The continuous development of the service economy and an aging society with fewer children is expected to lead to a shortage of workers in the near future. In addition, the growth of the service economy would require service providers to meet various service requirements. In this regard, self-service technology (SST) is a promising alternative to securing labor in both developed and emerging countries. SST is expected to coordinate the controllable productive properties in order to optimize resources and minimize consumer stress. As services are characterized by simultaneity and inseparability, a smoother operation in cooperation with the consumer is required to provide a certain level of service. This study focuses on passenger handling in an airport departure lobby with the objective of optimizing multiple service resources comprising interpersonal service staff and self-service kiosks. Our aim is to elucidate the passenger decision-making mechanism of choosing either interpersonal service or self-service as the check-in option, and to apply it to analyze several scenarios to determine the best practice. The experimental space is studied and an agent-based model is proposed to analyze the operational efficiency via a simulation. We expand on a previous SST adoption model, which is enhanced by introducing the concept of individual traits. We focus on the decision-making of individuals who are neutral toward the service option, by tracking the actual activity of passengers and mapping their behavior into the model. A new method of validation that follows a different approach is proposed to ensure that this model approximates real-world situations. A scenario analysis is then carried out with the aim of exploring the best operational practice to minimize the stress experienced by the air travelers and to meet the business needs of the airline managers at the airport. We collected actual data from the Departure Control System of an airline to map the real-world data to the proposed model. Passenger behavior was extracted by front-line service experts and clarified through consecutive on-site observations.
This paper discusses how the neighbours affect the decision of consumer behaviour over diffusion of innovation. An agent-based model of diffusion is proposed on an online social network which have both "scale-free" and "regular" properties. The findings of the studies of consumer activity in order to show the following points: 1) the informative effect can cause a takeoff , but it is not sufficient to reach the completion of diffusion, 2) the combination of the informative and normative effects can easily bring a takeoff , which is a point in time within the adoption curve that the existence of a sufficient amount of adopters of an innovation or a product. After the takeoff , the diffusion is accelerated and reaches the completion in the end, 3) the informative effect makes information propagate fast, and so does the normative effect over a network that has characteristics of scale-free and high cluster, 4) in a selective advertisement, the most effective approach is non-selective advertisement for all consumers. This paper shows that it is inadequate to think that opinion leaders only adopt a product and transmit the information of usability impressions to other consumers in order to trigger diffusion on online human-relationship networks. Rather, diffusion is promoted entirely by active communication among non-opinion leaders which have received such information from opinion leaders.
This study proposes a simulation model of rubella. SIR (Susceptible, Infected, Recovered) model has been widely used to analyse infectious diseases such as influenza, smallpox, bioterrorism, to name a few. On the other hand, agentbased model begins to spread in recent years. The model enables to represent the behaviour of each person on the computer. It also reveals the spread of infection by simulation of the contact process among people in the model. The study designs a model based on smallpox and Ebola fever model in which several health policies are decided such as vaccination, the gender-specific workplace and so on. The infectious simulation of rubella, which has not yet vaccinated completely for men in Japan, is implemented in the model. As results of experiments using the model, it has been found that preventive vaccine to all the men is crucial factors to prevent the spread in women.
In this study, we investigate what would happen in a Chinese historical family line. We have analyzed a particular family line which had a great many candidates who passed the very tough examinations for Chinese government officials over 500 years. First, we studied the genealogical records Zokufu in China. Second, based on the study, we implemented an agent-based model with the family line network as an adjacency matrix, and the personal profile data as an attribution matrix. Third, using the "inverse simulation" technique, we optimized the agent-based model in order to fit the simulation profiles to the real profile data. From the intensive experiments, we have found that both grandfather and mother have a profound impact within a family in (1) transmitting cultural capital to the children, and (2) maintaining the norm system of the family. We conclude that advanced agent-based models are able to contribute to the discovery of new knowledge in the fields of historical science.
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