• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.183-203
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• 2018

News articles are the most suitable medium for examining the events occurring at home and abroad. Especially, as the development of information and communication technology has brought various kinds of online news media, the news about the events occurring in society has increased greatly. So automatically summarizing key events from massive amounts of news data will help users to look at many of the events at a glance. In addition, if we build and provide an event network based on the relevance of events, it will be able to greatly help the reader in understanding the current events. In this study, we propose a method for extracting event networks from large news text data. To this end, we first collected Korean political and social articles from March 2016 to March 2017, and integrated the synonyms by leaving only meaningful words through preprocessing using NPMI and Word2Vec. Latent Dirichlet allocation (LDA) topic modeling was used to calculate the subject distribution by date and to find the peak of the subject distribution and to detect the event. A total of 32 topics were extracted from the topic modeling, and the point of occurrence of the event was deduced by looking at the point at which each subject distribution surged. As a result, a total of 85 events were detected, but the final 16 events were filtered and presented using the Gaussian smoothing technique. We also calculated the relevance score between events detected to construct the event network. Using the cosine coefficient between the co-occurred events, we calculated the relevance between the events and connected the events to construct the event network. Finally, we set up the event network by setting each event to each vertex and the relevance score between events to the vertices connecting the vertices. The event network constructed in our methods helped us to sort out major events in the political and social fields in Korea that occurred in the last one year in chronological order and at the same time identify which events are related to certain events. Our approach differs from existing event detection methods in that LDA topic modeling makes it possible to easily analyze large amounts of data and to identify the relevance of events that were difficult to detect in existing event detection. We applied various text mining techniques and Word2vec technique in the text preprocessing to improve the accuracy of the extraction of proper nouns and synthetic nouns, which have been difficult in analyzing existing Korean texts, can be found. In this study, the detection and network configuration techniques of the event have the following advantages in practical application. First, LDA topic modeling, which is unsupervised learning, can easily analyze subject and topic words and distribution from huge amount of data. Also, by using the date information of the collected news articles, it is possible to express the distribution by topic in a time series. Second, we can find out the connection of events in the form of present and summarized form by calculating relevance score and constructing event network by using simultaneous occurrence of topics that are difficult to grasp in existing event detection. It can be seen from the fact that the inter-event relevance-based event network proposed in this study was actually constructed in order of occurrence time. It is also possible to identify what happened as a starting point for a series of events through the event network. The limitation of this study is that the characteristics of LDA topic modeling have different results according to the initial parameters and the number of subjects, and the subject and event name of the analysis result should be given by the subjective judgment of the researcher. Also, since each topic is assumed to be exclusive and independent, it does not take into account the relevance between themes. Subsequent studies need to calculate the relevance between events that are not covered in this study or those that belong to the same subject.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.97-117
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• 2020

