Purpose
Due to the complexity of policy environment in modern society, it is accepted as common basics of policy design to mix up a variety of policy instruments aiming the multiple functions. However, under the current situation of written-down policy specification, not only the public officers but also the policy researchers cannot easily grasp such frameworks as policy portfolio. The purpose of this study is to develop “Policy Attributes Taxonomy” identifying and classifying the public programs to help making decisions for allocative efficiency with effectiveness-based information.
Design/methodology/approach
To figure out the main scheme and classification criteria of Policy Attributes Taxonomy which represents characteristics of public policies, previous theories and researches on policy components were explored. In addition, to test taxonomic feasibility of certain information system, a set of “Feasibility Standards” was drawn from “requirements for well-organized criteria” of eminent taxonomy literatures. Finally, current government classification system in the area of social service was tested to visualize the application of Taxonomy and Standards.
Findings
Program Taxonomy Schemes were set including “policy goals”, “policy targets”, “policy tools”, “logical relation” and “delivery system”. Each program and project could be condensed into these attributes, making their design more easily distinguishable. Policy portfolio could be readily made out by extracting certain characteristics according to this scheme. Moreover, this taxonomy could be used for rearrangement of present “Program Budget System” or estimation of “Basic Income”.

Purpose
Local governments in each region actively hold local festivals for the purpose of promoting the region and revitalizing the local economy. Existing studies related to local festivals have been actively conducted in tourism and related academic fields. Empirical studies to understand the effects of latent variables on local festivals and studies to analyze the regional economic impacts of festivals occupy a large proportion. Despite of practical need, since few researches have been conducted to predict the number of visitors, one of the criteria for evaluating the performance of local festivals, this study developed a model for predicting the number of visitors through various observed variables using a machine learning algorithm and derived its implications.
Design/methodology/approach
For a total of 593 festivals held in 2018, 6 variables related to the region considering population size, administrative division, and accessibility, and 15 variables related to the festival such as the degree of publicity and word of mouth, invitation singer, weather and budget were set for the training data in machine learning algorithm. Since the number of visitors is a continuous numerical data, random forest, Adaboost, and linear regression that can perform regression analysis among the machine learning algorithms were used.
Findings
This study confirmed that a prediction of the number of visitors to local festivals is possible using a machine learning algorithm, and the possibility of using machine learning in research in the tourism and related academic fields, including the study of local festivals, was captured. From a practical point of view, the model developed in this study is used to predict the number of visitors to the festival to be held in the future, so that the festival can be evaluated in advance and the demand for related facilities, etc. can be utilized. In addition, the RReliefF rank result can be used. Considering this, it will be possible to improve the existing local festivals or refer to the planning of a new festival.

Purpose
Despite the continued rise in the popularity of mobile MMORPGs, the previous studies mainly focused on negative results of playing MMORPG such as violence and game addiction. In addition, previous studies that verified game play motives were not enough to explain user's fundamental motives for MMORPG play. To verify the positive role of playing MMORPG, this study focused on stress coping strategies on continued play MMORPG, and divided factors into two dimensions: escapism and coping with system. The purpose of this study is to comprehensively identify the discriminatory effects of each factor on users' self-control, and the effect of self-constrol and behavioral adapatation on continued use intention for MMORPG.
Design/methodology/approach
This study was designed to examine the structural relationships among MMORPG users' escapism, system coping, self-control, behavioral adaptation and continued use intention for MMORPG.
Findings
The results from this study are as follow. First, the participants' effect-based escapism had a positive impact on self-control, whereas cause-based escapism had a negative impact on self-control. Next, proactive coping and reactive coping for system problem had a positive impact on self-control. In addition, Self-control had a positive impact on behavioral adaptation and continued use intention for MMORPG. Lastly, the behavioral adaptation to MMORPG had a positive impact continued use intention.
The results of our study can suggest the positive effects of coping strategy on users' self-control and behavioral adaptation by applying the theory of both escapism and coping with system. Based on our results, game companies should develop contents that can gratify users' coping motives and enhance self-control and behavioral adaptation.

Purpose
Internet only bank is one of the promising untact businesses which are rising in COVID-19 pandemic. Kakaobank and Kbank have been offering banking services since 2017 in Korea and WeBank and MYbank in China opened the services since 2015. It is necessary to examining determinants of customer loyalty beyond intention to use. The purpose of the present study is to analyze factors influencing customer loyalty and their determinants. A research model integrating the relationships among customer loyalty, service satisfaction, trust, and social influence, and their determinants was proposed.
Design/methodology/approach
Structural equation modelling for validating the research model was employed. 235 valid data were collected from users of Internet only bank in China. Nine latent variables grouped from 38 indicators were reliable and valid and nine hypotheses were tested by using SmartPLS.
Findings
All nine hypotheses were supported at significant levels of 0.001 and 0.01. Trust, service satisfaction, and social influence had influenced on customer loyalty. Trust not only affected customer loyalty directly, but also had influence on it indirectly through service satisfaction. Empathy and convenience are determinants of trust. Perceived expertise and dispositions believing acquaints had a positive influence on social influence, whereas users' anxiety for technology or change had a negative influence on social influence. Implications for academics and practitioners were suggested.

Purpose
Recently, various issues related to toxic comments on web portal sites and SNS are becoming a major social problem. Toxic comments can threaten Internet users in the type of defamation, personal attacks, and invasion of privacy. Over past few years, academia and industry have been conducting research in various ways to solve this problem. The purpose of this study is to develop the deep learning modeling for toxic comments classification.
Design/methodology/approach
This study analyzed 7,878 internet news comments through CNN classification modeling based on Highway Network and OOV process.
Findings
The bias and hate expressions of toxic comments were classified into three classes, and achieved 67.49% of the weighted f1 score. In terms of weighted f1 score performance level, this was superior to approximate 50~60% of the previous studies.

