• Journal of Intelligence and Information Systems
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• v.27 no.2
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• pp.1-15
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• 2021

If it is difficult for us to make decisions, we ask for advice from friends or people around us. When we decide to buy products online, we read anonymous reviews and buy them. With the advent of the Data-driven era, IT technology's development is spilling out many data from individuals to objects. Companies or individuals have accumulated, processed, and analyzed such a large amount of data that they can now make decisions or execute directly using data that used to depend on experts. Nowadays, the recommender system plays a vital role in determining the user's preferences to purchase goods and uses a recommender system to induce clicks on web services (Facebook, Amazon, Netflix, Youtube). For example, Youtube's recommender system, which is used by 1 billion people worldwide every month, includes videos that users like, "like" and videos they watched. Recommended system research is deeply linked to practical business. Therefore, many researchers are interested in building better solutions. Recommender systems use the information obtained from their users to generate recommendations because the development of the provided recommender systems requires information on items that are likely to be preferred by the user. We began to trust patterns and rules derived from data rather than empirical intuition through the recommender systems. The capacity and development of data have led machine learning to develop deep learning. However, such recommender systems are not all solutions. Proceeding with the recommender systems, there should be no scarcity in all data and a sufficient amount. Also, it requires detailed information about the individual. The recommender systems work correctly when these conditions operate. The recommender systems become a complex problem for both consumers and sellers when the interaction log is insufficient. Because the seller's perspective needs to make recommendations at a personal level to the consumer and receive appropriate recommendations with reliable data from the consumer's perspective. In this paper, to improve the accuracy problem for "appropriate recommendation" to consumers, the recommender systems are proposed in combination with context-based deep learning. This research is to combine user-based data to create hybrid Recommender Systems. The hybrid approach developed is not a collaborative type of Recommender Systems, but a collaborative extension that integrates user data with deep learning. Customer review data were used for the data set. Consumers buy products in online shopping malls and then evaluate product reviews. Rating reviews are based on reviews from buyers who have already purchased, giving users confidence before purchasing the product. However, the recommendation system mainly uses scores or ratings rather than reviews to suggest items purchased by many users. In fact, consumer reviews include product opinions and user sentiment that will be spent on evaluation. By incorporating these parts into the study, this paper aims to improve the recommendation system. This study is an algorithm used when individuals have difficulty in selecting an item. Consumer reviews and record patterns made it possible to rely on recommendations appropriately. The algorithm implements a recommendation system through collaborative filtering. This study's predictive accuracy is measured by Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). Netflix is strategically using the referral system in its programs through competitions that reduce RMSE every year, making fair use of predictive accuracy. Research on hybrid recommender systems combining the NLP approach for personalization recommender systems, deep learning base, etc. has been increasing. Among NLP studies, sentiment analysis began to take shape in the mid-2000s as user review data increased. Sentiment analysis is a text classification task based on machine learning. The machine learning-based sentiment analysis has a disadvantage in that it is difficult to identify the review's information expression because it is challenging to consider the text's characteristics. In this study, we propose a deep learning recommender system that utilizes BERT's sentiment analysis by minimizing the disadvantages of machine learning. This study offers a deep learning recommender system that uses BERT's sentiment analysis by reducing the disadvantages of machine learning. The comparison model was performed through a recommender system based on Naive-CF(collaborative filtering), SVD(singular value decomposition)-CF, MF(matrix factorization)-CF, BPR-MF(Bayesian personalized ranking matrix factorization)-CF, LSTM, CNN-LSTM, GRU(Gated Recurrent Units). As a result of the experiment, the recommender system based on BERT was the best.

• Journal of Global Scholars of Marketing Science
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• v.20 no.1
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• pp.89-97
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• 2010