Product evaluation criteria is an indicator describing attributes or values of products, which enable users or manufacturers measure and understand the products. When companies analyze their products or compare them with competitors, appropriate criteria must be selected for objective evaluation. The criteria should show the features of products that consumers considered when they purchased, used and evaluated the products. However, current evaluation criteria do not reflect different consumers' opinion from product to product. Previous studies tried to used online reviews from e-commerce sites that reflect consumer opinions to extract the features and topics of products and use them as evaluation criteria. However, there is still a limit that they produce irrelevant criteria to products due to extracted or improper words are not refined. To overcome this limitation, this research suggests LDA-k-NN model which extracts possible criteria words from online reviews by using LDA and refines them with k-nearest neighbor. Proposed approach starts with preparation phase, which is constructed with 6 steps. At first, it collects review data from e-commerce websites. Most e-commerce websites classify their selling items by high-level, middle-level, and low-level categories. Review data for preparation phase are gathered from each middle-level category and collapsed later, which is to present single high-level category. Next, nouns, adjectives, adverbs, and verbs are extracted from reviews by getting part of speech information using morpheme analysis module. After preprocessing, words per each topic from review are shown with LDA and only nouns in topic words are chosen as potential words for criteria. Then, words are tagged based on possibility of criteria for each middle-level category. Next, every tagged word is vectorized by pre-trained word embedding model. Finally, k-nearest neighbor case-based approach is used to classify each word with tags. After setting up preparation phase, criteria extraction phase is conducted with low-level categories. This phase starts with crawling reviews in the corresponding low-level category. Same preprocessing as preparation phase is conducted using morpheme analysis module and LDA. Possible criteria words are extracted by getting nouns from the data and vectorized by pre-trained word embedding model. Finally, evaluation criteria are extracted by refining possible criteria words using k-nearest neighbor approach and reference proportion of each word in the words set. To evaluate the performance of the proposed model, an experiment was conducted with review on '11st', one of the biggest e-commerce companies in Korea. Review data were from 'Electronics/Digital' section, one of high-level categories in 11st. For performance evaluation of suggested model, three other models were used for comparing with the suggested model; actual criteria of 11st, a model that extracts nouns by morpheme analysis module and refines them according to word frequency, and a model that extracts nouns from LDA topics and refines them by word frequency. The performance evaluation was set to predict evaluation criteria of 10 low-level categories with the suggested model and 3 models above. Criteria words extracted from each model were combined into a single words set and it was used for survey questionnaires. In the survey, respondents chose every item they consider as appropriate criteria for each category. Each model got its score when chosen words were extracted from that model. The suggested model had higher scores than other models in 8 out of 10 low-level categories. By conducting paired t-tests on scores of each model, we confirmed that the suggested model shows better performance in 26 tests out of 30. In addition, the suggested model was the best model in terms of accuracy. This research proposes evaluation criteria extracting method that combines topic extraction using LDA and refinement with k-nearest neighbor approach. This method overcomes the limits of previous dictionary-based models and frequency-based refinement models. This study can contribute to improve review analysis for deriving business insights in e-commerce market.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.1-21
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• 2020

With the rapid development of artificial intelligence technology, various techniques have been developed to extract meaningful information from unstructured text data which constitutes a large portion of big data. Over the past decades, text mining technologies have been utilized in various industries for practical applications. In the field of business intelligence, it has been employed to discover new market and/or technology opportunities and support rational decision making of business participants. The market information such as market size, market growth rate, and market share is essential for setting companies' business strategies. There has been a continuous demand in various fields for specific product level-market information. However, the information has been generally provided at industry level or broad categories based on classification standards, making it difficult to obtain specific and proper information. In this regard, we propose a new methodology that can estimate the market sizes of product groups at more detailed levels than that of previously offered. We applied Word2Vec algorithm, a neural network based semantic word embedding model, to enable automatic market size estimation from individual companies' product information in a bottom-up manner. The overall process is as follows: First, the data related to product information is collected, refined, and restructured into suitable form for applying Word2Vec model. Next, the preprocessed data is embedded into vector space by Word2Vec and then the product groups are derived by extracting similar products names based on cosine similarity calculation. Finally, the sales data on the extracted products is summated to estimate the market size of the product groups. As an experimental data, text data of product names from Statistics Korea's microdata (345,103 cases) were mapped in multidimensional vector space by Word2Vec training. We performed parameters optimization for training and then applied vector dimension of 300 and window size of 15 as optimized parameters for further experiments. We employed index words of Korean Standard Industry Classification (KSIC) as a product name dataset to more efficiently cluster product groups. The product names which are similar to KSIC indexes were extracted based on cosine similarity. The market size of extracted products as one product category was calculated from individual companies' sales data. The market sizes of 11,654 specific product lines were automatically estimated by the proposed model. For the performance verification, the results were compared with actual market size of some items. The Pearson's correlation coefficient was 0.513. Our approach has several advantages differing from the previous studies. First, text mining and machine learning techniques were applied for the first time on market size estimation, overcoming the limitations of traditional sampling based- or multiple assumption required-methods. In addition, the level of market category can be easily and efficiently adjusted according to the purpose of information use by changing cosine similarity threshold. Furthermore, it has a high potential of practical applications since it can resolve unmet needs for detailed market size information in public and private sectors. Specifically, it can be utilized in technology evaluation and technology commercialization support program conducted by governmental institutions, as well as business strategies consulting and market analysis report publishing by private firms. The limitation of our study is that the presented model needs to be improved in terms of accuracy and reliability. The semantic-based word embedding module can be advanced by giving a proper order in the preprocessed dataset or by combining another algorithm such as Jaccard similarity with Word2Vec. Also, the methods of product group clustering can be changed to other types of unsupervised machine learning algorithm. Our group is currently working on subsequent studies and we expect that it can further improve the performance of the conceptually proposed basic model in this study.

