• Journal of Global Scholars of Marketing Science
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• v.18 no.1
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• pp.33-58
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• 2008

Previous research has presupposed that the evaluation of consumer who received any recovery after experiencing product failure should be better than the evaluation of consumer who did not receive any recovery. The major purposes of this article are to examine impacts of product defect failures rather than service failures, and to explore effects of recovery on postrecovery product attitudes. First, this article deals with the occurrence of severe and unsevere failure and corresponding service recovery toward tangible products rather than intangible services. Contrary to intangible services, purchase and usage are separable for tangible products. This difference makes it clear that executing an recovery strategy toward tangible products is not plausible right after consumers find out product failures. The consumers may think about backgrounds and causes for the unpleasant events during the time gap between product failure and recovery. The deliberation may dilutes positive effects of recovery efforts. The recovery strategies which are provided to consumers experiencing product failures can be classified into three types. A recovery strategy can be implemented to provide consumers with a new product replacing the old defective product, a complimentary product for free, a discount at the time of the failure incident, or a coupon that can be used on the next visit. This strategy is defined as "a rewarding effort." Meanwhile a product failure may arise in exchange for its benefit. Then the product provider can suggest a detail explanation that the defect is hard to escape since it relates highly to the specific advantage to the product. The strategy may be called as "a strengthening effort." Another possible strategy is to recover negative attitude toward own brand by giving prominence to the disadvantages of a competing brand rather than the advantages of its own brand. The strategy is reflected as "a weakening effort." This paper emphasizes that, in order to confirm its effectiveness, a recovery strategy should be compared to being nothing done in response to the product failure. So the three types of recovery efforts is discussed in comparison to the situation involving no recovery effort. The strengthening strategy is to claim high relatedness of the product failure with another advantage, and expects the two-sidedness to ease consumers' complaints. The weakening strategy is to emphasize non-aversiveness of product failure, even if consumers choose another competitive brand. The two strategies can be effective in restoring to the original state, by providing plausible motives to accept the condition of product failure or by informing consumers of non-responsibility in the failure case. However the two may be less effective strategies than the rewarding strategy, since it tries to take care of the rehabilitation needs of consumers. Especially, the relative effect between the strengthening effort and the weakening effort may differ in terms of the severity of the product failure. A consumer who realizes a highly severe failure is likely to attach importance to the property which caused the failure. This implies that the strengthening effort would be less effective under the condition of high product severity. Meanwhile, the failing property is not diagnostic information in the condition of low failure severity. Consumers would not pay attention to non-diagnostic information, and with which they are not likely to change their attitudes. This implies that the strengthening effort would be more effective under the condition of low product severity. A 2 (product failure severity: high or low) X 4 (recovery strategies: rewarding, strengthening, weakening, or doing nothing) between-subjects design was employed. The particular levels of product failure severity and the types of recovery strategies were determined after a series of expert interviews. The dependent variable was product attitude after the recovery effort was provided. Subjects were 284 consumers who had an experience of cosmetics. Subjects were first given a product failure scenario and were asked to rate the comprehensibility of the failure scenario, the probability of raising complaints against the failure, and the subjective severity of the failure. After a recovery scenario was presented, its comprehensibility and overall evaluation were measured. The subjects assigned to the condition of no recovery effort were exposed to a short news article on the cosmetic industry. Next, subjects answered filler questions: 42 items of the need for cognitive closure and 16 items of need-to-evaluate. In the succeeding page a subject's product attitude was measured on an five-item, six-point scale, and a subject's repurchase intention on an three-item, six-point scale. After demographic variables of age and sex were asked, ten items of the subject's objective knowledge was checked. The results showed that the subjects formed more favorable evaluations after receiving rewarding efforts than after receiving either strengthening or weakening efforts. This is consistent with Hoffman, Kelley, and Rotalsky (1995) in that a tangible service recovery could be more effective that intangible efforts. Strengthening and weakening efforts also were effective compared to no recovery effort. So we found that generally any recovery increased products attitudes. The results hint us that a recovery strategy such as strengthening or weakening efforts, although it does not contain a specific reward, may have an effect on consumers experiencing severe unsatisfaction and strong complaint. Meanwhile, strengthening and weakening efforts were not expected to increase product attitudes under the condition of low severity of product failure. We can conclude that only a physical recovery effort may be recognized favorably as a firm's willingness to recover its fault by consumers experiencing low involvements. Results of the present experiment are explained in terms of the attribution theory. This article has a limitation that it utilized fictitious scenarios. Future research deserves to test a realistic effect of recovery for actual consumers. Recovery involves a direct, firsthand experience of ex-users. Recovery does not apply to non-users. The experience of receiving recovery efforts can be relatively more salient and accessible for the ex-users than for non-users. A recovery effort might be more likely to improve product attitude for the ex-users than for non-users. Also the present experiment did not include consumers who did not have an experience of the products and who did not perceive the occurrence of product failure. For the non-users and the ignorant consumers, the recovery efforts might lead to decreased product attitude and purchase intention. This is because the recovery trials may give an opportunity for them to notice the product failure.

