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Once introduced and conceptualized as a factor that causes erosion and decay of social institutions and subsequent deinstitutionalization, the notion of entropy is at odds with predictions of institutional isomorphism and seems to directly contradict the tendency toward ever-increasing institutionalization. The purpose of this paper is to offer a resolution of this theoretical inconsistency by revisiting the meaning of entropy and reconceptualizing institutionalization from an information-theoretic point of view.

It is a theoretical paper that offers an information perspective on institutionalization.

A mistaken understanding of the nature and role of entropy in the institutional theory is caused by conceptualizing it as a force that counteracts institutional tendencies and acts in opposite direction. Once institutionalization and homogeneity are seen as a product of natural tendencies in the organizational field, the role of entropy becomes clear. Entropy manifests itself at the level of information processing and corresponds with increasing uncertainty and the decrease of the value of information. Institutionalization thus can be seen as a special case of an increase in entropy and a decrease of knowledge. Institutionalization is a state of maximum entropy.

It is explained why institutionalization and institutional persistence are what to be expected in the long run and why information entropy contributes to this tendency. Contrary to the tenets of the institutional work perspective, no intentional efforts of individuals and collective actors are needed to maintain institutions. In this respect, the paper contributes to the view of institutional theory as a theory of self-organization.

The main aim of this paper is to establish a reasonable and scientific evaluation index system to assess the high quality and full employment (HQaFE).

This paper uses a novel Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) multi-criteria framework to evaluate the quality and quantity of employment, wherein the integrated weights of attributes are determined by the combined the Criteria Importance Through Inter-criteria Correlation (CRITIC) and entropy approaches.

Firstly, the gap in the Yangtze River Delta in employment quality is narrowing year by year; secondly, employment skills as well as employment supply and demand are the primary indicators that determine the HQaFE; finally, the evaluation scores are clearly hierarchical, in the order of Shanghai, Jiangsu, Zhejiang and Anhui.

A scientific and reasonable evaluation index system is constructed. A novel CRITIC-entropy-TOPSIS evaluation is proposed to make the results more objective. Some policy recommendations that can promote the achievement of HQaFE are proposed.

Computer-assisted instruction (CAI) has been used to combat reading challenges, namely reading accuracy and rate for learners with intellectual, developmental and learning disabilities (IDLD). Whilst most reading CAI effectiveness has been studied in English, other transparent languages have less evidence. This study provides a systematic review and meta-analysis of CAI effectiveness for transparent language reading for K-3 learners with IDLD.

This study systematically reviews academic peer-reviewed studies from 2010 to 2023 with either randomised controlled treatment (RCT) or single-case treatments. Articles were searched from the ACM Digital Library, Google Scholar, IEEE Xplore, ERIC, PsychINFO and Science Direct databases, references and systematic review articles. Reading component skills effect sizes were computed using the random effect sizes model.

11 RCT studies of reading CAI for transparent languages with 510 learners with IDLD were found. A random effect sizes (Cohen’s d) of CAI on individual reading component skills were d = 0.24, p-value = 0.063 and confidence interval (CI) 95% (−0.068–0.551) for phonics and phonemic awareness d = 0.41, p-value = 0.000 and CI 95% (0.175–0.644). Given an average intervention dosage of 1.8 h weekly for a maximum of 16 weeks, CAI had better retention with d = 1.13, p-value = 0.066 and CI 95%(−0.339–2.588). However, these results must be interpreted with a concern of only using published studies.

The study contributes to quantitative CAI effectiveness for transparent language reading components for learners with IDLD.

In times of crisis, such as the Covid-19 global pandemic, journalists who write about biomedical information must have the strategic aim to be clearly and easily understood by everyone. In this study, we assume that journalistic discourse could benefit from language redundancy to improve clarity and simplicity aimed at science popularization. The concept of language redundancy is theoretically discussed with the support of discourse analysis and information theory. The methodology adopted is a corpus-based qualitative approach. Two corpora samples with Brazilian Portuguese (BP) texts on Covid-19 were collected. One with texts from a monthly science digital magazine called Pesquisa FAPESP aimed at students and researchers for scientific information dissemination and the other with popular language texts from a news Portal G1 (Rede Globo) aimed at unspecified and/or non-specialized readers. The materials were filtered with two descriptors: “vaccine” and “test.” Preliminary analysis of examples from these materials revealed two categories of redundancy: paraphrastic and polysemic. Paraphrastic redundancy is based on concomitant language reformulation of words, sentences, text excerpts, or even larger units. Polysemic redundancy does not easily show material evidence, but is based on cognitively predictable semantic association in socio-cultural domains. Both kinds of redundancy contribute, each in their own way, to improving text readability for science popularization in Brazil.

Project portfolio risk (PPR) management plays an important role in promoting the smooth implementation of a project portfolio (PP). Accurate PPR prediction helps managers cope with risks timely in complicated PP environments. However, studies on accurate PPR impact degree prediction, which consists of both risk occurrence probabilities and risk impact consequences considering project interactions, are limited. This study aims to model PPR prediction and expand PPR prediction tools.

In this study, the authors build a PPR prediction model based on a genetic algorithm and back-propagation neural network (GA-BPNN) integrated with entropy-trapezoidal fuzzy numbers. Then, the authors verify the proposed model with real data and obtain PPR impact degrees.

The test results indicate that the proposed method achieves an average absolute error of 0.002 and an average prediction accuracy rate of 97.8%. The former is reduced by 0.038, while the latter is improved by 32.1% when compared with the results of the original BPNN model. Finally, the authors conduct an index sensitivity analysis for identifying critical risks to effectively control them.

