Portraying Competence, Benevolence, or Party Loyalty? Political Propaganda and the Image-Building of Political Elites in China

The aim of this study is to derive the important factors that influence levels of happiness in Korea, and to identify which factors are particularly important among these influencing factors. To achieve this goal, topic modelling analysis, machine learning analysis and neural network analysis methods were utilized. The Netminer 4.5 program was used for topic modelling analysis and machine learning analysis, and SPSS MODELER 18 was used to perform neural network analysis. Two types of analysis data were used in this study. The first consisted of 1 000 papers relating to happiness published in academic journals managed by the Springer publishing company, which were used to derive happiness-influencing factors. The second consisted of a survey conducted in 2020 by the Community Well-being Center of the Graduate School of Public Administration at Seoul National University in Korea. A total of 16 655 people responded to this survey. The analysis results of the study are as follows. Important variables that affect the level of happiness of Korean residents are: family life, social status, income, health, and perceptions of inequality. Analysis using neural network analysis of the most important factors influencing happiness showed that satisfaction with family life had the most important influence. This suggests that policies that can improve the quality of family life, such as family-friendly work environments, childcare support, and domestic violence prevention and response programmes, will become important in the future

Transformational leadership among core hospital leaders boosts medical organizations' competitiveness, adaptability, and sustainability, which is jointly affected by individual, organizational and environmental factors. This study aims to unpack its configurational framework and propose strategies to strengthen core hospital leaders' transformational leadership. Data were collected from an online questionnaire among 31 core hospital leaders. The fuzzy-set qualitative comparative analysis (fsQCA) was used to explore the causal mechanism of high-level transformational leadership. We enrich this mechanism by professional background, critical thinking, initiative spirit, family-work conflict, job satisfaction, subordinates' followership, and work pressure. Result shows initiative spirit is the only necessary condition (consistency=0.911) for the formation of high-level transformational leadership among core hospital leaders. Three configurations are the sufficient conditions that lead to high-level transformational leadership among core hospital leaders with two different professional backgrounds (overall solution consistency= 0.952). Core hospital leaders' initiative spirit is an indispensable condition for improving high-level transformational leadership, emphasizing the necessity for core leaders to be proactive in order to develop such leadership. Besides, the study also uncovered three configurations are the sufficient conditions for core hospital leaders with diverse professional backgrounds to achieve high-level transformational leadership. This finding offers significant insights into hospital management practices, suggesting that core hospital leaders' work should be managed in a personalized manner based on their professional backgrounds, thereby fostering favorable conditions conducive to the development of their high-level transformational leadership capabilities. Furthermore, the central insight of this study is that the formation of high-level transformational leadership contingent upon the collaboration of professional background, critical thinking, initiative spirit, family-work conflict, job satisfaction, subordinates' followership, and work pressure, contributing to a holistic and more rigorous view for the development of transformational leadership.

Research shows that effective teaching behavior is important for students' learning and outcomes, and scholars have developed various instruments for measuring effective teaching behavior domains. Although student assessments are frequently used for evaluating teaching behavior, they are mainly in Likert-scale or categorical forms, which precludes students from freely expressing their perceptions of teaching. Drawing on an open-ended questionnaire from large-scale student surveys, this study uses a machine learning tool aiming to extract teaching behavior topics from large-scale students’ open-ended answers and to test the convergent validity of the outcomes by comparing them with theory-driven manual coding outcomes based on expert judgments. We applied a latent Dirichlet allocation (LDA) topic modeling analysis, together with a visualization tool (LDAvis), to qualitative data collected from 173,858 secondary education students in the Netherlands. This data-driven machine learning analysis yielded eight topics of teaching behavior domains: Clear explanation, Student-centered supportive learning climate, Lesson variety, Likable characteristics of the teacher, Evoking interest, Monitoring understanding, Inclusiveness and equity, Lesson objectives and formative assessment. In addition, we subjected 864 randomly selected student responses from the same dataset to manual coding, and performed theory-driven content analysis, which resulted in nine teaching behavior domains and 19 sub-domains. Results suggest that the relation between machine learning and human analysis is complementary. By comparing the bottom-up (machine learning analysis) and top-down (content analysis), we found that the proposed topic modeling approach reveals unique domains of teaching behavior, and confirmed the validity of the topic modeling outcomes evident from the overlapping topics.

