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Subject: AEJ 98 LinC CTM Predicting online service adoption among nonsubscribers
From: Elliott Parker <[log in to unmask]>
Reply-To:AEJMC Conference Papers <[log in to unmask]>
Date:Thu, 31 Dec 1998 15:57:41 EST

TEXT/PLAIN (1064 lines)

Predicting Online Service Adoption Likelihood
 Among Nonsubscribers
Carolyn A. Lin (Ph.D., Michigan State, 1987) is an associate professor in the
Department of Communication at Cleveland State University, E. 21st and Euclid
Avenue, Cleveland, Ohio 44115
(e-mail: [log in to unmask];  phone: 216-687-4641).
Predicting Online Service Adoption Likelihood
Among Nonsubscribers:
      As we approach the dawn of the digital television revolution, the
convergence between television and online services continues to progress along
technological as well as content dimensions.  With further erosion of the
television audience on the horizon, it is speculated that PC-TV use will one day
displace traditional TV use.  This study investigates the relations between
perceived television use and online access motives among non-online
subscribers--the audience segment that is being courted by the online industry,
and how such relations influence the likelihood of online service adoption.
Predicting Online Service Adoption Likelihood
Among Nonsubscribers
      As we approach the dawn of the digital television revolution, the
convergence between television and online services continues along technological
as well as content dimensions.  Resulting from this inevitable transition is a
further erosion of the television audience, as evidenced by the dwindling
primetime television audience share generated by all six broadcast networks,
which dipped to 42.8% in 1997 from 47.6% in 1995 (Nielsen Media Research,
    Concerns regarding this potential media substitution phenomenon have
prompted expert predictions that online or PC-TV services would eventually
replace regular television services, due to their added interactive
capabilities.  These predictions, although not completely speculative, seem to
rest on one or both of the following assumptions: (1) television and online
content is mutually substitutable, and (2) television use motives are similar to
online service use motives.
     It's reasonable to assert that certain aspects of the online content do
emulate television content and such emulation will rise as the PC-TV phenomena
grows.  However, whether audience television use motives parallel those of
online service use remains a mystery.
     This study intends to investigate that relationship among non-online
subscribers--the audience segment that is being intensely courted by the online
industry.  Specifically, it attempts to explain whether these two sets of
motives are indeed substitutable and, if so, how they each may influence the
perceived likelihood of online service adoption.
Uses and Gratifications Perspective
     The uses and gratifications perspective is considered one of the most
appropriate theoretical frameworks to study psychological and behavioral
tendencies in association with mediated communication (Lin, 1996).  As echoed by
others, this particular theoretical approach is also well-suited for studying
computer mediated communication such as Internet use (e.g., Kuehn, 1994; Morris
& Ogan, 1996; Rafaeli, 1986; Newhagan & Rafaeli, 1996).
     However, while mediated communication such as television viewing has been
widely examined under the uses and gratifica-tions paradigm, computer-mediated
communication--such as online service use on the Internet--demands renewed
theoretical attention as well as empirical effort (Morris & Ogan, 1996).
      In the tradition of uses and gratifications research, audience media use
is said to be associated with a set of psychological motives.  These
psychological motives motivate the audience to purposefully select certain media
and media content for consumption in order to satisfy a set of psychological
needs behind those motives (e.g., Blumler, 1979; Katz, Blumler & Gurevitch,
     Under this theoretical umbrella, television viewing motives --such as
surveillance, entertainment, personal identity, escape and companionship (e.g.,
Rubin, 1981, 1983)--have been empirically linked with distinct channel selection
decisions and viewing content choices as well as varying viewing levels and
viewing gratifications-obtained (e.g., Levy & Windahl, 1984; Palmgreen, Wenner &
Rosengren, 1985).  These audience behaviors in essence point to a relatively
utility- or goal-oriented active viewing public (e.g., Levy & Windahl, 1984;
Rubin & Perse, 1987).
       In contrast, parallel past empirical findings have also established the
relationship between audience computer-mediated communication behavior and the
uses and gratifications approach.  For instance, a study of electronic political
bulletin boards in 1986 (Garramone, Harris & Anderson, 1986) indicates that the
need for surveillance, personal identity and diversion all equally contributed
to electronic political bulletin board use.
     Nearly a decade later, James, Worting and Forrest (1995) found that the
most cited psychological motives for using electronic bulletin boards include
informational learning and socialization.  Other relevant studies found, for
instance, that surveillance needs are a strong predictor of potential adoption
of news and information services via a videotext system (Lin, 1994a).