• 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.23 no.1
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• pp.127-141
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• 2017

Advances in Internet technologies and the proliferation of mobile devices enabled consumers to approach a wide range of goods and services, while causing an adverse effect that they have hard time reaching their congenial items even if they devote much time to searching for them. Accordingly, businesses are using the recommender systems to provide tools for consumers to find the desired items more easily. Association Rule Mining (ARM) technology is advantageous to recommender systems in that ARM provides intuitive form of a rule with interestingness measures (support, confidence, and lift) describing the relationship between items. Given an item, its relevant items can be distinguished with the help of the measures that show the strength of relationship between items. Based on the strength, the most pertinent items can be chosen among other items and exposed to a given item's web page. However, the diversity of the measures may confuse which items are more recommendable. Given two rules, for example, one rule's support and confidence may not be concurrently superior to the other rule's. Such discrepancy of the measures in distinguishing one rule's superiority from other rules may cause difficulty in selecting proper items for recommendation. In addition, in an online environment where a web page or mobile screen can provide a limited number of recommendations that attract consumer interest, the prudent selection of items to be included in the list of recommendations is very important. The exposure of items of little interest may lead consumers to ignore the recommendations. Then, such consumers will possibly not pay attention to other forms of marketing activities. Therefore, the measures should be aligned with the probability of consumer's acceptance of recommendations. For this reason, this study proposes a model-based approach to combine those measures into one unified measure that can consistently determine the ranking of recommended items. A regression model was designed to describe how well the measures (independent variables; i.e., support, confidence, and lift) explain consumer's acceptance of recommendations (dependent variables, hit rate of recommended items). The model is intuitive to understand and easy to use in that the equation consists of the commonly used measures for ARM and can be used in the estimation of hit rates. The experiment using transaction data from one of the Korea's largest online shopping malls was conducted to show that the proposed model can improve the hit rates of recommendations. From the top of the list to 13th place, recommended items in the higher rakings from the proposed model show the higher hit rates than those from the competitive model's. The result shows that the proposed model's performance is superior to the competitive model's in online recommendation environment. In a web page, consumers are provided around ten recommendations with which the proposed model outperforms. Moreover, a mobile device cannot expose many items simultaneously due to its limited screen size. Therefore, the result shows that the newly devised recommendation technique is suitable for the mobile recommender systems. While this study has been conducted to cover the cross-selling in online shopping malls that handle merchandise, the proposed method can be expected to be applied in various situations under which association rules apply. For example, this model can be applied to medical diagnostic systems that predict candidate diseases from a patient's symptoms. To increase the efficiency of the model, additional variables will need to be considered for the elaboration of the model in future studies. For example, price can be a good candidate for an explanatory variable because it has a major impact on consumer purchase decisions. If the prices of recommended items are much higher than the items in which a consumer is interested, the consumer may hesitate to accept the recommendations.

