| Summary: | 碩士 === 實踐大學 === 產品與建築設計研究所 === 96 === As the market patterns of the marketing trends are shifting from traditionally production oriented to customer oriented, only by possessing acute sense of consumers’ perceptions and needs can designers convey the product images to consumers precisely. Among many factors that compose a product, “ form “ is the major factor to image perceptions and style recognition. In connection to consumers’ style-image recognition progress towards a product, “ Gestalt Theory “ argues that product images are constructed through “ overall appearance “ ; whereas , “ Feature Analysis Theory “ argues that product images are constructed through different “ partial features”. This research is based on the relevant theories of form recognition and by using products of Giant bicycles, we observe consumers’ processing progress towards form recognitions and figure out whether their recognitions are based on feature components or the overall features. If total images of partial features of the bicycles equal overall images, it confirms that consumers’ processing progress is based on “Feature Analysis Theory “. If not, it confirms that the progress is based on “ Gestialt Theory “.
For researches conducted previously on marking the sources of feature components of product style images, consumers’ “ subjective “ reports and descriptions are obtained mostly through surveys. When conducting this research, in addition to using a survey, the eye tracking method is added. When interviewees are watching items or sceneries, eye tracking technologies will collect the messages of eye movements and measure the areas where the interviewees look or drift. The areas that the interviewees are interested in are, therefore, figured out. Eye movement messages include unconscious and objective physiological reactions which are different from subjective and conscious self-descriptions or reports. Two different research approaches will help designers probe further into consumers’ different psychological factors of form recognition progress.
Main results are as follow :
1. Experiment 1 uses subjective –report survey method to evaluate the images of bicycles as a whole. Three images are included: comfort, feminine and exercise & fitness. In addition, 5 bicycles are chosen as testing materials for subsequent experiments.
2. Research purposes of Experiment 2 and 3 are to choose crucial feature components of bicycles that best represent the images of bicycles from images of comfort, feminine and exercise & fitness. The differences between these 2 experiments are their research methods. Experiment 2 uses subjective-report- questionnaire method and Experiment 3 uses eye-tracking technologies.
3. Experiment 4 uses subjective-report survey to analyze the images of the crucial feature components (including bicycle heads, wheels, frames, seats, power transmission parts and suspensions) , then, add up all image categories, calculating total image scores for 3 groups of crucial feature components of each bicycle. The first group is the total image scores of the crucial feature components (including bicycle heads, wheels, frames , seats , power transmission parts and suspensions) selected by the questionnaires of Experiment 2 and the eye-movement of Experiment 3. The second group is the total image scores of the crucial feature components (including bicycle heads, seats, frames) selected by the subjective questionnaires of Experiment 2. The third group is the total image scores of the crucial feature components (Comfort image type: bicycle heads, frames, seats. Feminine image type: wheels, frames, power transmission parts. Athlete image type: wheels, frames, suspensions) selected by eye-movement of Experiment 3.
4. Although the procedures and purposes of bicycle images as a whole in Experiment 1 are different from the total image scores of the crucial feature components in Experiment 4, the cross-image analysis can examine the most important issue of this research: style-image recognition progress towards a product is either “Feature Analysis Theory“ or “Gestalt Theory“ . The results of the cross-image analysis show these 2 experiments have a highly significantly correlated coefficient (r = 0.958) which means consumers’ perceptions of the total image scores of bicycle feature components and the entire images are closely met. The empirical results are closer to the arguments of “Feature Analysis Theory“. Therefore, when designers are designing new bicycle styles, they are advised to emphasize more on the feature components of the product so that the images of the bicycle will be conveyed to consumers effectively.
In addition, the bicycle images as a whole are matched with the total image scores of the crucial feature components chosen by subjective description. Their correlated coefficient (r = 0.993) is slightly higher than the correlated coefficient (r = 0.991) of the whole image and the total image scores of the crucial feature components chosen by the eye tracking movement. It means the crucial feature components chosen by subjective description have more influence on consumers’ image recognition process than the eye tracking movement. However, since the difference is very little, it is advised to keep on paying attentions to the feature components chosen by consumers’ eye tracking movement.
5. The research purposes of the subjective questionnaires of Experiment 2 and the eye movement tracking of Experiment 3 are both choosing the most crucial bicycle feature components in terms of expressing images. For experiments of the same research purposes, using different research methods can examine if the results of consumers’ subjective questionnaires are consistent with objective eye movement when they are identifying image perceptions. The results show when consumers are filling in questionnaires with their subjective consciousness, the top three feature components for identifying comfort, feminine and exercise & fitness are bicycle heads, seats and frames. However, objective eye movement data has very different results. Among the top three feature components, only frames appear in the list. Subjective and objective methods are only correlated moderately (r = 0.539). It means when consumers are identifying images of bicycles, consumers’ subjective report and subconscious eye movement response have very limited similarities. The orders of top three feature components (seats > bicycle heads > frames) in comfort image chosen subjectively and objectively are consistent. The similarities for top three feature components in feminine images and exercise & fitness images chosen subjectively and objectively are very little, only frames appear in both. According to these results, designers are suggested to refer to consumers’ perceptions of objective descriptions but they still have to reserve their judgment on consumers’ physiological level which is not expressed verbally.
6. Scores of the feature component images obtained from using the entire or partial feature components to present different experimental methods are statistically significant but the correlated coefficients are not high. Experiments 2 and 3 are proceeded with presenting the entire bicycle while Experiment 4 is proceeded with presenting a single feature component. The relevance of feature component image scores between Experiments 2 and 3 is r = 0.569, while that of Experiments 3 and 4 is r = 0.475. Although it is significant but the correlation is not high which means when presented the entire bicycle or presented the feature components with a single one, participants’ image perceptions towards a single component may seem similar but they are actually not quite the same. Designers are advised that when determining the images of feature components, they should be aware that feature components should be analyzed independently or leave it in the car frame background for interpretation.
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