• Journal of Global Scholars of Marketing Science
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• v.19 no.4
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• pp.44-56
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• 2009

Impulse purchasing is defined as an immediate purchase with no pre-shopping intentions. Previous studies of impulse buying have focused primarily on factors linked to marketing mix variables, situational factors, and consumer demographics and traits. In previous studies, marketing mix variables such as product category, product type, and atmospheric factors including advertising, coupons, sales events, promotional stimuli at the point of sale, and media format have been used to evaluate product information. Some authors have also focused on situational factors surrounding the consumer. Factors such as the availability of credit card usage, time available, transportability of the products, and the presence and number of shopping companions were found to have a positive impact on impulse buying and/or impulse tendency. Research has also been conducted to evaluate the effects of individual characteristics such as the age, gender, and educational level of the consumer, as well as perceived crowding, stimulation, and the need for touch, on impulse purchasing. In summary, previous studies have found that all products can be purchased impulsively (Vohs and Faber, 2007), that situational factors affect and/or at least facilitate impulse purchasing behavior, and that various individual traits are closely linked to impulse buying. The recent introduction of new distribution channels such as home shopping channels, discount stores, and Internet stores that are open 24 hours a day increases the probability of impulse purchasing. However, previous literature has focused predominantly on situational and marketing variables and thus studies that consider critical consumer characteristics are still lacking. To fill this gap in the literature, the present study builds on this third tradition of research and focuses on individual trait variables, which have rarely been studied. More specifically, the current study investigates whether impulse buying tendency has a positive impact on impulse buying behavior, and evaluates how consumer characteristics such as the need for cognitive closure (NFCC), psychological wellbeing, and susceptibility to interpersonal influences affect the tendency of consumers towards impulse buying. The survey results reveal that while consumer affective impulsivity has a strong positive impact on impulse buying behavior, cognitive impulsivity has no impact on impulse buying behavior. Furthermore, affective impulse buying tendency is driven by sub-components of NFCC such as decisiveness and discomfort with ambiguity, psychological wellbeing constructs such as environmental control and purpose in life, and by normative and informational influences. In addition, cognitive impulse tendency is driven by sub-components of NFCC such as decisiveness, discomfort with ambiguity, and close-mindedness, and the psychological wellbeing constructs of environmental control, as well as normative and informational influences. The present study has significant theoretical implications. First, affective impulsivity has a strong impact on impulse purchase behavior. Previous studies based on affectivity and flow theories proposed that low to moderate levels of impulsivity are driven by reduced self-control or a failure of self-regulatory mechanisms. The present study confirms the above proposition. Second, the present study also contributes to the literature by confirming that impulse buying tendency can be viewed as a two-dimensional concept with both affective and cognitive dimensions, and illustrates that impulse purchase behavior is explained mainly by affective impulsivity, not by cognitive impulsivity. Third, the current study accommodates new constructs such as psychological wellbeing and NFCC as potential influencing factors in the research model, thereby contributing to the existing literature. Fourth, by incorporating multi-dimensional concepts such as psychological wellbeing and NFCC, more diverse aspects of consumer information processing can be evaluated. Fifth, the current study also extends the existing literature by confirming the two competing routes of normative and informational influences. Normative influence occurs when individuals conform to the expectations of others or to enhance his/her self-image. Whereas informational influence occurs when individuals search for information from knowledgeable others or making inferences based upon observations of the behavior of others. The present study shows that these two competing routes of social influence can be attributed to different sources of influence power. The current study also has many practical implications. First, it suggests that people with affective impulsivity may be primary targets to whom companies should pay closer attention. Cultivating a more amenable and mood-elevating shopping environment will appeal to this segment. Second, the present results demonstrate that NFCC is closely related to the cognitive dimension of impulsivity. These people are driven by careless thoughts, not by feelings or excitement. Rational advertising at the point of purchase will attract these customers. Third, people susceptible to normative influences are another potential target market. Retailers and manufacturers could appeal to this segment by advertising their products and/or services as products that can be used to identify with or conform to the expectations of others in the aspiration group. However, retailers should avoid targeting people susceptible to informational influences as a segment market. These people are engaged in an extensive information search relevant to their purchase, and therefore more elaborate, long-term rational advertising messages, which can be internalized into these consumers' thought processes, will appeal to this segment. The current findings should be interpreted with caution for several reasons. The study used a small convenience sample, and only investigated behavior in two dimensions. Accordingly, future studies should incorporate a sample with more diverse characteristics and measure different aspects of behavior. Future studies should also investigate personality traits closely related to affectivity theories. Trait variables such as sensory curiosity, interpersonal curiosity, and atmospheric responsiveness are interesting areas for future investigation.