• The Journal of Korean Society for Radiation Therapy
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• v.27 no.1
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• pp.45-52
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• 2015

Purpose : The dose distribution of organ at risk (OAR) and normal tissue is affected by treatment technique in postoperative radiation therapy for prostate cancer. The aim of this study was to compare dose distribution characteristic and to evaluate treatment efficiency by devising VMAT plans according to applying differed number of arc and IMRT plan for postoperative patient of prostate cancer radiation therapy using a rectal balloon. Materials and Methods : Ten patients who received postoperative prostate radiation therapy in our hospital were compared. CT images of patients who inserted rectal balloon were acquired with 3 mm thickness and 10 MV energy of HD120MLC equipped Truebeam STx (Varian, Palo Alto, USA) was applied by using Eclipse (Version 11.0, Varian, Palo Alto, USA). 1 Arc, 2 Arc VMAT plans and 7-field IMRT plan were devised for each patient and same values were applied for dose volume constraint and plan normalization. To evaluate these plans, PTV coverage, conformity index (CI) and homogeneity index (HI) were compared and $R_{50%}$ was calculated to assess low dose spillage as per treatment plan. $D_{25%}$ of rectum and bladder Dmean were compared on OAR. And to evaluate the treatment efficiency, total monitor units(MU) and delivery time were considered. Each assessed result was analyzed by average value of 10 patients. Additionally, portal dosimetry was carried out for accuracy verification of beam delivery. Results : There was no significant difference on PTV coverage and HI among 3 plans. Especially CI and $R_{50%}$ on 7F-IMRT were the highest as 1.230, 3.991 respectively(p=0.00). Rectum $D_{25%}$ was similar between 1A-VMAT and 2A-VMAT. But approximately 7% higher value was observed on 7F-IMRT compare to the others(p=0.02) and bladder Dmean were similar among the all plan(P>0.05). Total MU were 494.7, 479.7, 757.9 respectively(P=0.00) for 1A-VMAT, 2A-VMAT, 7F-IMRT and at the most on 7F-IMRT. The delivery time were 65.2sec, 133.1sec, 145.5sec respectively(p=0.00). The obvious shortest time was observed on 1A-VMAT. All plans indicated over 99.5%(p=0.00) of gamma pass rate (2 mm, 2%) in portal dosimetry quality assurance. Conclusion : As a result of study, postoperative prostate cancer radiation therapy for patient using a rectal balloon, there was no significant difference of PTV coverage but 1A-VMAT and 2A-VMAT were more efficient for dose reduction of normal tissue and OARs. Between VMAT plans. $R_{50%}$ and MU were little lower in 2A-VMAT but 1A-VMAT has the shortest delivery time. So it is regarded to be an effective plan and it can reduce intra-fractional motion of patient also.

• Journal of Acupuncture Research
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• v.29 no.1
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• pp.139-150
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• 2012

