension, as both allow instantaneous responses and access to
information.  The inability of this research to uncover relationships with other
time-saving devices (e.g., speaker-phones, auto-dialers and cellular phones)
mitigates against the convenience dimension noted in diffusion theory (Rogers,
1995).
               Drawing from that work, we seek to explore compatible media uses
across a wider range of conventional media than that studied earlier.  Given
that fax machine usage has been associated with more utilitarian applications,
we hypothesize that it will be related to a different set of media than those
fulfilling primarily entertainment needs.  More formally,
          H2:   Use of audiotext and 1-900 phone services will be related to
               use of functionally similar entertainment media (e.g., TV,
movies) while
               those used for utilitarian purposes (e.g., fax) will be
unrelated.
 
              Focusing on motivational measures, Keller (1990, B1) suggests that
audiotext users display a fear of the unknown in needing to know "what the
future holds."  O'Keefe and Sulanowski (1992) identified several instrumental
and entertainment gratifications associated with the adoption of audiotext.
Drawing from that work, Jeffres and Atkin (1994) argue that researchers need to
shift the focus toward communication variables and away from technological
hardware.  Their own results suggest that attitudinal variables, particularly
those addressing communication needs served by computer technology, are more
explanatory than demographics.  Based on that work, we hypothesize that,
          H3:   Use of audiotext, 1-900 and fax services will be more
               powerfully explained by personal communication needs than media
use or
               demographic variables.
              One promising construct set that may help explain adoption of
technologies that intersect with older hardwares is that of "quality of life."
Simply put, QOL assessments represent people's assessments of well-being
(Andrews, 1980; Andrews & Withey, 1976), and may correspond to generalized
states of pessimism or optimism about "how things are going in one's life."
Recent research (---, 1996) has identified QOL as an important predictor of
media choice.  In summarizing which domains of life contribute most to global
QOL measurements, Campbell (1981) examined people's assessments of the quality
of life available in the larger environment, e.g., neighborhoods, communities,
nations, as well as personal assessments of their family, home, friends, job and
health (Campbell, 1981, p. 159).
              Since these studies have generally ignored the influence of media,
it's useful to see how personal satisfaction measures relate to adoption of new
phone adjuncts.  Given the exploratory nature of this inquiry, we pose the
following research question:
          RQ:   What is the relative influence of demographics, media use,
               communication needs and QOL assessments on people's use of
audiotext, 1-900
               numbers, and fax services?
 
