• Asia pacific journal of information systems
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• v.20 no.1
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• pp.141-170
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• 2010

User created content (UCC) is created and shared by common users on line. From the user's perspective, the increase of UCCs has led to an expansion of alternative means of communications, while from the business perspective UCCs have formed an environment in which an abundant amount of new contents can be produced. Despite outward quantitative growth, however, many aspects of UCCs do not meet the expectations of general users in terms of quality, and this can be observed through pirated contents and user-copied contents. The purpose of this research is to investigate effective methods for fostering production of creative user-generated content. This study proposes two core elements, namely, reward and motivation, which are believed to enhance content creativity as well as the mediating factor and users' committement, which will be effective for bridging the increasing motivation and content creativity. Based on this perspective, this research takes an in-depth look at issues related to constructing the dimensions of reward and motivation in UCC services for creative content product, which are identified in three phases. First, three dimensions of rewards have been proposed: task dimension, social dimension, and organizational dimention. The task dimension rewards are related to the inherent characteristics of a task such as writing blog articles and pasting photos. Four concrete ways of providing task-related rewards in UCC environments are suggested in this study, which include skill variety, task significance, task identity, and autonomy. The social dimensioni rewards are related to the connected relationships among users. The organizational dimension consists of monetary payoff and recognition from others. Second, the two types of motivations are suggested to be affected by the diverse rewards schemes: intrinsic motivation and extrinsic motivation. Intrinsic motivation occurs when people create new UCC contents for its' own sake, whereas extrinsic motivation occurs when people create new contents for other purposes such as fame and money. Third, commitments are suggested to work as important mediating variables between motivation and content creativity. We believe commitments are especially important in online environments because they have been found to exert stronger impacts on the Internet users than other relevant factors do. Two types of commitments are suggested in this study: emotional commitment and continuity commitment. Finally, content creativity is proposed as the final dependent variable in this study. We provide a systematic method to measure the creativity of UCC content based on the prior studies in creativity measurement. The method includes expert evaluation of blog pages posted by the Internet users. In order to test the theoretical model of our study, 133 active blog users were recruited to participate in a group discussion as well as a survey. They were asked to fill out a questionnaire on their commitment, motivation and rewards of creating UCC contents. At the same time, their creativity was measured by independent experts using Torrance Tests of Creative Thinking. Finally, two independent users visited the study participants' blog pages and evaluated their content creativity using the Creative Products Semantic Scale. All the data were compiled and analyzed through structural equation modeling. We first conducted a confirmatory factor analysis to validate the measurement model of our research. It was found that measures used in our study satisfied the requirement of reliability, convergent validity as well as discriminant validity. Given the fact that our measurement model is valid and reliable, we proceeded to conduct a structural model analysis. The results indicated that all the variables in our model had higher than necessary explanatory powers in terms of R-square values. The study results identified several important reward shemes. First of all, skill variety, task importance, task identity, and automony were all found to have significant influences on the intrinsic motivation of creating UCC contents. Also, the relationship with other users was found to have strong influences upon both intrinsic and extrinsic motivation. Finally, the opportunity to get recognition for their UCC work was found to have a significant impact on the extrinsic motivation of UCC users. However, different from our expectation, monetary compensation was found not to have a significant impact on the extrinsic motivation. It was also found that commitment was an important mediating factor in UCC environment between motivation and content creativity. A more fully mediating model was found to have the highest explanation power compared to no-mediation or partially mediated models. This paper ends with implications of the study results. First, from the theoretical perspective this study proposes and empirically validates the commitment as an important mediating factor between motivation and content creativity. This result reflects the characteristics of online environment in which the UCC creation activities occur voluntarily. Second, from the practical perspective this study proposes several concrete reward factors that are germane to the UCC environment, and their effectiveness to the content creativity is estimated. In addition to the quantitive results of relative importance of the reward factrs, this study also proposes concrete ways to provide the rewards in the UCC environment based on the FGI data that are collected after our participants finish asnwering survey questions. Finally, from the methodological perspective, this study suggests and implements a way to measure the UCC content creativity independently from the content generators' creativity, which can be used later by future research on UCC creativity. In sum, this study proposes and validates important reward features and their relations to the motivation, commitment, and the content creativity in UCC environment, which is believed to be one of the most important factors for the success of UCC and Web 2.0. As such, this study can provide significant theoretical as well as practical bases for fostering creativity in UCC contents.