Objectives : The puropse of this study was to report the availability of Hyeongsang diagnosis compensating for visceral pattern identification in applying Sa-am acupuncture therapy. Methods : Eight cases was presented to substantiate the above. Results : According to the characteristic diagnostic method of Hyeongsang medicine by feature such as face, ears, eyes, nose and mouth shape, There are 8 pattern differentiations, including essence family, Qi family, spirit family, blood family, fish type, bird type, beast(running) type and crust(crustacea) type which are correlated with essence deficiency, heat harassing the heart spirit, Qi stagnation, blood stasis, kidney essence deficiency, intense heart fire, liver blood deficiency and lung Qi deficiency in the established visceral pattern identification, respectively. Eight patients was diagnosed by the above Hyeongsang 8 pattern differentiations, of whom Sinjeonggyeok(kidney reinforcing prescription) was applied to a patient with fish type and essence family to nourish kidney essence, and Giul prescription(Qi stagnation prescription) was given to a patient with Qi family for regulating Qi, and Sanghwa priscription(ministerial fire prescription) was delivered to a patient with Spirit family to clear the heart fire and tranquilize, and Sojangjeonggyeok(small intestine reinforcing prescription) was used for a patient with blood family to nourish blood and remove blood stasis, and Sinjeonggyeok(kidney reinforcing prescription), Simhangyeok(heart heat clearing prescription), Ganjeonggyeok(liver reinforcing prescription) and Pyejeonggyeok(lung reinforcing prescription) were utilized for fish type, bird type, beast(running) type and crust(crustacea) type respectively to reinforce the relevant visceral function. Conclusions : It was suggested that characteristic diagnostic method of Hyeongsang medicine should be helpful for enhancing the accuracy of the established visceral pattern identification, applying Sa-am acupuncture therapy more appropriately.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.149-169
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• 2018

Korea is currently accumulating a large amount of data in public institutions based on the public data open policy and the "Government 3.0". Especially, a lot of data is accumulated in the tourism field. However, the academic discussions utilizing the tourism data are still limited. Moreover, the openness of the data of restaurants, hotels, and online tourism information, and how to use SNS Big Data in tourism are still limited. Therefore, utilization through tourism big data analysis is still low. In this paper, we tried to analyze influencing factors on foreign tourists' satisfaction in Korea through numerical data using data mining technique and R programming technique. In this study, we tried to find ways to revitalize the tourism industry by analyzing about 36,000 big data of the "Survey on the actual situation of foreign tourists from 2013 to 2015" surveyed by the Korea Culture & Tourism Research Institute. To do this, we analyzed the factors that have high influence on the 'Satisfaction', 'Revisit intention', and 'Recommendation' variables of foreign tourists. Furthermore, we analyzed the practical influences of the variables that are mentioned above. As a procedure of this study, we first integrated survey data of foreign tourists conducted by Korea Culture & Tourism Research Institute, which is stored in the tourist information system from 2013 to 2015, and eliminate unnecessary variables that are inconsistent with the research purpose among the integrated data. Some variables were modified to improve the accuracy of the analysis. And we analyzed the factors affecting the dependent variables by using data-mining methods: decision tree(C5.0, CART, CHAID, QUEST), artificial neural network, and logistic regression analysis of SPSS IBM Modeler 16.0. The seven variables that have the greatest effect on each dependent variable were derived. As a result of data analysis, it was found that seven major variables influencing 'overall satisfaction' were sightseeing spot attraction, food satisfaction, accommodation satisfaction, traffic satisfaction, guide service satisfaction, number of visiting places, and country. Variables that had a great influence appeared food satisfaction and sightseeing spot attraction. The seven variables that had the greatest influence on 'revisit intention' were the country, travel motivation, activity, food satisfaction, best activity, guide service satisfaction and sightseeing spot attraction. The most influential variables were food satisfaction and travel motivation for Korean style. Lastly, the seven variables that have the greatest influence on the 'recommendation intention' were the country, sightseeing spot attraction, number of visiting places, food satisfaction, activity, tour guide service satisfaction and cost. And then the variables that had the greatest influence were the country, sightseeing spot attraction, and food satisfaction. In addition, in order to grasp the influence of each independent variables more deeply, we used R programming to identify the influence of independent variables. As a result, it was found that the food satisfaction and sightseeing spot attraction were higher than other variables in overall satisfaction and had a greater effect than other influential variables. Revisit intention had a higher ${\beta}$ value in the travel motive as the purpose of Korean Wave than other variables. It will be necessary to have a policy that will lead to a substantial revisit of tourists by enhancing tourist attractions for the purpose of Korean Wave. Lastly, the recommendation had the same result of satisfaction as the sightseeing spot attraction and food satisfaction have higher ${\beta}$ value than other variables. From this analysis, we found that 'food satisfaction' and 'sightseeing spot attraction' variables were the common factors to influence three dependent variables that are mentioned above('Overall satisfaction', 'Revisit intention' and 'Recommendation'), and that those factors affected the satisfaction of travel in Korea significantly. The purpose of this study is to examine how to activate foreign tourists in Korea through big data analysis. It is expected to be used as basic data for analyzing tourism data and establishing effective tourism policy. It is expected to be used as a material to establish an activation plan that can contribute to tourism development in Korea in the future.