Purpose
Real estate usually takes charge of the highest proportion of physical properties which individual, organizations, and government hold and instability of real estate market affects the economic condition seriously for each economic subject. Consequently, practices for predicting the real estate market have attention for various reasons, such as financial investment, administrative convenience, and wealth management. Additionally, development of machine learning algorithms and computing hardware enhances the expectation for more precise and useful prediction models in real estate market.
Design/methodology/approach
In response to the demand, this paper aims to provide a framework for forecasting the real estate market with machine learning algorithms. The framework consists of demonstrating the prediction efficiency of each machine learning algorithm, interpreting the interior feature effects of prediction model with a state-of-art algorithm, LIME(Local Interpretable Model-agnostic Explanation), and comparing the results in different cities.
Findings
This research could not only enhance the academic base for information system and real estate fields, but also resolve information asymmetry on real estate market among economic subjects. This research revealed that macroeconomic indicators, real estate-related indicators, and Google Trends search indexes can predict real-estate prices quite well.

Purpose
This study proposed an approach for predicting the efficiency rating of the cultural tourism festivals using DEA and machine learning techniques. The cultural tourism festivals are selected for the best festivals through peer reviews by tourism experts. However, only 10% of the festivals which are held in a year could be evaluated in the view of effectiveness without considering the efficiency of festivals.
Design/methodology/approach
Efficiency scores were derived from the results of DEA for the prediction of efficiency ratings. This study utilized BCC models to reflect the size effect of festivals and classified the festivals into four ratings according the efficiency scores. Multi-classification method were considered to build the prediction of four ratings for the festivals in this study. We utilized neural networks and SVMs with OAO(one-against-one), OAR(one-against-rest), C&S(crammer & singer) with Korea festival data from 2013 to 2018.
Findings
The number of total visitors in low efficient rating of DEA is more larger than the number of total visitors in high efficient ratings although the total expenditure of visitors is the highest in the most efficient rating when we analyzed the results of DEA for the characteristics of four ratings. SVM with OAO model showed the most superior performance in accuracy as SVM with OAR model was not trained well because of the imbalanced distribution between efficient rating and the other ratings. Our approach could predict the efficiency of festivals which were not included in the review process of culture tourism festivals without rebuilding DEA models each time. This enables us to manage the festivals efficiently with the proposed machine learning models.

Purpose
The purpose of this study is to demonstrate the relationship between ICT(Information & communication technology) infrastructure, investment environment, and innovation performance from a national perspective.
Design/methodology/approach
We derived 5 hypotheses based on the literature review. To measure each concept, We converted the scores of each country provided by the Global Innovation Index into a 7 Likert scale and performed a regression analysis.
Findings
According to the empirical analysis result, this study confirmed that knowledge and technology output mediated the relationship between ICT infrastructure and creative goods and services. In addition, this study confirmed that the investment environment has a moderating effect between ICT infrastructure and knowledge and technology output.

Purpose & Design/methodology/approach
Amidst the COVID-19 spread, interest in telework has been increasing as a way to respond to environmental changes and improve organizational performance. In this study, causal loop diagram, thinking process, and four principles of TRIZ were used to find various ways to achieve work-life balance for workers and firms in teleworking environment.
Findings
The result of the causal loop diagram shows that keeping work-life balance is needed to provide free extra time for workers and achieve the firm's performance through efficient coordination between departments in teleworking environment. Using thinking process of TOC, we proposed a evaporating cloud for work-life balance and reviewed assumptions and premises to maintain extra free time for workers and achieve firm's performance paradoxically, and suggested solutions through the breakdown of the assumptions and premises.
Resolving the contradiction(RC) by spatial separation is to divide working spaces into home, smart center, and mobile work types, to find both ways to achieve the organizational performance, and to provide extra time for workers. RC by temporal separation is to divide working time into various unit time, and to find ways to achieve organizational performance through doing work at office or home periodically or shiftly according to a predetermined schedule and time setting. RC by situational separation is to give options of flexible methods of performing work according to the conditions of the company or the worker. RC by separation between part and a whole is to find ways to organize and do the work so that the entire work can be completed efficiently even if each work is performed separately in teleworking way.
This study is meaningful in that it provides basic thinking framework for achieving work-life balance in teleworking environment and proposes ways to resolve conflict from various perspectives, but it is limited in that it has not been tested empirically.

Purpose
This study examines the influence of product development competence and IT competence on new product development (NPD) performance in the context of Korean companies. To achieve this goal, this study presents and empirically tests a model of how NPD competence and IT competence can be exploited to positively influence NPD performance through convergence capabilities.
Design/methodology/approach
The NPD competence are based on the research construct developed by Zhang et al. (2013). IT competence is based on the research construct developed by Lu and Ramamurthy(2011) and the NPD performance are based on the performance construct developed by Sivadas and Dwyer (2000). To complete the investigation, we conducted a survey from Korean 1000 big companies, which enrolled in Korean stock market. Randomly contacted 171 Korean companies, including firms of all sizes and types. To test our hypotheses, structured equation model (SEM) with partial least squares (PLS) method was employed.
Findings
The findings indicate that NPD competence and IT competence are antecedent to convergence capabilities, while IT competence is higher influence than NPD competence. Also, convergence capabilities has very significant relationship with NPD performance. This study provides a better understanding of the relationship between NPD competence, IT competence, convergence capabilities, and NPD performance. So companies should focus on improving NPD and IT competence on NPD performance through convergence capabilities.