     As little empirical research has addressed the topic of online service uses
and gratifications (in the Internet environment), a recent industry study
nevertheless indicates that online audience activity is motivated by seeking
gratifications in escape, entertainment, interaction and surveillance (Miller,
1996).  In addition, Jeffres and Atkin (1996) reported that Internet adoption
intention was predicted by needs for communication.  Moreover, Eighmey (1997)
discovers both entertainment value and personal identity (or personal
involvement and relevance) are the strong motivational factors behind commercial
web site adoption (1997).
      The summative findings gathered from the literature herein imply that the
basic audience motives for seeking either traditional mediated content or online
content are similar.  As stated earlier, online content does emulate (and
perhaps even extend) traditional mediated content.  It is logical, then, to
expect that perceived audience motives for traditional mediated content use and
online service use may both be potential antecedent variables to likely online
service adoption.
H1: Perceived motives for television use will be positively
    related to perceived motives for online service use.
H2: Perceived motives for television use will be positive
    predictors for online service adoption likelihood.
H3: Perceived motives for online service use will be positive
    predictors for perceived online service adoption likelihood.
Media Substitution Hypothesis
     According to the media substitution hypothesis, audience members may
substitute the use of a functionally similar medium for another when such a
substitution need arises and the circumstance presents itself.  The classic
example for this type of media substitution dynamic reflects the displacement of
radio by television as the most widely adopted mass entertainment medium
(Laswell, 1948).  The more recent substitution model, for instance,
characterizes prerecorded video playbacks on a VCR as a replacement for
movie-outing activity (e.g., Childers & Krugman, 1987; Henke & Donohue, 1989;
Lin, 1993), as such home video entertainment activity provides better audience
control over the household budget and leisure-time allocations.
     Such a media substitution mechanism is not often so transparent between
other functionally similar media, however.  This is especially true between
traditional mediated and computer-mediated communication channels, even when the
channels under comparison provide similar content.
     For instance, audience use of on-line services was found to have little
effect on their patronage of television, newspapers and other traditional news
sources (Jessell, 1995).  Jeffres and Atkin (1996) also failed to find any
significant correlations between interest in using online services on the
Internet and other traditional mass media.  A recent national survey reported
that the percentage of people who preferred to consume traditional mass media
instead of getting on line ranged from 70% to 77% (Snider, 1997).
Contrastingly, other studies report a slight reduction of television viewing
time among online users (e.g., Berniker, 1995; Crispell, 1997).
     This lack of apparent substitution mechanisms between traditional media and
online service use seems to signal additional audience media choice models.
That is, between any two media choices, there could exist a displacement (or
substitu-tion), complementary or supplementary relationship.
     As discussed above, a displacement relation illustrates a mutually
exclusive opportunity for audience choice between two media.  A complementary
relation, on the other hand, reflects a situation where the use of one medium
makes the utility of another medium more complete.  For instance, newspapers and
television were perceived as functional complements in that television was seen
as a medium for fulfilling a general surveillance need, while newspapers were
deemed a tool for specific information seeking, knowledge acquisition and
election education (Weaver & Budeenbaum, 1979).  By the same token, as the VCR
allows for time-shifting of television program viewing, the VCR complements the
television viewing experience (Lin, 1993).
     In contrast, as online service use has not yet impacted the level of
traditional media use at a noticeable level (e.g., Jessel, 1995), usage patterns
between the two media may be considered largely orthogonal at this point in
time.  Thus, online content can be regarded as a functional supplement to
traditional mediated content.  As such, online content access enhances or
enriches the traditional mediated content consumption experience.
     The onset of this supplement, complement or displacement mechanism should
depend on whether "new media" can effectively compete with "old media" for
cost-efficiency, perceived communication utilities and gratification
expectations that concern the user at a sufficient level (Lin, 1994).  At this
particular juncture, it appears that online services are not yet effectively
competitive against the more established traditional mediated channels in any of
those categories.  However, it should also be recognized that audience leisure
time is a property of fixed avail.  Much like the home video competition with
both TV viewing and movie-going activity (Lin, 1993), the more leisure time the
audience devotes to online access, the less of such time will be allocated to TV
viewing and vice versa.  Since this competitive dynamic is likely to become the
trend rather than mere speculation, the following hypothesis is posited:
H4: Level of television use will be unrelated or inversely
    related to perceived level of online service use.