• Management & Information Systems Review
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• v.33 no.5
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• pp.97-115
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• 2014

The purpose of this study is to illustrating how the store selection factors affect the customer's satisfaction about the store and the intention of revisit base on the analyzing the store selection factors. At the same time, the difference between the influence on the customer's satisfaction and revisit intention of that in Korea and in China is also compared. Accordingly, through the notional understanding of configuration variables and the investigation of previous research, the Research hypothesis was set and the relevance between the two was inspected. The survey was aimed at the Korean customers who visit the large supermarkets in Korea and Chinese customers who visit the large supermarkets in China. After that, the reliability and validity of the collected data was verified and the research hypothesis was validated by structure equation modeling. The result of this study can by concluded as follows: First, in Korea the customer's satisfaction is showed to be affected by store selection factors, product property, service property and physical environment. Second, in China the customer's satisfaction is showed to be affected by store selection factors, product property, service property and physical environment. Third, in Korea the revisit intention is showed to be affected by customer's satisfaction. Forth, in China the revisit intention is showed to be affected by customer's satisfaction. Fifth, it shows little difference between the store selection factors of the customers visiting large supermarkets in Korea and in China. According to the research results above, the implications can be drawn as the customer's satisfaction of those who visit the large supermarkets may be affected by store selection factors (store property, product property, service property and physical environment). In recent years, more and more overseas large supermarkets are opening in both Korea and China and the competition among each is become more intense day by day. Every larger supermarket is trying their best to refine their store property, product property, service property and physical environment, in order to enhance the customer's satisfaction. The biggest factor that affects the customer's satisfaction and revisit intention in Korea is service property, So that the services requires proper measures and improvement. In China, the factor that affects most on the customer's satisfaction and revisit intention is physical environment. In order to enhance the customer's positive consciousness of stores, the physical environment needs to be well constructed. Lastly, in the compared research between Korea and China, the distribution of survey responders was limited from certain areas. Therefore, the further study can be implemented by more research in various geographical areas and more development in store selection factors.

• Management & Information Systems Review
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• v.33 no.3
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• pp.129-154
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• 2014

We are making products and services that we didn't easily think or even imagine when young and living using them. Consumer needs become more diversified, markets and technologies are being constantly changed, and corporate global competitiveness is getting intensified. Now, companies that don't change nor create new values cannot survive. The cycle of new products is faster, and therefore corporations should improve previous products continuously or create new values according to such a trend. This era requires 'creativity' for companies to overcome uncertainty and survive. Corporate creativity is not easily activated as financial and physical compensation unlike the work ability or performance. When there is an environment that people with various abilities, experiences and backgrounds can express their opinions freely, it becomes possible. Despite the importance, the studies have focused on creativity education so far instead of the viewpoint of corporate business administration. In addition, most studies have dealt with job characteristics and employees' personality factors rather than the process that is connected to actual performance. In other words, the studies on environmental factors that can lead motivation as situational characteristics of the employee's company in a current non-monetary and non-physical way are very insufficient. Therefore, this study aims to comprehensively examine the process of creating creative performance in the environment inside the company that the employee could approach most closely from the corporate deinstitutional perspective. As the major variables, job characteristics and supervisor's ethical leadership were selected, and the process that is connected to the creative performance was explained by means of motivation necessary to understand creativity of the organization and creative process engagement(CPE). The summary of the verification results is following. First, job characteristics and supervisor's attitude(ethical leadership) had positive effects on intrinsic motivation and creativity process engagement. Second, intrinsic motivation and creativity process engagement had positive effects on creative performance. Third, intrinsic motivation and creativity process engagement had mediated the relationship between job characteristics, supervisor's attitude and intrinsic motivation, creativity process engagement

• Management & Information Systems Review
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• v.32 no.5
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• pp.69-88
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• 2013

