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The purpose of this paper is to examine the results of an empirical exploration of the relationship between learning transfer climates and organizational innovation. Additionally, factors associated with learning transfer climate that could account for innovation in Korean public and private organizations have been explored.

This study relies on quantitative data obtained from two survey questionnaires. The sample consists of 390 employees working for seven private and five public organizations; further, each employee has completed at least one training program within a one-year period.

Results of the study demonstrate that private, rather than public organizations, have significantly higher mean scores for all five learning transfer climate variables and for perceived organizational innovation. The results of multiple regression analyses reveal that openness to change and performance coaching have common and significant impacts on perceived innovation in both private and public organizations. However, the other three variables (i.e., transfer effort-performance expectations, performance-outcomes expectations, and performance self-efficacy) have varied effects on perceived innovation depending on organizational type. Specifically, transfer effort-performance expectations has a relatively meaningful impact on perceived innovation in public organizations. Performance-outcomes expectations and performance self-efficacy have relatively meaningful effects on perceived innovation in private organizations.

The sample for this study consists of employees solely from Korean organizations. Therefore, further studies encompassing a greater sampling variety are required to determine the generalizability of these results. In addition, this study is limited to an investigation of the possible differences between public and private organizations with respect to their learning transfer climates and innovation. In studies to follow, researchers can further investigate these relationships in segmented organizations.

The results of this study will assist human resource practitioners to promote innovation effectively and efficiently based on organizational type.

In an effort to reduce ambiguity around customs clearance of items deemed pernicious to public morality and present lessons for customs administration, this research aims to explore ways to improve customs clearance for RealDolls. Furthermore, by suggesting specific ideas to improve the process, this research also aims to contribute to the future customs clearance of socially controversial and legally ambiguous items.

The authors have decided to review studies from ethical and legal perspectives to focus on the legislation and its enforcement issues. The literature review is designed to provide insights on how to incorporate legal and ethical reasoning in the customs clearance process for RealDolls. Supplementary interviews were also conducted with criminal lawyers and customs officials to obtain expert knowledge on domestic legislation and customs control against the sex toys industry.

As a result of the study, it is found that a complete ban on RealDoll infringes on individuals' right to pursue happiness guaranteed by the Constitution and in most cases, cannot be implemented without loopholes. Therefore, it is suggested that the import regulation on RealDolls should be changed to the negative list system, which selectively disapproves certain RealDolls based on a list of RealDolls previously denied clearance by either the Supreme Court or the Customs Clearance Screening Committee. In addition, to have sufficient ethical and legal grounds, it is necessary to expand research on RealDolls regulation and actively introduce the process of obtaining social consensus.

There has been little discussion on regulating RealDolls on the customs clearance level; no specific criterion exists except the public morality clause. This study is vital in that it reviews issues of RealDolls customs clearance from legal and ethical perspectives. Doing so also has a practical significance of providing implications for the customs policies regarding items deemed pernicious to public morality.

The purpose of this paper is to analyze the relationship between new product diffusion and consumer internet search patterns using big data and to investigate whether such data can be used in forecasting new product diffusion.

This research proposes a new product diffusion model based on the Bass diffusion model by incorporating consumer internet search behavior. Actual data from search engine queries and new vehicle sales for each vehicle class and region are used to estimate the proposed model. Statistical analyses are used to interpret the estimated results, and the prediction performance of the proposed method is compared with other methods to validate the usefulness of data for internet search engine queries in forecasting new product diffusion.

The estimated coefficients of the proposed model provide a clear interpretation of the relationship between new product diffusion and internet search volume. In 83.62 percent of 218 cases, analyzing the internet search pattern data are significant to explain new product diffusion and that internet search volume helps to predict new product diffusion. Therefore, marketing that seeks to increase internet search volume could positively affect vehicle sales. In addition, the demand forecasting performance of the proposed diffusion model is superior to those of other models for both long-term and short-term predictions.

As search queries have only been available since 2004, comparisons with data from earlier years are not possible. The proposed model can be extended using other big data from additional sources.