 
                                    METHOD
               Study data are based on a telephone survey involving  a regional
probability sample from a metropolitan area of the Midwest.  It was conducted
during 1993, yielding a sample of 331 respondents using traditional random-digit
dialing techniques and a CATI system.  The survey was presented as a general
poll about current issues, and contained items tapping respondents' opinions on
a wide variety of items.
                The dependent measures of use of audio information services used the
following phrasings:  "In the last month, how many times have you called a 1-900
telephone number?"  ". . . how many times have you called a phone-based
information service (for example, time, weather, sports scores)?"  ". . . how
many times have you sent or received a fax?"  The metric value of each
respondent's answer was retained.
                An assortment of demographics and other social indicators were
tapped:  Gender, race, age, income, level of education, number of people in the
household, number of children in the household, degree of liberalism (vs.
conservatism; on a 5-point scale), and degree of Republicanism (vs. Democratism;
on a 5-point scale).
                Several items assessed respondents' quality-of-life assessments.
First, respondents were asked to "imagine a scale from 0 to 10, with 0 being the
worst place to live and 10 being the best place to live.  On this scale, how
would you rank the ___ area?"  They also were asked to use a 0-10 evaluation
scale (0=not at all confident, 10=highly confident) to rank their confidence in
the local schools, local police, and area media.  Several additional items
tapped respondents' levels of optimism or pessimism regarding critical
contemporary issues:  AIDS, the economy, and sexual harassment.  Two 11-point
Likert-type items measured responses to the following statements:  "I am
concerned that I will get AIDS," and "I am better off economically now, than I
was four years ago."  Actual exposure to sexual harassment was measured via the
item:  "How many times in your life do you feel that you've been a victim of
sexual harassment?"
                Each respondent's pattern of personal communication activity was
quantified via a set of eight items requesting estimated frequency of verbal
interaction with people in the household, the neighborhood, in public places
(neighborhood and outside the neighborhood), elsewhere in the city, at work, and
on the telephone (local and outside of the area).  The three
home-and-neighborhood items were added in an index of localite personal
communication, and the two outside-the-neighborhood (but non-work, non-phone)
items were added in an index of city personal communication.  This resulted in a
final set of five personal communication activity measures.
                Use of "traditional" media was operationalized using
commonly-accepted measures which asked people for:  The number of hours
yesterday they spent watching TV, watching premium cable channels, and listening
to the radio, the number of days last week they read a newspaper, the number of
magazines they read regularly, the number of books read in the past six months,
and the number of films seen in a theater in the past month.
                Adoption of several newer media also was recorded.  In addition to
the three audio technologies described above, the measures included the number
of videos viewed in the last month, whether there was a personal computer in the
household, and how many years of experience the respondent had using a personal
computer.
                The median household income was in the category of $20,000-30,000,
50% were married, 64% were female, 92% were high school graduates or above and
34% were college graduates.
               In terms of penetration for the newer media, roughly a third
(31.8%) reported having a personal computer at home, with 49.4% having used a PC
at some time.  Almost two-thirds (64.4%) reported watching videos in the last
month, and 16.4% watched a premium cable channel yesterday.  All had access to a
phone and nearly a third (32.9%) had used fax in the last month.  Only 4%
reported having called a 1-900 number in the past month, while 33.8% said they
had called phone-based information services.
               Correlational and multiple regression analyses were used to test
the hypotheses.  A hierarchical, forced-entry model was used to assess the
relative contributions of the blocks:  (1) social indicators, (2) quality of
life measures, (3) personal communication patterns, (4) traditional media use,
and (5) new media use.  Standard tests for multicollinearity (i.e., inspection
of condition indexes and regression coefficient variance-decomposition matrices)
revealed no significant problem for any of the three regressions.
 
                                    RESULTS
              Correlations for call-in service adoption and use of other
technologies are displayed in the first column in each of Tables 1-3.  As
expected, an indicator of social status (education) is negatively related to
1-900 use (Table 1; r=-.13, p<.05).  The lack of any other relationships
involving income or any other social indicators also contradicts the "upscale"
adopter profile characteristic of diffusion research.  Use of 1-900 numbers is
also significantly related to a lower QOL in general (r=-.13), and lower
confidence in the local schools and police (r=-13 and r=-.15).  1-900 users are
also more concerned with getting AIDS (r=.14), adding up to a profile of a more
pessimistic, somewhat disenfranchised individual.  Use of 1-900 is also linked
to greater personal communication in the home and neighborhood (r=.20), greater
movie attendance (r=.45), higher rate of video viewing (r=.21), and greater
phone-based information service usage (r=.33).
          ______________________________
 
          Table 1 about here
          ______________________________
              Media correlates of audiotext adoption (Table 2) include use of
1-900 phone services (r=.33), cinema attendance (r=.22), subscription to premium
cable (r=.16) and the proclivity to communicate with others in one's home and
neighborhood (r=.20).   Audiotext adoption is inversely related to the belief
that the economy has recently improved (r=-.15).
          ______________________________
 
          Table 2 about here
          ______________________________
               Unlike 1-900 use, use of fax machines is related to income
(r=.18), consistent with class-driven theories of adoption.  However, fax use is
inversely related to age (r=-.22).  In addition, fax users are more likely to
indicate that they use telephones frequently for local calls (r=.24) and long
distance calls (r=.25).  They are more likely to communication frequently at
work (r=.23).  Fax users are also more likely to indicate that they have been a
victim of sexual harrassment (r=.15).  They're also more likely to attend cinema
(r=.17) and have more experience with using a PC (r=.28).
          ______________________________
 
