Results
Bivariate Relationships . Pearson correlations provided partial support for hypotheses 1, 2, and 3. There was no support for hypotheses 6 or 7. Hypotheses 4 and 5, concerning the impact of VCR time‑shifting and rental, were not tested in Study 2.
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Table 2 about here
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Fear of crime was positively related to broadcast channel repertoire (r  = .09, p  < .05), consistent with hypothesis 1. Fear of crime was negatively related to cable channel repertoire (r  = -.20, p  < .001) and VCR ownership (r  = ‑.12, p  < .01), as predicted by hypothesis 2 and 3, respectively.
Mistrust was linked to broadcast channel repertoire (r  = .10,p  < .01), consistent with the hypothesis 1. Mistrust was not related to cable channel repertoire or VCR ownership, failing to support to hypothesis 2 and 3. The Pearson correlations also found no support for hypothesis 6 or 7. There was no relationship between RCD ownership and use and fear of crime or mistrust.
Fear of crime (r = .18, p < .001), but not mistrust (r  = .04, p  = .19), was related to the amount of television viewing. The relationships between television viewing and fear of crime (r  = .17, p  < .001) and mistrust (r  = .01, p  = .43) were reduced somewhat after controlling for new technology use (cable subscription, VCR ownership, RCD ownership, broadcast and cable channel repertoires, and channel changing). The relationships between television viewing and fear of crime (r  = .09, p  < .05) and mistrust (r  = ‑.02, p  = .34) were also reduced by further controlling for age, sex, education, and occupational level.
Predicting fear of crime . The hypotheses in this study are based on the idea that new television technology ownership and use would differentially affect cultivation effects beyond the influence of television viewing. Hierarchical multiple regression was used to test hypotheses derived from this reasoning. Demographic variables (age, sex, education, and occupational level) were entered on the first step to control for any variance they might contribute to cultivation effects. The regression is summarized in Table 3.
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Table 3 about here
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The demographics accounted for 24.9% (p  < .001) of the variance on the first step. At this step, sex (female), age, and occupational level were significant positive predictors. At the second step, amount of exposure to television accounted for a significant increase of 0.7% (p  < .05) of the variance. Amount of television viewing was a significant positive predictor of fear. At this stage, occupational level was no longer a significant predictor in the equation. The new technology variables, entered at the third step, added an additional 2.5% (p  < .01) to the variance.
The final equation accounted for 28.2% of the variance in fear of crime (R  = .53, adjusted R 2 = 26.3%). Cable channel repertoire (ß = -.11, p  < .05) and VCR ownership (ß = -.09, p  < .05) were significant negative predictors, supporting the second and third hypotheses. Sex (female) (ß = .42, p  < .001), age (ß = .08,p  < .05), and amount of television exposure (ß = .10,p  = .01) remained significant positive contributors. Broadcast channel repertoire, RCD ownership, and channel changing were not significant predictors of fear; there was no support for hypothesis 1, 6, or 7.4
Predicting mistrust . It was anticipated that mistrust would also be explained by differential use of new television technologies. Hierarchical multiple regression tested the study’s hypotheses. Once again, demographics were entered on the first step. The regression is summarized in Table 3.
Age, sex, education, and occupational level accounted for 6.3% (p  < .001) in the variance in mistrust. Sex (male) and education were significant negative predictors at this stage. At step two, amount of television exposure failed to account for any additional variance. The new television technology variables, entered at step three, added 2.4% to the variance (p  < .05).