This study aimed to analyze the effects that the exchange relationship among employees engaged in the wedding industry in Busan, Ulsan and Gyeongnam area exerted on job satisfaction and voluntary service intentions. For this purpose, a theoretical model was presented based on previous studies and the relationship among variables was analyzed based on the analysis results through a survey. There have recently been great difficulties in the whole wedding industry due to the global economic depression and the low birthrate. Moreover, it is hard to optimize the future business environment. In particular, the low birthrate has weakened the demand base of the wedding industry. As a result, it is expected that the competition among companies will be more fierce in the wedding industry. Employees' voluntary service intention become much more important to overcome difficulties in this industry. Employees' voluntary service intentions can be possible when their job satisfaction is achieved. Many studies report the result that this job satisfaction can naturally be achieved when the high quality exchange relationship among a leader, team members (co-workers) and customers is formed. It is viewed that employees' voluntary service intentions should be induced through job satisfaction to improve the profitability through the efficient operation and management of the system. A leader of the organization should make an effort to form the high-quality exchange relationship with members, support so that team members(coworkers) and other employees can get along together and the teamwork can be improved and induce them to improve a high level of service quality with friendly customer services. In spite of the significance of the study discussed above, there are the following limitations in this study. There is a limit to a certain extent in generalizing study conclusions because the study was limited to the Busan, Ulsan and Gyeongnam area. If practical surveys which full- and part-time employees are included in the composition of samples are conducted for wedding suppliers in the country in the future, the quality of study will be able to improve.

• 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.

• Journal of Internet Computing and Services
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• v.14 no.6
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• pp.71-84
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• 2013

Log data, which record the multitude of information created when operating computer systems, are utilized in many processes, from carrying out computer system inspection and process optimization to providing customized user optimization. In this paper, we propose a MongoDB-based unstructured log processing system in a cloud environment for processing the massive amount of log data of banks. Most of the log data generated during banking operations come from handling a client's business. Therefore, in order to gather, store, categorize, and analyze the log data generated while processing the client's business, a separate log data processing system needs to be established. However, the realization of flexible storage expansion functions for processing a massive amount of unstructured log data and executing a considerable number of functions to categorize and analyze the stored unstructured log data is difficult in existing computer environments. Thus, in this study, we use cloud computing technology to realize a cloud-based log data processing system for processing unstructured log data that are difficult to process using the existing computing infrastructure's analysis tools and management system. The proposed system uses the IaaS (Infrastructure as a Service) cloud environment to provide a flexible expansion of computing resources and includes the ability to flexibly expand resources such as storage space and memory under conditions such as extended storage or rapid increase in log data. Moreover, to overcome the processing limits of the existing analysis tool when a real-time analysis of the aggregated unstructured log data is required, the proposed system includes a Hadoop-based analysis module for quick and reliable parallel-distributed processing of the massive amount of log data. Furthermore, because the HDFS (Hadoop Distributed File System) stores data by generating copies of the block units of the aggregated log data, the proposed system offers automatic restore functions for the system to continually operate after it recovers from a malfunction. Finally, by establishing a distributed database using the NoSQL-based Mongo DB, the proposed system provides methods of effectively processing unstructured log data. Relational databases such as the MySQL databases have complex schemas that are inappropriate for processing unstructured log data. Further, strict schemas like those of relational databases cannot expand nodes in the case wherein the stored data are distributed to various nodes when the amount of data rapidly increases. NoSQL does not provide the complex computations that relational databases may provide but can easily expand the database through node dispersion when the amount of data increases rapidly; it is a non-relational database with an appropriate structure for processing unstructured data. The data models of the NoSQL are usually classified as Key-Value, column-oriented, and document-oriented types. Of these, the representative document-oriented data model, MongoDB, which has a free schema structure, is used in the proposed system. MongoDB is introduced to the proposed system because it makes it easy to process unstructured log data through a flexible schema structure, facilitates flexible node expansion when the amount of data is rapidly increasing, and provides an Auto-Sharding function that automatically expands storage. The proposed system is composed of a log collector module, a log graph generator module, a MongoDB module, a Hadoop-based analysis module, and a MySQL module. When the log data generated over the entire client business process of each bank are sent to the cloud server, the log collector module collects and classifies data according to the type of log data and distributes it to the MongoDB module and the MySQL module. The log graph generator module generates the results of the log analysis of the MongoDB module, Hadoop-based analysis module, and the MySQL module per analysis time and type of the aggregated log data, and provides them to the user through a web interface. Log data that require a real-time log data analysis are stored in the MySQL module and provided real-time by the log graph generator module. The aggregated log data per unit time are stored in the MongoDB module and plotted in a graph according to the user's various analysis conditions. The aggregated log data in the MongoDB module are parallel-distributed and processed by the Hadoop-based analysis module. A comparative evaluation is carried out against a log data processing system that uses only MySQL for inserting log data and estimating query performance; this evaluation proves the proposed system's superiority. Moreover, an optimal chunk size is confirmed through the log data insert performance evaluation of MongoDB for various chunk sizes.

