The seasonal and meteorological nature of television viewing is typically explained by arguing that viewing competes for time with other leisure activities, and that people tend to watch less when the amount of daylight and the weather conditions permit them to be outside and engage in outdoor activities, especially warm weather out-of-home activities (Gensch and Shaman 1980; Gould et al. 1984; Roe and Vandebosch 1996). Good weather helps people choose activities that they can do outdoors. If the weather conditions are nice, they take the time to go out, participate in outdoor physical activities, visit relatives or be on the go. Inclement and uncomfortable weather conditions in contrast, such as accumulated snow and a cold outside temperature, pose a barrier to leisure activity engagement by negatively influencing mechanical or thermal comfort (Spinney and Millward 2010). While adverse weather conditions pose an opportunity for some specific outdoor activities (e.g., high winds for sailing and temperatures below freezing for ice-dependent sports such as skating), it poses a mechanical barrier for many other outdoor activities and for travel to indoor leisure activities. Inclement and uncomfortable weather reduces the range of recreation and leisure opportunities, physical and social, and can act as a mechanical or perceived barrier for accessibility and mobility to out-of-home leisure opportunities, while promoting home-based and physically passive leisure activities, such as reading and watching television. The latter is the world’s most popular sedentary pastime. On average, individuals in the industrialized world devote more than 3 h a day to traditional TV—more than half their leisure time and more than on any single activity save work and sleep (Kubey and Csikszentmihalyi 2002). Thus, the purported annual cycle in television viewing is assumed to be due to by-products of the weather. These by-products are related to diminished opportunities for being outside, including restricted travel opportunities and limited participation in recreational and other outdoor activities.
Weather and changing weather conditions not only affect leisure activity engagement. They are also popularly believed to affect people’s emotional state or mood (Persinger 1980; Watson 2000). Cold, cloudy and rainy days are typically considered to lower human moods whereas warm, sunny and dry days are taken to heighten their mood feelings. For some people, relentless rain and persistent grey skies may be depressive making the day feel long and tiring, especially when they would normally expect to be spending long warm days and evenings outside. Also, some population groups, such as the elderly, can be adversely affected by persistent precipitation, which makes it difficult for them to get out of the house, and can leave them feeling isolated and lonely, which may trigger a lower mood. Hence, inclement and uncomfortable weather is taken to be ‘bad’ weather, at least for certain activities, and bad weather is considered to make people feel ‘bad’.
It should be noted that empirical research on the association between weather and human mood is limited and of mixed results. Several studies reported that mood is responsive to weather fluctuations and that high mood is associated with more sunshine hours (Persinger 1975), high levels of sunlight (Cunningham 1979; Parrott and Sabini 1990; Schwarz and Clore 1983), low levels of humidity (Persinger 1975; Sanders and Brizzolara 1982; Howarth and Hoffman 1984), high barometric pressure (Goldstein 1972), and high temperature (Cunningham 1979; Howarth and Hoffman 1984). Mood sensitivity to weather fluctuations in these studies proves to be modest, however. Also, some studies (e.g., Clark and Watson 1988; Watson 2000) found no significant relationship between mood and any of the assessed weather variables (sunshine, barometric pressure, temperature and precipitation). Recently, Denissen et al. (2008) examined the impact of six different daily weather factors (temperature, wind, sunlight, precipitation, air pressure, and length of day) and concluded that the average effect of ‘good’ weather on positive mood was minimal. Windy, cool, and darker days seemed to have just a slight negative effect on mood, with many people reporting that they felt tired or sluggish. The researchers strained to draw a final conclusion, however, and determined in the end that “people differ in their sensitivity to daily weather changes” (Denissen et al. 2008, p. 667). This seems to corroborate the findings reported by Barnston (1988), who argued that psychologically troubled people are generally more susceptible to fluctuations in mood stemming from weather conditions than others. Also, the weather’s impact on people’s mood may vary by season. Keller et al. (2005), for example, found that moods do rise with higher temperature, but only in spring. And even during the spring, only people who spend time outside in the sun are likely to be measurably happier. These results are consistent with findings on seasonal affective disorder, a condition where depressions in fall and winter alternate with non-depressed periods in spring and summer (Rosenthal 2005), and suggest that pleasant weather (i.e., longer days with more sunlight) improves mood and broadens cognition in the spring because people have been deprived of such weather during the winter (Ennis and McConville 2004).
