The purpose of this study was to investigate preservice teachers’ understanding of democratic citizenship. This study utilized the democratic citizenship frame to assess 17 participants’ comprehension of this concept. The researcher designed a method course where participants in groups analyzed science activities to identify democratic citizenship components. Through the analysis of two science activities—one on energy and the other on climate change—and the development of science panels addressing various global issues, preservice teachers' understanding of democratic citizenship was enhanced. Preservice teachers showed a good understanding of critical thinking, communication and collaboration, and STS (science, technology, and society); and the most enhanced understanding of empathy, which was the least perceived in pre-survey, component of democratic citizenship. The democratic citizenship frame proved to be a valuable tool for teaching and learning this topic, particularly when applied to socioscientific issues in the classroom. More research-based revisions of the science curriculum are necessary, and more systematic practices with reflections are essential in teacher education.

This study aimed to verify the validity of a measurement tool for Vietnamese high school students’ systems thinking abilities. Two quantitative assessment tools, the Systems Thinking Measuring Instrument (Lee et al., 2013) and the Systems Thinking Scale (Dolansky et al., 2020), were used to measure students’ systems thinking after translation into Vietnamese. As a result, it was revealed that Cronbach-α for each tool (i.e., STMI and STS) was .917 and .950, respectively, indicating high reliability for both. To validate the construct validity of the translated questionnaire, exploratory factor analysis was performed using SPSS 26.0, and confirmatory factor analysis was performed using AMOS 21.0. For concurrent validity, correlation analysis using structural equation modeling was performed to validate the translated questionnaire. Exploratory factor analysis revealed that 10 items from the STMI and 12 items from the STS loaded on the intended factors and appropriate factor loading values were obtained. For confirmatory factor analysis, a structural equation model organized with 10 items from the STMI and 12 items from the STS was used. The result of this showed that the convergent validity values of the model were all appropriate, and the model fit indices were analyzed to be χ 2 /df of 1.892, CFI of .928, TLI of .919, SRMR of .047, and RMSEA of .063, indicating that the model consisting of the 22 items of the two questionnaires was appropriate. Analysis of the concurrent validity of the two tools indicated a high correlation coefficient (.903) and high correlation (.571-.846) among the subfactors. In conclusion, both the STMI and STS are valid quantitative measures of systems thinking, and it can be inferred that the systems thinking of Vietnamese high-school students can be quantitatively measured using the 22 items identified in our analysis. Using the tool validated in this study with other tools (e.g., qualitative assessment) can help accurately measure Vietnamese high school students’ systems thinking abilities. Furthermore, these tools can be used to collect evidence and support effective education in ODA projects and volunteer programs.

Civil defense originally refers to the protection of civilians in the events of war-related disasters. Today, it encompasses a wide range of protection, rescue, and recovery activities not only aimed at minimizing the damage caused by war but also addressing natural and man-made disasters such as wildfires, typhoons, and floods. Civil defense uniforms are worn by volunteers who participate in civil defense training organized by practitioners, government officials, and military personnel. Therefore, more than 3,350,000 persons (+6.5 % of the Korean population) likely should wear civil defense uniforms. As such, the functionalities of civil defense uniforms need to be reexamined and improved in response to the wider and newer responsibilities of the civil defense system to protect the civil defense personnel from potential hazards and to help better serve the Korean citizens. The essential functions of civil defense uniforms were proposed by analyzing various civil defense activities. Per the level and type of safety hazards, the design and materials for civil defense uniforms should ideally differ, similar to civil defense uniforms in other countries. We surveyed the current Korean civil defense uniforms’ design, material compositions and important functionalities such as water repellency, water pressure resistance, and flame resistance. Compared to common functional workwear, we suggest room for improvement in the functional properties of civil defense uniforms. This research paves a new road for the functional design of civil defense uniforms to address a wider range of hazards, potentially leading to rational guidelines for protective workwear and government uniform development.