• Journal of Intelligence and Information Systems
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• v.27 no.4
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• pp.73-95
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• 2021

This study uses Node2vec graph embedding method and Light GBM link prediction to explore undeveloped export candidate countries in Korea's food and beverage industry. Node2vec is the method that improves the limit of the structural equivalence representation of the network, which is known to be relatively weak compared to the existing link prediction method based on the number of common neighbors of the network. Therefore, the method is known to show excellent performance in both community detection and structural equivalence of the network. The vector value obtained by embedding the network in this way operates under the condition of a constant length from an arbitrarily designated starting point node. Therefore, it has the advantage that it is easy to apply the sequence of nodes as an input value to the model for downstream tasks such as Logistic Regression, Support Vector Machine, and Random Forest. Based on these features of the Node2vec graph embedding method, this study applied the above method to the international trade information of the Korean food and beverage industry. Through this, we intend to contribute to creating the effect of extensive margin diversification in Korea in the global value chain relationship of the industry. The optimal predictive model derived from the results of this study recorded a precision of 0.95 and a recall of 0.79, and an F1 score of 0.86, showing excellent performance. This performance was shown to be superior to that of the binary classifier based on Logistic Regression set as the baseline model. In the baseline model, a precision of 0.95 and a recall of 0.73 were recorded, and an F1 score of 0.83 was recorded. In addition, the light GBM-based optimal prediction model derived from this study showed superior performance than the link prediction model of previous studies, which is set as a benchmarking model in this study. The predictive model of the previous study recorded only a recall rate of 0.75, but the proposed model of this study showed better performance which recall rate is 0.79. The difference in the performance of the prediction results between benchmarking model and this study model is due to the model learning strategy. In this study, groups were classified by the trade value scale, and prediction models were trained differently for these groups. Specific methods are (1) a method of randomly masking and learning a model for all trades without setting specific conditions for trade value, (2) arbitrarily masking a part of the trades with an average trade value or higher and using the model method, and (3) a method of arbitrarily masking some of the trades with the top 25% or higher trade value and learning the model. As a result of the experiment, it was confirmed that the performance of the model trained by randomly masking some of the trades with the above-average trade value in this method was the best and appeared stably. It was found that most of the results of potential export candidates for Korea derived through the above model appeared appropriate through additional investigation. Combining the above, this study could suggest the practical utility of the link prediction method applying Node2vec and Light GBM. In addition, useful implications could be derived for weight update strategies that can perform better link prediction while training the model. On the other hand, this study also has policy utility because it is applied to trade transactions that have not been performed much in the research related to link prediction based on graph embedding. The results of this study support a rapid response to changes in the global value chain such as the recent US-China trade conflict or Japan's export regulations, and I think that it has sufficient usefulness as a tool for policy decision-making.

• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.119-138
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• 2016

Since the Industrial Revolution, which made the mass production and mass distribution of standardized goods possible, machine-made (manufactured) products have accounted for the majority of the market. However, in recent years, the phenomenon of purchasing even more expensive handmade products has become a noticeable trend as consumers have started to acknowledge the value of handmade products, such as the craftsman's commitment, belief in their quality and scarcity, and the sense of self-esteem from having them,. Consumer interest in these handmade products has shown explosive growth and has been coupled with the recent development of three-dimensional (3D) printing technologies. Etsy.com is the world's largest online handmade platform. It is no different from any other online platform; it provides an online market where buyers and sellers virtually meet to share information and transact business. However, Etsy.com is different in that shops within this platform only deal with handmade products in a variety of categories, ranging from jewelry to toys. Since its establishment in 2005, despite being limited to handmade products, Etsy.com has enjoyed rapid growth in membership, transaction volume, and revenue. Most recently in April 2015, it raised funds through an initial public offering (IPO) of more than 1.8 billion USD, which demonstrates the huge potential of online handmade platforms. After the success of Etsy.com, various types of online handmade platforms such as Handmade at Amazon, ArtFire, DaWanda, and Craft is ART have emerged and are now competing with each other, at the same time, which has increased the size of the market. According to Deloitte's 2015 holiday survey on which types of gifts the respondents plan to buy during the holiday season, about 16% of U.S. consumers chose "homemade or craft items (e.g., Etsy purchase)," which was the same rate as those for the computer game and shoes categories. This indicates that consumer interests in online handmade platforms will continue to rise in the future. However, this high interest in the market for handmade products and their platforms has not yet led to academic research. Most extant studies have only focused on machine-made products and intelligent services for them. This indicates a lack of studies on handmade products and their intelligent services on virtual platforms. Therefore, this study used signaling theory and prior research on the effects of sellers' characteristics on their performance (e.g., total sales and price premiums) in the buyer-seller relationship to identify the key influencing e-Image factors (e.g., reputation, size, information sharing, and length of relationship). Then, their impacts on the performance of shops within the online handmade platform were empirically examined; the dataset was collected from Etsy.com through the application of web harvesting technology. The results from the structural equation modeling revealed that the reputation, size, and information sharing have significant effects on the total sales, while the reputation and length of relationship influence price premiums. This study extended the online platform research into online handmade platform research by identifying key influencing e-Image factors on within-platform shop's total sales and price premiums based on signaling theory and then performed a statistical investigation. These findings are expected to be a stepping stone for future studies on intelligent online handmade services as well as handmade products themselves. Furthermore, the findings of the study provide online handmade platform operators with practical guidelines on how to implement intelligent online handmade services. They should also help shop managers build their marketing strategies in a more specific and effective manner by suggesting key influencing e-Image factors. The results of this study should contribute to the vitalization of intelligent online handmade services by providing clues on how to maximize within-platform shops' total sales and price premiums.

