• Journal of Animal Science and Technology
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• v.50 no.3
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• pp.391-400
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• 2008

Odor management is significantly concerned with sustainable livestock production because odor nuisance is a primary cause for complaint to neighbors. This study was conducted to measure the concentration of odorous compounds, odor intensity, and odor offensiveness at unit process in animal waste treatment facility combined composting and methane fermentation process by an instrumental analysis and direct olfactory method. Ammonia, sulfur-containing compounds, and volatile fatty acid were analyzed at each process units and boundary area in summer and winter, respectively. Higher concentration of odorants occurred in the summer than in the winter due to high ambient temperature. The maximum concentration of odorants was detected in composting pile when mixed manure was being turned followed by inlet, curing, outlet, and screen & packing process. Highest concentration of detected odorous compounds was ammonia ranging from 3.4 to 224.7 ppm. Among the sulfur-containing compounds measured, hydrogen sulfide was a maximum level of 2.3 ppm and most of them exceeded reported odor detection thresholds. Acetic acid was the largest proportion of VFA generated, reaching a maximum of 51 to 89%, followed by propionic and butyric acid at 1.9 to 35% and 1.8 to 15%, respectively. Malodor assessment by a human panel appeared a similar tendency in instrumental analysis data. Odor quotient for predicting major odor-causing compounds was calculated by dividing concentrations measured in process units by odor detection thresholds. In the composting process, hydrogen sulfide, ammonia, dimethyl sulfide, and methyl mercaptan were deeply associated with odor-causing compounds, while the major malodor compounds in the inlet process were methyl mercaptan, hydrogen sulfide, and butyric acid.

• Journal of Nutrition and Health
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• v.51 no.3
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• pp.228-241
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• 2018

Purpose: The health risk of women increases after menopause. This study evaluated the effectiveness of a public health center-based nutrition education program for hypertension in women older than 50 years of age. Methods: The program included 8-week nutrition education and 8-week follow-up with keeping a health diary and nutrition counseling. The program was evaluated three times: before and after the nutrition education, and after the follow-up. The subjects were classified into hypertensives (n = 44) or normotensives (n = 71). Results: The rate of taking antihypertensive drugs in the hypertensive group was 86.4%. The systolic blood pressure decreased in the hypertensive and normotensive groups after nutrition education (p < 0.05). The body weight (p < 0.001), BMI (p < 0.001), waist circumference (p < 0.001), and percent body fat (p < 0.01) were also decreased after nutrition education in both groups. The hypertensive group showed an increase in HDL-cholesterol level (p < 0.001) and decreases in triglycerides (p < 0.01) and LDL-cholesterol (p < 0.05) levels after completion of the program. The normotensive group also displayed significant changes in HDL-cholesterol (p < 0.001) and triglycerides (p < 0.01). The dietary habits and nutrition knowledge on sodium and hypertension were improved in both groups (p < 0.001). The total score of dietary behavior related to the sodium intake was improved in the normotensive group (p < 0.001). The total score of the high sodium dish frequency questionnaire decreased in both groups after nutrition education and completion of the program compared to that before the program. Decreases in the consumption frequencies of noodles, pot stews and stews, Kimchi, and beverages were significant. The total self-efficacy score was increased in both groups by the program (p < 0.001). In particular, the hypertensive group showed improvement in all items. Conclusion: This public health center-based nutrition education program may contribute to the prevention and management of hypertension and chronic diseases in women over 50 years of age.

