We herein performed animal safety assessment in accordance with Good Laboratory Practice (GLP) regulations with the aim of developing sialic acid from glycomacropeptide (hereafter referred to as "GMP") as an index ingredient and functional component in functional foods. GMP is a type of whey protein derived from milk and a safe food, with multiple functions, such as antiviral activity. A test substance was produced containing 7% (w/w) sialic acid and mostly-hydrolyzed whey protein (hereafter referred to as "7%-GNANA") by enzymatic treatment of substrate GMP. The maximum intake test dose level was selected based on 5,000 mg/kg/day dose set for male NOEL (no-observed-effect-level) and female NOAEL (no-observed-adverse-effect-level) determined by a dose-range finding (DRF) test (GLP Center of Catholic University of Daegu, Report No. 15-NREO-001) that was previously conducted with the same test substance. To evaluate the toxicity of a repeated oral dose of the test substance in connection with the previous DRF study, 1,250, 2,500, and 5,000 mg/kg of the substance were administered by a probe into the stomachs of 6-week-old SPF Sprague-Dawley male and female rats for 90 d. Each test group consisted of 10 male and 10 female rats. To determine the toxicity index, all parameters, such as observation of common signs; measurements of body weight and food consumption; ophthalmic examination; urinalysis, electrolyte, hematological, and serum biochemical examination; measurement of organ weights during autopsy; and visual and histopathological examinations were conducted according to GLP standards. After evaluating the results based on the test toxicity assessment criteria, it was determined that NOAEL of the test substance, 7%-GNANA, was 5,000 mg/kg/day, for both male and female rats. No animal death was noted in any of the test groups, including the control group, during the study period, and there was no significant difference associated with test substance, as compared with the control group, with respect to general symptoms, body weight changes, food consumption, ophthalmic examination, urinalysis, hematological and serum biochemical examination, and electrolyte and blood coagulation tests during the administration period (P<0.05). As assessed by the effects of the test substance on organ weights, food consumption, autopsy, and histopathological safety, change in kidney weight as an indicator of male NOAEL revealed up to 20% kidney weight increase in the high-dose group (5,000 mg/kg/day) compared with the change in the control group. However, it was concluded that this effect of the test substance was minor. In the case of female rats, reduction of food consumption, increase of kidney weight, and decrease of thymus weight were observed in the high-dose group. The kidney weight increased by 10.2% (left) and 8.9% (right) in the high-dose group, with a slight dose-dependency compared with that of the control group. It was observed that the thymus weight decreased by 25.3% in the high-dose group, but it was a minor test substance-associated effect. During the autopsy, botryoid tumor was detected on the ribs of one subject in the high-dose group, but we concluded that the tumor has been caused by a naturally occurring (non-test) substance. Histopathological examination revealed lesions on the kidney, liver, spleen, and other organs in the low-dose test group. Since these lesions were considered a separate phenomenon, or naturally occurring and associated with aging, it was checked whether any target organ showed clear symptoms caused by the test substance. In conclusion, different concentrations of the test substance were fed to rats and, consequently, it was verified that only a minor effect was associated with the test substance in the high-dose (5,000 mg/kg/day) group of both male and female rats, without any other significant effects associated with the test substance. Therefore, it was concluded that NOAEL of 7%-GNANA (product name: Helicobactrol) with male and female rats as test animals was 5,000 mg/kg/day, and it thus was determined that the substance is safe for the ultimate use as an ingredient of health functional foods.
Service failure is one of the major reasons for customer defection. As the business environment gets tougher and more competitive, a single service failure might bring about fatal consequences to a service provider or a firm. Sometimes a failure won't end up with an unsatisfied customer's simple complaining but with a wide-spread animosity against the service provider or the firm, leading to a threat to the firm's survival itself in the society. Therefore, we are in need of comprehensive understandings of complainants' attitudes and behaviors toward service failures and firm's recovery efforts. Even though a failure itself couldn't be fixed completely, marketers should repair the mind and heart of unsatisfied customers, which can be regarded as an successful recovery strategy in the end. As the outcome of recovery efforts exerted by service providers or firms, recovery of the relationship between customer and service provider need to put on the top in the recovery goal list. With these motivations, the study investigates how service failure and recovery makes the changes in dynamics of fundamental elements of customer-firm relationship, such as customer affection, customer trust and loyalty intention by comparing two time points, before the service failure and after the recovery, focusing on the effects of recovery satisfaction and the failure severity. We adopted La & Choi (2012)'s framework for development of the research model that was based on the previous research stream like Yim et al. (2008) and Thomson et al. (2005). The pivotal background theories of the model are mainly from relationship marketing and social relationships of social psychology. For example, Love, Emotional attachment, Intimacy, and Equity theories regarding human relationships were reviewed. As the results, when recovery satisfaction is high, customer affection and customer trust that were established before the service failure are carried over to the future after the recovery. However, when recovery satisfaction is low, customer-firm relationship that had already established in the past are not carried over but broken up. Regardless of the degree of recovery satisfaction, once a failure occurs loyalty intention is not carried over to the future and the impact of customer trust on loyalty intention becomes stronger. Such changes imply that customers become more prudent and more risk-aversive than the time prior to service failure. The impact of severity of failure on customer affection and customer trust matters only when recovery satisfaction is low. When recovery satisfaction is high, customer affection and customer trust become severity-proof. Interestingly, regardless of the degree of recovery satisfaction, failure severity has a significant negative influence on loyalty intention. Loyalty intention is the most fragile target when a service failure occurs no matter how severe the failure criticality is. Consequently, the ultimate goal of service recovery should be the restoration of customer-firm relationship and recovery of customer trust should be the primary objective to accomplish for a successful recovery performance. Especially when failure severity is high, service recovery should be perceived highly satisfied by the complainants because failure severity matters more when recovery satisfaction is low. Marketers can implement recovery strategies to enhance emotional appeals as well as fair treatments since the both impacts of affection and trust on loyalty intention are significant. In the case of high severity of failure, recovery efforts should be exerted to overreach customer expectation, designed to directly repair customer trust and elaborately designed in the focus of customer-firm communications during the interactional recovery process to affect customer trust rebuilding indirectly. Because it is a longer and harder way to rebuild customer-firm relationship for high severity cases, low recovery satisfaction cannot guarantee customer retention. To prevent customer defection due to service failure of high severity, unexpected rewards as a recovery will be likely to be useful since those will lead to customer delight or customer gratitude toward the service firm. Based on the results of analyses, theoretical and managerial implications are presented. Limitations and future research ideas are also discussed.
