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Abstract
It is widely argued that the rise of multichannel television services, together with other ‘new media,’ have led to a decline in family television viewing and the emergence of more individualized media culture within the household. This study, however, argues that family viewing can be treated as a variable shaping people's use and evaluations of the medium. More specifically, the survey data analysis focuses on how individuals' perceptions of family television viewing preferences influence their consumption and evaluation of both conventional mass broadcast television and multichannel television services in Hong Kong. The results show that consumption and evaluation of mass broadcast television relate positively to preference for family viewing and negatively to perceived family television preference heterogeneity. Meanwhile, multichannel television service subscription relates positively to perceived family television preference heterogeneity. Yet actual consumption and evaluation of the medium remains positively related to preference for family viewing, thus pointing towards the dual nature of the medium.
Notes
1. More specifically, doing so is likely to substantially and adversely affect the response rate of a telephone survey because: (1) the co-operation of multiple family members is more difficult to obtain, (2) the total interviewing time would be lengthened, and (3) in many cases interviews of different family members on different days would be required.
2. Information was taken from the Association's website: http://www.thepowerofpaytv.com
3. The two figures were calculated following AAPOR RR1 and RR6 respectively. A maximum response rate of about 50% is typical of survey research in the city.
4. Weighting the data by income can also reduce the discrepancies in age and education because the three demographics are correlated.
5. More precisely, 3.0% of Hong Kong households included ‘related persons forming no nuclear family’, whereas 2.2% included ‘unrelated persons only’. Besides, it can also be noted that attempts to exclude ‘non-familial households’ may be controversial because people living with ‘related persons’ or even ‘unrelated persons’ may nonetheless feel that they are living in a family of some sort. This is another reason why the minor discrepancy between ‘household’ and ‘family’ should be pose serious challenge to the present study.
6. An ? value of .58 is lower than what is usually accepted as adequate (even the most lenient conventional standard would require .60). But it should be noted that ? is sensitive to the number of items involved. The larger the number of items, the higher the ? value tends to be. For perceived family television preference heterogeneity, there are only two items involved. Hence it is difficult to achieve a high ? value. More importantly, when only two items are involved, Pearson r can be the basis for judging whether the two items can be averaged. In the present case, the averaging of the two items is based on the adequate Pearson r-value.
7. Analyzing channel repertoire with regard to free TV would not be too meaningful in the Hong Kong context given the very limited number of channels (i.e., 4) available.
8. Some US studies have shown relationships between number of children in households and cable television subscription (Collins, Reagan, & Abel, Citation1983; Ducey, Krugman, & Eckrich, Citation1983). Meanwhile, number of people and number of children are used as separate variables because they may have different implications on television consumption. The two are positively but not overwhelmingly correlated (r=.41).
9. The three statements are: (1) watching TV is an important leisure activity for me, (2) I actually don't have many other entertainment/leisure activities besides watching TV, and (3) among various leisure activities, watching TV is one of my favorites. The second statement was reverse-coded before the averaging (α=.73, M=2.61, SD=0.89).
10. While the adjusted R 2 value of the regression model is 5.5% for overall pay TV watching, the adjusted R 2 values of the same model range from 5.5% to 15.2% for consumptions of specific channels.
11. Other control variables also have varying relationships with consumption of the 12 types of channels. For example, education relates positively with consumption of informational channel and negatively with consumption of horse-racing, local entertainment, and Hong Kong movies channels.