• Journal of Intelligence and Information Systems
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• v.18 no.1
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• pp.23-38
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• 2012

There is a large difference between purchasing patterns in an online shopping mall and in an offline market. This difference may be caused mainly by the difference in accessibility of online and offline markets. It means that an interval between the initial purchasing decision and its realization appears to be relatively short in an online shopping mall, because a customer can make an order immediately. Because of the short interval between a purchasing decision and its realization, an online shopping mall transaction usually contains fewer items than that of an offline market. In an offline market, customers usually keep some items in mind and buy them all at once a few days after deciding to buy them, instead of buying each item individually and immediately. On the contrary, more than 70% of online shopping mall transactions contain only one item. This statistic implies that traditional data mining techniques cannot be directly applied to online market analysis, because hardly any association rules can survive with an acceptable level of Support because of too many Null Transactions. Most market basket analyses on online shopping mall transactions, therefore, have been performed by expanding the co-occurrence criteria of traditional association rule mining. While the traditional co-occurrence criteria defines items purchased in one transaction as concurrently purchased items, the expanded co-occurrence criteria regards items purchased by a customer during some predefined period (e.g., a day) as concurrently purchased items. In studies using expanded co-occurrence criteria, however, the criteria has been defined arbitrarily by researchers without any theoretical grounds or agreement. The lack of clear grounds of adopting a certain co-occurrence criteria degrades the reliability of the analytical results. Moreover, it is hard to derive new meaningful findings by combining the outcomes of previous individual studies. In this paper, we attempt to compare expanded co-occurrence criteria and propose a guideline for selecting an appropriate one. First of all, we compare the accuracy of association rules discovered according to various co-occurrence criteria. By doing this experiment we expect that we can provide a guideline for selecting appropriate co-occurrence criteria that corresponds to the purpose of the analysis. Additionally, we will perform similar experiments with several groups of customers that are segmented by each customer's average duration between orders. By this experiment, we attempt to discover the relationship between the optimal co-occurrence criteria and the customer's average duration between orders. Finally, by a series of experiments, we expect that we can provide basic guidelines for developing customized recommendation systems. Our experiments use a real dataset acquired from one of the largest internet shopping malls in Korea. We use 66,278 transactions of 3,847 customers conducted during the last two years. Overall results show that the accuracy of association rules of frequent shoppers (whose average duration between orders is relatively short) is higher than that of causal shoppers. In addition we discover that with frequent shoppers, the accuracy of association rules appears very high when the co-occurrence criteria of the training set corresponds to the validation set (i.e., target set). It implies that the co-occurrence criteria of frequent shoppers should be set according to the application purpose period. For example, an analyzer should use a day as a co-occurrence criterion if he/she wants to offer a coupon valid only for a day to potential customers who will use the coupon. On the contrary, an analyzer should use a month as a co-occurrence criterion if he/she wants to publish a coupon book that can be used for a month. In the case of causal shoppers, the accuracy of association rules appears to not be affected by the period of the application purposes. The accuracy of the causal shoppers' association rules becomes higher when the longer co-occurrence criterion has been adopted. It implies that an analyzer has to set the co-occurrence criterion for as long as possible, regardless of the application purpose period.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.39-54
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• 2013