• Journal of Chest Surgery
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• v.39 no.5 s.262
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• pp.354-358
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• 2006

Background: Pulsatile pumps for extracorporeal circulation have been known to be better for tissue perfusion than non-pulsatile pumps but be detrimental to blood corpuscles. This study is intended to examine the risks and benefits of $T-PLS^{TM}$ through the comparison of clinical effects of $T-PLS^{TM}$ (pulsatile pump) and $Bio-pump^{TM}$ (non-pulsatile pump) used for coronary bypass surgery. Material and Method: The comparison was made on 40 patients who had coronary bypass using $T-PLS^{TM}\;and\;Bio-pump^{TM}$ (20 patients for each) from April 2003 to June 2005. All of the surgeries were operated on pump beating coronary artery bypass graft using cardiopulmonary extra-corporeal circulation. Risk factors before surgery and the condition during surgery and the results were compared. Result: There was no significant difference in age, gender ratio, and risk factors before surgery such as history of diabetes, hypertension, smoking, obstructive pulmonary disease, coronary infarction, and renal failure between the two groups. Surgery duration, hours of heart-lung machine operation, used shunt and grafted coronary branch were little different between the two groups. The two groups had a similar level of systolic arterial pressure, diastolic arterial pressure and mean arterial pressure, but pulse pressure was measured higher in the group with $T-PLS^{TM}\;(46{\pm}15\;mmHg\;in\;T-PLS^{TM}\;vs\;35{\pm}13\;mmHg\;in\;Bio-pump^{TM},\;p<0.05)$. The $T-PLS^{TM}$-operated patients tended to produce more urine volume during surgery, but the difference was not statistically significant $(9.7{\pm}3.9\;cc/min\;in\;T-PLS^{TM}\;vs\;8.9{\pm}3.6\;cc/min\;in\;Bio-pump^{TM},\;p=0.20)$. There was no significant difference in mean duration of respirator usage and 24-hour blood loss after surgery between the two groups. Plasma free Hb was measured lower in the group with $T-PLS^{TM}\;(24.5{\pm}21.7\;mg/dL\;in\;T-PLS^{TM}\;versus\;46.8{\pm}23.0mg/dL\;in\;Bio-pump^{TM},\;p<0.05)$. There was no significant difference in coronary infarction, arrhythmia, renal failure and morbidity rate of cerebrovascular disease. There was a case of death after surgery (death rate of 5%) in the group tested with $T-PLS^{TM}$, but the death rate was not statistically significant. Conclusion: Coronary bypass was operated with $T-PLS^{TM}$ (Pulsatile flow pump) using a heart-lung machine. There was no unexpected event caused by mechanical error during surgery, and the clinical process of the surgery was the same as the surgery for which $Bio-pump^{TM}$ was used. In addition, $T-PLS^{TM}$ used surgery was found to be less detrimental to blood corpuscles than the pulsatile flow has been known to be. Authors of this study could confirm the safety of $T-PLS^{TM}$.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.1041-1043
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• 1993

Fuzzy logic based Control Theory has gained much interest in the industrial world, thanks to its ability to formalize and solve in a very natural way many problems that are very difficult to quantify at an analytical level. This paper shows a solution for treating membership function inside hardware circuits. The proposed hardware structure optimizes the memoried size by using particular form of the vectorial representation. The process of memorizing fuzzy sets, i.e. their membership function, has always been one of the more problematic issues for the hardware implementation, due to the quite large memory space that is needed. To simplify such an implementation, it is commonly [1,2,8,9,10,11] used to limit the membership functions either to those having triangular or trapezoidal shape, or pre-definite shape. These kinds of functions are able to cover a large spectrum of applications with a limited usage of memory, since they can be memorized by specifying very few parameters ( ight, base, critical points, etc.). This however results in a loss of computational power due to computation on the medium points. A solution to this problem is obtained by discretizing the universe of discourse U, i.e. by fixing a finite number of points and memorizing the value of the membership functions on such points [3,10,14,15]. Such a solution provides a satisfying computational speed, a very high precision of definitions and gives the users the opportunity