Drawing on resource dependency theory and the resource-absorbing perspective of risk-taking, this article examines how political connections provide firms with opportunities to gain government funding support to enhance financial slack, which can in turn benefit their entrepreneurial risk-taking. We employ both symmetrical (partial least squares structural equation modelling (PLS-SEM)) and configurational approaches (fuzzy-set qualitative comparative analysis (fsQCA)) using a sample of 202 small and medium-sized enterprises (SMEs) in China. Our results from PLS-SEM suggest government financial funding support and financial slack play sequential mediating roles in the relationship between political connections and entrepreneurial risk-taking. The results of fsQCA further highlight the importance of political connections and financial slack as core conditions for entrepreneurial risk-taking and uncover the multiple pathways through which political connections influence entrepreneurial risk-taking. These findings advance our understanding of how entrepreneurial risk-taking in SMEs may require configurations of different resources.

This study aims to examine the temporal evolution and changes of research interests and trends in the educational augmented reality (AR) literature. To this end, 3718 articles published in the 2003–2022 period and indexed in the Scopus database were analyzed through machine learning-based semantic topic modeling and descriptive analysis. The findings indicate a notable upswing in studies on educational AR, particularly since 2015. The articles were categorized into eleven primary themes through topic modeling analysis. The three most prevalent topics in terms of volume are “Augmented Reality in Education and Cultural Heritage”, “Medical Education and Patient Care”, and “Enhancing Safety and Information in Food Consumption”. Observations across different times reveal that “Augmented Reality in Electrical and Electronic Systems” and “Gesture-Based Instruction and Maintenance” were studied in the initial periods. Since 2015, there has been a notable increase in applications falling under the “Serious Games” category. The least voluminous and slowest-evolving topics are identified as “Serious Games for Children with Autism Spectrum Disorder”, “Augmented Reality in Chemistry and Biology Laboratories”, and “Augmented Reality for Safe and Efficient Driving”. Considering the recent momentum gained by these topics, it is anticipated that they will become popular topics for future studies. This study represents a significant milestone as the first and most comprehensive research using machine learning in its field, not only explaining the current state of the field but also providing valuable information for future research efforts.

Given widespread perceptions of risk and uncertainty in nondemocratic systems and developing democracies, why do some citizens still take action and make complaints to authorities? The resource mobilization model identifies the importance of time, money, and civic skills as resources that are necessary for participation. In this paper we build on this model and argue that political connections – close personal ties to someone working in government – can also constitute a critical resource, especially in contexts with weak democratic institutions. Using data from both urban and rural China, we find that individuals with political connections are more likely to contact authorities with complaints about government public services, despite the fact that they do not have higher levels of dissatisfaction with public service provision. We conduct various robustness checks, including a sensitivity analysis, and show that this relationship is unlikely to be driven by an incorrect model specification or unobserved confounding variables.