The final equation accounted for 8.7% of the variance in mistrust (R  = .29, adjusted R 2 = 6.7%). Sex (male, ß = ‑.14, p  < .01) and education (ß = ‑.15,p  < .01) remained significant, negative predictors. Cable subscription (ß = .13, p  < .05) was a significant, positive predictor. Broadcast channel repertoire (ß = .10,p  < .05) was also a significant, positive predictor, supporting hypothesis 1. Cable channel repertoire (ß = ‑.13,p  < .05) was a significant, negative predictor, supporting hypothesis 2. VCR ownership, RCD ownership, and channel changing were not significant predictors of mistrust. There was no support for hypothesis 3, 6, or 7.5
Discussion
The results of this study have an important implication for cultivation research. The changing television environment has an impact on the cultivation process. As Bryant (1986) pointed out, new television technologies offer increased program diversity and audience selectivity. These newer technologies do not reinforce cultivation effects; they modify them. When new television technologies replace typical broadcast content, cultivation effects are reduced.
Our studies’ findings suggest that cable television has the strongest impact on the relationship between television exposure and cultivation effects. Cable subscription itself is positively related to some cultivation perceptions. In both Study 1 and 2, was cable subscription linked to stronger feelings of interpersonal mistrust. But, channel repertoire, or the channels that viewers typically watch, was a stronger predictor of both fear of crime and interpersonal mistrust. Consistent with our hypotheses, broadcast channel repertoire, or watching channels that present programming most like that traditionally carried by the television networks, was a positive, significant predictor of interpersonal mistrust. When viewers are exposure to ”more of the same” programming, cultivation relationships are consistent with previous research.
But, cable television does not carry only broadcast‑type channels. Cable offers new sorts of channels that present specialized and alternative programming. Consistent with expectations, higher cable channel repertoire, or watching channels that carry diverse programming, was related to negatively to cultivation perceptions. In study 2, higher cable channel repertoire was a significant, negative predictors of fear of crime and mistrust.
These findings suggest that cable might change the traditional effects of television. Cable’s specialized programming appears to reduce the impact of broadcast television’s consistent messages. Over time, cable’s impact might become even stronger. There is evidence that as people become more familiar with cable, they watch more of the specialized programming (Webster, 1986), place greater value those specialized channels (Sparkes & Kang, 1986), and find viewing them to be more satisfying than ”traditional” channels (Garay, 1988).
It is clear that channel repertoire should be an important element of media uses and effect research. Research should work to conceptualize and measure channel repertoire and understand the implications of different types of channel repertoires. The results of this study point out that it is useful to consider broadcast and cable repertoires as signals of exposure to different types of programs. Future research should explore additional meanings to channel repertoires (Ferguson & Perse, 1992).
Because exposure to a wider range of diverse cable channels was associated with resistance to cultivation effects, future research should examine influences on channel repertoire. Earlier studies suggested that media environment, types of audience activity, and the reasons people watch television affect the range of channels watched (Ferguson, 1992a; Ferguson & Perse, 1992; Heeter et al., 1988). Research should explore how to increase channel repertoires and how to encourage viewers to sample new types of programs and channels.
Videocassette recorders also seem to affect cultivation effects. This study found partial support for the hypothesized relationships between VCR ownership and reduced cultivation effects. VCR ownership was linked to lower perceptions of mistrust/anomie in Study 1 and to less fear of crime in Study 2. While VCR ownership may reflect a greater commitment to video entertainment (Morgan et al., 1990), it may also signal a more selective use of time‑shifted and rented content (Lin, 1990).
Although we found no support for hypotheses 4 and 5 in Study 1, the relationships between video rental, time‑shifting, and cultivation perceptions were negative, suggesting that renting and time‑shifting may also reduce cultivation effects. Future research should explore the ”repertoires” of video rentals and time‑shifted viewing. Levy and Fink (1984) observed that people tend to specialize in time‑shifted content. Perhaps some VCR users rent and time‑shift programming that reinforces the dominant messages of broadcast television while others specialize in more diverse content. Studies should examine whether VCR selectivity may have differential effects on the cultivation process.
Contrary to our hypotheses, ownership and use of the remote control device were unrelated to cultivation effects. Remote control devices and channel changing may have their biggest impact on media effects through their impact on channel repertoire. Higher levels of channel changing have been related to higher channel repertoires (Ferguson, 1992a; Heeter, 1985). Future research should examine the role of the RCD as a facilitator of broadcast and cable channel repertoires.