to choose membership functions of any shape. However, a significant memory waste can as well be registered. It is indeed possible that for each of the given fuzzy sets many elements of the universe of discourse have a membership value equal to zero. It has also been noticed that almost in all cases common points among fuzzy sets, i.e. points with non null membership values are very few. More specifically, in many applications, for each element u of U, there exists at most three fuzzy sets for which the membership value is ot null [3,5,6,7,12,13]. Our proposal is based on such hypotheses. Moreover, we use a technique that even though it does not restrict the shapes of membership functions, it reduces strongly the computational time for the membership values and optimizes the function memorization. In figure 1 it is represented a term set whose characteristics are common for fuzzy controllers and to which we will refer in the following. The above term set has a universe of discourse with 128 elements (so to have a good resolution), 8 fuzzy sets that describe the term set, 32 levels of discretization for the membership values. Clearly, the number of bits necessary for the given specifications are 5 for 32 truth levels, 3 for 8 membership functions and 7 for 128 levels of resolution. The memory depth is given by the dimension of the universe of the discourse (128 in our case) and it will be represented by the memory rows. The length of a world of memory is defined by: Length = nem (dm(m)＋dm(fm) Where: fm is the maximum number of non null values in every element of the universe of the discourse, dm(m) is the dimension of the values of the membership function m, dm(fm) is the dimension of the word to represent the index of the highest membership function. In our case then Length=24. The memory dimension is therefore 128*24 bits. If we had chosen to memorize all values of the membership functions we would have needed to memorize on each memory row the membership value of each element. Fuzzy sets word dimension is 8*5 bits. Therefore, the dimension of the memory would have been 128*40 bits. Coherently with our hypothesis, in fig. 1 each element of universe of the discourse has a non null membership value on at most three fuzzy sets. Focusing on the elements 32,64,96 of the universe of discourse, they will be memorized as follows: The computation of the rule weights is done by comparing those bits that represent the index of the membership function, with the word of the program memor . The output bus of the Program Memory (μCOD), is given as input a comparator (Combinatory Net). If the index is equal to the bus value then one of the non null weight derives from the rule and it is produced as output, otherwise the output is zero (fig. 2). It is clear, that the memory dimension of the antecedent is in this way reduced since only non null values are memorized. Moreover, the time performance of the system is equivalent to the performance of a system using vectorial memorization of all weights. The dimensioning of the word is influenced by some parameters of the input variable. The most important parameter is the maximum number membership functions (nfm) having a non null value in each element of the universe of discourse. From our study in the field of fuzzy system, we see that typically nfm 3 and there are at most 16 membership function. At any rate, such a value can be increased up to the physical dimensional limit of the antecedent memory. A less important role n the optimization process of the word dimension is played by the number of membership functions defined for each linguistic term. The table below shows the request word dimension as a function of such parameters and compares our proposed method with the method of vectorial memorization[10]. Summing up, the characteristics of our method are: Users are not restricted to membership functions with specific shapes. The number of the fuzzy sets and the resolution of the vertical axis have a very small influence in increasing memory space. Weight computations are done by combinatorial network and therefore the time performance of the system is equivalent to the one of the vectorial method. The number of non null membership values on any element of the universe of discourse is limited. Such a constraint is usually non very restrictive since many controllers obtain a good precision with only three non null weights. The method here briefly described has been adopted by our group in the design of an optimized version of the coprocessor described in [10].