This research directly demonstrates the relationship between new product diffusion and consumer internet search pattern and investigates whether internet search queries can be used to forecast new product diffusion by product type and region. Based on the estimated results, increasing internet search volume could positively affect vehicle sales across product types and regions. Because the proposed model had the best prediction power compared with the other considered models for all cases with large margins, it can be successfully utilized in forecasting demand for new products.

Among a wide array of information and communication technologies (ICTs) used to directly or indirectly support the mega events are reality boosting technologies and smart tourism technologies. Building upon the halo effect, this study aims to explain the connection between satisfaction with ICTs used in mega event, national image and event participants’ future behavior.

The analyzed data included survey responses from 246 foreigners who visited PyeongChang as a visitor for the 2018 Winter Olympics.

The results showed that both reality boosting technologies and smart tourism technologies directly or indirectly influence overall experience satisfaction by way of transaction satisfaction. Furthermore, the two types of satisfaction were found to positively influence the national image of the host country, which consequently has a positive effect on visitors’ future behavior.

This study aimed to explore two different roles of ICTs in mega events by focusing more on the visitors who came to PyeongChang for the Olympics. The originality of this study lies in its attempt to examine the mechanisms in which visitors’ satisfaction from ICT-based experiences in mega events contributes to forming a positive image toward the host country.

现实促进技术和智能旅游技术在各种 ICT中被广泛应用来直接或间接支持大型活动的。基于光环效应, 这项研究解释了对大型活动中使用的 ICT 的满意度, 国家形象, 活动参与者未来行为之间的联系。

分析的数据包括 246 位在2018 年冬季奥运会访问平昌的外国游客。

结果表明, 现实促进技术和智慧旅游技术通过直接或者间接方式接通过交易满意度影响整体体验满意度。此外, 研究发现两种类型的满意度均对东道国的国家形象形成产生积极影响, 从而对吸引未来访客的有正面影响。

通过聚焦来平昌参加奥运会的游客, 本研究旨在探索信息通信技术在大型活动中的两种不同作用。本研究的原创性在于试图检验游客在大型活动中基于 ICT 的体验满意度对形成对东道国正面的国家形象的机制。

Receiving Michelin stars is considered an effective marketing tool and a prestigious certification in the restaurant industry. However, the direct effects of Michelin stars on restaurant performance remain unclear. To bridge this gap, this study aims to empirically validate the “Michelin effect” on the consumption values of restaurant patrons before and after receiving Michelin stars.

The data, collected from OpenTable, consist of over 160,000 reviews written for 218 restaurants, including 109 Michelin-starred (treatment group) and 109 nonstarred restaurants (control group). The authors measure perceived consumption value using the collected user-generated review data. The authors estimate fixed-effect difference-in-differences regressions to validate the Michelin effect.

Michelin stars enhance social, hedonic and service quality values, which are nonfunctional values. However, no significant effects on functional consumption values, such as economic, food quality and ambience quality values, are observed, even though Michelin stars are pure awards for kitchen performance.

Michelin stars can be an effective marketing tool for fine-dining restaurants because customers consider emotional and nonfunctional benefits such as hedonic and social values, which are more important than functional benefits such as food value. Furthermore, Michelin effects are heterogeneous depending on the number of stars awarded, price range and customers’ gastronomic involvement. These offer a strong rationale for monitoring social media, which may help managers better understand their customers and improve their performance.

This study extends the current literature on the Michelin effect by quantifying consumption values using user-generated review data. To the best of the authors’ knowledge, this study offers the first empirical evidence that directly validates the Michelin effect.

In this paper, a three-dimensional (3D) printer-based manufacturing line and supporting system, which supports personalized/customized manufacturing for individual businesses or start-up companies, was studied to evaluate the practicality of using additive manufacturing for personalization/mass customization.