This research focuses on the behavior of political elite taxpayers and their political connections during the COVID-19 pandemic. The research object is the political elite, entrepreneurs, and entrepreneurs who have connections with the political elite in Balikpapan. Using Husserl's transdental phenomenological study to gain an in-depth understanding of the behavior and meaning of tax with tax subjects using the terms "politically elite taxpayers" and "politically connected taxpayers", as a form of perspective on the other side of exploring taxpayer behavior and the meaning of tax by adding a subject to new research other than what has been done by previous researchers (e.g., WP-OP, WP-UMKM, WP-UMK). Meanwhile, it is known that 22 of the 45 political elites in the Balikpapan city legislature have business backgrounds. Even though he later left the management of the company, unless he was still registered as a director for a certain reason along with his existing political connections, it left questions about his independence from his duties as a legislative political elite and his partiality for his business or for his group. Rent-seeking opportunities, budgeting for people's welfare projects, and the distribution of donations for handling COVID-19 are things that they see for themselves to explore compliance, the meaning of taxes, and their behavior in light of the policies that they set in their company. Tax-related policies, this research only focuses on the cost of COVID-19 donations due to indications of large tax aggressiveness and indications of the legitimacy of companies and themselves as political elites with political connections. As a result of the research, based on the reduction of deep meaning found in information from informants, there is suspicion of other political elites prioritizing personal or group interests in project planning and implementation. Political elite taxpayer informants and politically connected taxpayers chose not to follow the rules for implementing the distribution of COVID-19 assistance and chose not to take existing tax facilities to reduce the company's tax burden on humanitarian grounds, and it was difficult to prepare data according to tax rules.

Many political elites in Indonesia have strategic position in companies. Uniquely, in Indonesia,there are also military elites hold this position. On the contrary, the level of corruption in Indonesia is still very high, and the tax ratio is also low. Therefore, this study is conducted to determine and analyze the effect of political and military connection on tax avoidance in Indonesia. The research was conducted on mining companies listed on the IDX in 2017 – 2019, and the data were analyzed using SmartPLS. The results of this study indicate that political connection has an effect on tax avoidance through audit delay. Top management can intervene in the audit report process to get the expected result. So, the process takes longer time and it indicates the occurrence of tax avoidance efforts. Meanwhile, the military connected company has no effect, because the proportion is relatively small and they have characteristics to comply more with the rules in submitting audit reports. Ultimately, this study is expected to be a valid source of input in making regulations about tax collecting activities. Moreover, it can be taken into consideration in making decisions regarding the proportion of the top management who are connected politically and militarily.

In this study, in order to analyze nanotechnology trends, topic modeling analysis and text network analysis were conducted by collecting nanotechnology summary information that was developed between 2014 and 2019 and registered in NTIS. First, key keywords were selected through topic modeling analysis, and then a text network was built based on these keywords. As a result of classifying topics through topic modeling analysis, topics were classified into five topics based on the five major nanotechnology fields classified by the government. In addition, as a result of conducting a network analysis to examine the factors that affect nanotechnology performance, carbon, battery, metal, electrode, fiber, substrate, and graphene were found as factors that mainly influence nanotechnology. Next, as a result of analyzing the nanotechnology field according to the time period, the five major fields were from 2014 to 2015 (time 1), 2016 to 2017 (time 2), and 2018 to 2019 (time 3). The results of technology development have occurred evenly. However, it was confirmed that the degree of technology development by field differs depending on the period. As for the text network analysis, the most active development was in the nano-energy and environment field in the period 1, the nanomaterials and the nanodevices in the period 2, and the nanomaterials in the period 3.KeywordsNanotechnologyTechnology trendTechnology performance structureText network analysisTopic modeling

This study investigates the conditions that influence organizational success in protecting the legiti-macy of large capital investment projects in plural and complex institutional contexts. Following a bottom-up approach, we distinguish and measure the different components that characterize plural and complex institutional contexts. Then, we investigate under which context-specific conditions are different organizational responses linked to low organizational success? Our results show that contexts of volatile complexity lead to low success irrespective of the type of organizational re-sponse. Additionally, we find that incompatibility alone, a key feature of complex institutional con-texts according to prior research, does not seriously compromise organizational success. Our da-taset is comprised of the hearing transcripts of the regulatory review processes of 35 oil pipeline project proposals in Canada (1993 – 2018). Our research design combines topic modeling and fuzzy set Qualitative Comparative Analysis (fsQCA).