Research Methods and Procedures
      A telephone survey utilizing the computer-aided telephone interview (CATI)
system was conducted for data collection during the spring of 1996.  Random
digits were generated to compose the telephone survey sample.  The survey area
covered a geographic region rich in ethnic diversity, with a population base
close to 2 million.  Overall, 348 completed surveys were gathered, reflecting a
60% response rate.
Sample Profile
      The present sample's personal computer ownership was at 36% in 1996,
compared with 37% national penetration (Sandberg, 1997).  The average ownership
duration for PCs was 4.34 years.  Respondent average media use frequencies
reflect: (1) 3.5 hours of daily TV viewing, (2) 2.9 hours of daily radio
listening, (3) 4.4 days of newspaper reading during the week, and (4) 2.5 days
of magazine reading during the week.  Other sample SES indicators show that (1)
mean age is around 40, (2) average annual household income is $40,000 and (3)
the mean education level is "some college".  The sample gender split is 43%
males and 57% females.  While 47% of the sample have children; an average family
has 2 children.
   Perceived Television Use Motives.  Respondents were asked to rate how often
they engage in television viewing for a series of 18 psychological motives.  A
five-point Likert scale, ranging from "very often" to "never," was used.  These
items were adapted from past uses and gratifications studies (e.g., Rubin, 1981,
1983).  Factor analysis (with Varimax rotation) employed to find variable
groupings yielded three final three factors with acceptable inter-item
reliability coefficients, including Surveillance, Escape/Companionship and
personal identity--with Cronbach's alphas reaching .86, .76 and .88,
respectively (see Table 1).
   Perceived Online Service Use Motives.  Respondents were asked to assess their
potential online service use motives based on the 18 psychological motive items
given, after explanations were provided for the function and content of the
online use phenomenon.  These items, worded in a similar fashion to those of the
television use motive items, were measured by a five-point Likert scale, ranging
from "very likely" to "very unlikely."  The factor analysis procedure (with
Varimax rotation) resulted in three final factors: Surveillance,
Escape/Companionship/Identity and Entertainment--with Cronbach's alphas at .88,
.91 and .88, in that order (see Table 2).
   Perceived Online Service Adoption Likelihood.  Altogether, 23 categories of
regular online service features were ranked by respondents in terms of their
perceived adoption likelihood.  A five-point Likert scale, ranging from "very
likely" to "very unlikely", was used to gauge responses.  Three final online
service adoption likelihood groupings were generated via factor analyses (with
Varimax rotation), including Shopping services, Information services and
Infotainment services--with correspond-ing Cronbach's alphas of .86, .89 and .89
(see Table 3).
   Television Use Level.  Television viewing was measured by asking the
respondent to report the number of hours spent watching television on a weekday
and on a weekend.  The average for the sum of weekday and weekend viewing hours
was obtained to reflect television use level.
      The Pearson Correlation results (Table 4) revealed that
six out of nine correlation coefficients--pairing perceived TV use and online
service use motives--are statistically significant.  In particular, as perceived
Entertainment and Escape/Companionship motives for TV use are not related to the
perceived online Surveillance motives, the latter is unrelated to perceived TV
Escape/Companionship/Identity motive.  Based on these findings, Hypothesis one
is thus partially supported.
     Table 5 presents the hierarchical multiple regression results for three
different equations.  The first equation, featuring perceived adoption
likelihood for Infotainment services as the criterion variable, reveals that all
perceived online service use motives are significant predictors, while the same
is not true for all perceived TV use motives.  Overall, 45% of the variance is
explained by the equation, with perceived TV use motives accounting for a meager
1% of that variance.  Beta values for perceived online service motives are .35
for Surveillance motives, .23 for Escape/Companionship/Identity motives and .19
for the Entertainment motive.
     The equation predicting adoption likelihood of Information services
accounted for 49% of the variance in the criterion variable; perceived TV use
motive measures are responsible for 2% of the total variance explained.
Significant predictors include perceived Surveillance (B = .32),
Escape/Companionship/Identity (B = .26) and Entertainment (B = .24) motives for
online service use as well as an inversely related perceived Identity (B = -.11)
motive for TV use.