• Journal of agricultural medicine and community health
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• v.11 no.1
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• pp.44-54
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• 1986

This study was aimed at investigating the health seeking behaviors of patients; For the purpose of analyzing the research theme we classified the study into two phase. First, the types of patients' health seeking behavior were categorized into a scheme according to what medical care resources were utilized in patients' coping process. Second, from patients' first visits to third visits to medical resources, we analyzed variations of factors which noted as crucial elements in constituting the patients' sickness career. To grasp the generalized characteristics from complicated empirical data, we limited the scope of our analysis to third stage of health seeking. A total of 121 persons who had beer suffering from chronic diseases more than 3 months was sampled among the residents of Banwol-Eup, the target Area of Korea University Health Project. The findings are as follows ; 1) In the course of visiting medical care resources, 34 different types of health seeking Behavior were found. From this result we inferred the idea that patients in Banwol-Eup had not any stable norms to cope with their pains. Clinics, hospital, pharmacy, Herb-doctors', folkways (self-treatment) were accessed by patients in orders. But more than half of patients who had utilized clinics or hospitals from their first to third visits, changed medical care resources to others, for example herb doctors or folkways, which had fundamentally different treatment models. Upon these two facts, the diversified types and capricious patterns in the health seeking behavior of Banwol patients, we observed a typical Shopping-Around phenomenon. 2) Factors which influenced patients' to their sickness career were changed along the courses of health seeking, from first to third visits as follows ; $\cdot$ Perceived seriousness of diseases were tended to decrease. $\cdot$ Professional medical personnel tended to be influencial in the patients' sickness career, (5.0%, 25.0% and 65.7%). The influence of the primary interaction groups such as parents, friends, neighbours, tended to decrease ; (90.9%, 71.2% and 30.0%). $\cdot$ The subjective reasons why to choose such a medical care resource were related to economic affordability and disease-itself as main motives. Credibility of health resources tended to increase 14.9%, 24.0% and 31.4 sequently. $\cdot$ Geographic accessibility factors did not change significantly. Most of patients had utilized health resources in Banwol and Anyang area. 3) Cultural inclination in the shopping-around phenomenon has shown difference among age groups. The age group' over 50 years' preferred traditional health resources to modern health resources. 4) Consistency of health seeking behavior on the shopping around phenomenon has shown difference according to the degrees of patients' economic affordability and those of psychological satisfaction toward modern health services. However, there were some restrictions in this thesis ; a) the study was limited to the 3rd health seeking career so it did not allow us to collect more informations after that, b) the study was not able to carry out causal analysis on patients health behavior determinated by explanatory model of health resources, and c) the study was not able to take into consideration of factors connected with social structural circumstances. Despite of restrictions described above, we are sure that this thesis would promote health providers' understanding toward patients' inclinations, through which they could provide efficient and accurate medical service.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.8 no.4
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• pp.145-159
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• 2013