Recent explosive increase of electronic commerce provides many advantageous purchase opportunities to customers. In this situation, customers who do not have enough knowledge about their purchases, may accept product recommendations. Product recommender systems automatically reflect user's preference and provide recommendation list to the users. Thus, product recommender system in online shopping store has been known as one of the most popular tools for one-to-one marketing. However, recommender systems which do not properly reflect user's preference cause user's disappointment and waste of time. In this study, we propose a novel recommender system which uses data mining and multi-model ensemble techniques to enhance the recommendation performance through reflecting the precise user's preference. The research data is collected from the real-world online shopping store, which deals products from famous art galleries and museums in Korea. The data initially contain 5759 transaction data, but finally remain 3167 transaction data after deletion of null data. In this study, we transform the categorical variables into dummy variables and exclude outlier data. The proposed model consists of two steps. The first step predicts customers who have high likelihood to purchase products in the online shopping store. In this step, we first use logistic regression, decision trees, and artificial neural networks to predict customers who have high likelihood to purchase products in each product group. We perform above data mining techniques using SAS E-Miner software. In this study, we partition datasets into two sets as modeling and validation sets for the logistic regression and decision trees. We also partition datasets into three sets as training, test, and validation sets for the artificial neural network model. The validation dataset is equal for the all experiments. Then we composite the results of each predictor using the multi-model ensemble techniques such as bagging and bumping. Bagging is the abbreviation of "Bootstrap Aggregation" and it composite outputs from several machine learning techniques for raising the performance and stability of prediction or classification. This technique is special form of the averaging method. Bumping is the abbreviation of "Bootstrap Umbrella of Model Parameter," and it only considers the model which has the lowest error value. The results show that bumping outperforms bagging and the other predictors except for "Poster" product group. For the "Poster" product group, artificial neural network model performs better than the other models. In the second step, we use the market basket analysis to extract association rules for co-purchased products. We can extract thirty one association rules according to values of Lift, Support, and Confidence measure. We set the minimum transaction frequency to support associations as 5%, maximum number of items in an association as 4, and minimum confidence for rule generation as 10%. This study also excludes the extracted association rules below 1 of lift value. We finally get fifteen association rules by excluding duplicate rules. Among the fifteen association rules, eleven rules contain association between products in "Office Supplies" product group, one rules include the association between "Office Supplies" and "Fashion" product groups, and other three rules contain association between "Office Supplies" and "Home Decoration" product groups. Finally, the proposed product recommender systems provides list of recommendations to the proper customers. We test the usability of the proposed system by using prototype and real-world transaction and profile data. For this end, we construct the prototype system by using the ASP, Java Script and Microsoft Access. In addition, we survey about user satisfaction for the recommended product list from the proposed system and the randomly selected product lists. The participants for the survey are 173 persons who use MSN Messenger, Daum Caf$\acute{e}$, and P2P services. We evaluate the user satisfaction using five-scale Likert measure. This study also performs "Paired Sample T-test" for the results of the survey. The results show that the proposed model outperforms the random selection model with 1% statistical significance level. It means that the users satisfied the recommended product list significantly. The results also show that the proposed system may be useful in real-world online shopping store.

• Korean Security Journal
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• no.29
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• pp.139-162
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• 2011

The purpose of this study is to compare with the general and behavioral characteristics between simple and serial arsonists using the data derived from Scientific Crime Analysis System, Criminal Filing Search System, and Crime Information Management System. The analysis and findings reported here are derived from data extracted from 160 arsonists arrested by police officer. The independent variables included such socio-economic characteristic as arsonists' gender, age, occupation, education level, and previous criminal records of arsonists, and finally the general characteristics of the scene of fire settings. The dependent variable is whether or not serial fire setter. To achieve the purpose, the analysis of frequencies and cross-tab were conducted. According to frequence and cross-tab analysis, there are great differences of the general and behavior characteristics between two groups. In the comparison of simple and serial arsonists, serial arsonists are more likely to have previous criminal records, low socio-economic status, unmarried and no cohabitants than simple arsonists. furthermore, serial arsonists are more likely to use garbage papers for fire setting in the scene of the crime, to have mental or psychological problems, and to get involved in fire setting for the psychological pleasure than simple arsonists do. The present research has some obvious limitations. First, the analysis is based only on arsonists arrested by police officers. These may be considerable differences in arsonists arrested by police officers and fire setters not arrested by them. Additional research is needed to assess the extent to which these findings would apply to fire setters not arrested by police officer in Korea. Secondly, the data in this study are cross-sectional and simple cross-tab analysis are used. Potential limitation of cross-sectional data concerns the inability to specify the changes in measures as arsonists behavioral characteristics. Therefore, further studies need to use longitudinal data and more complicate statistical techniques such as correlation analysis, multiple regression analysis, or LISREL models to specify the casual relationships between dependent and independent variables for fire settings. Even if this study has some limitations, it is meaningful in which it first investigated the comparison of simple and serial arsonists focusing on the general and behavioral characteristics between two groups in Korea.