Purpose : To evaluate our clinical experience with the combination of teletherapy and intraluminal brachytherapy in patients with unresectable or inoperable esophageal cancers. Materials and Methods : From Nov 1989 to Mar 1993, twenty patients with esophageal cancer were treated with radical radiotherapy and intraluminal brachytherapy at Yonsei Cancer Center. All patients had squamous histolgy and stage distribution was as follows: stage II, 4(
Previous studies have shown that the most important factor affecting customer loyalty in the service industry is service quality. However, on the subject of whether service quality has a direct or indirect effect on customer loyalty, scholars' views apparently vary. Some studies suggest that service quality has a direct and fundamental influence on customer loyalty (Bai and Liu, 2002). However, others have shown that service quality not only directly affects customer loyalty, it also has an indirect impact on customer loyalty by influencing customer satisfaction and perceived value (Cronin, Brady, and Hult, 2000). Currently, there are few domestic articles that specifically address the relationship between service quality and customer loyalty in the mobile communication industry. Moreover, research has studied customer loyalty as a whole variable, rather than breaking it down further into multiple dimensions. Based on this analysis, this paper summarizes previous study results, establishes an effect mechanism model among service quality, customer satisfaction, and customer loyalty in the mobile communication industry, and presents a statistical test on model assumptions by using customer investigation data from Heilongjiang Mobile Company. It provides theoretical guidance for mobile service management based on the discussion of the hypothesis test results. For data collection, the sample comprised mobile users in Harbin city, and the survey was taken by random sampling. Out of a total of 300 questionnaires, 276 (92.9%) were recovered. After excluding invalid questionnaires, 249 remained, for an effective rate of 82.6 percent for the study. Cronbach's
Recommender system has become one of the most important technologies in e-commerce in these days. The ultimate reason to shop online, for many consumers, is to reduce the efforts for information search and purchase. Recommender system is a key technology to serve these needs. Many of the past studies about recommender systems have been devoted to developing and improving recommendation algorithms and collaborative filtering (CF) is known to be the most successful one. Despite its success, however, CF has several shortcomings such as cold-start, sparsity, gray sheep problems. In order to be able to generate recommendations, ordinary CF algorithms require evaluations or preference information directly from users. For new users who do not have any evaluations or preference information, therefore, CF cannot come up with recommendations (Cold-star problem). As the numbers of products and customers increase, the scale of the data increases exponentially and most of the data cells are empty. This sparse dataset makes computation for recommendation extremely hard (Sparsity problem). Since CF is based on the assumption that there are groups of users sharing common preferences or tastes, CF becomes inaccurate if there are many users with rare and unique tastes (Gray sheep problem). This study proposes a new algorithm that utilizes Social Network Analysis (SNA) techniques to resolve the gray sheep problem. We utilize 'degree centrality' in SNA to identify users with unique preferences (gray sheep). Degree centrality in SNA refers to the number of direct links to and from a node. In a network of users who are connected through common preferences or tastes, those with unique tastes have fewer links to other users (nodes) and they are isolated from other users. Therefore, gray sheep can be identified by calculating degree centrality of each node. We divide the dataset into two, gray sheep and others, based on the degree centrality of the users. Then, different similarity measures and recommendation methods are applied to these two datasets. More detail algorithm is as follows: Step 1: Convert the initial data which is a two-mode network (user to item) into an one-mode network (user to user). Step 2: Calculate degree centrality of each node and separate those nodes having degree centrality values lower than the pre-set threshold. The threshold value is determined by simulations such that the accuracy of CF for the remaining dataset is maximized. Step 3: Ordinary CF algorithm is applied to the remaining dataset. Step 4: Since the separated dataset consist of users with unique tastes, an ordinary CF algorithm cannot generate recommendations for them. A 'popular item' method is used to generate recommendations for these users. The F measures of the two datasets are weighted by the numbers of nodes and summed to be used as the final performance metric. In order to test performance improvement by this new algorithm, an empirical study was conducted using a publically available dataset - the MovieLens data by GroupLens research team. We used 100,000 evaluations by 943 users on 1,682 movies. The proposed algorithm was compared with an ordinary CF algorithm utilizing 'Best-N-neighbors' and 'Cosine' similarity method. The empirical results show that F measure was improved about 11% on average when the proposed algorithm was used