          Table 3 about here
          ______________________________
               Focusing on the regression models, the prediction of 1-900 use
(Table 1) explained a rather robust proportion of variance (R2=.37).  The first
block, that of social indicators, contributed 5% explained variance, a
non-significant increment.  (The two social indicators to emerge as significant
unique contributors are both negatively related to 1-900 use: Number of people
in the household (beta=-.13) and eduction (beta=-.12).)         All others blocks
contributed significant increments to the 1-900 equation.  QOL variables
explained 5% of the variance (p=.04); while no significant individual
contributors emerged, the pattern for the block is one of greater 1-900 use by
more pessimistic individuals.  Personal communication activities contributed 4%
to the equation (p=.02); no individual variables were significant, but the
pattern is one of greater 1-900 use by those who communicate frequently in a
localite fashion.
                The traditional media use block was a strong contributor, explaining
17% of the variance (p<.0001).  The single significant beta (.33) was that for
theatrical moviegoing.  And, the new media block added 5% to the equation
(p=.002), with use of phone-based information services the greatest individual
contributor (beta=.22).
              In contrast, the equation predicting use of audiotext services
(Table 2) explained 23% of the variance.  Use of this service was significantly
predicted by the personal communication, traditional media, and new media blocks
(contributing 6%, 6%, and 5%, respectively).  Significant unique predictors
included home and neighborhood interpersonal communication (beta=.18), premium
cable viewership (beta=.16) and 1-900 use (beta=.27).
              The regression model predicting fax use (Table 3) resulted in a
moderate degree (28%) of variance explained.  Here we see that use of
functionally similar computer technology was a strong individual contributor
(beta=.16), but that the new media block in which it was included did not
provide a significant incremental R2 (inc.=3%, p=.10).
                Three other preceding blocks were significant.  The social indicators
block contributed a sizable 9% of variance (p=.001), and although no individual
betas were significant, the pattern is one of greater fax use by those who are
more conservative politically.  The QOL block contributed 5% (p=.04), with
sexual harassment victimization the biggest predictor (beta=.17) and a mixed
pattern of confidence/pessimism with regard to the other independent variables
in the block.  Personal communication variables provided a respectable 7%
variance explained, with work discussions (beta=.17), non-local phone
conversations (beta=.15), and communication outside the neighborhood but within
the city (beta=-.15) serving as significant unique contributors.  The
traditional media block did not explain a significant amount of variance.
 