• Korean Journal of Heritage: History & Science
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• v.48 no.3
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• pp.96-119
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• 2015

This research has studied the changes of Gwi-po(轉角包) by taking the cases of China's medieval wooden buildings as objects. The purpose of the study is to examine the time-periodic transition process of Gwi-po through the cases of 71 wooden buildings which were built from Tang(唐) dynasty(AD 618~690 & 705~907) until Jin(金) dynasty(AD 1115~1234) and also designated as 'Major Historical and Cultural Sites Protected at the National Level'. This research has taken note of various frame types of Jwau-dae(左右隊), which are architectural components of Gwi-po, to study the changes and development process of Gwi-po. The results are as follows. An important factor in the transformations of Gwi-po format is the changes in perception of the craftsmen about Jwau-dae, who took charge in the building process. In the early periods, the principles of Yidou sanshen dougong(一斗三升) in constructing ancons of Gwi-po had been well-maintained, while there appeared many different types of Gwi-po in later periods, due to the usage of Jwau-dae and $Shu{\check{a}}$ $t{\acute{o}}u$(?頭) in each Chulmok of Gwi-po. Transitional types of Gwi-po, which were evolved from the earlier ones, are divided into 3 categories by different forms of Jwau-dae, placed on odd number stages. The first one is 'none-$f{\bar{a}}ng$ $t{\acute{o}}u$(無枋頭) type' of Song(AD 960~1127, 1127~1279) and Liao dynasty(AD 907~1125) buildings, which doesn't have $f{\bar{a}}ng$ $t{\acute{o}}u$(枋頭)s, for the reason that Jwau-dae(左右隊) is in direct contact with Gwihan-dae(耳限大). The second one is '$Shu{\check{a}}$ $t{\acute{o}}u$ $f{\bar{a}}ng$ $t{\acute{o}}u$(?頭枋頭) type' of Song(AD 960~1127, 1127~1279) and Jin dynasty(AD 1115~1234), that has $f{\bar{a}}ng$ $t{\acute{o}}u$(枋頭)s of Jwau-dae(左右隊) identical to $Shu{\check{a}}$ $t{\acute{o}}u$(?頭) in form. The last one is '$Xi{\check{a}}o$ $g{\check{o}}ng$ $t{\acute{o}}u$(小?頭) type' of Jin(AD 1115~1234) and Yuan dynasty(AD 1271~1368), which has $f{\bar{a}}ng$ $t{\acute{o}}u$(枋頭)s of Jwau-dae identical to $Xi{\check{a}}o$ $g{\check{o}}ng$ $t{\acute{o}}u$(小?頭) in form. The earlier forms of Gwi-po, which appeared between Tang dynasty(AD 618~690 & 705~907) and Five Dynasties periods(907~960) went through transitional forms of 'non-$f{\bar{a}}ng$ $t{\acute{o}}u$(無枋頭) type', '$Shu{\check{a}}$ $t{\acute{o}}u$ $f{\bar{a}}ng$ $t{\acute{o}}u$(?頭枋頭) type' and '$Xi{\check{a}}o$ $g{\check{o}}ng$ $t{\acute{o}}u$(小?頭) type' and finally had its form settled between Yuan(元, AD 1271~1368) and Ming(明. AD 1368~1644) dynasty periods. In Liao(遼) dynasty period(AD 907~1125), as the buildings got bigger and the tendency of longer eave-exposure was implemented, there grew a certain need to structurally reinforce Gwi-po, on which load of the whole roof is concentrated. Especially, the transition from Tōuxīn $z{\grave{a}}o$(偸心造) style to Jì xīn $z{\grave{a}}o$(計心造) style in this period had a great influence on standardization of Gwi-po, along with None-${\acute{A}}ng$(無仰) style. Furthermore, Wing-type Gong(翼型?), which developed in Liao dynasty(AD 907~1125), is also thought to have had a great influence on the transition from Tōuxīn $z{\grave{a}}o$(偸心造) style to Jì xīn $z{\grave{a}}o$(計心造) style by changing the forms of Gongs(?), such as Gwi-po. However, unlike None-${\acute{A}}ng$(無仰) style, there occurred a gradual change from '$Shu{\check{a}}$ $t{\acute{o}}u$ $f{\bar{a}}ng$ $t{\acute{o}}u$(?頭枋頭) type' to '$Xi{\check{a}}o$ $g{\check{o}}ng$ $t{\acute{o}}u$(小?頭) type' of Gwi-po in $Xi{\grave{a}}$ ${\acute{a}}ng$ style.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.173-198
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• 2020