• Journal of the Korean Society of Radiology
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• v.8 no.4
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• pp.181-187
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• 2014

The purpose of this study were to analyze the characteristic of the glow curves in order to the glow temperature of the thermoluminescent dosimeters (TLDs) for the absorbed dose measurement of the radiation therapy. In this study, we was used the TLDs of the LiF:Mg${\cdot}$Ti, LiF:Mg${\cdot}$Cu${\cdot}$P, $CaF_2$:Dy, $CaF_2$:Mn (Thermo Fisher Scientific Inc., USA). The source-to-solid dry phantom (RW3 slab, IBA Dosmetry, Germany) surface distance was set at 100 cm, and the exposure dose of 100 MU (monitor unit) was used 6- and 15-MV X-rays, and 6- and 12-MeV electron beams in the reference depth, respectively. After the radiations exposure, we were to analyze the glow curves by using the TL reader (Hashaw 3500, Thermo Fisher Scientific Inc., USA) at the fixed heating rate of $15^{\circ}C/sec$ from $50^{\circ}C$ to $260^{\circ}C$. The glow peaks, the trapping level in the captured electrons and holes combined with the emitted light, were discovered the two or three peak. When the definite increasing the temperature of the TLDs, the maximum glow peak representing the glow temperature was follow as; $LiF:Mg{\cdot}Ti$: $185.5{\pm}1.3^{\circ}C$, $LiF:Mg{\cdot}Ti$: $135.0{\pm}5.1^{\circ}C$, $CaF_2$:Dy: $144.0{\pm}1.6^{\circ}C$, $CaF_2$:Mn: $294.3{\pm}3.8^{\circ}C$, respectively. Because the glow emission probability of the captured electrons depend on the heating temperature after the exposure radiation, TLDs by applying the fixed heating rate, the accuracy of measurement will be able to improve within the absorbed dose measurement of the radiation therapy.

• Pediatric Gastroenterology, Hepatology & Nutrition
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• v.9 no.1
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• pp.23-30
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• 2006

Purpose: The estimation of fluid deficit is crucial to the proper management of dehydrated children. Without well-documented serial weights on the same scale, the estimation of any given child's fluid deficit is imprecise and dependent largely on subjective clinical criteria. Despite the abundance of literature on clinical and laboratory evaluation of dehydration, few studies have focused on serum uric acid. So, we examined the usefulness of scrum uric acid in gastroenteritis patients with dehydration. Methods: Medical records of 90 gastroenteritis patients were retrospectively reviewed. By the body weight loss, we classified patients with mild, moderate, and severe dehydration groups. We studied the relevance of laboratory data (BUN, creatinine, serum bicarbonate, glucose, urine specific gravity, and uric acid) with dehydration. Results: 54 children (60%) were dehydrated mildly, 24 (26%) dehydrated and moderately, and 12 (14%) dehydrated severely. Statistically significant differences in BUN, creatinine, serum bicarbonate, glucose, and urine specific gravity could not be observed. But there was significant relationship between uric acid and the degree of dehydration. Data analysis suggested that the level of 7.0 mg/dL is the best cut-off value for predicting the development of moderate or severe dehydration. At this cut-off value, the sensitivity and specificity were 66.6% and 87.1%. Conclusion: Our study supports that the measurement of serum uric acid with traditional scale is useful for predicting the development of dehydration. But, in order 10 be used as the indicator for proper treatment at an earlier stage, further validation about serum uric acid is necessary.