          DISCUSSION
               Looking across our different prediction equations, we see that
explanatory power varied greatly for each of the dependent technology variables.
Media-use blocks were more explanatory of adoption than demographics for two of
the three phone adjuncts studied--1-900 and audiotext use.[4]  This leaves
Hypothesis 1 with mixed support.
               With regard to the functional similarity dynamic posited in
Hypothesis 2, audiotext and 1-900 use are most powerfully predicted by each
other, while both are related to use of such entertainment media as cinema.
Utilitarian media such as the fax and computer, on the other hand, are unrelated
to these telephone adjuncts.  The only media predictor of fax use, in turn, is
the computer.  This indicates the existence of two distinct media clusters that,
while not comprehensive, support the compatible media uses outlined in
Hypothesis 2.
               Turning to the relative influence of personal communication needs
across our equations, we see they are among the more powerful predictor blocks
in explaining audiotext and fax use.  However, such needs were less explanatory
than demographics in predicting 1-900 use and fax use, contrary to Hypothesis 3.
This leaves the hypothesis with only weak support.
               Finally, in addressing the relative influence QOL variables in
comparison with more conventional demographic, personal communication and media
predictor blocks, we see that they are among the least explanatory of the
predictor blocks across all of our equations.  QOL variables did explain a
modest yet significant proportion of variance for 1-900 user and fax use.
               On balance, the high degree of variance explained by our
                              communication items       compares favorably with that
noted in
          past studies of videotext (Ducey, 1986; Reagan, 1987), audio
information services (LaRose & Atkin, 1992; O'Keefe & Sulanowski, 1992) and
cable (LaRose & Atkin, 1988; Reagan, 1991).  Thus, on an aggregate level, our
findings establish the importance of augmenting conventional demographic
locators with a wider range of subjective measures, including people's
communication needs.
                Our findings generally confirm the expectation that demographics and
other traditional social indicators are not important in the prediction of use
of these several audio information services.  The modest role played by
demographics here reinforces past findings (e.g., Atkin & LaRose, 1994a;
Garramone, Harris, & Pizante, 1986; Jeffres & Atkin, 1994; LaRose & Atkin, 1992;
O'Keefe & Sulanowski, 1992; Reagan, 1987, 1991), suggesting that their
explanatory influence has, indeed, weakened over time.  On the other hand, some
research continues to identify socioeconomic correlates of new media adoption
(including a meta-analysis of eleven surveys on the diffusion of home computers,
by Dutton, Rogers, & Jun, 1987b; see also James, Wotring, & Forrest, 1995).  Our
findings suggest a need to reconceptualize class-driven theories of media
adoption and use, formulated under the assumption that media use is a one-way,
passively received process.
                An explanation for this trend away from social indicators as
predictors may be developed from notions presented by Dozier, Valente, and
Severn (1986) and by Dizard (1989).  First, one may posit that demographic
characteristics matter little when an innovation's hardware is linked to
established delivery systems, i.e., when the innovation is more "continuous"
(Dozier et al., 1986).[5]  Second, if one accepts that the age of the
"Integrated Grid" (Dizard, 1989) has arrived, it seems probable that over time,
the importance of social indicators in the prediction of new technology adoption
and use will continue to erode.
                In the face of such erosion, attitudinal variables may be established
as most explanatory.  One tradition that should help contextualize adoption of
new media is the uses and gratifications paradigm.  Although some (Dimmick,
Sikand, & Patterson, 1994; Train, McFadden, & Ben-Najiv, 1987) have investigated
gratifications associated with general telephone use, little work has focused on
telephone adjuncts.  Traditionally, gratifications sought have been an important
predictor for media exposure level (Palmgreen, Wenner & Rosengren, 1985; Lin,
1993), although this is not consistently the case (Wenner, 1986).  The strength
of those needs, motives and expectations ultimately determines media content
selection (Lin, 1993) as well as new media adoption (Garramone, Harris &
Pizante, 1986).  Most importantly, the eventual gratifications associated with a
technology are not always self-evident.
                For example, there exists intriguing evidence that initial
expectations that PCs would be primarily an entertainment medium were faulty,
with home utilitarian uses eclipsing entertainment at a fairly early stage of
the innovation's development (Dutton, Rogers, & Jun, 1987b).  And, James,
Wotring, and Forrest (1995) found information/education to be the top-cited
function for BBS use, far exceeding entertainment (38% vs. 9% of responses,
respectively).
                This study discovered at least one rather puzzling finding that seems
to contradict conventional wisdom regarding innovation clusters and uses and
gratifications.  Film-going, identified in previous research as related to the
perceived importance of the highly "passive" media function of the traditional
audience role as receiver (Jeffres & Atkin, 1994), was found to hold positive
zero-order relationships to all three technologies studied here.  Given the
positive prediction of all but fax by home/neighborhood communication, one
explanation may be that 1-900 and phone-based information service users tend to
be social types--preferring to co-view their visual entertainment at the
theater, and talking frequently close-to-home.
                The related construct arena of communication needs also merits future
investigation.  This study found some important linkages between interpersonal
communication activity and new audio media use.  Ducey (1986) found a strong
systemic relationship between communication needs and characteristics of
computer-based services.
                The integration of several bodies of literature--including diffusion
theory, media communication needs, and QOL--is valuable since the distinctions
between mass media communication and computer communication has become
increasingly blurred.
                We expect the future to bring a more diverse set of uses and
gratifications for "newer" technologies.  As the novelty of disparate hardwares
diminishes, and as the "integrated grid" expands, emphasis will fall on the
functionality of technologies.  Figure 1 presents a posited typology (adapted
from Atkin & LaRose, 1994b) that even within the relatively narrow range of
"mass audience phone services," differentiates according to (a) primary mode of
delivery (mass vs. interpersonal) and (b) typical content (entertainment vs.
information).  Such an analysis may in fact explain key differences in the three
regression models developed here, and provide a template for future analyses
regarding innovations that use primarily pre-existing hardware.
          ______________________________
 