First, factory-as-a-service (FaaS) system, which provides factory as a service to customers, was proposed and designed to manufacture various products within a distributed manufacturing environment. This system includes 3D printer-based material extrusion processes, vapor machine/computer numerical control machines as post-processes and assembly and inspection processes with an automated material handling robot in the factory. Second, a virtualization module for the FaaS factory was developed using a simulation model interfaced with a cloud-based order and production-planning system and an internet-of-things-based control and monitoring system. This is part of the system for manufacturing operations, which is capable of dynamic scheduling in a distributed manufacturing environment. In addition, simulation-based virtual production was conducted to verify and evaluate the FaaS factory for the target production scenario. Main information of the simulation also has been identified and included in the virtualization module. Finally, the established system was applied in a sample production scenario to evaluate its practicality and efficiency.

Additive manufacturing is a reliable, feasible and applicable technology, and it can be a core element in smart manufacturing and the realization of personalization/mass customization.

Various studies on additive manufacturing have been conducted with regard to replacing the existing manufacturing methods or integrating with them, but these studies mostly focused on materials or types of additive manufacturing, with few advanced or applied studies on the establishment of a new manufacturing environment for personalization/mass customization.

This study aims to develop a machine learning model to detect structure fire fatalities using a dataset comprising 11,341 cases from 2011 to 2019.

Exploratory data analysis (EDA) was conducted prior to modelling, in which ten machine learning models were experimented with.

The main fatal structure fire risk factors were fires originating from bedrooms, living areas and the cooking/dining areas. The highest fatality rate (20.69%) was reported for fires ignited due to bedding (23.43%), despite a low fire incident rate (3.50%). Using 21 structure fire features, Random Forest (RF) yielded the best detection performance with 86% accuracy, followed by Decision Tree (DT) with bagging (accuracy = 84.7%).

Limitations of the study are pertaining to data quality and grouping of categories in the data pre-processing stage, which could affect the performance of the models.

The study is the first of its kind to manipulate risk factors to detect fatal structure classification, particularly focussing on structure fire fatalities. Most of the previous studies examined the importance of fire risk factors and their relationship to the fire risk level.

The Hanbo (meaning Korean treasure) scandal or “Hanbogate” occurred on January 23, 1997, with the bankruptcy of Hanbo Iron and Steel Company, the second largest steel company and 14th largest conglomerate in South Korea, as its debt had accumulated to US$5.6 billion. Hanbo's bankruptcy triggered an investigation by the Public Prosecutor's Office that resulted in the imprisonment for 15 years of Hanbo's founder, Chung Tae-Soo, for bribing politicians and bankers to pressure banks to extend hugh bank loans to Hanbo. Nine other persons were also convicted including Chung's son, who was jailed for three years for bribery and embezzlement, and Kim Hyun-Chol, the second son of President Kim Young-Sam, who was sentenced to three years jail and fined US$1.5 million (New York Times, 1997).

The purpose of this study is to gain a better understanding of the employment figures in the labour market after the two Korean financial crises (1997 and 2008), focusing on the gender gap across different characteristics. Based on several data points, a general trend becomes evident whereby, as companies retrenched, they shifted the demographics of their employees to those perceived as most valuable, i.e. workers with university educations. However, when distinguished by gender, it is evident that their priorities changed. This discovery suggests that as the world faces another global health crisis (COVID-19) with its associated impact on organisational retrenchment, the gender gap in Korea could widen further.

The analysis was conducted by applying a simple compounded average growth rate (CAGR) analysis to determine the impact of the two crises on employment by the educational level.

A general trend becomes evident whereby, as companies retrenched, they shifted the demographics of their employees to those perceived as most valuable, i.e. workers with university educations. However, when distinguished by gender, it is evident that their priorities changed.

Secondary data were used for the analysis as data for unemployed, who had given up looking for work, were not available.

Managers can use the findings when making decisions about laying-off staff during times of financial/economic crisis.

There is a perceived negative impact upon highly educated Korean women.

The paper advances the pay gap literature by providing evidence from Korea. CAGR analysis has never been used previously in analysing the pattern of labour market data to reveal gender discrimination. With a global health crisis (COVID-19) with its associated impact on organisational retrenchment, the gender gap in Korea could widen further.