This study investigated which factors impact esports viewership for League of Legends matches through Live and highLight streaming video. This research anaLyzed League of Legends viewership factors' differences between the three Leagues (LCK, LCS, and LEC) where viewership factors infLuence esports viewership demand through LOL’s Live and highLight streaming video. League of Legends not onLy has the highest audience among different various esports, aLso has formed a huge market from diverse countries. In order to soLve each League's unbaLance viewership trend issue between the three Leagues, and facing worLdwide viewership decrease, this study examined viewership impact factors from three Leagues with the highest viewership ratings: Korea's LCK, North America's LCS, and Europe's LEC. After that, differences in viewership impact factors for the research were anaLyzed and compared to each League. SpecificaLLy, this study investigated which factors impact esports viewership for League of Legends (LOL) matches. Data (N=1581) of reguLar season matches from the LCK, LCS, and LEC Leagues (2020 spring to 2021 summer) were coLLected and anaLyzed. This study anaLyzed the average number of visitors per minute of Twitch Live streaming and the number of views on YouTube highLights. Five main viewership factors(the uncertainty of match, consumer preferences, match importance, team attributes, match content, and controL variabLes), and seventeen independent variabLes were verified by muLtipLe regression anaLysis using STATA 15. As a resuLt of the anaLysis, the modeL with the number of reaL-time audience as the dependent variabLe demonstrated different trends between Leagues in terms of the uncertainty of match factors (rank difference, win rate difference, goLd difference, kiLL difference, object difference), and consumer preference factors(fan, operating period of both teams, number of worLd championship wins, the number of championship semifinaL advances). WhiLe the match importance factors(week, top-rank matches) and match content factors (the sum of kiLLs, the sum of assists, the sum of objects) indicated somewhat simiLar tendencies between Leagues. In the modeL with the number of highLights views as the dependent variabLe, consumer preference factors and match importance factors tended to be somewhat simiLar, but match content factors prone to be sLightLy different between Leagues. In addition, the uncertainty of the match factors did not show a significant infLuence across the three Leagues. Findings from this research expand and appLy the variabLes derived from the demand determination theory of traditionaL sports to e-sports demand research. This study heLps stakehoLders understand various esports cuLtures from different regions. Furthermore, it is meaningfuL in that it provides information that wiLL contribute to the growth of the entire League of Legends market through baLanced deveLopment between Leagues.

A typical consumer is exposed to more than 5,000 advertisements per day (Story 2015) by exposure to around 500 advertising messages before ending breakfast (Marshall, 2015), and it is impossible for consumers to remember most of the advertisement images and messages. Thus, one consistent yet not thoroughly investigated question for advertisers is how advertisers draw consumers' attention by differentiating their brand from competitors' brands. One suggestion from academia is making more "creative" advertising (Dahlén et al., 2008; Lehnert et al., 2014; Rosengren et al., 2013; Smith et al., 2008). However, it is still questionable the exact meaning of "advertising creativity," and the effects of creative advertising on consumer evaluation have not been fully investigated the effects of creativity in advertising evaluation by considering various boundary conditions. The objective of this research is to redefine advertising creativity, to understand how advertising creativity shapes consumers' evaluation, and how these effects are moderated by the different types of boundary conditions, such as industry category, by analyzing more than 100,000 advertising images and copies using a cutting-edge transfer learning technique. The results of the transfer learning algorithm indicate that both cognitive dimensions (e.g., novelty of image) and affective dimensions (e.g., awe and coolness) simultaneously affect the consumers' perception of the advertising creativity, and the current algorithm enables to detect of creative advertising image with 92% accuracy rate.

Blockchain is an immutable ledger that records transactions and tracks assets using a common communication protocol. It stores a copy of the blockchain and implements a consensus function to verify transactions. Blockchain is applied to industries beyond finance, such as retail, to maintain security and transparency. Consumers with knowledge of blockchain technology are likely to be affected when evaluating products with blockchain embedded, impacting their product evaluation. The study investigates the impact of blockchain technology on consumers' product evaluation and how knowledge of blockchain and product quality moderate its effects.

This paper examines how consumers evaluate digital products with multiple Non-fungible Tokens (NFTs), which are blockchain-enabled cryptographic assets that represent proof-of-ownership for digital objects. The study predicts that people prefer fractioned NFTs (unique fractions of a digital object) versus duplicated NFTs (identical digital objects with distinct numbers) due to their preference for uniqueness. The study also examines the moderating role of product attributes, predicting that preferences for fractioned NFTs will be reduced when the product provides unique aspects, such as a serial number.

This research demonstrates that consumers with high (vs. low) usage of social networking sites (SNSs) prefer NFTs representing a partial but the only digital piece (vs. one of the limited digital copies) of artwork. NFT uniqueness is identified as the psychological mechanism underlying the SNS usage effect on NFT preference.