          Figure 1 about here
          ______________________________
                All three technologies studied here, and most clearly 1-900 use and
use of phone-based information services, are continuous rather than
discontinuous innovations (Dozier et al., 1986).  The manner in which this
continuity maintains is particularly noteworthy.  That is, the very obstacles to
adoption identified as critical by other new-technology scholars are
circumvented via use of the tried-and-true, audio-based delivery system of the
telephone.  Much less relevant are Garramone, Harris, and Pizante's (1986)
hindrances to adoption--expense (that "tends to favor economic and intellectual
elites" (p. 446) and the print-based nature of interactive media (that
discriminates against the less-educated).
               As the demand for telematic services grows, helping define a
larger information economy, it will be important to understand user profiles for
information services.  Given that they provide a gateway to a growing number of
information services, phone delivery systems are a major contributing factor to
an understanding of attendance to information technologies in general.  The
implications for the strong consumer interest shown in audiotext applications
are many.   For, it presents an entree through which more than just the business
sector can utilize advanced (i.e., nonconversational) phone services.  As
emerging ISDNs promise to expand these offerings, to include as standard such
services as voice-mail, call-back, call forwarding and call blocking, we can
expect to see increased consumer uses for voice and data services.  This,
combined with the low costs for service providers, should enhance the potential
for text services.
                These new applications may help overcome the malaise observers have
noted with past electronic services.  For, as past observers have noted, the
failure of videotext in the U.S. stemmed from the fact that it was nothing more
than an expensive, less convenient substitute for services that were already
available to customers (e. g. catalogs, automatic tellers, etc).  If other text
services had solved the "chicken/egg problem" of having too few users to support
innovative services--and no innovative services to attract users--then they may
have experienced greater success.
                In the same way that this study finds uses of phone-based innovations
undifferentiated socioeconomically, we would posit that future studies will
identify a similar pattern for computer-based innovations.  A recent
non-probability survey provides startling evidence that those under age 25 are
as comfortable with computers as previous generations were with the telephone.
The study found 99% of people born after 1971 to have used a computer before the
age of 10 (compared with 7% of those older).  More than 66% of those born after
1971 call themselves "intermediate," "expert," or "power" PC users (compared
with 19% of older respondents) (Beniger, 1996).
                As we move fully to the era of the "integrated grid" elaborated by
Dizard (1989), a convergence of users is expected, corresponding to a world of
perhaps no discontinuous innovations.  The continued evolution of more complex
information services, such as the "net," is forcing scholars to reconceptualize
the notion of communication.  Findings reported here should help researchers
develop more reliable measures of innovation attributes and adoption intentions.
It will be important, then, in later work to continue our exploration of
technology clusters based on functional similarities and need met by existing
media.
 
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     Table 1.  Hierarchical Regression Predicting Use of 1-900 Telephone System.
                                                                Final
                                                Corr.   Beta            Inc. R2 F & p
 
     Block 1:  Social Indicators                                        .05             1.639,272, .11
     FEMALE                                      .07             .06
     NONWHITE                                   -.04            -.13*
     Q45-People in household             .03            -.15
     Q33-Liberalism                             -.03            -.09
     Q51-Education                              -.13*   -.12*
     Q52-Income                         -.02            -.02
     Q48-Age                                    -.08            -.02
     Q32-Republicanism                  -.05            -.09
     Q46-Children                                .08             .15
 
     Block 2:  Quality of Life                                  .05             2.197,265, .04
     Q28-Sexual har. victim              .04            -.01
     Q5-Conf. in local schools  -.13*   -.08
     Q10-Better off econ.               -.10            -.04
     Q1-QOL metro. area                 -.13*   -.06
     Q22-Concerned getting AIDS  .14*    .11
     Q7-Conf. in area media             -.02             .02
     Q6-Conf. in local police           -.15*   -.03
 