The purpose of this study is to analyze the structural relationship as to how learning flow and self-directed learning are linked to learning motives and academic self-efficacy in the learning setting of high school students. To accomplish such purpose, based on theoretical backgrounds and preceding research findings evaluation models were put to verification for a valid research model for this study. The initial hypothetical model was that self-directed learning ability would have a direct influence on learning motive, academic efficacy and learning flow, while having an indirect influence on learning flow with learning motive and self-efficacy acting as a mediating variable. But the hypothetical model showed low significance level between self-directed learning and learning motive, and learning motive and learning flow. Therefore, links were adjusted to create the final model within the scope that the adequacy of the model might not be compromised. To verify the model, 900 high school students in Seoul were surveyed and the collected data were statistically analyzed using AMOS v21.0 and SPSS v21.0 But 815 surveys were excluded because they were not sufficiently answered. From the analysis, it was found that self-directed learning and academic efficacy have a direct influence on learning flow while self-directed learning and academic efficacy have an indirect leaning motive and learning flow. This finding means that, in the relationship of self-directed learning and learning flow, learning motive and learning efficacy are positive factors that help high school students experience learning flow. Thus, in order to enhance the experience of self-directed learning ability of high school students, various educational endeavors are needed to draw the experience of learning flow during the regular course of study. In addition, customized educational methods and environments are required to increase academic efficacy of the students.

• Journal of The Korean Astronomical Society
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• v.29 no.spc1
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• pp.63-64
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• 1996

The nature of distant faint blue field galaxies remains a mystery, despite the fact that much attention has been devoted to this subject in the last decade. Galaxy counts, particularly those in the optical and near ultraviolet bandpasses, have been demonstrated to be well in excess of those expected in the 'no-evolution' scenario. This has usually been taken to imply that galaxies were brighter in the past, presumably due to a higher rate of star formation. More recently, redshift surveys of galaxies as faint as B$\~$24 have shown that the mean redshift of faint blue galaxies is lower than that predicted by standard evolutionary models (de-signed to fit the galaxy counts). The galaxy number count data and redshift data suggest that evolutionary effects are most prominent at the faint end of the galaxy luminosity function. While these data constrain the form of evolution of the overall luminosity function, they do not constrain evolution in individual galaxies. We are carrying out a series of observations as part of a long-term program aimed at a better understanding of the nature and amount of luminosity evolution in individual galaxies. Our study uses the luminosity-linewidth relation (Tully-Fisher relation) for disk galaxies as a tool to study luminosity evolution. Several studies of a related nature are being carried out by other groups. A specific experiment to test a 'no-evolution' hypothesis is presented here. We have used the AUTOFIB multifibre spectro-graph on the 4-metre Anglo-Australian Telescope (AAT) and the Rutgers Fabry-Perot imager on the Cerro Tolalo lnteramerican Observatory (CTIO) 4-metre tele-scope to measure the internal kinematics of a representative sample of faint blue field galaxies in the red-shift range z = 0.15-0.4. The emission line profiles of [OII] and [OIII] in a typical sample galaxy are significantly broader than the instrumental resolution (100-120 km $s^{-l}$), and it is possible to make a reliable de-termination of the linewidth. Detailed and realistic simulations based on the properties of nearby, low-luminosity spirals are used to convert the measured linewidth into an estimate of the characteristic rotation speed, making statistical corrections for the effects of inclination, non-uniform distribution of ionized gas, rotation curve shape, finite fibre aperture, etc.. The (corrected) mean characteristic rotation speed for our distant galaxy sample is compared to the mean rotation speed of local galaxies of comparable blue luminosity and colour. The typical galaxy in our distant sample has a B-band luminosity of about 0.25 L$\ast$ and a colour that corresponds to the Sb-Sd/Im range of Hub-ble types. Details of the AUTOFIB fibre spectroscopic study are described by Rix et al. (1996). Follow-up deep near infrared imaging with the 10-metre Keck tele-scope+ NIRC combination and high angular resolution imaging with the Hubble Space Telescope's WFPC2 are being used to determine the structural and orientation parameters of galaxies on an individual basis. This information is being combined with the spatially resolved CTIO Fabry-Perot data to study the internal kinematics of distant galaxies (Ing et al. 1996). The two main questions addressed by these (preliminary studies) are: 1. Do galaxies of a given luminosity and colour have the same characteristic rotation speed in the distant and local Universe? The distant galaxies in our AUTOFIB sample have a mean characteristic rotation speed of $\~$70 km $s^{-l}$ after correction for measurement bias (Fig. 1); this is inconsistent with the characteristic rotation speed of local galaxies of comparable photometric proper-ties (105 km $s^{-l}$) at the > $99\%$ significance level (Fig. 2). A straightforward explanation for this discrepancy is that faint blue galaxies were about 1-1.5 mag brighter (in the B band) at z $\~$ 0.25 than their present-day counterparts. 2. What is the nature of the internal kinematics of faint field galaxies? The linewidths of these faint galaxies appear to be dominated by the global disk rotation. The larger galaxies in our sample are about 2"-.5" in diameter so one can get direct insight into the nature of their internal velocity field from the $\~$ I" seeing CTIO Fabry-Perot data. A montage of Fabry-Perot data is shown in Fig. 3. The linewidths are too large (by. $5\sigma$) to be caused by turbulence in giant HII regions.

