• Asia Marketing Journal
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• v.12 no.1
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• pp.1-30
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• 2010

Customers often experience waiting for buying service. Managing customers' waiting time is important for service providers since customers who are dissatisfied with waiting, secede from a service place at last. Not a few studies have been done to solve waiting time problem and improve customers' waiting experience. Hui & Tse(1996) identify evaluation factors in customers' behavioral mechanism as customers wait. That is, customers experience perceived waiting time, waiting acceptability and emotional response to the wait when they wait. Since customers evaluate the wait using these factors, service provider should manage these factors in order to minimize customers' dissatisfaction. Therefore, this study explores that evaluation factors of waiting are influenced by customers' situational and experiential characteristics, which include customer loyalty, transaction importance for customer and waiting expectation level. Those situational and experiential characteristics are usually given to service providers so they can't control these at waiting point. The major findings derived from two exploratory studies can be summarized as follows. First, according to the result from the study 1 (restaurant setting), customers' transaction importance has the greatest positive influence on waiting experience. The results show restaurant service provider could prevent customers' separation effectively through strategies which raise customers' transaction importance, like giving special coupons for important events. Second, in study 2 (amusement part setting) customer loyalty has large positive impact on waiting experience as well as transaction importance. This results show that service provider could minimize customers' dissatisfaction using strategies which raise customer loyalty continuously. This results show customer perceives waiting experience differently according to characteristics of service place and service itself. Therefore, service provider should grasp the unique customers' situational and experiential characters for each service and service place. It could provide an effective strategy for waiting time management. Third, the study finds transaction importance and waiting expectation level have direct influence customers' waiting experience as independent variables, while existing studies treated them as moderators. Customer loyalty which has not been incorporated in previous waiting time research is known to affect waiting experience. It suggests that marketing strategy which builds up customer loyalty for long period of time is also quite effective, compared to short term tactics to help customers endure waiting time. Fourth, this study reveals the importance of actual waiting time along with perceived waiting time. So far most studies only focus on customers' perceived waiting time. Especially, this study incorporates the concept of patient limit on waiting time to investigate effect of actual waiting time. The results show that there were various responses to the wait depending on how actual waiting time exceeds individual's patent limit on waiting time or not, even though customers wait about the same period of time. Finally, using structural equation model, conceptual path between behavioral responses is verified. As customer perceives waiting time, then she decides whether she can endure it or not, and then her emotional response occurs. This result are somewhat different from Hui & Tse(1996)'s study. The study also includes theoretical contributions as well as practical implications.

• Journal of Korean Academy of Nursing Administration
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• v.16 no.3
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• pp.219-228
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• 2010

Purpose: The purpose of this study was to explore the degree of delay in waiting time, and the relationships of waiting time, patient satisfaction, and revisiting intention of outpatient in general hospitals. Methods: The data were collected from June 22 to July 4, 2009. A total of 536 outpatients who visited 21 clinics of a general hospital were subjected to evaluate the waiting time. The survey tools used were the Korea Health Industry Development Institutes (2008) tool for patient satisfaction and Reichheld & Sasser (1990) for revisiting intention. The data were analyzed by SAS version 9.1, descriptive statistics, t-test, ANOVA, and Pearson correlation coefficient. Results: The mean patient's waiting time was $28.3{\pm}30.7\;min$, the revealed mean score of patient's satisfaction was 2.92, and the revisiting intention showed was 4.56. The waiting time was negatively correlated with patient's satisfaction (r=-.10, p<.019). Patient's satisfaction was positively correlated with revisiting intention (r=-.51, p<.001). Conclusion: Waiting time management is an important factor of increasing patient's satisfaction and revisiting intention in general hospitals. It is mandatory that reservation management systems take into account the patient's characteristics of visiting outpatient department in order to shorten the real waiting time.