The spread of COVID-19 changes consumer preferences and behaviors greatly across the world. Extant literature has demonstrated that when there is a threat to disease, people stay away from those who do not seem healthy as they can be potentially infectious. Based on the previous literature, this research shows that individuals exposed to disease threat avoid products of which designs are high in visual complexity. When disease threat was present, individuals had lower purchase intention for products with complex designs. The perceived uncleanliness mediated the effect of visual complexity and disease threat on purchase intention. The findings provide a novel insight into the effect of disease salience on consumer perception of product design.

In this article, we address this shortcoming by exploring the concept of AI-based sustainable service—an offering that embeds artificial intelligence in ways that meet the needs of current consumers by contributing to socio-economic equalities and conserving the natural environment.

Social media have emerged as one of the most important tools for firms to engage customers (e.g., Chandrasekaran et al., 2022; Cheng & Edwards, 2015; Lee et al., 2018; Wedel & Kannan, 2016). Within the tourism industry, scholars have investigated the role of social media communication in various contexts, such as online travel information search (Xiang & Gretzel, 2010), sharing travel experiences (So et al., 2018; Wang et al., 2022) and establishing positive customer relationships (Jamshidi et al., 2021). Insights into which social media content makes for generating positive engagement are, however, still largely based on marketers’ intuitions or focusing on message factors of social media posts such as message appeals (e.g., Wang & Lehto, 2020). It also often neglects the importance of the visual component of social media posts, and only a few research have investigated the effects of the image in social media on the travel industry (e.g., Fusté-Forné, 2022). The objective of this research is, therefore, to understand how textual features and image features generate user engagement in social media utilizing cutting-edge transfer learning techniques and to propose how these features should be customized to maximize user engagement for online travel shopping companies. We collect and analyze more than 10,000 Instagram posts from three online travel shopping companies, including Expedia, Priceline, and Kayak. The results from transfer learning algorithms utilizing 24 features, such as the number of people in the image, emotions expressed in the people in the image, hue, and RGB value, successfully predict the level of engagement measured by the number of likes and comments.

Instagram marketing is a popular tool for communication in the tourism industry. However, research into effective execution tactics has been limited to the content, recipient characteristics, and sender types (e.g., celebrities) (i.e., Ferwerda & Tkalcic, 2018; Jin et al., 2019; Singh, 2020). The layouts of Instagram ads themselves have received little scholarly attention. Instagram ads typically convey multiple pieces of information in a single advertisement unit, raising concerns about the significance of the order in which the information is presented. Specifically, we must understand how different orders of presenting information affect a firm’s performance. Therefore, this study investigates the effect of presentation order on consumer behavioral intentions toward tour programs promoted on Instagram.

Drawing from the uses and gratification theory (UGT), the study intends to investigate audiences’ motivation to watch Livestream and its influence on subscription and donation. The roles of viewers’ parasocial relationships and identification with live streamers were also examined. The results also indicate the presence of a mediation effect.

How do people perceive new technology-embedded machines? Based on the previous literature on mind perception, this research proposes how people perceive the mind of machines including artificial intelligence (AI), robots, recommendation systems, chatbots, and self-service technologies (SSTs).

From the past to the present, we are living in an age of social comparison. Above all, upward social comparison to those who are superior in terms of their social and economic status makes individuals feel psychologically deficient and threatened. As a result, individuals oftentimes exhibit compensatory consumption to relieve their unfulfilled desires such as stress, disappointment, and self-esteem deficiency by purchasing high-end products or brands (Mandel, Petrova, & Cialdini, 2006; Pettit & Sivanathan, 2011).

For the commercialization of hydrogen energy, a technology enabling safe storage and the transport of large amounts of hydrogen is needed. Porous materials are attracting attention as hydrogen storage material; however, their gravimetric hydrogen storage capacity (GHSC) at room temperature (RT) is insufficient for actual use. In an effort to overcome this limitation, we present a N-doped microporous carbon that contains large proportion of micropores with diameters below 1 nm and small amounts of N elements imparted by the nitrogen plasma treatment. The N-doped microporous carbon exhibits the highest total GHSC (1.59 wt%) at RT, and we compare the hydrogen storage capacities of our sample with those of metal alloys, showing their advantages and disadvantages as hydrogen storage materials.