     The third equation yielded a robust 64% of variance explained for the
criterion variable, Shopping service adoption likelihood.  As none of the
perceived TV use motives are significant predictors for the equation, they help
account for 1% of the total variance explained.  All three perceived online
service use motives are significant predictors, namely, Entertainment (B = .28),
Surveillance (B = .27) and
Escape/Companionship/Identity (B = .19) motives.
     By summing up these findings, it is apparent that H2 is not supported by
the analyses, as only one perceived TV use motive is significantly predictive of
perceived online service adoption likelihood.  Alternatively, H3 is supported by
the data, as all perceived online service use motives are significant predictors
for all three multiple regression equations.
     Table 6 illustrates the results from a Multidimensional Scaling procedure.
While the overall R2 value reaches .952 and the Kruskal's stress measure is at
.112, it is apparent that the two-dimensional model describes the relative
perceptual distances between all variables in the model almost perfectly.  As
demonstrated by Figure 1 and Figure 2, perceived TV use motives are clearly
distant from the dimension which is clustered with perceived online service use
motives and online service adoption likelihood.
     Finally, with regard to the relationship between the level of TV use and
perceived likely online utility (see Table 4), the only significant but inverse
correlation exists between TV use level and perceived Information service use (r
= .11, p < .05).  Hypothesis 4 is thus supported by these findings.
      The significant but moderate empirical parallel found between perceived
motives associated with the use of both television and likely online access
helps validate the theoretical assumption that audience media use motives are
often similar, even between traditional mediated and computer-mediated
communication channels (see Table 4).  The only insignificant correlations
between perceived TV and perceived online use motives involve the perceived
Surveillance motives for both TV and online access, as the latter appears to be
relevant only for the former, or the Identity-seeking motive for TV use.
Overall, these findings suggest that those who are compelled by a set of
psychological motives to seek certain cognitive, affective or behavioral
gratifications from TV viewing may also be prompted  by similar
motives--including "entertainment"--from a computer-mediated source such as the
online universe.
     This assessment can be further examined to explicate a surprise finding
obtained through the factor analysis procedure, which fails to generate the
traditional "entertainment" dimension of TV use motive (excluded from further
analysis) with sufficient scale reliability (Cronbach's alpha=.66).  By
contrast, the perceived "entertainment" motive for online use is a solid factor
with high scale reliability (Cronbach's alpha=.88).  Could this be emblematic of
the changing nature of audience TV viewing motives, such that the audience no
longer considers TV viewing a strong source for "pure entertainment"?  In this
rapidly expanding multichannel viewing universe--where the average cable TV
system now carries between 75 and 80 channels and around 52 million people in
the U.S. are Internet users in 1997 (Nielsen Media Research, 1997b)--the answer
to this question remains unclear.
     These ambiguous relations between the audience, their TV viewing and likely
online service adoption activity are further complicated by the intriguing
results from the multiple regression tests.  While all three perceived TV use
motives are significantly but weakly correlated with either one or two types of
likely online service adoption, and even though most similar  perceived
psychological motives for TV use and online access are intercorrelated, those
motives associated with TV use are nevertheless largely insignificant predictors
for likely online service adoption (see Table 5).
     The only exception involves the Identity-seeking motives for TV use, which
is a significant but negative predictor for likely adoption of Information
services.  This finding contradicts Garramone, Harris and Anderson's (1986)
discovery of a strong association between personal identity needs and the
interactive nature of political bulletin board use, as the former is inversely
related to online content of a more impersonal business nature.
     Nonetheless, all three perceived online use motives are strong and
significant predictors for likely adoption of all three online service
groupings.  It is then reasonable to assert that these online services are being
perceived by those non-online subscribers as a source potentially capable of
meeting their psychological needs for entertainment, surveillance, escape,
companionship and personal identity.
     It is perhaps not difficult to have fostered that positive perception for
potential online content access, if one examines both the "brand identity" and
"brand equity" of online media that has been perpetuated by--ironically
enough--the traditional media.  For the average person, the "brand identity" of
the online universe represents universal interconnectivity, versatility and
infinite breadth and depth of content substance
(Miller, 1996).  As for the perceived "brand equity" of the online medium, it
also rides high in the public mind as an infallible source for information,
entertainment and interpersonal communication utilities (e.g., Tetzeli, 1994).
