This research explores how imported automobile companies can develop their strategies to improve the outcome of their recalls. For this, the researchers analyzed patterns of recall demand, classified recall types based on the demand patterns and examined response strategies, considering plans on how to procure parts and induce customers to visit workshops, recall execution capacity and costs. As a result, recalls are classified into four types: U-type, reverse U-type, L- type and reverse L-type. Also, as determinants of the types, the following factors are further categorized into four types and 12 sub-types of recalls: the height of maximum demand, which indicates the volatility of recall demand; the number of peaks, which are the patterns of demand variations; and the tail length of the demand curve, which indicates the speed of recalls. The classification resulted in the following: L-type, or customer-driven recall, is the most common type of recalls, taking up 25 out of the total 36 cases, followed by five U-type, four reverse L-type, and two reverse U-type cases. Prior studies show that the types of recalls are determined by factors influencing recall execution rates: severity, the number of cars to be recalled, recall execution rate, government policies, time since model launch, and recall costs, etc. As a component demand forecast model for automobile recalls, this study estimated the ARIMA model. ARIMA models were shown in three models: ARIMA (1,0,0), ARIMA (0,0,1) and ARIMA (0,0,0). These all three ARIMA models appear to be significant for all recall patterns, indicating that the ARIMA model is very valid as a predictive model for car recall patterns. Based on the classification of recall types, we drew some strategic implications for recall response according to types of recalls. The conclusion section of this research suggests the implications for several aspects: how to improve the recall outcome (execution rate), customer satisfaction, brand image, recall costs, and response to the regulatory authority.

In this study, we propose a link between L2 rhetorical concepts and ELF as a way of the analysis of the development of a single concept, of an EFL college student’s rhetorical knowledge. Using Vygotskian sociocultural theory as analytical lenses, we examine whether L2 rhetoric can be mastered and internalized as a culturally neutral concept, i.e., the formulaic knowledge of L2 writing the student has learned from the NEST through instruction; and how the student’s L1 rhetorical concept and ELF performance together mediate his L2 concept development in his academic writing. The data consist of a student’s personal narratives, text-based interviews and academic writings. Rather than the mastery of a single variety of English, he produced texts that reflect the flexibility and variability inherent in written ELF. From ELF perspectives, this study offers an opportunity of establishing a new normal, in which rhetorical conventions of texts should be viewed as constructs that are dynamic, emergent, and therefore negotiable and adaptable.

In order to satisfy customers, it is important to identify the quality elements that affect customers’ satisfaction. The Kano model has been widely used in identifying multi-dimensional quality attributes in this purpose. However, the model suffers from various shortcomings and limitations, especially those related to survey practices such as the data amount, reply attitude and cost. In this research, a model based on the text sentiment analysis is proposed, which aims to substitute the survey-based data gathering process of Kano models with sentiment analysis. In this model, from the set of opinion text, quality elements for the research are extracted using the morpheme analysis. The opinions’ polarity attributes are evaluated using text sentiment analysis, and those polarity text items are transformed into equivalent Kano survey questions. Replies for the transformed survey questions are generated based on the total score of the original data. Then, the question-reply set is analyzed using both the original Kano evaluation method and the satisfaction index method. The proposed research model has been tested using a large amount of data of public IT service project evaluations. The result shows that it can replace the existing practice and it promises advantages in terms of quality and cost of data gathering. The authors hope that the proposed model of this research may serve as a new quality analysis model for a wide range of areas.

Recently, M/G/1 priority queues with a finite buffer for high-priority customers and an infinite buffer for low-priority customers have applied to the analysis of communication systems with two heterogeneous traffics : delay-sensitive traffic and loss-sensitive traffic. However, these studies are limited to M/G/1 priority queues with finite and infinite buffers under a work-conserving priority discipline such as the nonpreemptive or preemptive resume priority discipline. In many situations, if a service is preempted, then the preempted service should be completely repeated when the server is available for it. This study extends the previous studies to M/G/1 priority queues with finite and infinite buffers under the preemptive repeat-different and preemptive repeat-identical priority disciplines. We derive the loss probability of high-priority customers and the waiting time distributions of high- and low-priority customers. In order to do this, we utilize the delay cycle analysis of finite-buffer M/G/1/K queues, which has been recently developed for the analysis of M/G/1 priority queues with finite and infinite buffers, and combine it with the analysis of the service time structure of a low-priority customer for the preemptive-repeat and preemptive-identical priority disciplines. We also present numerical examples to explore the impact of the size of the finite buffer and the arrival rates and service distributions of both classes on the system performance for various preemptive priority disciplines.

Effects of pH and salt concentration on rheological and thermal properties of Pacific whiting surimi were investigated. As pH increased from 6 to 7.5, failure shear stress and strain values increased from 15.4 to 31.32 kPa, whereas lightness values (L * ) decreased from 86.22 to 78.86. Storage modulus (G') followed a trend similar to failure shear stress. A linear relationship (r 2 =0.89) was found between G' and failure stress values as a function of pH. Increasing salt concentration up to 1% increased failure shear stress to 27.14 kPa without salt to 34.30 kPa, and strain values from 1.73 to 1.91, whereas further increase had a negative effect. The relationship between dynamic rheological data and failure shear stress was not linear, indicating that salt concentration cannot be used as an index for estimating final gel quality. The transition temperatures obtained from temperature sweep measurements of Pacific whiting surimi at different salt concentrations showed similar peak temperatures as DSC thermograms, indicating lower stability due to increased salt concentration.