This paper presents a case study of developing a blended reading program for Korean EFL middle school students using a process drama methodology to generate intrinsic motivation to read, improve reading comprehension and language fluency, and foster twenty-first-century skills such as creativity, communication, and cooperation. For the development of the reading program, a needs analysis was conducted using student questionnaires and diagnostic tests. From results of the needs analysis, sample lessons were designed based on the process drama methodology following its planning principles and strategies. Based on feedback from teachers and students after piloting the sample lessons, the blended reading program was finalized. The proposed blended reading program is expected to help Korean EFL middle school students build a positive attitude toward reading English books and develop reading comprehension, language fluency, and 21st-century core competencies. It will also encourage English teachers to use innovative teaching methods in English education.

While metadiscourse use has been well-attended in second language (L2) writing research, relatively less effort has been made in documenting changing patterns of metadiscourse use among L2 writers. The present study addressed this gap by probing a diachronic change of interactive metadiscourse in research articles published in English Teaching across a span of 40 years. Using the corpus of 931 articles written by Korean L2 writers, we examined whether, and to what extent, interactive metadiscourse use in academic writing had changed over time. Our findings revealed an overall increase in the frequency of interactive resources mainly driven by a significant increase of evidentials. The observed pattern of change in interactives suggests that academic discourse within the applied linguistics community in Korea is becoming more persuasive and reader-oriented over time, consistent with Hyland and Jiang (2018) who reported a dramatic rise in interactive metadiscourse in the global discourse community of applied linguistics.

This research proposes a novel approach to tackle the challenge of categorizing unstructured customer complaints in the automotive industry. The goal is to identify potential vehicle defects based on the findings of our algorithm, which can assist automakers in mitigating significant losses and reputational damage caused by mass claims. To achieve this goal, our model uses the Word2Vec method to analyze large volumes of unstructured customer complaint data from the National Highway Traffic Safety Administration (NHTSA). By developing a score dictionary for eight pre-selected criteria, our algorithm can efficiently categorize complaints and detect potential vehicle defects. By calculating the score of each complaint, our algorithm can identify patterns and correlations that can indicate potential defects in the vehicle. One of the key benefits of this approach is its ability to handle a large volume of unstructured data, which can be challenging for traditional methods. By using machine learning techniques, we can extract meaningful insights from customer complaints, which can help automakers prioritize and address potential defects before they become widespread issues. In conclusion, this research provides a promising approach to categorize unstructured customer complaints in the automotive industry and identify potential vehicle defects. By leveraging the power of machine learning, we can help automakers improve the quality of their products and enhance customer satisfaction. Further studies can build upon this approach to explore other potential applications and expand its scope to other industries.

The Animal and Plant Quarantine Agency conducts a targeted sampling plan and analysis for veterinary drugs within the country every year. Target compounds included tetrachlorvinphos as an organophosphate, diminazene as an anti-infective medication, ketoprofen as a nonsteroidal anti-inflammatory drug, triclabendazole and clorsulon as flukicides in 2022. These compounds were not included in National Residues Program (NRP), despite their high sales ranking. A total of 94 bovine muscle samples and 20 equine muscle samples were collected from various locations across the country. The analysis of target compounds in muscle was performed using LC-MS/MS coupled with Food code 8.3.1 revised in 2022. A 2 g sample of muscle tissue was extracted using a water: acetonitrile (1:4, v/v) solution, then cleaned up with C18 and hexane saturated with acetonitrile. Compounds were separated with C18 column and mobile phases consisted of 0.1% formic acid in water (A) and 0.1% formic acid in acetonitrile (B). All analytes exhibited good linearity with correlation coefficients (R2) higher than 0.992. The limit of quantification (LOQ) of these compounds ranged from 0.21 to 2.79 μg/kg except for diminazene (3.85~6.86 μg/kg). The average recoveries of these analytes were 89.45~129.13% in muscle at spiked level of 10 or 20 μg/kg. Relative standard deviations (%) (intra-day and inter-day) were lower than 20% for all target compounds, except for diminazene and triclabendazole, whose intra-day RSD % was slightly higher than 20% in equine muscle. Testing confirmed that all 94 bovine and 20 equine muscle samples from 9 provinces were free from residues of veterinary drugs. Monitoring of compounds not included in the NRP should continue to ensure consumer health and food safety.