      More importantly though, the patterns of merged dimensions among perceived
online access motives are perhaps a finding worthy of further theoretical
exploration.  Traditional media use motives typically encompass most of the
following unique but intercorrelated dimensions--entertainment, escape (or
diversion), personal identity, surveillance, information learning, parasocial
communication and companionship (Rubin, 1983).  The perceived online access
motives clustered from the present data, however, collapse them into a smaller
number of multidimensional composites.  In the case of escape, companionship and
personal identity motives, a side by side comparison of these two sets of
perceived motivational dimensions shows that--while the former two motives are
fused into a single perceived TV use motive--all three motives are conjoined to
form a converged perceived online access motive.
     It is possible that the online access process as an interactive activity
evokes the perception of a medium associated with the more "intimate" feeling of
personal physical "connection."  Moreover, the medium's ability to furnish the
audience the ultimate control over its interactive process for online content
access--which could be a rather daunting task in itself--may also help stipulate
an emotional "bond" in the audience with the medium.  This intimate personal
connection and emotional bond then may be cultivated into a personal identity
with the interactive process which intrinsically defines the medium itself.
     To further dissect this somewhat curious phenomenon, one can consider the
parallel between shopping via television channels versus online channels.  Where
home shopping services on television may be linked to audience motives of
seeking companionship, escape and even parasocial gratifications (e.g., Grant,
Guthrie & Rokeach, 1991), online shopping services, however, may be linked to
additional motives such as surveillance or even personal identity.  This is
because online shopping services tend to be a repository of new, fashionable
and/or specialized products or services.  While online shoppers display the
tendency to browse and screen these more innovative products, online shopping
activity itself can be interpreted as either a conscious or unintended
status-conferral act (Miller, 1995).  The majority of online shoppers, according
to that same work, are typically men who order technology or finance oriented
products or services and who tend to strongly identify with the brand equity of
those products and services (e.g., Hawkins, 1944; Miller, 1996).
      With this perceived versatility of online services in mind, online
services--a relatively nascent medium in structural developmental terms--is far
from being seen as the designated successor to the dominant storyteller and
master marketer of our time, television.  As confirmed by the test results of
Hypothesis 4, the presumed "supplement" function of online services in relation
to television use is supported.  This finding hence helps affirm past study
evidence which, on the one hand, negates the displacement function of online
services over traditional mediated communication use (e.g., Jessell, 1995), and
on the other, embraces the supplementary relationship from online services to
the ubiquitous television medium (e.g., Berniker, 1995; James, et. al., 1995).
     The supplementary mechanism of online service access in relation to
television use, as validated here, provides a rather succinct theoretical
exposition for the parallel but distinct nature of a new versus an old medium.
It is difficult to surmise when exactly such a relation between online service
access and television use would, instead, be transformed into an emulated
version of either a complementary or displacement mechanism.  The conditions on
which these transformations may take place, however, can be envisioned.  For
this particular shift of technology paradigm to occur, online content of
interest to an average TV audience would have to become as attractive as
conventional TV content, and online access would also need to be as cost- and
energy-efficient as TV viewing access.
     Lastly, the perceptual mapping results (see Table 6, Figure 1 and Figure 2)
illustrated a near perfect description for the attitudinal distances of
perceived TV use motives, perceived online access motives and perceived online
service adoption likelihood.  As perceived TV use motives spread across in a
separate vertical dimension from perceived online access motives, they also
stretch into rather distant locations from perceived online access motives in
the horizontal dimension.  These results then further evidence the validity of
the theoretical assumptions forwarded in this study, and in particular, the
respective multiple regression models.
      While the present study is successful in examining online service adoption
likelihood within the uses and gratifications framework, it lends further
credence to the utility of this theoretical perspective in investigating
computer-mediated communication (Kuehn, 1194; Morris & Ogan, 1996; Rafaeli,
1986; Newhagen & Rafaeli, 1996).  The theoretical parallel found between
perceived motives for TV use and online access, supported by the relatively weak
to moderate empirical evidence, points to the following several conclusions.
    Audience motives for media use decisions could be similar across both new
and old media modalities--such as the television and online media.  However, in
spite of the similarities, these motives are perceptually distinct from each
other when media adoption decisions are made, as each set of these motives is
invariably linked to a specific type of likely media adoption choice.  This
specific media adoption choice is inherently dictated by the nature of the media
technology in question, as envisaged by the audience.  In essence, the
non-interactive versus interactive communication nature of television and the
online medium helps delineate the unique perceptual dimension associated with
the perceived audience media use motives.