• Geophysics and Geophysical Exploration
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• v.11 no.1
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• pp.1-14
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• 2008

We present a method for interpreting seismic records with arrivals and waveforms having characteristics which could be generated by extremely inhomogeneous velocity structures, such as non-typical oceanic crust, decollement at subduction zones, and seamounts in oceanic regions, by comparing them with synthetic waveforms. Recent extensive refraction and wide-angle reflection surveys in oceanic regions have provided us with a huge number of high-resolution and high-quality seismic records containing characteristic arrivals and waveforms, besides first arrivals and major reflected phases such as PmP. Some characteristic waveforms, with significant later reflected phases or anomalous amplitude decay with offset distance, are difficult to interpret using only a conventional interpretation method such as the traveltime tomographic inversion method. We find the best process for investigating such characteristic phases is to use an interactive interpretation method to compare observed data with synthetic waveforms, and calculate raypaths and traveltimes. This approach enables us to construct a reasonable structural model that includes all of the major characteristics of the observed waveforms. We present results here with some actual observed examples that might be of great help in the interpretation of such problematic phases. Our approach to the analysis of waveform characteristics is endorsed as an innovative method for constructing high-resolution and high-quality crustal structure models, not only in oceanic regions, but also in the continental regions.

• Korea journal of population studies
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• v.23 no.2
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• pp.79-112
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• 2000

This study was focused on the contrasting pattern of part-time employment between Japan and Taiwan where the environments are similar in terms of expanding service industries and increasing flexibility of labor. In Japan, the expansion of part-time employment and its feminization have occurred, whereas they have not at all in Taiwan. The purpose of this study was to examine the reasons behind this phenomena, and to explore what relations they might have with the supply of women\`s labor in each country. Data analysis showed the following results. First, when the phenomena of part-time employment in Japan and Taiwan are summarized as \`active\` and \`inactive\` models, the difference could be explained by a structure-oriented approach rather than an individual-oriented approach. In other words, the difference between the two countries is mainly because of the structural characteristics of the labor market. a combination of capitalism and patriarchy, and an effect of state welfare and family policies rather than a \`voluntaristic choice\` due tn household work and child rearing. In light of this. the labor market segmentation and flexibility of labor theory in particular provided a useful frame for explanation. Second, with regard to the supply of women\`s labor, the difference between Japan and Taiwan could be found in the structure of the labor market and in family response strategies. The large corporation-oriented and strictly divided labor market structure in Japan activated part-time employment and its feminization, whereas, the small family-oriented businesses and less divided labor market in Taiwan supported the continuity of full-time employment of married women. There was also a room for informal employment in Taiwan which made part-time employment unnecessary. This study showed that even within similar environments of expanding service industry and pursuing flexibility of labor different measures and adaptations were possible. The case of Taiwan in particular, showed the significance of an informal labor market which was a part of industrialization process and a strategy of producing various products through a subcontracting network.

• Asia pacific journal of information systems
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• v.19 no.2
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• pp.157-178
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• 2009