• The Korean Journal of Nuclear Medicine Technology
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• v.14 no.1
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• pp.67-72
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• 2010

Purpose: In Asan Medical Center we perform myocardial perfusion SPECT to evaluate cardiac event risk level for non-cardiac surgery patients. In case of patients with cancer, we check tumor metastasis using whole body bone scan and whole body PET scan and then perform myocardial perfusion SPECT to reduce unnecessary exam. In case of short term in patients, we perform $^{201}Tl$ myocardial perfusion SPECT after whole body bone scan a minimum 16 hours in order to reduce hospitalization period but it is still the actual condition in which the evaluation about the affect of the crosstalk contamination due to the each other dissimilar isotope administration doesn't properly realize. So in our experiments, we try to evaluate crosstalk contamination influence on $^{201}Tl$ myocardial perfusion SPECT using anthropomorphic torso phantom and patient's data. Materials and Methods: From 2009 August to September, we analyzed 87 patients with $^{201}Tl$ myocardial perfusion SPECT. According to $^{201}Tl$ myocardial perfusion SPECT yesterday whole body bone scan possibility of carrying out, a patient was classified. The image data are obtained by using the dual energy window in $^{201}Tl$ myocardial perfusion SPECT. We analyzed $^{201}Tl$ and $^{99m}Tc$ counts ratio in each patients groups obtained image data. We utilized anthropomorphic torso phantom in our experiment and administrated $^{201}Tl$ 14.8 MBq (0.4 mCi) at myocardium and $^{99m}Tc$ 44.4 MBq (1.2 mCi) at extracardiac region. We obtained image by $^{201}Tl$ myocardial perfusion SPECT without gate method application and analyzed spatial resolution using Xeleris ver 2.0551. Results: In case of $^{201}Tl$ window and the counts rate comparison result yesterday whole body bone scan of being counted in $^{99m}Tc$ window, the difference in which a rate to 24 hours exponential-functionally notes in 1:0.114 with Ventri (GE Healthcare, Wisconsin, USA), 1:0.249 after the bone tracer injection in 12 hours in 1:0.411 with 1:0.79 with Infinia (GE healthcare, Wisconsin, USA) according to a reduction a time-out was shown (Ventri p=0.001, Infinia p=0.001). Moreover, the rate of the case in which it doesn't perform the whole body bone scan showed up as the average 1:$0.067{\pm}0.6$ of Ventri, and 1:$0.063{\pm}0.7$ of Infinia. According to the phantom after experiment spatial resolution measurement result, and an addition or no and time-out of $^{99m}Tc$ administrated, it doesn't note any change of FWHM (p=0.134). Conclusion: Through the experiments using anthropomorphic torso phantom and patients data, we found that $^{201}Tl$ myocardium perfusion SPECT image later carried out after the bone tracer injection with 16 hours this confirmed that it doesn't receive notable influence in spatial resolution by $^{99m}Tc$. But this investigation is only aimed to image quality, so it needs more investigation in patient's radiation dose and exam accuracy and precision. The exact guideline presentation about the exam interval should be made of the validation test which is exact and in which it is standardized about the affect of the crosstalk contamination according to the isotope use in which it is different later on.