     This lack of unidimensional perception in media use motives is further
reflected by the supplementary relation from the online medium to the television
medium.  What challenges this relational presumption is the anticipated arrival
of digital television, a media modality which intends to fashion itself into a
sort of TV-PC service in the near future.  Meanwhile, the forthcoming PC-TV may
also rival both TV-PC and online services for audience adoption preference.
When and if both TV-PC and PC-TV become a viable media choice for the audience,
what would then be the technical and perceptual definitions for television and
online services and what would their relations become?
     It is more than apparent that a great deal of research energy and
theorizing effort will be needed to disentangle this complex "web" of new media
with interchangeable technical traits but distinct content characteristics.
This perplexity notwith-standing, the research challenges ahead also provide the
impetus for studying the changing nature of mediated communication, an ever
increasing part of our human communication infrastructure.
Table 1     Factor Analysis for Perceived Television Use Motives
                  Factor   Factor   Factor   Factor   Factor
1        2        3        4        5
Fun                                           .81
Entertainment                                 .82
Excitement                                    .57
Relaxation                                    .49
Local News         .77
National News      .92
World News         .89
Intellectual       .52
Kill Time                            .69
Companionship                        .75
Boredom Relief                        .78
Forget Problems                      .56
Reality Escape                       .43
Problem Solving
Solve Problems                                          .68
Learn Skills                                            .79
Personal Identity
Belief Identity             .84                                   Attitude
Identity           .88
Behavior Identity           .85
% Variance          25      15.1     9.5       7.2       6.8
Table 2     Factor Analysis for Perceived Online Access Motives
                                Factor Loadings
Variables                     1         2        3
Seek Companionship           .74
Relief Boredom               .67
Solve Problem                .66
Forget Problems              .79
Escape Problems              .67
Relax                        .60
Chat On Line                 .51
Make Friends On Line         .54
Get Local News                        .79
Get National News                     .86
Get World News                        .85
Enhance Intellectual Growth           .59
Have Fun                                       .78
Find Excitement                                .77
Be Entertained                                 .78
Variance Explained            52%      10%      7%
Eigenvalue                    8.2      1.7     1.1
Table 3   Factor Analysis For Likely Online Service Adoption
                                     Factor Loadings
Variables                             1     2     3
Shopping Services
General Merchandise Orders                       .74
Grocery Orders/Delivery                          .79
Restaurant Reservation/Delivery                  .76
Travel Reservation                               .53
Entertainment Ticket Reservation                 .55
Information Services
Electronic Mail                     .52
Yellow Pages                        .67
Taxes                               .55
Banking                             .61
Financial Market                    .69
Library Search                      .81
Encyclopedia                        .73
Infotainment Services
Newspapers                                 .59
Magazines                                  .62
Television News                            .75
Sports News/Information                    .65
TV Program/Content Guide                   .72
Movie News/Reviews                         .74
Weather Forecasts                          .58
Retail Ads                                 .55
Variance Explained                 49.1%   6.8%    6.1%
Eigenvalues                        10.2    1.5     1.3
Table 4   Zero-Order Correlations between Perceived Motives for
              TV Use and Online Service Use, TV Use level
                  and Likely Online Service Adoption
            Likely Online Service Adoption     Perceived Online Access Motives
                Shopp-  Informa-  Info-   Entertain-  Surveill-  Companionship/
                 ing     tion    tainment    ment        ance       Identity
Perceived TV
Use Motives
Surveillance     .06     .11*     .03        -.03       .23**     -.01
Identity         .14*    .07      .17**       .18*      .23**      .29**
Escape/          .17**   .03      .20**       .25**     .07        .35**
TV Use Level    -.07    -.11*    -.02        -.03       .14**      .16**
Perceived Online
Access Motives
Entertainment    .58**   .59**    .58**        --        --         --
Surveillance     .54**   .60**    .59**        --        --         --
Escape/          .58**   .59**    .58**        --        --         --
*  represents two-tailed significance level p < .05.
