Effect of fatigue on consumers' advertising processing

Huge advertising budgets are invested by firms to reach and convince potential consumers to buy their products. To optimize these investments, it is fundamental not only to ensure that appropriate consumers will be reached, but also that they will be in appropriate reception conditions. Marketing research has focused on the way consumers react to advertising, as well as on some individual and contextual factors that could mediate or moderate the ad impact on consumers (e.g. motivation and ability to process information or attitudes toward advertising). Nevertheless, a factor that potentially influences consumers’ advertising reactions has not yet been studied in marketing research: fatigue. Fatigue can yet impact key variables of advertising processing, such as cognitive resources availability (Lieury 2004). Fatigue is felt when the body warns to stop an activity (or inactivity) to have some rest, allowing the individual to compensate for fatigue effects. Dittner et al. (2004) defines it as “the state of weariness following a period of exertion, mental or physical, characterized by a decreased capacity for work and reduced efficiency to respond to stimuli.’’ It signals that resources will lack if we continue with the ongoing activity. According to Schmidtke (1969), fatigue leads to troubles in information reception, in perception, in coordination, in attention getting, in concentration and in thinking. In addition, for Markle (1984) fatigue generates a decrease in memory, and in communication ability, whereas it increases time reaction, and number of errors. Thus, fatigue may have large effects on advertising processing. We suggest that fatigue determines the level of available resources. Some research about consumer responses to advertising claim that complexity is a fundamental element to take into consideration. Complexity determines the cognitive efforts the consumer must provide to understand the message (Putrevu et al. 2004). Thus, we suggest that complexity determines the level of required resources. To study this complex question about need and provision of cognitive resources, we draw upon Resource Matching Theory. Anand and Sternthal (1989, 1990) are the first to state the Resource Matching principle, saying that an ad is most persuasive when the resources required to process it match the resources the viewer is willing and able to provide. They show that when the required resources exceed those available, the message is not entirely processed by the consumer. And when there are too many available resources comparing to those required, the viewer elaborates critical or unrelated thoughts. According to the Resource Matching theory, the level of resource demanded by an ad can be high or low, and is mostly determined by the ad’s layout (Peracchio and Myers-Levy, 1997). We manipulate the level of required resources using three levels of ad complexity (low – high – extremely high). On the other side, the resource availability of an ad viewer is determined by lots of contextual and individual variables. We manipulate the level of available resources using two levels of fatigue (low – high). Tired viewers want to limit the processing effort to minimal resource requirements by making heuristics, forming overall impression at first glance. It will be easier for them to decode the message when ads are very simple. On the contrary, the most effective ads for viewers who are not tired are complex enough to draw their attention and fully use their resources. They will use more analytical strategies, looking at the details of the ad. However, if ads are too complex, they will be too difficult to understand. The viewer will be discouraged to process information and will overlook the ad. The objective of our research is to study fatigue as a moderating variable of advertising information processing. We run two experimental studies to assess the effect of fatigue on visual strategies, comprehension, persuasion and memorization. In study 1, thirty-five undergraduate students enrolled in a marketing research course participated in the experiment. The experimental design is 2 (tiredness level: between subjects) x 3 (ad complexity level: within subjects). Participants were randomly assigned a schedule time (morning: 8-10 am or evening: 10-12 pm) to perform the experiment. We chose to test subjects at various moments of the day to obtain maximum variance in their fatigue level. We use Morningness / Eveningness tendency of participants (Horne & Ostberg, 1976) as a control variable. We assess fatigue level using subjective measures - questionnaire with fatigue scales - and objective measures - reaction time and number of errors. Regarding complexity levels, we have designed our own ads in order to keep aspects other than complexity equal. We ran a pretest using the Resource Demands scale (Keller and Bloch 1997) and by rating them on complexity like Morrison and Dainoff (1972) to check for our complexity manipulation. We found three significantly different levels. After having completed the fatigue scales, participants are asked to view the ads on a screen, while their eye movements are recorded by the eye-tracker. Eye-tracking allows us to find out patterns of visual attention (Pieters and Warlop 1999). We are then able to infer specific respondents’ visual strategies according to their level of fatigue. Comprehension is assessed with a comprehension test. We collect measures of attitude change for persuasion and measures of recall and recognition at various points of time for memorization. Once the effect of fatigue will be determined across the student population, it is interesting to account for individual differences in fatigue severity and perception. Therefore, we run study 2, which is similar to the previous one except for the design: time of day is now within-subjects and complexity becomes between-subjects

Mach bands and multiscale models of spatial vision:the role of first, second, and third derivative operators in encoding bars and edges

Ernst Mach observed that light or dark bands could be seen at abrupt changes of luminance gradient in the absence of peaks or troughs in luminance. Many models of feature detection share the idea that bars, lines, and Mach bands are found at peaks and troughs in the output of even-symmetric spatial filters. Our experiments assessed the appearance of Mach bands (position and width) and the probability of seeing them on a novel set of generalized Gaussian edges. Mach band probability was mainly determined by the shape of the luminance profile and increased with the sharpness of its corners, controlled by a single parameter (n). Doubling or halving the size of the images had no significant effect. Variations in contrast (20%-80%) and duration (50-300 ms) had relatively minor effects. These results rule out the idea that Mach bands depend simply on the amplitude of the second derivative, but a multiscale model, based on Gaussian-smoothed first- and second-derivative filtering, can account accurately for the probability and perceived spatial layout of the bands. A key idea is that Mach band visibility depends on the ratio of second- to first-derivative responses at peaks in the second-derivative scale-space map. This ratio is approximately scale-invariant and increases with the sharpness of the corners of the luminance ramp, as observed. The edges of Mach bands pose a surprisingly difficult challenge for models of edge detection, but a nonlinear third-derivative operation is shown to predict the locations of Mach band edges strikingly well. Mach bands thus shed new light on the role of multiscale filtering systems in feature coding. © 2012 ARVO.