• Radiation Oncology Journal
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• v.28 no.3
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• pp.155-165
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• 2010

Purpose: In order to evaluate the positional uncertainty of internal organs during radiation therapy for treatment of liver cancer, we measured differences in inter- and intra-fractional variation of the tumor position and tidal amplitude using 4-dimentional computed radiograph (DCT) images and gated orthogonal setup kilovolt (KV) images taken on every treatment using the on board imaging (OBI) and real time position management (RPM) system. Materials and Methods: Twenty consecutive patients who underwent 3-dimensional (3D) conformal radiation therapy for treatment of liver cancer participated in this study. All patients received a 4DCT simulation with an RT16 scanner and an RPM system. Lipiodol, which was updated near the target volume after transarterial chemoembolization or diaphragm was chosen as a surrogate for the evaluation of the position difference of internal organs. Two reference orthogonal (anterior and lateral) digital reconstructed radiograph (DRR) images were generated using CT image sets of 0% and 50% into the respiratory phases. The maximum tidal amplitude of the surrogate was measured from 3D conformal treatment planning. After setting the patient up with laser markings on the skin, orthogonal gated setup images at 50% into the respiratory phase were acquired at each treatment session with OBI and registered on reference DRR images by setting each beam center. Online inter-fractional variation was determined with the surrogate. After adjusting the patient setup error, orthogonal setup images at 0% and 50% into the respiratory phases were obtained and tidal amplitude of the surrogate was measured. Measured tidal amplitude was compared with data from 4DCT. For evaluation of intra-fractional variation, an orthogonal gated setup image at 50% into the respiratory phase was promptly acquired after treatment and compared with the same image taken just before treatment. In addition, a statistical analysis for the quantitative evaluation was performed. Results: Medians of inter-fractional variation for twenty patients were 0.00 cm (range, -0.50 to 0.90 cm), 0.00 cm (range, -2.40 to 1.60 cm), and 0.00 cm (range, -1.10 to 0.50 cm) in the X (transaxial), Y (superior-inferior), and Z (anterior-posterior) directions, respectively. Significant inter-fractional variations over 0.5 cm were observed in four patients. Min addition, the median tidal amplitude differences between 4DCTs and the gated orthogonal setup images were -0.05 cm (range, -0.83 to 0.60 cm), -0.15 cm (range, -2.58 to 1.18 cm), and -0.02 cm (range, -1.37 to 0.59 cm) in the X, Y, and Z directions, respectively. Large differences of over 1 cm were detected in 3 patients in the Y direction, while differences of more than 0.5 but less than 1 cm were observed in 5 patients in Y and Z directions. Median intra-fractional variation was 0.00 cm (range, -0.30 to 0.40 cm), -0.03 cm (range, -1.14 to 0.50 cm), 0.05 cm (range, -0.30 to 0.50 cm) in the X, Y, and Z directions, respectively. Significant intra-fractional variation of over 1 cm was observed in 2 patients in Y direction. Conclusion: Gated setup images provided a clear image quality for the detection of organ motion without a motion artifact. Significant intra- and inter-fractional variation and tidal amplitude differences between 4DCT and gated setup images were detected in some patients during the radiation treatment period, and therefore, should be considered when setting up the target margin. Monitoring of positional uncertainty and its adaptive feedback system can enhance the accuracy of treatments.

• Journal of Chest Surgery
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• v.42 no.5
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• pp.624-629
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• 2009