** represents two-tailed significance level p < .01.
Table 5       Multiple Regression Analysis for Predicting
                  Likelihood of Online Service Adoption
                    Media          Information         Shopping
                   Services          Services          Services
 Variables        Beta    p         Beta    p         Beta    p
Perceived Online
Access Motives
Entertainment     .19   .005        .24   .002        .28   .0001
Surveillance      .35   .000        .32   .000        .27   .0001
Escape/           .23   .001        .26   .0001       .19   .009
Multiple R        .665  .000        .69   .000        .63   .000
R2 Change         .44   .000        .47   .000        .40   .000
Perceived TV
Use Motives
Identity         -.04   .43        -.11   .03        -.06   .23   Surveillance
-.05   .33         .06   .21         .02   .66    Escape/           .06   .27
-.06   .26         .03   .63
Multiple R        .67   .000        .70   .000        .64   .000
R2 Change         .01   .000        .02   .000        .01   .000
Final R2          .45   .000        .49   .000        .64   .000
Table 6   MDS Concept Coordinates for All Variables
                         Dimension1  Dimension2
Variables                  Online    Television
Likelihood of
Online Access
Shopping Services           .91        -.06
Information Services        .98        -.36
Media Services              .85         .13
Perceived Online
Access Motives
Entertainment               .78         .45
Surveillance                .53        -.63
Escape/                     .42         .60
Perceived TV
Use Motives
Surveillance              -1.34       -2.06
Identity                  -1.78         .51
Escape/                   -1.34        1.43
Stress = .112
R2     = .952
Table 6   MDS Concept Coordinates for All Variables
                         Dimension1  Dimension2
Variables                  Online    Television
Likelihood of
Online Access
Shopping Services           .91        -.06
Information Services        .98        -.36
Media Services              .85         .13
Perceived Online
Access Motives
Entertainment               .78         .45
Surveillance                .53        -.63
Escape/                     .42         .60
Perceived TV
Use Motives
Surveillance              -1.34       -2.06
Identity                  -1.78         .51
Escape/                   -1.34        1.43
Stress = .112
R2     = .952
        Figure 1   Derived Stimulus Configuration Plot
             Dimension 1 (Horizontal)  Vs  Dimension 2 (Vertical)
             :                         :                         :
        2.1 -+                         :                         +
             :                         :                         :
             :                         :                         :
             :                         :                         :
             :            9            :                         :
             :                         :                         :
        1.0 -+                         :                         +
             :                         :                         :
             :                         :                         :
             :       8                 :   6   4                 :
             :                         :                         :
             :                         :       3                 :
        0.0 -+----------------------------------1----------------+
             :                         :                         :
             :                         :         2               :
             :                         :                         :
             :                         :    5                    :
             :                         :                         :
       -1.0 -+                         :                         +
             :                         :                         :
             :                         :                         :
             :                         :                         :
             :                         :                         :
             :                         :                         :
       -2.1 -+            7            :                         +
             :                         :                         :
            -2.5      -1.5      -0.5       0.5       1.5       2.5
        *Plot Symbol:
         1 - Shopping Service Adoption Likelihood
         2 - Information Service Adoption Likelihood
         3 - Infotainment Service Adoption Likelihood
         4 - Perceived Entertainment Motive for Online Access
         5 - Perceived Surveillance Motive for Online Access
         6 - Perceived Escape/Companionship/Identify Motive for
             Online Access
         7 - Perceived Surveillance Motive for TV Use
         8 - Perceived Personal Identity Motive for TV Use
         9 - Perceived Escape/Companionship Motive for TV Use
         Figure 2   Scatterplot of Linear Fit
        3.8 -+                                                   +
             :                                                   :
             :                                             X     :
             :                                                   :
             :                                            X      :
             :                                          XX       :
        3.0 -+                                       XX          +
             :                                      X X          :
             :                                   2 X  X          :
             :                                X XX X             :
             :                                                   :
             :                               XX                  :
        2.2 -+                             X                     +
             :                          X                        :
             :                                                   :
             :                                                   :
             :                                                   :
             :                                                   :
        1.3 -+            X                                      +
             :           X  X                                    :
             :                     X                             :
             :            2 X                                    :
             :               X                                   :
             :             X                                     :
        0.5 -+X          XX                                      +
             : 2           X                                     :
             :      X                                            :
             0.0       0.7       1.5       2.2       3.0       3.7
     Berniker, H. (1995, November 6). Internet begins to cut into TV viewing.
Broadcasting & Cable, p. 113.
     Blumler, J. G. (1979). The role of theory in uses and
gratifications studies. Communication Research, 6, 9-36.
     Childers, T., & Krugman, D. (1987).  The competitive
environment of pay-per-view. Journal of Broadcasting &
Electronic Media, 31, 335-342.
     Crispell, D. (1997, May). The Internet of TV. American      Demographics,
     Eighmey, J. (1997). Profiling user responses to commercial
web sites, Journal of Advertising Research, 37, 59-66.