Corporate credit rating is a very important factor in the market for corporate debt. Information concerning corporate operations is often disseminated to market participants through the changes in credit ratings that are published by professional rating agencies, such as Standard and Poor's (S&P) and Moody's Investor Service. Since these agencies generally require a large fee for the service, and the periodically provided ratings sometimes do not reflect the default risk of the company at the time, it may be advantageous for bond-market participants to be able to classify credit ratings before the agencies actually publish them. As a result, it is very important for companies (especially, financial companies) to develop a proper model of credit rating. From a technical perspective, the credit rating constitutes a typical, multiclass, classification problem because rating agencies generally have ten or more categories of ratings. For example, S&P's ratings range from AAA for the highest-quality bonds to D for the lowest-quality bonds. The professional rating agencies emphasize the importance of analysts' subjective judgments in the determination of credit ratings. However, in practice, a mathematical model that uses the financial variables of companies plays an important role in determining credit ratings, since it is convenient to apply and cost efficient. These financial variables include the ratios that represent a company's leverage status, liquidity status, and profitability status. Several statistical and artificial intelligence (AI) techniques have been applied as tools for predicting credit ratings. Among them, artificial neural networks are most prevalent in the area of finance because of their broad applicability to many business problems and their preeminent ability to adapt. However, artificial neural networks also have many defects, including the difficulty in determining the values of the control parameters and the number of processing elements in the layer as well as the risk of over-fitting. Of late, because of their robustness and high accuracy, support vector machines (SVMs) have become popular as a solution for problems with generating accurate prediction. An SVM's solution may be globally optimal because SVMs seek to minimize structural risk. On the other hand, artificial neural network models may tend to find locally optimal solutions because they seek to minimize empirical risk. In addition, no parameters need to be tuned in SVMs, barring the upper bound for non-separable cases in linear SVMs. Since SVMs were originally devised for binary classification, however they are not intrinsically geared for multiclass classifications as in credit ratings. Thus, researchers have tried to extend the original SVM to multiclass classification. Hitherto, a variety of techniques to extend standard SVMs to multiclass SVMs (MSVMs) has been proposed in the literature Only a few types of MSVM are, however, tested using prior studies that apply MSVMs to credit ratings studies. In this study, we examined six different techniques of MSVMs: (1) One-Against-One, (2) One-Against-AIL (3) DAGSVM, (4) ECOC, (5) Method of Weston and Watkins, and (6) Method of Crammer and Singer. In addition, we examined the prediction accuracy of some modified version of conventional MSVM techniques. To find the most appropriate technique of MSVMs for corporate bond rating, we applied all the techniques of MSVMs to a real-world case of credit rating in Korea. The best application is in corporate bond rating, which is the most frequently studied area of credit rating for specific debt issues or other financial obligations. For our study the research data were collected from National Information and Credit Evaluation, Inc., a major bond-rating company in Korea. The data set is comprised of the bond-ratings for the year 2002 and various financial variables for 1,295 companies from the manufacturing industry in Korea. We compared the results of these techniques with one another, and with those of traditional methods for credit ratings, such as multiple discriminant analysis (MDA), multinomial logistic regression (MLOGIT), and artificial neural networks (ANNs). As a result, we found that DAGSVM with an ordered list was the best approach for the prediction of bond rating. In addition, we found that the modified version of ECOC approach can yield higher prediction accuracy for the cases showing clear patterns.

• Knowledge Management Research
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• v.21 no.3
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• pp.249-269
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• 2020

Recently, as the online market has matured, it is facing many problems to prevent the growth. The most common problem is the homogenization of online products, which fails to increase the number of customers any more. Moreover, although the portion of the online market has increased significantly, it now becomes essential to expand offline for further development. In response, many online firms have recently sought to expand their businesses and marketing channels by securing offline spaces that can complement the limitations of online platforms, on top of their existing advantages of online channels. Based on their competitive advantage in terms of analyzing large volumes of customer data utilizing information technologies (e.g., big data and artificial intelligence), they are reinforcing their offline influence as well through this online for offline (O4O) business model. On the other hand, most of the existing research has primarily focused on online to offline (O2O) business model, and there is still a lack of research on O4O business models, which have been actively attempted in various industrial fields in recent years. Since a few of O4O-related studies have been conducted only in an experience marketing setting following a case study method, it is critical to conduct an empirical study on O4O selection attributes and their impact on customer satisfaction and loyalty. Therefore, focusing on China's representative O4O business model, 'Fresh Hema,' this study attempts to identify some key selection attributes specialized for O4O services from the customers' viewpoint and examine the impact of these attributes on customer satisfaction and loyalty. The results of the structural equation modeling (SEM) with 300 O4O (Fresh Hema) experienced customers, reveal that, out of seven O4O selection attributes, four (mobile app quality, mobile payment, product quality, and store facilities) have an impact on customer satisfaction, which also leads to customer loyalty (reuse intention, recommendation intention, and brand attachment). This study would help managers in an O4O area well adapt to rapidly changing customer needs and provide them with some guidelines for enhancing both customer satisfaction and loyalty by allocating more resources to more significant selection attributes, rather than less significant ones.