For a long time, many studies have been conducted on predicting the success of campaigns for customers in academia, and prediction models applying various techniques are still being studied. Recently, as campaign channels have been expanded in various ways due to the rapid revitalization of online, various types of campaigns are being carried out by companies at a level that cannot be compared to the past. However, customers tend to perceive it as spam as the fatigue of campaigns due to duplicate exposure increases. Also, from a corporate standpoint, there is a problem that the effectiveness of the campaign itself is decreasing, such as increasing the cost of investing in the campaign, which leads to the low actual campaign success rate. Accordingly, various studies are ongoing to improve the effectiveness of the campaign in practice. This campaign system has the ultimate purpose to increase the success rate of various campaigns by collecting and analyzing various data related to customers and using them for campaigns. In particular, recent attempts to make various predictions related to the response of campaigns using machine learning have been made. It is very important to select appropriate features due to the various features of campaign data. If all of the input data are used in the process of classifying a large amount of data, it takes a lot of learning time as the classification class expands, so the minimum input data set must be extracted and used from the entire data. In addition, when a trained model is generated by using too many features, prediction accuracy may be degraded due to overfitting or correlation between features. Therefore, in order to improve accuracy, a feature selection technique that removes features close to noise should be applied, and feature selection is a necessary process in order to analyze a high-dimensional data set. Among the greedy algorithms, SFS (Sequential Forward Selection), SBS (Sequential Backward Selection), SFFS (Sequential Floating Forward Selection), etc. are widely used as traditional feature selection techniques. It is also true that if there are many risks and many features, there is a limitation in that the performance for classification prediction is poor and it takes a lot of learning time. Therefore, in this study, we propose an improved feature selection algorithm to enhance the effectiveness of the existing campaign. The purpose of this study is to improve the existing SFFS sequential method in the process of searching for feature subsets that are the basis for improving machine learning model performance using statistical characteristics of the data to be processed in the campaign system. Through this, features that have a lot of influence on performance are first derived, features that have a negative effect are removed, and then the sequential method is applied to increase the efficiency for search performance and to apply an improved algorithm to enable generalized prediction. Through this, it was confirmed that the proposed model showed better search and prediction performance than the traditional greed algorithm. Compared with the original data set, greed algorithm, genetic algorithm (GA), and recursive feature elimination (RFE), the campaign success prediction was higher. In addition, when performing campaign success prediction, the improved feature selection algorithm was found to be helpful in analyzing and interpreting the prediction results by providing the importance of the derived features. This is important features such as age, customer rating, and sales, which were previously known statistically. Unlike the previous campaign planners, features such as the combined product name, average 3-month data consumption rate, and the last 3-month wireless data usage were unexpectedly selected as important features for the campaign response, which they rarely used to select campaign targets. It was confirmed that base attributes can also be very important features depending on the type of campaign. Through this, it is possible to analyze and understand the important characteristics of each campaign type.