These null findings also point out the ambiguity in explaining the use of the RCD (Ferguson, 1992a; Perse, 1990; Walker & Bellamy, 1991). On one hand, channel changing can be interpreted as a selective search for or avoidance of specific content. On the other hand, channel changing has been linked to a less attentive use of television (Perse, 1990). Several researchers have argued that understanding the reasons why people change channels provides a better explanation of channel changing and other related viewing outcomes (Ferguson, 1992a; Ferguson & Perse, 1992; Walker & Bellamy, 1991). Future studies might explore whether different reasons for changing channels influence cultivation and other media effects.
The results of this study support scholars who argue that cultivation is best explained by selective exposure to specific television content (Hawkins & Pingree, 1981; Potter & Chang, 1990; Rubin et al., 1988). The widespread adoption and use of newer television technologies require that mass communication researchers reevaluate traditional media effects. With cable, VCRs, and RCDs, television content is less likely to be uniform and viewing is less likely to be habitual. Cable television, VCRs, and RCDs allow people to construct their own media environment and leads to greater audience fragmentation and polarization (Webster, 1989). Television’s traditional impact via cultivation, and other traditional media effects, may be slowly, but dramatically altered.
Notes
1The largest cable system in the county has a subscription rate of 70% (personal communication, Eric Trefz, sales manager, Heritage Cablevision, March 27, 1992).
2The four mistrust/anomie statements were: ”Most people are just looking out for themselves,” ”You can’t be too careful in dealing with people,” ”In spite of what people say, life for the average person is getting worse,” and ”It’s hardly fair to bring a child into the world the way things look for the future.”
The two fear items were: ”Crime is rising” and ”I have a good chance of becoming a crime victim.”
3The three fear items were: ”It is dangerous to walk alone in a city at night, ”I am afraid to walk alone in a city at night,” and ”I am afraid to walk alone in my own neighborhood at night.”
The three mistrust items were: ”Most of the time, people are just looking out for themselves,” ”Generally speaking, most people can be trusted,” and ”Most of the time, people try to be helpful.” The last two items were recoded in reverse to make them measures of mistrust.
4Because Morgan’s earlier work (Morgan & Rothschild, 1983; Morgan et al., 1990) suggests that new technology use interacts with television viewing in the cultivation process, we created interaction terms (cable subscription, VCR ownership, and RCD ownership and television exposure) and conducted an additional regression analysis. Demographics were entered on the first step, new technology variables (channel changing, cable CR, and broadcast CR) were entered at the second step, and the interaction terms entered on the third step of the equation. The interaction terms contributed significantly to the variance (R 2 change = .012,p  < .05). Cable CR (ß = ‑.13,p  < .01), sex (female, ß = .42,p  < .001), and the interaction between RCD ownership and television viewing (ß = .17, p  < .05) were significant predictors of fear of crime.
5Once again we tested the impact of cable‑, VCR‑, and RCD‑television viewing interaction terms on mistrust. The three interaction terms added only 1.0% to the variance (p  = .13). Sex (male, ß = -.13, p  < .01), education (ß = ‑.15,p  < .01), cable CR (ß = ‑.12, p  < .05) and the interaction between cable subscription and television exposure significantly predicted mistrust (ß = .16, p  < .05).