• Asia pacific journal of information systems
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• v.19 no.4
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• pp.101-124
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• 2009

According to Wikipedia, virtual reality (VR) is defined as a technology that allows a user to interact with a computer-simulated environment. Due to a rapid growth in information technology (IT), the cost of virtual reality has been decreasing while the utility of virtual reality advertisements has dramatically increased. Nevertheless, only a few studies have investigated the effects of virtual reality advertisement on consumer behaviors. Therefore, the objective of this study is to empirically examine the effects of virtual reality advertisement. Compared to traditional online advertisements, virtual reality advertisement enables consumers to experience products realistically over the Internet by providing high media richness, interactivity, and telepresence (Suh and Lee, 2005). Advertisements with high media richness facilitate consumers' understanding of advertised products by providing them with a large amount and a high variety of information on the products. Interactivity also provides consumers with a high level of control over the computer-simulated environment in terms of their abilities to adjust the information according to their individual interests and concerns and to be active rather than passive in their engagement with the information (Pimentel and Teixera, 1994). Through high media richness and interactivity, virtual reality advertisements can generate compelling feelings of "telepresence" (Suh and Lee, 2005). Telepresence is a sense of being there in an environment by means of a communication medium (Steuer, 1992). Virtual reality advertisements enable consumers to create a perceptual illusion of being present and highly engaged in a simulated environment, while they are in reality physically present in another place (Biocca, 1997). Based on the characteristics of virtual reality advertisements, a research model has been proposed to explain consumer responses to the virtual reality advertisements. The proposed model includes two dimensions of consumer responses. One dimension is consumers' rational response, which is based on the Information Processing Theory. Based on the Information Processing Theory, product knowledge and perceived risk are selected as antecedents of intention to purchase. The other dimension is emotional response of consumers, which is based on the Attitude-Structure Theory. Based on the Attitude-Structure Theory, arousal, flow, and positive affect are selected as antecedents of intention to purchase. Because it has been criticized to have investigated only one of the two dimensions of consumer response in prior studies, our research model has been built so as to incorporate both dimensions. Based on the Attitude-Structure Theory, we hypothesized the path of consumers' emotional responses to a virtual reality advertisement: (H1) Arousal by the virtual reality advertisement increases flow; (H2) Flow increases positive affect; and (H3) Positive affect increases intension to purchase. In addition, we hypothesized the path of consumers' rational responses to the virtual reality advertisement based on the Information Processing Theory: (H4) Increased product knowledge through the virtual reality advertisement decreases perceived risk; and (H5) Perceived risk decreases intension to purchase. Based on literature of flow, we additionally hypothesized the relationship between flow and product knowledge: (H6) Flow increases product knowledge. To test the hypotheses, we conducted a free simulation experiment [Fromkin and Streufert, 1976] with 300 people. Subjects were asked to use the virtual reality advertisement of a cellular phone on the Internet and then answer questions about the variables. To check whether subjects fully experienced the virtual reality advertisement, they were asked to answer a quiz about the virtual reality advertisement itself. Responses of 26 subjects were dropped because of their incomplete answers. Responses of 274 subjects were used to test the hypotheses. It was found that all of six hypotheses are accepted. In addition, we found that consumers' emotional response has stronger impact on their intention to purchase than their rational response does. This study sheds much light into practical implications for both IS researchers and managers. First of all, while most of previous research has analyzed only one of the customers' rational and emotional responses, we theoretically incorporated and empirically examined both of the two sides. Second, we empirically showed that mediators such as arousal, flow, positive affect, product knowledge, and perceived risk play an important role between virtual reality advertisement and customer's intention to purchase. In addition, the findings of this study can provide a basis of practical strategies for managers. It was found that consumers' emotional response is stronger than their rational response. This result indicates that advertisements using virtual reality should focus on the emotional side, and that virtual reality can be served as an appropriate advertisement tool for fancy products that require their online advertisements to give an impetus to customers' emotion. Finally, even if this study examined the effects of virtual reality advertisement of cellular phone, its findings could be applied to other products that are suited for virtual experience. However, this research has some limitations. We were unable to control different kinds of consumers and different attributes of products on consumers' intention to purchase. It is, therefore, deemed important for future research to control the consumer and product types for more reliable results. In addition to the consumer and product attributes, other variables could affect consumers' intention to purchase. Thus, the future research needs to find ways t control other variables.