Purpose Various technologically driven online food services, including food delivery applications, substantially contribute to food waste generation. This study utilizes the stimulus-organism-behaviour-consequence (SOBC) paradigm to explore how food delivery applications (FDAs) drive food waste by inducing over-ordering behaviour. Design/methodology/approach A total of 344 responses from FDA users were collected through an online survey. The data were analysed using structural equation modelling and fuzzy set qualitative comparative analysis (FSQCA). Findings The findings indicated that personalization does not influence functional value but exerts a positive influence on hedonic value. Similarly, social influence showed a significant association with hedonic value but no association with functional value. Furthermore, functional value does not exhibit a positive relationship with over-ordering behaviour, whereas hedonic value shows a positive association with over-ordering behaviour. Finally, over-ordering behaviour demonstrates a significant positive relationship with food waste. Additionally, FSQCA revealed that the combination of personalization, hedonic value and over-ordering behaviour significantly contributes to food waste in the context of food delivery apps. Practical implications The findings suggest that food delivery app service providers should focus on managing hedonic triggers and social influence to reduce over-ordering behaviour, which leads to food waste. Personalization strategies should be designed to encourage mindful and sustainable ordering habits, thereby promoting responsible consumption within the digital food delivery ecosystem. Originality/value This research extends the SOBC framework to explore food waste generation induced by food delivery apps (FDAs). The dual analyses using SEM and FSQCA provide a comprehensive perspective, combining insights into linear relationships with a configurational understanding of the factors driving food waste.

As the digital economy and society rapidly grow, individual and organizational adaptation to technology has gained substantial concern across various sectors. However, this process involves many challenges, including uncertainty and complexity arising from factors such as the reliability, feasibility, and compatibility of technologies. Based on evidence from existing literature, this study proposes applying the fuzzy set qualitative comparative analysis (fsQCA) approach as a valuable tool in investigating associated challenges and complex configurations of influential factors within the context of individual and organizational technology decision-making in technology adoption. The fsQCA has emerged as a popular tool in qualitative analysis, particularly in recent years, where its use has grown substantially. This paper conducts a systematic literature review of journal articles published between 2015 and 2023 using fsQCA, focusing on digital transformation, AI, IoT, e- and m-commerce applications, digital assistants, business analytics, sustainable development, and machine learning. This study offers a detailed review of related research, the implications of the identified trends, and the potential for future research utilizing fsQCA to explore performance and human behavior in technology adoption and organizational technology decision-making contexts.

This study focuses on how start-ups use machine learning technology to create and appropriate value. A firm’s use of machine learning can activate data network effects. These data network effects can then create perceived value for users. This study examines the interaction between the activation of data network effects by start-ups and the value that they are able to create and appropriate based on their business model. A neo-configurational approach built on fuzzy-set qualitative comparative analysis (fsQCA) explores how the design of a firm’s business model interacts with various aspects to explain value creation and appropriation using machine learning. The study uses a sample of 122 European start-ups created between 2019 and 2022. It explores the system of interactions between business model value drivers and value creation factors under the theory of data network effects. The findings show that start-ups primarily activate the efficiency and novelty elements of value creation and value capture.

Few political systems are completely closed to citizen participation, but in nondemocratic systems and developing democracies, such participation may come with risks. In these contexts where fear and uncertainty may be high, why do some citizens still take action and make complaints to authorities? The resource mobilization model identifies the importance of time, money, and civic skills as resources that are necessary for participation. In this paper, we build on this model and argue that political connections—close personal ties to someone working in government—can also constitute a critical resource, especially in contexts with weak democratic institutions. Using data from both urban and rural China, we find that individuals with political connections are more likely to contact authorities with complaints about government public services, despite the fact that they do not have higher levels of dissatisfaction with public service provision. We conduct various robustness checks, including a sensitivity analysis, and show that this relationship is unlikely to be driven by an incorrect model specification or unobserved confounding variables.

Carbon reduction effect of comprehensive land consolidation and its configuration paths at the township level: A case study of Zhejiang Province, China.