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

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

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.85-109
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• 2018

Recommender system recommends the items expected to be purchased by a customer in the future according to his or her previous purchase behaviors. It has been served as a tool for realizing one-to-one personalization for an e-commerce service company. Traditional recommender systems, especially the recommender systems based on collaborative filtering (CF), which is the most popular recommendation algorithm in both academy and industry, are designed to generate the items list for recommendation by using 'overall rating' - a single criterion. However, it has critical limitations in understanding the customers' preferences in detail. Recently, to mitigate these limitations, some leading e-commerce companies have begun to get feedback from their customers in a form of 'multicritera ratings'. Multicriteria ratings enable the companies to understand their customers' preferences from the multidimensional viewpoints. Moreover, it is easy to handle and analyze the multidimensional ratings because they are quantitative. But, the recommendation using multicritera ratings also has limitation that it may omit detail information on a user's preference because it only considers three-to-five predetermined criteria in most cases. Under this background, this study proposes a novel hybrid recommendation system, which selectively uses the results from 'traditional CF' and 'CF using multicriteria ratings'. Our proposed system is based on the premise that some people have holistic preference scheme, whereas others have composite preference scheme. Thus, our system is designed to use traditional CF using overall rating for the users with holistic preference, and to use CF using multicriteria ratings for the users with composite preference. To validate the usefulness of the proposed system, we applied it to a real-world dataset regarding the recommendation for POI (point-of-interests). Providing personalized POI recommendation is getting more attentions as the popularity of the location-based services such as Yelp and Foursquare increases. The dataset was collected from university students via a Web-based online survey system. Using the survey system, we collected the overall ratings as well as the ratings for each criterion for 48 POIs that are located near K university in Seoul, South Korea. The criteria include 'food or taste', 'price' and 'service or mood'. As a result, we obtain 2,878 valid ratings from 112 users. Among 48 items, 38 items (80%) are used as training dataset, and the remaining 10 items (20%) are used as validation dataset. To examine the effectiveness of the proposed system (i.e. hybrid selective model), we compared its performance to the performances of two comparison models - the traditional CF and the CF with multicriteria ratings. The performances of recommender systems were evaluated by using two metrics - average MAE(mean absolute error) and precision-in-top-N. Precision-in-top-N represents the percentage of truly high overall ratings among those that the model predicted would be the N most relevant items for each user. The experimental system was developed using Microsoft Visual Basic for Applications (VBA). The experimental results showed that our proposed system (avg. MAE = 0.584) outperformed traditional CF (avg. MAE = 0.591) as well as multicriteria CF (avg. AVE = 0.608). We also found that multicriteria CF showed worse performance compared to traditional CF in our data set, which is contradictory to the results in the most previous studies. This result supports the premise of our study that people have two different types of preference schemes - holistic and composite. Besides MAE, the proposed system outperformed all the comparison models in precision-in-top-3, precision-in-top-5, and precision-in-top-7. The results from the paired samples t-test presented that our proposed system outperformed traditional CF with 10% statistical significance level, and multicriteria CF with 1% statistical significance level from the perspective of average MAE. The proposed system sheds light on how to understand and utilize user's preference schemes in recommender systems domain.