Several experimental studies have shown that people typically choose activities in such a way as to maintain good moods (Isen and Levin 1972; Isen and Simmonds 1978). Moreover, those in lower mood engage in various pleasure-producing activities to self-regulate their mood to comfortable levels or to keep them from getting worse (Morris and Reilly 1987; Thayer et al. 1994; Parkinson et al. 1996). One possible device for self-regulating mood is distraction, i.e., the redirection of attention away from some disturbing stimulus, and one of the most readily available sources of distraction and escape in modern society is watching television (Morris and Reilly 1987; Thayer et al. 1994).
This conjecture ties in well with mood management theory proposed by Zillmann (1988). His theory posits that television consumption is capable of altering mood states and that the selection of specific media content categories for consumption serves the optimization of mood. According to mood management theory, entertainment programs bring about mood change more effectively than do information programs (Zillmann 1988). People who suffer from lower mood therefore typically refrain from exposure to informational programs such as news and documentaries and resort to the consumption of entertainment programs, such as comedy, as it makes them feel better (Anderson et al. 1996; Zillmann 2000). Mood management through television consumption is most apparent in situations where people have little influence over the conditions that foster lower moods, such as poor weather conditions. Individuals who cannot alter the circumstances that put them in lower moods are likely to choose mood enhancers such as cheerful entertainment programs (Zillmann 1988). They, so to speak, watch their troubled moods away (Moskalenko and Heine 2007).
Taken together, we propose a specific interaction between weather and program content and its effect upon television viewing time. We assume that inclement and uncomfortable weather conditions manifest themselves in lower human moods. People who suffer from such moods turn on the television to cheer themselves up and seek appropriate positive stimuli in the television program offerings available to them (Finn and Gorr 1988). They typically turn to entertainment as it holds greater promise of improving such mood than do informational programs. We therefore hypothesize that people spend more time watching television if inclement and uncomfortable weather conditions (low temperatures, little sunshine, much precipitation, strong winds, little daylight) coincide with more broadcast time for entertainment programs (i.e., amusement, fiction, sports, children’s television, and music). Relatedly, we also predict that they spend less time watching television if the same weather conditions coincide with more broadcast time for informational programs (i.e., news, serious information and education).
To test these hypotheses, we use aggregate time series data on daily television viewing time of the Dutch audience. Given the aggregate data available to us, we are unable to empirically examine the mediating role of human mood in the effect of weather on television viewing. Instead, mood and mood management theory are used as a heuristic tool to link weather and program airtime to particular changes in TV viewing time. We test the value of this theory and the efficacy of the predictions it provides.
In addition to annual peaks and valleys in television viewing, there are other significant cycles and trends. In the Netherlands, as in many other countries, viewing peaks on Sunday and hits bottom on Thursday (Peeters et al. 2005), and under normal conditions, this weekly pattern is repeated over time. The intra-weekly cycle is the result of social factors affecting the demand for television, such as the amount of leisure time, but it is also partly due to television itself (Gensch and Shaman 1980). The horizontal programming by network schedulers requires viewers to watch television series and programs at fixed 7-day time slots. Moreover, the amount of broadcast airtime has a cyclic pattern too, with lower amounts of daily airtime on weekdays compared to the weekend, and most broadcast airtime transmitted on Sunday. The same supply and demand effects cause viewing time to peak on national holidays and breaks and on special occasions when one-time happenings or recurrent (e.g., sports) events take place. Also, the television program content cycle in network scheduling has an annual cycle in that attractive new programs (popular series and films) are broadcast in fall and winter, and less attractive end-of-season older programs (repeats and reruns) in spring and summer. Finally, there is a secular upward trend in television viewing time over the years due to, amongst others, the increase in channels and platforms and the resulting expanded range of television airtime, the increase in leisure time, the diffusion of television technology innovations, and the alteration of everyday use of time. The model we use in this study takes these cyclical patterns, high viewing days and upward trend into account.