• Asia pacific journal of information systems
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• v.24 no.4
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• pp.611-635
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• 2014

Information systems (IS) have become ubiquitous and changed every aspect of how people live their lives. While some IS have been successfully adopted and widely used, others have failed to be adopted and crowded out in spite of remarkable progress in technologies. Both the technology acceptance model (TAM) and the IS Success Model (ISSM), among many others, have contributed to explain the reasons of success as well as failure in IS adoption and usage. While the TAM suggests that intention to use and perceived usefulness lead to actual IS usage, the ISSM indicates that information quality, system quality, and service quality affect IS usage and user satisfaction. Upon literature review, however, we found a significant void in theoretical development and its applications that employ either of the two models, and we raise research questions. First of all, in spite of the causal relationship between intention to use and actual usage, in most previous studies, only intention to use was employed as a dependent variable without overt explaining its relationship with actual usage. Moreover, even in a few studies that employed actual IS usage as a dependent variable, the degree of actual usage was measured based on users' perceptual responses to survey questionnaires. However, the measurement of actual usage based on survey responses might not be 'actual' usage in a strict sense that responders' perception may be distorted due to their selective perceptions or stereotypes. By the same token, the degree of system quality that IS users perceive might not be 'real' quality as well. This study seeks to fill this void by measuring the variables of actual usage and system quality using 'fact' data such as system logs and specifications of users' information and communications technology (ICT) devices. More specifically, we propose an integrated research model that bring together the TAM and the ISSM. The integrated model is composed of both the variables that are to be measured using fact as well as survey data. By employing the integrated model, we expect to reveal the difference between real and perceived degree of system quality, and to investigate the relationship between the perception-based measure of intention to use and the fact-based measure of actual usage. Furthermore, we also aim to add empirical findings on the general research question: what factors influence actual IS usage and how? In order to address the research question and to examine the research model, we selected a mobile campus application (MCA). We collected both fact data and survey data. For fact data, we retrieved them from the system logs such information as menu usage counts, user's device performance, display size, and operating system revision version number. At the same time, we conducted a survey among university students who use an MCA, and collected 180 valid responses. A partial least square (PLS) method was employed to validate our research model. Among nine hypotheses developed, we found five were supported while four were not. In detail, the relationships between (1) perceived system quality and perceived usefulness, (2) perceived system quality and perceived intention to use, (3) perceived usefulness and perceived intention to use, (4) quality of device platform and actual IS usage, and (5) perceived intention to use and actual IS usage were found to be significant. In comparison, the relationships between (1) quality of device platform and perceived system quality, (2) quality of device platform and perceived usefulness, (3) quality of device platform and perceived intention to use, and (4) perceived system quality and actual IS usage were not significant. The results of the study reveal notable differences from those of previous studies. First, although perceived intention to use shows a positive effect on actual IS usage, its explanatory power is very weak ($R^2$=0.064). Second, fact-based system quality (quality of user's device platform) shows a direct impact on actual IS usage without the mediating role of intention to use. Lastly, the relationships between perceived system quality (perception-based system quality) and other constructs show completely different results from those between quality of device platform (fact-based system quality) and other constructs. In the post-hoc analysis, IS users' past behavior was additionally included in the research model to further investigate the cause of such a low explanatory power of actual IS usage. The results show that past IS usage has a strong positive effect on current IS usage while intention to use does not have, implying that IS usage has already become a habitual behavior. This study provides the following several implications. First, we verify that fact-based data (i.e., system logs of real usage records) are more likely to reflect IS users' actual usage than perception-based data. In addition, by identifying the direct impact of quality of device platform on actual IS usage (without any mediating roles of attitude or intention), this study triggers further research on other potential factors that may directly influence actual IS usage. Furthermore, the results of the study provide practical strategic implications that organizations equipped with high-quality systems may directly expect high level of system usage.

