隨著智慧型手機、平板電腦以及行動網路的普及與流行，使得人們使用3C產品的時間也隨之增加。平均每個使用者每天盯看智慧型手持裝置螢幕的時間超過1小時，但是眼睛是靈魂之窗，過度使用3C產品會對於視力產生不良影響。根據相關研究，許多3C產品，包括平板顯示器、LED霓虹燈、螢光燈、電腦顯示器、手機螢幕等，所發出之背景光源皆含有異常的高能藍光。而這種高能藍光會對視網膜產生傷害，造成眼睛疲勞、乾澀、痠痛等困擾。長期下來將引發相關眼部疾病，主要包括像是黃斑部病變、白內障惡化等，因此市面上出現了許多以抗藍光為主軸的保健眼鏡。然而消費者對於眼鏡的需求從過去看重功能性的情況，近年來轉變為重視外觀、流行感的需求。因此對於製造抗藍光眼鏡之廠商而言，如何利用網路熱潮以及考量消費者需求來拓展市占率則成為主要目標。所以，本研究方法利用線性機率模型（Linear Probability Model），探討消費者在購買抗藍光眼鏡時，有哪些影響購買意願的因素，並依結果提出建議。本研究針對網路及3C產品使用者，共發出400份調查問卷，並回收305份有效問卷。透過SPSS統計軟體分析得出研究結果，顯示在p=0.01顯著水準之下，產品外觀因素、眼科醫師因素以及3C產品使用習慣因素等，對於是否購買抗藍光眼鏡會有顯著影響。因此本研究建議相關產品需考量流行元素，同時與專科醫師合作推廣。另外，研究也發現不同年齡層以及每週不同的3C使用時間也會影響消費者購買抗藍光眼鏡的意願，因此建議廠商可以針對不同消費族群推出不同的行銷方式來應變。
關鍵字：抗藍光眼鏡、線性機率模型、購買意願

Extended Abstract
The Key Factors that Affect Consumers Purchase Anti-blue Ray Glasses Using Online Shopping Platforms
Author's Name：Wu, Jung-Tsung
Advisor's Name：Kang, Hsin-Hong
Chuang, Shuang-Shii
Executive Master of Business Administration of National Cheng Kung University

SUMMARY
With the popularity of the smart phones, tablet PCs, and the mobile network, the use time of the 3C products are quickly increased. The average time of each user to stare at the screen is more than 1hour per day. It will bring the undesirable effect to our eyes. According to research, most of the background light in 3C products including flat panel displays, LED neon lights, fluorescent lamps, computer monitors, and mobile phone screens emit the high-energy blue ray. This high-energy blue ray may harm the retina and cause eye fatigue, dryness, pain and other problems. Therefore, the corresponding product of anti-blue ray glasses is provided and which claims that can protect from the damage of the blue ray. Compared with the functionality of the product, the consumers are better concerned the appearance of the product in recently. How to expand the market share in the anti-blue ray glasses market by using the mobile network and considering the favorites of the consumers is the first priority to the anti-blue ray manufactures. For this reason, this study adopts the Linear Probability Model to analyze the critical factors of the purchase intention when consumers buy anti-blue glasses. 400 copies of the questionnaires are launched and return 305 valid copies. The results show that the appearance factor, the ophthalmologist factor and the habit of the 3C product usage factor have significant effect to the purchasing intention of the anti-blue ray glasses under the significant level of p = 0.01. We also find that different age and different 3C use time will influence too.
Keywords: Anti-blue ray glasses, Linear probability model, Purchasing intention

INTRODUCTION
The study wants to realize the critical factors for consumer to purchase the anti-blue ray glasses. With the increasing notice in sight health protection, the purchasing intention of the consumers in buying the anti-blue ray glasses should be taken account of. On the other hand, the great progress in mobile network brings the new business opportunity for anti-blue glasses market. Therefore, the purpose of this thesis is to find out in what reason that the consumer will buy the anti-blue ray glasses and whether the consumers will buy the anti-blue glasses through the network transaction platform by giving the questionnaire. According to this research conclusion, we can realize the important factor for consumer to making a purchasing decision.

MATERIAL AND METHODS
Linear probability model can be used to handle the binary question. The result of consumer to make a buying decision can be divided into two scenarios: one is buying and the other is not buying. This is a classical binary classification problem. The linear probability model can be used to handle the binary classification problem and performs a satisfactory job in this task. The linear probability model of our study was represented as follows:
Y=a_1 x_1+a_2 x_2+a_3 x_3+a_4 x_4+a_5 x_5+a_6 x_6+a_7 x_7+a_8 x_8+a_9 x_9+a_10 x_10+a_11 x_11+a_12 x_12+a_13 D_1+a_14 D_2+a_15 D_3+a_16 D_4+a_17 D_5+a_18 D_6+ε

RESULTS AND DISCUSSION
The network transaction platform and other consumer related factors may affect the choice of buying anti-blue ray glasses, this research take general network consumer as the research object and issue 400 questionnaires. By deleting the invalid samples, the valid questionnaires are 305 copies. This study performs a linear probability model to deal with our research issues. The result is shown in Table 1, and the hypothesis 6, the hypothesis 8, and the hypothesis 12 are significant under the 0.01 p-value level. As the result, we can find that the appearance factor, the ophthalmologist factor and the habit of the 3C product usage factor will significantly influence the decision of buying anti-blue ray glasses. As to the dummy variables (Table 2), the result shows that the age under 30 is more significant than the age between 30 and 50. It means that the younger the consumer is the more possibility that he/she buy the anti-blue ray glasses. The result also shows that the 3C use time under 2 hours is more significant than the time over 6 hours. And this evident identifies that the longer the users use their smart device the less they care able their eyes.

Table 1. The result of the study
Variable Coefficient t-Statistic Prob.
X1 -0.042 -0.719 0.473
X2 -0.121 -1.818 0.07*
X3 -0.137 -2.167 0.031**
X4 0.041 0.691 0.49
X5 0.159 2.461 0.014**
X6 0.153 2.777 0.006***
X7 -0.002 -0.036 0.971
X8 0.202 2.74 0.007***
X9 -0.173 -0.2.435 0.016**
X10 -0.038 -0.469 0.64
X11 -0.097 -1.391 0.165
X12 -0.219 -3.133 0.002***
***(P ＜0. 01)

Table 2. Dummy Variables
Variable Coefficient t-Statistic Prob.
D1 0.211 3.237 0.001***
D2 -0.144 -2.469 0.014**
D3 -0.089 -1.497 0.136
D4 0.037 0.519 0.604
D5 0.425 6.72 0.000***
D6 -0.009 -0.162 0.872
***(P ＜0. 01)

CONCLUSION
According our finding, we can see that the product appearance have a great effect to the consumers when purchase the anti-blue ray glasses. This is because that the consumer not only takes the anti-blue ray glasses as a health protection product but also a fashion adornment. Also, the recommendation of the ophthalmologist will enhance the purchasing intention. With the specialized recommendation, the consumers will have a better confidence in this product. Finally, the consumers who have the habit of the using 3C product will are intended not to buy the anti-blue ray product because of their habitual 3C using behavior. The more they are used to using the 3C product, the more they disregard the damage which is brought by the blue ray.

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