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Table 1
Pearson and Partial Correlations
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Fear Mistrust TV CAB VCR VID TSV RCD CC
________________________________________________________________________________
Mistrust .39 ‑‑
TV Viewing .26 .41 ‑‑
Cable Subscription .18 .17 .11 ‑‑
VCR Ownership ‑.16 ‑.20 ‑.24 .13 ‑‑
Watch Rented Videos ‑.15 ‑.12 ‑.22 .08 .33 ‑‑
Time‑shift Viewing ‑.15 ‑.08 ‑.07 .02 .19 ‑.35 ‑‑
RCD Ownership .08 .10 .02 .56 .35 .22 .09 ‑‑
Channel Changing .03 .08 .23 .29 .06 ‑.13 .10 .35 ‑‑
Age ‑.12 ‑.16 ‑.12 ‑.11 .20 .02 ‑.12 ‑.18 ‑.19
Sex .03 ‑.20 ‑.11 ‑.22 .11 .01 .02 ‑.19 ‑.42
Educ ‑.31 ‑.22 ‑.23 ‑.23 .08 .15 .10 ‑.11 ‑.14
Income ‑.19 ‑.35 ‑.35 .08 .09 .18 .10 .21 .04
TV Viewing .14 .27
(6th‑order partial)
TV Viewing .15 .26
(10th‑order partial)
_______________________________________________________________________________
Note . r = .18, p < .05; r = .24,p < .01; r = .35, p < .001.
6th‑order partials control for cable subscription, channel changing, VCR
ownership, time‑shifting, and tape renting. 10th‑order partials also
control for age, sex, educational level, and income.
Table 2
Pearson and Partial Correlations
______________________________________________________________________________
Fear Mistrust TV CAB VCR RCD CC CCR BCR
______________________________________________________________________________
Mistrust .04 –
TV Viewing .18 .04 –
Cable Subscription -.08 .03 .12 –
VCR Ownership -.12 -.07 -.01 .21 –
RCD Ownership .02 .00 .13 .30 .28 –
Channel Changing -.01 .06 .08 .24 .17 .81
Cable Channel -.20 -.06 .07 .53 .15 .21 .22 –
Repertoire
Broadcast Channel .09 .10 .28 .10 .07 .16 .12 .19 –
Repertoire
Age .11 .14 ‑.01 .11 .09 -.08 ‑.05 .12 .02
Sex .10 ‑.06 ‑.08 ‑.03 ‑.24 -.12 .03 .48 .03
Education ‑.22 .08 .12 .01 .19 -.05 ‑.13 ‑.16 -.15
Occupation .19 .16 .25 ‑.16 ‑.23 ‑.07 .09 ‑.19 .11
TV Viewing .17 .01
(6th‑order)
TV Viewing .09 ‑.02
(10th‑order)
______________________________________________________________________________
Note . r = .08, p < .05; r = .11,p < .01; r = .14, p < .001.
6th‑order partials control for cable subscription, VCR ownership, RCD ownership, channel changing, broadcast and cable channel repertoires. 10th‑order partials also control for age, sex, educational level, and occupation.
Table 3
Hierarchical Multiple Regression Summary:
Regressing Cultivation Effects
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Fear Mistrust
Step R 2 Final R 2 Final
Entered R 2 Change ß pR 2 Change ß p
______________________________________________________________________________
Demographics 1 .25 .25 .000 .06 .06 .000
Age .08 .043 ‑.06 .188
Sex .42 .000 ‑.13 .004
Education .00 .929 ‑.15 .007
Occupation .09 .235 .06 .323
Viewing Behavior 2 .25 .01 .265 .06 .00 .537
TV Exposure .08 .037 ‑.03 .913
Technology 3 .28 .03 .008 .09 .02 .043
Cable Subscription ‑.03 .539 .12 .019
VCR Ownership ‑.09 .032 ‑.06 .170
RCD Ownership .06 .300 ‑.01 .893
Channel Changing .04 .489 .04 .503
Cable Channel Repertoire ‑.11 .019 ‑.14 .012
Broadcast Channel Repertoire .06 .128 .10 .036
______________________________________________________________________________
Note . Step 1: F (4, 516) = 42.45, p < .001. F (4, 517) = 8.69, p < .001.
Step 2: F (5, 515) = 35.22, p < .001. F (5, 516) = 6.94, p < .001.
Step 3: F (11, 507) = 18.08, p < .001.F (11, 510) = 4.39, p < .001