• Asia pacific journal of information systems
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• v.24 no.4
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• pp.493-513
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• 2014

With the development of related technologies, Location-Based Services (LBS) are growing fast and being used in many ways. Past LBS studies have focused on adoption of LBS because of the fact that LBS users have privacy concerns regarding revealing their location information. Meanwhile, the number of LBS users and revenues from LBS are growing rapidly because users can get some benefits by revealing their location information. Little research has been done on how LBS affects consumers' information search behavior in product purchase. The purpose of this paper is examining the effect of LBS information filtering on buyers' uncertainty and their information search behavior. When consumers purchase a product, they try to reduce uncertainty by searching information. Generally, there are two types of uncertainties - knowledge uncertainty and choice uncertainty. Knowledge uncertainty refers to the lack of information on what kinds of alternatives are available in the market and/or their important attributes. Therefore, consumers having knowledge uncertainty will have difficulties in identifying what alternatives exist in the market to fulfil their needs. Choice uncertainty refers to the lack of information about consumers' own preferences and which alternative will fit in their needs. Therefore, consumers with choice uncertainty have difficulties selecting best product among available alternatives.. According to economics of information theory, consumers narrow the scope of information search when knowledge uncertainty is high. It is because consumers' information search cost is high when their knowledge uncertainty is high. If people do not know available alternatives and their attributes, it takes time and cognitive efforts for them to acquire information about available alternatives. Therefore, they will reduce search breadth. For people with high knowledge uncertainty, the information about products and their attributes is new and of high value for them. Therefore, they will conduct searches more in-depth because they have incentive to acquire more information. When people have high choice uncertainty, people tend to search information about more alternatives. It is because increased search breadth will improve their chances to find better alternative for them. On the other hand, since human's cognitive capacity is limited, the increased search breadth (more alternatives) will reduce the depth of information search for each alternative. Consumers with high choice uncertainty will spend less time and effort for each alternative because considering more alternatives will increase their utility. LBS provides users with the capability to screen alternatives based on the distance from them, which reduces information search costs. Therefore, it is expected that LBS will help users consider more alternatives even when they have high knowledge uncertainty. LBS provides distance information, which helps users choose alternatives appropriate for them. Therefore, users will perceive lower choice uncertainty when they use LBS. In order to test the hypotheses, we selected 80 students and assigned them to one of the two experiment groups. One group was asked to use LBS to search surrounding restaurants and the other group was asked to not use LBS to search nearby restaurants. The experimental tasks and measures items were validated in a pilot experiment. The final measurement items are shown in Appendix A. Each subject was asked to read one of the two scenarios - with or without LBS - and use a smartphone application to pick a restaurant. All behaviors on smartphone were recorded using a recording application. Search breadth was measured by the number of restaurants clicked by each subject. Search depths was measured by two metrics - the average number of sub-level pages each subject visited and the average time spent on each restaurant. The hypotheses were tested using SPSS and PLS. The results show that knowledge uncertainty reduces search breadth (H1a). However, there was no significant correlation between knowledge uncertainty and search depth (H1b). Choice uncertainty significantly reduces search depth (H2b), but no significant relationship was found between choice uncertainty and search breadth (H2a). LBS information filtering significantly reduces the buyers' choice uncertainty (H4) and reduces the negative relationship between knowledge uncertainty and search breadth (H3). This research provides some important implications for service providers. Service providers should use different strategies based on their service properties. For those service providers who are not well-known to consumers (high knowledge uncertainty) should encourage their customers to use LBS. This is because LBS would increase buyers' consideration sets when the knowledge uncertainty is high. Therefore, less known services have chances to be included in consumers' consideration sets with LBS. On the other hand, LBS information filtering decrease choice uncertainty and the near service providers are more likely to be selected than without LBS. Hence, service providers should analyze geographically approximate competitors' strength and try to reduce the gap so that they can have chances to be included in the consideration set.