     Block 3:  Personal Comm.                                           .04             2.725,260, .02
     TALK1-Home & neighborhood   .20**   .09
     TALK2-City, outside neigh.  .07             .08
     TALK3-At work                              -.02            -.04
     TALK4-Local phone                  -.02            -.03
     TALK5-Non-local phone               .00             .02
 
     Block 4:  Traditional Media                                        .17             8.807,253, .0000
     Q37-Radio                                  -.04            -.08
     Q36-Premium cable                   .02            -.07
     Q42-Books                                   .07             .02
     Q43B-Movies at theatre              .45**   .33**
     Q39-Magazines                               .08             .00
     Q38-Newspaper                              -.03             .02
     Q35-Television                             -.00            -.02
 
     Block 5:  New Media                                                        .05             4.025,248, .002
     Q43D-Phone-based info.              .33**   .22**
     Q40-PC in home                              .02             .02
     Q43E-FAX                                    .09             .06
     Q43A-Videos                                 .21**   .07
     Q41-Years used PC                   .12             .07
 
     TOTAL EQUATION:                                                    .37             4.3433,248, .0000
 
 
     * - r or beta sig. at p < .05
     ** - r or beta sig. at p < .01
     Table 2.  Hierarchical Regression Predicting Use of Phone-based Information
     Services.
                                                                Final
                                                Corr.   Beta            Inc. R2 F & p
 
     Block 1:  Social Indicators                                        .02             0.489,272, .89
     FEMALE                                     -.01            -.04
     NONWHITE                                    .01            -.00
     Q45-People in household             .04            -.02
     Q33-Liberalism                              .03             .03
     Q51-Education                               .04     .09
     Q52-Income                         -.01             .05
     Q48-Age                                    -.06             .01
     Q32-Republicanism                  -.08            -.05
     Q46-Children                                .06             .02
 
     Block 2:  Quality of Life                                  .04             1.637,265, .13
     Q28-Sexual har. victim              .02            -.02
     Q5-Conf. in local schools  -.06            -.03
     Q10-Better off econ.               -.15*   -.12*
     Q1-QOL metro. area                 -.01             .07
     Q22-Concerned getting AIDS  .06    -.02
     Q7-Conf. in area media              .09             .14*
     Q6-Conf. in local police           -.10    -.07
 
     Block 3:  Personal Comm.                                           .06             3.605,260, .004
     TALK1-Home & neighborhood   .20**   .18**
     TALK2-City, outside neigh.  .01            -.09
     TALK3-At work                               .04             .02
     TALK4-Local phone                   .07             .00
     TALK5-Non-local phone               .09             .11
 
     Block 4:  Traditional Media                                        .06             2.527,253, .02
     Q37-Radio                                  -.00            -.01
     Q36-Premium cable                   .16*    .16*
     Q42-Books                                   .07             .03
     Q43B-Movies at theatre              .22**   .09
     Q39-Magazines                               .04             .00
     Q38-Newspaper                              -.01             .02
     Q35-Television                              .02            -.06
 
     Block 5:  New Media                                                        .05             3.315,248, .001
     Q43C-1-900 phone                    .33**   .27**
     Q40-PC in home                              .01            -.02
     Q43E-FAX                                    .03            -.00
     Q43A-Videos                                 .09    -.01
     Q41-Years used PC                  -.01            -.07
 
     TOTAL EQUATION:                                                    .23             2.2033,248, .0004
 
     * - r or beta sig. at p < .05
     ** - r or beta sig. at p < .01
     Table 3.  Hierarchical Regression Predicting Use of FAX.
 