This study researches the effects of common features on a no-choice option with respect to regulatory focus theory. The primary interest is in three factors and their interrelationship: common features, no-choice option, and regulatory focus. Prior studies have compiled vast body of research in these areas. First, the "common features effect" has been observed bymany noted marketing researchers. Tversky (1972) proposed the seminal theory, the EBA model: elimination by aspect. According to this theory, consumers are prone to focus only on unique features during comparison processing, thereby dismissing any common features as redundant information. Recently, however, more provocative ideas have attacked the EBA model by asserting that common features really do affect consumer judgment. Chernev (1997) first reported that adding common features mitigates the choice gap because of the increasing perception of similarity among alternatives. Later, however, Chernev (2001) published a critically developed study against his prior perspective with the proposition that common features may be a cognitive load to consumers, and thus consumers are possible that they are prone to prefer the heuristic processing to the systematic processing. This tends to bring one question to the forefront: Do "common features" affect consumer choice? If so, what are the concrete effects? This study tries to answer the question with respect to the "no-choice" option and regulatory focus. Second, some researchers hold that the no-choice option is another best alternative of consumers, who are likely to avoid having to choose in the context of knotty trade-off settings or mental conflicts. Hope for the future also may increase the no-choice option in the context of optimism or the expectancy of a more satisfactory alternative appearing later. Other issues reported in this domain are time pressure, consumer confidence, and alternative numbers (Dhar and Nowlis 1999; Lin and Wu 2005; Zakay and Tsal 1993). This study casts the no-choice option in yet another perspective: the interactive effects between common features and regulatory focus. Third, "regulatory focus theory" is a very popular theme in recent marketing research. It suggests that consumers have two focal goals facing each other: promotion vs. prevention. A promotion focus deals with the concepts of hope, inspiration, achievement, or gain, whereas prevention focus involves duty, responsibility, safety, or loss-aversion. Thus, while consumers with a promotion focus tend to take risks for gain, the same does not hold true for a prevention focus. Regulatory focus theory predicts consumers' emotions, creativity, attitudes, memory, performance, and judgment, as documented in a vast field of marketing and psychology articles. The perspective of the current study in exploring consumer choice and common features is a somewhat creative viewpoint in the area of regulatory focus. These reviews inspire this study of the interaction possibility between regulatory focus and common features with a no-choice option. Specifically, adding common features rather than omitting them may increase the no-choice option ratio in the choice setting only to prevention-focused consumers, but vice versa to promotion-focused consumers. The reasoning is that when prevention-focused consumers come in contact with common features, they may perceive higher similarity among the alternatives. This conflict among similar options would increase the no-choice ratio. Promotion-focused consumers, however, are possible that they perceive common features as a cue of confirmation bias. And thus their confirmation processing would make their prior preference more robust, then the no-choice ratio may shrink. This logic is verified in two experiments. The first is a $2{\times}2$ between-subject design (whether common features or not X regulatory focus) using a digital cameras as the relevant stimulus-a product very familiar to young subjects. Specifically, the regulatory focus variable is median split through a measure of eleven items. Common features included zoom, weight, memory, and battery, whereas the other two attributes (pixel and price) were unique features. Results supported our hypothesis that adding common features enhanced the no-choice ratio only to prevention-focus consumers, not to those with a promotion focus. These results confirm our hypothesis - the interactive effects between a regulatory focus and the common features. Prior research had suggested that including common features had a effect on consumer choice, but this study shows that common features affect choice by consumer segmentation. The second experiment was used to replicate the results of the first experiment. This experimental study is equal to the prior except only two - priming manipulation and another stimulus. For the promotion focus condition, subjects had to write an essay using words such as profit, inspiration, pleasure, achievement, development, hedonic, change, pursuit, etc. For prevention, however, they had to use the words persistence, safety, protection, aversion, loss, responsibility, stability etc. The room for rent had common features (sunshine, facility, ventilation) and unique features (distance time and building state). These attributes implied various levels and valence for replication of the prior experiment. Our hypothesis was supported repeatedly in the results, and the interaction effects were significant between regulatory focus and common features. Thus, these studies showed the dual effects of common features on consumer choice for a no-choice option. Adding common features may enhance or mitigate no-choice, contradictory as it may sound. Under a prevention focus, adding common features is likely to enhance the no-choice ratio because of increasing mental conflict; under the promotion focus, it is prone to shrink the ratio perhaps because of a "confirmation bias." The research has practical and theoretical implications for marketers, who may need to consider common features carefully in a practical display context according to consumer segmentation (i.e., promotion vs. prevention focus.) Theoretically, the results suggest some meaningful moderator variable between common features and no-choice in that the effect on no-choice option is partly dependent on a regulatory focus. This variable corresponds not only to a chronic perspective but also a situational perspective in our hypothesis domain. Finally, in light of some shortcomings in the research, such as overlooked attribute importance, low ratio of no-choice, or the external validity issue, we hope it influences future studies to explore the little-known world of the "no-choice option."

• Journal of Global Scholars of Marketing Science
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• v.18 no.2
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• pp.65-88
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• 2008

