Purpose The purpose of this study is to analize a psychological and behavioral mechanism for using profile picture management in digital service such as social network service. Profile picture management falls under metadata management and is performed only by those who want it. This means that it, is one of the typical makeup-type digital shadow works (DSWs) which have not been studied yet. Design/methodology/approach This study adopts ground theory method(GTM) as research methodology. GTM, which is one of qualitative methodologies, is for developing theories while most survey based methodologies, which are well adopted in much research for information systems, are for validation of theories. By interviewing ten users, the data are collected and analyzed by open coding, axis coding with paradigm model, and selective coding. Findings In result, 39 codes are found and classified into 29 sub classes and 15 classes. These 15 classes are organized by paradigm model which derives core code of profile picture management as 'voluntary management tasks to experience small pleasures with intermittent attention'. Finally, based on the paradigm model and the core code, the story line, which can explain profile picture management, is suggested.

Purpose Since COVID-19, the demand for online class platforms has increased. However, those platforms have not been clearly defined, and related research is also limited. In the context of the knowledge marketplace (KMs), this study examined the effects of class information and trust in creators on class preferences from the perspective of consumption value theory. Design/methodology/approach By establishing a web crawler through Python, this study collected 1,174 class data in Korea's leading knowledge marketplace, Class 101, focusing on diverse class-related information and the number of Instagram followers for individual class creators. Based on class information, this research analyzed the effects of consumers' utilitarian value, social value, and hedonic value on class preference. In addition, this study examined whether consumers' trust in creators moderates the relationship between class information and class preference. Findings According to analysis results, it was found that the higher the consumers' consumption value for each class on KMs, the more positive their preference for the class. Also, it was confirmed that consumers' trust in creators moderates the relationship between class information and class preference.

Purpose With the recent development of Big Data and Artificial Intelligence technology, self-driving technology has developed into three stages (partial self-driving) or four stages (conditional self-driving), it is expected to bring a new paradigm to transportation in the city. Although many researchers are researching related technologies, there is no research on self-driving for disabled persons. In this study, the basic research was conducted based on the assumption that the shared self-driving car used by the disabled person is similar to the special transportation currently driving. Design In this study, data analysis and machine learning techniques were utilized to analyze the mobility patterns of disabled persons by type and to search for leading factors affecting the traffic volume of special transportation. Findings The study found that external physical disorders and developmental disorders often visit general welfare centers, internal organ disorders often visit general hospitals, and the elderly and mental disorders have various destinations. In addition, machine learning analysis showed that the main transportation routes for the disabled person use arterial roads and auxiliary arterial roads and that the ratio of building usage-related variables affecting the use of special transportation for a disabled person is high. In addition, the distance to the subway and bus stops was also mentioned as a meaningful variable. Based on these analysis results, it is expected that the necessary infrastructure for shared self-driving cars for disability person traffic will be used as meaningful research data in the future.

Purpose This study quantified companies' views on the COVID-19 pandemic with sentiment analysis of U.S. public companies' disclosures. It aims to provide timely insights to shareholders, investors, and consumers by analyzing and visualizing sentiment changes over time as well as similarities and differences by industry. Design/methodology/approach From more than fifty thousand Form 10-K and Form 10-Q published between 2020 and 2021, we extracted over one million texts related to the COVID-19 pandemic. Using the FinBERT language model fine-tuned in the finance domain, we conducted sentiment analysis of the texts, and we quantified and classified the data into positive, negative, and neutral. In addition, we illustrated the analysis results using various visualization techniques for easy understanding of information. Findings The analysis results indicated that U.S. public companies' overall sentiment changed over time as the COVID-19 pandemic progressed. Positive sentiment gradually increased, and negative sentiment tended to decrease over time, but there was no trend in neutral sentiment. When comparing sentiment by industry, the pattern of changes in the amount of positive and negative sentiment and time-series changes were similar in all industries, but differences among industries were shown in neutral sentiment.

Purpose The purpose of this study is to clarify the conceptualizations of mandatory and reward that have come into focus in the definition of digital shadow work. And explore how users in a shared services environment view cost and coercion from the perspective of digital shadow work. Design/methodology/approach We conducted one-on-one interviews with 4 participants, with each interview being an average of 25 minutes. Based on literature review, stakeholder observation, and interviews on digital shadow work so far, very objective results can be derived through triangulation based on the basis of multiple sources. Findings According to the results of the preliminary study, there are some rewards for each type of digital shadow work, but time saving and service convenience are considered more than financial rewards. Unfair demands in determining whether to implement them in consideration of the difficulty and expected benefits of the demanding digital work can cause dissatisfaction with the service. Academic implications and future research directions are also discussed.

Purpose The purpose of this study is to examine whether customers' shadow work and risk perception have an impact on revisit intention to the fast-food restaurant, with mediating role of different types of attribution tendencies. We also added perceived unfairness in the research model, in order to verify its mediating effect on the relationship between attribution tendencies and revisit intention. Design/methodology/approach Applying the shadow work perspective and attribution theory, we designed a research model to confirm the hypotheses. After collecting the data from 331 customers who have used self-service technology in a fast-food restaurant, we conducted an analysis of measurement model and structural model by using Smart-PLS 3.0. Findings This study confirmed that Forced Use of self-service technology can enhance the tendency of dispositional attribution which would negatively affect perceived unfairness and revisit intention. On the other hand, affective risk perception was proven to affect situational attribution, which positively affects revisit intention.