                                                                Final
                                                Corr.   Beta            Inc. R2 F & p
 
     Block 1:  Social Indicators                                        .09             2.179,272, .001
     FEMALE                                     -.09            -.01
     NONWHITE                                   -.07            -.02
     Q45-People in household             .05             .05
     Q33-Liberalism                             -.07            -.05
     Q51-Education                               .10     .01
     Q52-Income                          .18**   .06
     Q48-Age                                    -.22**  -.04
     Q32-Republicanism                   .12             .03
     Q46-Children                               -.02            -.07
 
     Block 2:  Quality of Life                                  .05             2.147,265, .04
     Q28-Sexual har. victim              .15*    .17**
     Q5-Conf. in local schools   .04             .08
     Q10-Better off econ.                .12     .06
     Q1-QOL metro. area                  .08             .08
     Q22-Concerned getting AIDS -.00    -.03
     Q7-Conf. in area media             -.10            -.12
     Q6-Conf. in local police            .01    -.01
 
     Block 3:  Personal Comm.                                           .07             4.365,260, .0008
     TALK1-Home & neighborhood   .05    -.03
     TALK2-City, outside neigh.  .07            -.15*
     TALK3-At work                               .23**   .17*
     TALK4-Local phone                   .24**   .08
     TALK5-Non-local phone               .25**   .15*
 
     Block 4:  Traditional Media                                        .04             1.867,253, .08
     Q37-Radio                                   .09             .06
     Q36-Premium cable                  -.07    -.01
     Q42-Books                                  -.02            -.09
     Q43B-Movies at theatre              .17*    .10
     Q39-Magazines                               .12             .02
     Q38-Newspaper                              -.10            -.08
     Q35-Television                             -.10            -.00
 
     Block 5:  New Media                                                        .03             1.885,248, .10
     Q43C-1-900 phone                    .09     .06
     Q40-PC in home                             -.19            -.00
     Q43D-Phone-based info.              .03            -.00
     Q43A-Videos                                 .11     .07
     Q41-Years used PC                   .28**   .16*
 
     TOTAL EQUATION:                                                    .28             2.8633,248, .0000
 
     * - r or beta sig. at p < .05
     ** - r or beta sig. at p < .01
     Figure 1.  Mass audience phone service examples.
 
 
 
                               Primary mode of delivery
 
     Content*     |   Machine-facilitated         Operator-facilitated
                  |   (mass audience)             (interpersonal)
     -------------|------------------------------------------------
     Primarily    |   Audiotext, 1-900            Talk lines
     entertainment|
                  |
     Primarily    |   1-800 (business),           Live
     information  |   fax                         operator
 
 
     *--Based on content analyses and technology profiles reviewed in the
     literature; for a more comprehensive technology classification scheme, see
Atkin
     & LaRose (1994b).
 
 
 
                             ENDNOTES
 
            [1]  Print and electronic media have, in turn, become heavy users of
phone-delivered text (or audiotext) services.  Audiotext is being used in a
variety of media promotion schemes, including "play along" audience
participation games and instant polls.  By keying in personal identification
information, home participants in game shows or tune-in promotions can enter
drawings for "valuable prizes," and become part of an extended audience that is
actively in the game.
            [2]  Yet the same might be said of rallies, the Internet and other
public settings in which various groups jockey to present their views.  Research
suggests that activist sentiment is overrepre-sented in call-in polls (Bates &
Harmon, 1993) and computer polls (Bates & Harmon, 1991).
            [3]   As past work from Ettema indicates, videotext adopters are
more likely than nonadopters to rate market data as important to their needs,
though general news was less important for them.  Adopters also tended to be
younger, better educated and more likely to adopt products.  With regard to user
attitudes, adopters are most interested in access to market data, including
up-to-the-minute commodity reports.  Ettema concludes that adopters were most
concerned with business applications, rather than those oriented toward
consumers (e.g. shopping,cooking, banking or news services).
            [4]  This study's truncated range on 1-900 use produced an
overly-conservative test of its prediction--only 4% used last month; we need a
longer time span to capture full variance of the variable.  Given the strong
prediction of this variable, increasing the range of the variable would be
fruitful.
            [5]  Indeed, fax, the most dynamically continuous innovation studied
here, showed the strongest dependence on socioeconomic factors.

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