Since the implementation of economic reforms in 1978, the Chinese economy grows rapidly at an average annul growth rate of 9% over the post two decades. Franchising has been widely recognized as an important source of entrepreneurial activity. Trust is important in that it facilitates relational exchanges by permits partners to transcend short-run inequities or risks to concentrate on long-term profits or gains. In the relationship between the franchisors and franchisees, trust has been described as an important source of competitive advantage. However, little research has been done on the factors affecting trust in Chinese franchisor-franchisee relationships. The purpose of this study is to investigate what factors affect the trust in the franchise system in China, and to provide guidelines and insights to franchisors which enter Chinese market. In this study, according to Morgan and Hunt (1994), trust is defined as the extending when one party has confidence in an exchange partner's reliability and integrity. We offered a conceptual model of the empirical study. The model shows that the factors affecting the trust include franchisor's supports, communication, satisfaction with previous outcome and conflict. We also suggested the franchisor's supports and communication like to enhance the franchisee's satisfaction with previous outcome, and the franchisor's supports, communication and he franchisee's satisfaction with previous outcome tend to decrease conflict. Before the formal study, a pretest involving exploratory interviews with owners from three franchisees was conducted to make sure the questionnaire was relevant and clear to the respondents. The data were collected using trained interviewers to carry out personal interviews with the aid of an unidentified, muti-page, structured questionnaire. The respondents comprised of owners, managers, and owner managers of franchisee-owned food service franchises located in Beijing, China. Even though a total of 256 potential franchises were initially contacted, the finally usable sample consisted of 125 respondents. As expected, the sampling method was successful in soliciting respondents with waried personal and firm characteristics. Self-administrated questionnaires were used for all measures. And established scales were used to measure the latent constructs in this study. The measures tapped the franchisees' perceptions of the relationship with the referent franchisor. Five-point Likert-type scales ranging from "strongly disagree" (=1) to "strongly agree" (=7) were used throughout the constructs (trust, eight items; support, five items; communication, four items; satisfaction, six items; conflict, three items). The reliability measurements traditionally employed, such as the Cronbach's alpha, were used. All the reliabilities were greater than.80. The proposed measurement model was estimated using SPSS 12.0 and AMOS 5.0 analysis package. We conducted A series of exploratory factor analyses and confirmatory factor analyses to assess the convergent validity, discriminant validity, and reliability. The results indicate reasonable overall fits between the model and the observed data. The overall fit of measurement model were $X^2$= 159.699, p=0.004, d.f. = 116, GFI =.879, NFI =.898, CFI =.969, IFI =.970, TLI =.959, RMR =.058. The results demonstrated that the data reasonably fitted the model. We also examined construct reliability and reliability and average variance extracted (AVE). The construct reliability of each construct was greater than.80 and the AVE of each construct was greater than.50. According to the analysis of Structure Equation Modeling (SEM), the results of path model indicated an adequate fit of the model: $X^2$= 142.126, p = 0.044, d.f. = 115, GFI =.892, NFI =.909, CFI =.981, IFI =.981, TLI =.974, RMR =.057. As hypothesized, the results showed that it is strategically important to establish trust in a franchise system, and the franchisor's supports, communication and satisfaction with previous outcome tend to reinforce franchisee's trust. The results also showed trust seems to decrease as the experience of conflict episodes increases. And we also noticed that franchisor's supports and communication tend to enhance the franchisee's satisfaction with previous outcome, and communication tend to decrease conflict. If the trust between the franchisor and franchisee can be established in a franchise system, franchising offers many benefits and reduces many costs. To manage a mutual trust of relationship with their franchisees, franchisor's should provide support effectively to their franchisees. Effective assistant services have direct effect on franchisees' satisfaction with previous outcome and trust in franchisor. Especially, franchise sales process, orientation, and training in the start-up period are key elements for success of the franchise system. Franchisor's support is an accumulated separate satisfaction evaluation with different kind of service provided by the franchisor. And providing support definitely can improve the trustworthy image of the franchisor. In the franchise system, conflicts of interests and exertions of different power sources are very common. The experience of conflict episodes seems to negatively relate to trust. Therefore, it is important to reduce the negative side of the relationship conflicts. Communication actually plays a broader role in reducing conflict and establish mutual trust in franchisor-franchisee relationship. And effective communication between franchisors and franchisees can improve franchisees' satisfaction toward the franchise system. As the diversification of Chinese markets, both franchisors and franchisees must keep the relevant, timely, and reliable communication. And it is very important to improve the quality of communication. Satisfaction with precious outcomes seems to positively relate to trust. Franchisors and franchisees that are highly satisfied with the previous outcomes that flow from their relationship will perceive their partner as advancing their goal achievement. Therefore, it is necessary for both franchisor and their franchisees to make the welfare of partner with effort. Little literature has focused on what factors affect the trust between franchisors and their franchisees in China. This study developed the hypotheses regarding the factors affecting trust in the transaction relationship. The results of data analysis supported the hypotheses strongly. There are certain limitations in this study. First, we may point out that some other factors missed in this study could be significantly important. Second, the context of this study, food service industry, limits its potential generalizability for all franchise systems. More studies in different categories of franchise system are needed to broaden its generalizability. Third, the model was tested empirically in a sample in Beijing, more empirical tests of the proposed model in other Chinese areas are needed. Finally, the analysis in this study was solely based on the perception of franchisees and the opinions of franchisors were not included.