Purpose The purpose of this study was to investigate the intention of using a news media platform using block chain through media company workers in a situation where various platform services using block chain are being newly released in the media industry. Therefore, in this paper, we intend to explore the development direction of the news media platform service using the block chain in the future by deriving implications through the characteristics of the block chain, user characteristics, and self-determination factors. Design/methodology/approach This study conducted a survey on the main characteristics of blockchain, user characteristics, self-determination, resistance to innovation, etc., and designed a research model by integrating factors on the continuity of intention to use the news media platform. Findings According to the empirical analysis result, in this study, it was confirmed that the intention to use the blockchain news media platform is significantly related to decentralization, which is a characteristic variable of the blockchain, perceived risk, which is a user characteristic variable, and competence and relationship, which is a self-determination variable. In addition, it was confirmed that it affects the perceived ease of use with respect to the intention to use. In addition, in this study, news writers write more careful articles as they cannot edit articles once written, which can contribute to improving the quality of news content.

Purpose This study aims to identify and empirically prove the factors and influence relationships that affect the intention to continue using the video conferencing solution. Design/methodology/approach This study designed a research model by integrating self-efficacy, facilitation condition, event-related fear, and social influencing factors based on the post-acceptance model of IT continuance that explained the relationship between disconfirmation, post-usage usefulness, satisfaction, and continuance intention. Findings As a result of empirical analysis, in this study, it was confirmed that positive disconfirmation and post-usage usefulness had a positive effect on satisfaction, and satisfaction had a positive effect on continuance intention. In addition, we confirmed that self-efficacy and social influence had a positive effect on continuance intention, but event-related fear and facilitating condition had no significant effect.

Purpose This paper aims to prepare a full operational readiness by establishing an optimal flight plan considering the weather conditions in order to effectively perform the mission and operation of military aircraft. This paper suggests a flight prediction model and rules by analyzing the correlation between flight implementation and cancellation according to weather conditions by using big data collected from historical flight information of military aircraft supplied by Korean manufacturers and meteorological information from the Korea Meteorological Administration. In addition, by deriving flight rules according to weather information, it was possible to discover an efficient flight schedule establishment method in consideration of weather information. Design/methodology/approach This study is an analytic study using data mining techniques based on flight historical data of 44,558 flights of military aircraft accumulated by the Republic of Korea Air Force for a total of 36 months from January 2013 to December 2015 and meteorological information provided by the Korea Meteorological Administration. Four steps were taken to develop optimal flight prediction models and to derive rules for flight implementation and cancellation. First, a total of 10 independent variables and one dependent variable were used to develop the optimal model for flight implementation according to weather condition. Second, optimal flight prediction models were derived using algorithms such as logistics regression, Adaboost, KNN, Random forest and LightGBM, which are data mining techniques. Third, we collected the opinions of military aircraft pilots who have more than 25 years experience and evaluated importance level about independent variables using Python heatmap to develop flight implementation and cancellation rules according to weather conditions. Finally, the decision tree model was constructed, and the flight rules were derived to see how the weather conditions at each airport affect the implementation and cancellation of the flight. Findings Based on historical flight information of military aircraft and weather information of flight zone. We developed flight prediction model using data mining techniques. As a result of optimal flight prediction model development for each airbase, it was confirmed that the LightGBM algorithm had the best prediction rate in terms of recall rate. Each flight rules were checked according to the weather condition, and it was confirmed that precipitation, humidity, and the total cloud had a significant effect on flight cancellation. Whereas, the effect of visibility was found to be relatively insignificant. When a flight schedule was established, the rules will provide some insight to decide flight training more systematically and effectively.

Purpose The purpose of this study is to identifies the demand pattern categorization of repair parts of Automotive After-sales Service(A/S) and proposes a demand prediction model for Auto repair parts using Long Short-Term Memory (LSTM) of artificial neural networks (ANN). The optimal parts inventory quantity prediction model is implemented by applying daily, weekly, and monthly the parts demand data to the LSTM model for the Lumpy demand which is irregularly in a specific period among repair parts of the Automotive A/S service. Design/methodology/approach This study classified the four demand pattern categorization with 2 years demand time-series data of repair parts according to the Average demand interval(ADI) and coefficient of variation (CV²) of demand size. Of the 16,295 parts in the A/S service shop studied, 96.5% had a Lumpy demand pattern that large quantities occurred at a specific period. lumpy demand pattern's repair parts in the last three years is predicted by applying them to the LSTM for daily, weekly, and monthly time-series data. as the model prediction performance evaluation index, MAPE, RMSE, and RMSLE that can measure the error between the predicted value and the actual value were used. Findings As a result of this study, Daily time-series data were excellently predicted as indicators with the lowest MAPE, RMSE, and RMSLE values, followed by Weekly and Monthly time-series data. This is due to the decrease in training data for Weekly and Monthly. even if the demand period is extended to get the training data, the prediction performance is still low due to the discontinuation of current vehicle models and the use of alternative parts that they are contributed to no more demand. Therefore, sufficient training data is important, but the selection of the prediction demand period is also a critical factor.