• Management Science and Financial Engineering
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• v.14 no.1
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• pp.23-34
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• 2008

In this study, we consider stationary waiting times in a Poisson driven single-server m-node queues in series. We assume that service times at nodes are independent, and are either deterministic or non-overlapped. Each node excluding the first node has a finite waiting line and every node is operated under a FIFO service discipline and a communication blocking policy (blocking before service). By applying (max, +)-algebra to a corresponding stochastic event graph, a special case of timed Petri nets, we derive the explicit expressions for stationary waiting times at all areas, which are functions of finite buffer capacities. These expressions allow us to compute the performance measures of interest such as mean, higher moments, or tail probability of waiting time. Moreover, as applications of these results, we introduce optimization problems which determine either the biggest arrival rate or the smallest buffer capacities satisfying probabilistic constraints on waiting times. These results can be also applied to bounds of waiting times in more general systems. Numerical examples are also provided.

• Culinary science and hospitality research
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• v.20 no.3
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• pp.1-12
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• 2014

In recognition of the significance of waiting time in restaurant management, a quasi-experimental design was employed to measure the gap between actual and perceived waiting time in a real restaurant setting. In particular, this study focused on a comparison of Americans and Koreans to explore gender and culture differences in customer waiting behaviors. The results indicated that compared to American women, Korean women are more tolerant of waiting, and reported perceived waiting time as much longer than actual waiting time. However, there are no gender differences in both cultures. It is anticipated that managers will be able to adjust their operational strategies based on these results.

• Journal of Korea Port Economic Association
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• v.34 no.2
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• pp.69-82
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• 2018

The number of ships serviced at the container terminals in Busan is increasing by 2.9% per year. In spite of the increase in calling ships, there are no official records of waiting rate by the port authority. This study attempts to compare the theoretical ship waiting ratio and actual ship waiting ratio. The actual ship waiting ratio of container terminals is acquired from the 2014 to 2016 data of PORT-MIS and Terminal Operating System (TOS). Furthermore, methods and procedures to measure the actual ship's waiting rate of container terminal are proposed for ongoing measurement. In drawing the theoretical ship waiting ratio, the queuing theory is applied after deploying the ship arrival probability distribution and ship service probability distribution by the Chi Square method. As a result, the total number of ships waiting in a terminal for three years was 587, the average monthly service time and the average waiting time was 13.8 hours and 17.1 hours, respectively, and the monthly number of waiting ships was 16.3. Meanwhile, according to the queuing theory with multi servers, the ship waiting ratio is 31.1% on a 70% berth occupancy ratio. The reason behind the huge gap is the congested sailing in the peak days of the week, such as Sunday, Tuesday, and Wednesday. In addition, the number of waiting ships recorded on Sundays was twice as much as the average number of waiting ships.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2017.11a
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• pp.27-29
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• 2017

Container terminals in Northern Vietnam have recorded an impressive development in recent years. This development, however, also raises a fierce competition among local container terminals to attract customers. Beside the handling charges, the vessels' waiting cost is also an important factor that drive the opinion of users in choosing appropriate terminal. This research plans to estimate the waiting cost in different container terminals in Northern Vietnam by building regression equation that describe the relationship between the rate of throughput/capacity and waiting cost/TEU. Queuing theory with the application of Poisson distibution is used to estimate the waiting time of arrival vessels and uncertainty theory is applied to estimate the vessel's daily expenses. Previous studies suggested two different formation of the equation and according to the research results, cubic equation is more suitable in the given case. The research results are also useful for further research which require calculation of waiting cost per TEU in each container terminal in Northern Vietnam.

• Journal of the Korean Institute of Landscape Architecture
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• v.33 no.3 s.110
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• pp.56-64
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• 2005

Despite there have been m?my studies about the relationships between congestions and satisfaction in outdoor recreation field, the results indicated only weak impacts of congestions on the satisfaction. This study thus aims to investigate the influence of waiting situation on the whole valuation and satisfaction of guests at theme parks using new variables such as 'goal attainment' and then to suggest implications for waiting management. This study shows meaningful theoretical implications. First, congestion influences the satisfaction more strongly when it prevents guests from achieving their plan. Second, management of waiting time is important and the can be controlled well. When guests have something interested during the waiting time, they were satisfied with the quality of waiting time and overall experience in the theme park.