Background: Making the histologic diagnosis of small pulmonary nodules and ground glass opacity (GGO) lesions is difficult. CT-guided percutaneous needle biopsies often fail to provide enough specimen for making the diagnosis. Video-assisted thoracoscopic surgery (VATS) can be inefficient for treating non-palpable lesions. Preoperative localization of small intrapulmonary lesions provides a more obvious target to facilitate performing intraoperative. resection. We evaluated the efficacy of CT-guided localization with using a hook wire and this was followed by VATS for making the histologic diagnosis of small intrapulmonary nodules and GGO lesions. Material and Method: Eighteen patients (13 males) were included in this study from August 2005 to March 2008. 18 intrapulmonary lesions underwent preoperative localization by using a CT-guided a hook wire system prior to performing VATS resection for intrapulmonary lesions and GGO lesions. The clinical data such as the accuracy of localization, the rate of conversion-to-thoracotomy, the operation time, the postoperative complications and the histology of the pulmonary lesion were retrospectively collected. Result: Eighteen VATS resections were performed in 18 patients. Preoperative CT-guided localization with a hook-wire was successful in all the patients. Dislodgement of a hook wire was observed in one case. There was no conversion to thoracotomy, The median diameter of lesions was 8 mm (range: $3{\sim}15\;mm$). The median depth of the lesions from the pleural surfaces was 5.5 mm (range: $1{\sim}30\;mm$). The median interval between preoperative CT-guided with a hook-wire and VATS was 34.5 min (range: ($10{\sim}226$ min). The median operative time was 43.5.min (range: $26{\sim}83$ min). In two patients, clinically insignificant pneumothorax developed after CT-guided localization with a hook-wire and there were no other complications. Histological examinations confirmed 8 primary lung cancers, 3 cases of metastases, 3 cases of inflammation, 2 intrapulmonary lymph nodes and 2 other benign lesions. Conclusion: CT-guided localization with a hook-wire followed by VATS for treating small intrapulmonary nodules and GGO lesions provided a low conversion thoracotomy rate, a short operation time and few localization-related or postoperative complications. This procedure was efficient to confirm intrapulmonary lesions and GGO lesions.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.33-56
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• 2017

As Internet usage is becoming more common worldwide and smartphone become necessity in daily life, technologies and applications related to mobile Internet are developing rapidly. The results of the Internet usage patterns of consumers around the world imply that there are many potential new business opportunities for mobile Internet technologies and applications. The location-based service (LBS) is a service based on the location information of the mobile device. LBS has recently gotten much attention among many mobile applications and various LBSs are rapidly developing in numerous categories. However, even with the development of LBS related technologies and services, there is still a lack of empirical research on the intention to use LBS. The application of previous researches is limited because they focused on the effect of one particular factor and had not shown the direct relationship on the intention to use LBS. Therefore, this study presents a research model of factors that affect the intention to use and actual use of LBS whose market is expected to grow rapidly, and tested it by conducting a questionnaire survey of 330 users. The results of data analysis showed that service customization, service quality, and personal innovativeness have a positive effect on the intention to use LBS and the intention to use LBS has a positive effect on the actual use of LBS. These results implies that LBS providers can enhance the user's intention to use LBS by offering service customization through the provision of various LBSs based on users' needs, improving information service qualities such as accuracy, timeliness, sensitivity, and reliability, and encouraging personal innovativeness. However, privacy concerns in the context of LBS are not significantly affected by service customization and personal innovativeness and privacy concerns do not significantly affect the intention to use LBS. In fact, the information related to users' location collected by LBS is less sensitive when compared with the information that is used to perform financial transactions. Therefore, such outcomes on privacy concern are revealed. In addition, the advantages of using LBS are more important than the sensitivity of privacy protection to the users who use LBS than to the users who use information systems such as electronic commerce that involves financial transactions. Therefore, LBS are recommended to be treated differently from other information systems. This study is significant in the theoretical point of contribution that it proposed factors affecting the intention to use LBS in a multi-faceted perspective, proved the proposed research model empirically, brought new insights on LBS, and broadens understanding of the intention to use and actual use of LBS. Also, the empirical results of the customization of LBS affecting the user's intention to use the LBS suggest that the provision of customized LBS services based on the usage data analysis through utilizing technologies such as artificial intelligence can enhance the user's intention to use. In a practical point of view, the results of this study are expected to help LBS providers to develop a competitive strategy for responding to LBS users effectively and lead to the LBS market grows. We expect that there will be differences in using LBSs depending on some factors such as types of LBS, whether it is free of charge or not, privacy policies related to LBS, the levels of reliability related application and technology, the frequency of use, etc. Therefore, if we can make comparative studies with those factors, it will contribute to the development of the research areas of LBS. We hope this study can inspire many researchers and initiate many great researches in LBS fields.