     Garramone, G., Harris, A., & Anderson, R. (1986).  Uses of
political bulletin boards. Journal of Broadcasting & Electronic Media, 30,
     Grant, A. E., Guthrie, K. K., & Ball-Rokeach, S. B. (1991).   Television
shopping. Communication Research, 18(6), 773-798.
     Hawkins, D. T. (1994, March). Online information $y$term$.  Online, 26-38.
     Henke, L. L. & Donohue, T. R. (1989). Functional displacement of
traditional television viewing by VCR owners, Journal of Advertising Research,
29(2), 18-23.
     James, M. L., Worting, C. E., & Forrest, E. J. (1995).  An   exploratory
study of the perceived benefits of electronic
bulletin board use and their impact on other communication
activities.  Journal of Broadcasting & Electronic Media,
39, 30-50.
     Jeffres, L., & Atkin, D. (1996).  Predicting use of technologies for
consumer and communication needs.  Journal of Broadcasting & Electronic Media,
40, 318-330.
     Jessell, M. (1995, November 6).  Internet begins to cut into TV viewing.
Broadcasting & Cable, 113.
     Katz, E., Blumler, J. G., & Gurevitch, M. (1974). Utilization of mass
communication by the individual. In J. G. Blumler & E.
     Laswell, H. The structure and function of communication in society.  In L.
Bryson (Ed.), The Communication of Idea. New York: Harper.
     Levy, M. R. & Windahl, S. (1984). Audience activity and gratifications: a
conceptual clarification and exploration.
Communication Research, 11, 51-78.
     Lin, C. A. (1993). Exploring the role of VCR use in the emerging home
entertainment culture.
 Journalism Quarterly, 70, 833-842.
     Lin, C. A. (1994).  Audience fragmentation in a competitive
video marketplace.  Journal of Advertising Research, 34, 1-17.
     Lin, C. A. (1994a).  Exploring potential factors for home
videotext adoption. Advances in Telematics, 2, 111-121.
     Lin, C. A. (1996). Looking back: the contribution of Blumler and Katz's
Uses of Mass Communication to communication research.
Journal of Broadcasting & Electronic Media, 574-581.
     Miller, T. E. (1995, April). New markets for information.
American Demographics. 46-54.
     Miller, T. E. (1996, July). Segmenting the Internet. American Demographics,
     Morris, M., & Ogan, C. (1996). The Internet as mass medium.
Journal of Communication, 46(1), 39-50.
     Newhagen, J. E., & Rafaeli, S. (1996).  Why communication
researchers should study the Internet: A dialogue. Journal of
Communication, 46(1), 4-13.
     Nielsen Media Research (1997a).  Primetime network rating and shares
report. Cognizant Corporation, New York.
     Nielsen Media Research (1997b, December). CommerceNet/ Nielsen Media
Research Survey. Cognizant Corporation, New York.
     Palmgreen, P., Wenner, L. A. & Rosengren, K. E. (1985).
Uses and gratifications research: the past ten years.  In
K. E. Rosengren, L. A. Wenner, & P. Palmgreen (Eds.), Media
Gratifications Research: Current Perspectives (pp. 11-37),
Beverly Hills, CA: Sage.
     Rafaeli, S. (1986)   The electronic bulletin board: a computer-driven mass
medium. Computers and the Social Sciences, 2, 123-136.
     Rubin, A. M. (1981). An examination of television viewing
motivations. Communication Research, 8, 141-165.
     Rubin, A. M. (1984).  Ritualized versus instrumental television viewing.
Journal of Communication, 34(3), 67-77.
     Rubin, A. M. (1983).  Television uses and gratifications: the interactions
of viewing patterns and motivations. Journal of
Broadcasting, 27, 37-51.
     Rubin, A. M. & Perse, E. M. (1987). Audience activity and television news
gratifications. Communication Research, 14,
     Sanberg, J. (1997, March 6). PC makers' push into more homes may be
faltering. Wall Street Journal, p. B2.
     Snider, M. (1997, July 31).  As an entertainment, net is still behind TV,
reading. USA Today, p. 1D.
     Tetzeli, R. (1994, March 7). The internet and your business. Fortune, pp.
     Weaver, D. H. & Budeenbaum, J. M. (1979, April 20). Newspapers and
television: a review of research on uses and effects. ANPA News Research Report,
19. Washington, D. C.: ANPA.

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