• Journal of Navigation and Port Research
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• v.40 no.6
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• pp.421-430
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• 2016

In this study, a gate simulation model was developed to reduce the truck waiting time for trucking companies servicing container terminals. To verify the developed model, 4 weeks of truck gate-in/gate-out data was collected in December 2014 at the Port of Busan New Port. Also, the existing gate system was compared to the proposed gate system using the developed simulation model. The result showed that based on East gate-in, a maximum number of 50 waiting trucks with a maximum waiting time of 120 minutes. With the proposed system the maximum number of waiting trucks was 10 with a maximum waiting time of 5.3 minutes. Based on West gate-in, the maximum number of waiting trucks was 17 and the maximum waiting time was 34 minutes in the existing gate system. With the proposed system the maximum number of waiting trucks was 10 with a maximum waiting time of 5.3 minutes. Based on West gate-out, the maximum number of waiting trucks was 11 with a maximum waiting time of 5.5 minutes. With the proposed system the maximum number of waiting trucks was 9 with a maximum waiting time of 4.4 minutes. This developed model shows how many waiting trucks there are, depending on the gate-in/gate-out time of each truck. This system can be used to find optimal gate system operating standards by assuming and adjusting the gate-in/gate-out time of each truck in different situations.

• Korean Journal of Cognitive Science
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• v.23 no.1
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• pp.73-95
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• 2012

Two experiments were conducted to verify how the factors of attentional dispersion, reason for waiting, and cue of time flow affect the perceived waiting time. In experiment 1, based on the characteristics of waiting experience that Maister(1985) suggested, levels of attentional dispersion and whether or not offering a reason for waiting were manipulated. Participants estimated elapsed time(the objective time was 10 minutes) using either prospective or retrospective estimation method. Overall results were that they overestimated the elapsed time regardless of the experimental conditions. However, both main effects of the attentional dispersion and the reason for waiting were statistically significant. That is, when attention was more dispersed and when the reason was given, overestimation of elapsed time was reduced. No difference was found between the two estimation methods, and none of the interaction was significant. Experiment 2 was a replication of Experiment 1 except that a cue of time flow was added by using scroll bar on a computer screen. Because it has been suggested that the cue can help us to manage the waiting time and result in differences between the two time estimation methods. The results showed that main effects of the attentional dispersion and the reason for waiting were significant as those in Experiment 1. In addition, main effect of time estimation method and the three-way interaction were also significant. None of two-way interaction was significant. That is, the perceived waiting time is much shorter in the retrospective method, and the effects of the attentional dispersion and the reason of waiting were dependent upon the estimation methods. Both experiments showed that offering a clear reason for waiting is more important than the attentional dispersion in reducing the perceived waiting time. Some implications of these results for the service industry and the future direction of research were discussed in the final section.

• Journal of the Korea Safety Management & Science
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• v.12 no.2
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• pp.225-230
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• 2010

The purpose of the study was to apply and to expand the six sigma to reduce waiting times for computed tomography (CT) examination which manipulated by the department of radiology. It was preceded by DMAIC (Define, Measure, Analyze, Improve, and Control). In the stage of definition, it wereselected for total 5 critical to quality (CTQ), which were the kindness, the waiting time, the examination explanation, the waiting day and the waiting stand environment, that increased the reserved time of CT examination. In the stage of measurement, the number of examinations and of reservation waiting days performed and resulted in final CTQ(Y) which measured each 1.68 and 1.85 sigma. In the stage of analysis, the examination concentrated on morning time, non-scheduled examination of the day, the delayed time of booking, frequent telephone contacting and equipment malfunction were determined as variable key causes. In the stage of improvement, it were performed with expansion of the examination in the morning time, integration of laboratories that used to in each steps, developing the ability of simultaneous booking schedule for the multiple examinations, developing program of examination request, and the customer management team operations. For the control, the number of examinations and reserved waiting days were measured each 3.14 and 1.13 sigma.