This study develops a hybrid PPR prediction model that integrates a GA-BPNN with entropy-trapezoidal fuzzy numbers. The authors use this model to predict PPR impact degrees, which consist of both risk occurrence probabilities and risk impact consequences considering project interactions. The results provide insights into PPR management.

This is the application of the Entropy and TOPSIS model to assess the eco-efficiency of European financial institutions using environmental, social, and governance (ESG) strategies. The aim is to categorize financial institutions based on key factors such as environmental training and management and to examine the alignment between ideal ESG performance and eco-efficiency.

The study uses environmental, social, and governance (ESG) strategies to identify and categorize eco-entrepreneurs in European financial institutions. The study utilizes data to examine the structure between environmental training, effective management practices, and the green performance of financial institutions.

The study shows that European financial institutions exhibit varying degrees of eco-efficiency as assessed using the Entropy and TOPSIS model applied to ESG strategies. Surprisingly, the study found that institutions with a high ESG performance do not always match those with the highest eco-efficiency.

They emphasize the need for financial institutions to align their operations with sustainable practices. This research provides insights to increase eco-efficiency and improve the ESG performance of financial institutions. It also informs policy and decision-making in these institutions in relation to environmental training and management practices, contributing to the wider dialogue on sustainable finance.

This indicates a discrepancy between ESG ratings and actual eco-efficiency, emphasizing the need to reassess the ESG framework. The study findings are crucial for aligning financial institutions with sustainable practices and improving the effectiveness of the ESG framework, especially for institutions at the lower end of the eco-efficiency spectrum.

This study investigates the relationship between the Chief Executive Officer's (CEO) overconfidence and financial reporting complexity in Iran, a context characterized by weak corporate governance and heightened managerial discretion.

The sample consists of 1,445 firm-year observations from 2010 to 2021. CEO overconfidence (CEOOC) is evaluated using an investment-based index, specifically capital expenditures. Financial reporting complexity (Complexity) is measured through textual features, particularly three readability measures (Fog, SMOG and ARI) extracted from annual financial statements. The ordinary least squares (OLS) regression is employed to test the research hypothesis.

Results suggest that CEOOC is positively related to Complexity, leading to reduced readability. Additionally, robustness analyses demonstrate that the relationship between CEOOC and Complexity is more distinct and significant for firms with lower profitability than those with higher profitability. This implies that overconfident CEOs in underperforming firms tend to increase complexity. Also, firms with better financial performance present a more positive tone in their annual financial statements, reflecting their superior performance. The findings remain robust to alternative measures of CEOOC and Complexity and are consistent after accounting for endogeneity issues using firm fixed-effects, propensity score matching (PSM), entropy balancing approach and instrumental variables method.

This study adds to the literature by delving into the effect of CEOs' overconfidence on financial reporting complexity, a facet not thoroughly investigated in prior studies. The paper pioneers the use of textual analysis techniques on Persian texts, marking a unique approach in financial reporting and a first for the Persian language. However, due to the inherent challenges of text mining and feature extraction, the results should be approached with caution.

The insights from this study can guide investors in understanding the potential repercussions of CEOOC on financial reporting complexity. This will assist them in making informed investment decisions and monitoring the financial reporting practices of their invested companies. Policymakers and regulators can also reference this research when formulating policies to enhance financial reporting quality and ensure capital market transparency. The innovative application of textual analysis in this study might spur further research in other languages and contexts.

This research stands as the inaugural study to explore the relationship between CEOs' overconfidence and financial reporting complexity in both developed and developing capital markets. It thereby broadens the extant literature to include diverse capital market environments.

On the one hand, this paper is to further understand the residents' differentiated power consumption behaviors and tap the residential family characteristics labels from the perspective of electricity stability. On the other hand, this paper is to address the problem of lack of causal relationship in the existing research on the association analysis of residential electricity consumption behavior and basic information data.

First, the density-based spatial clustering of applications with noise method is used to extract the typical daily load curve of residents. Second, the degree of electricity consumption stability is described from three perspectives: daily minimum load rate, daily load rate and daily load fluctuation rate, and is evaluated comprehensively using the entropy weight method. Finally, residential customer labels are constructed from sociological characteristics, residential characteristics and energy use attitudes, and the enhanced FP-growth algorithm is employed to investigate any potential links between each factor and the stability of electricity consumption.

Compared with the original FP-growth algorithm, the improved algorithm can realize the excavation of rules containing specific attribute labels, which improves the excavation efficiency. In terms of factors influencing electricity stability, characteristics such as a large number of family members, being well employed, having children in the household and newer dwelling labels may all lead to poorer electricity stability, but residents' attitudes toward energy use and dwelling type are not significantly associated with electricity stability.

This paper aims to uncover household socioeconomic traits that influence the stability of home electricity use and to shed light on the intricate connections between them. Firstly, in this article, from the perspective of electricity stability, the characteristics of the power consumption of residents' users are refined. And the authors use the entropy weight method to comprehensively evaluate the stability of electricity usage. Secondly, the labels of residential users' household characteristics are screened and organized. Finally, the improved FP-growth algorithm is used to mine the residential household characteristic labels that are strongly associated with electricity consumption stability.

1. The stability of electricity consumption is important to the stable operation of the grid.

2. An improved FP-growth algorithm is employed to explore the influencing factors.

3. The improved algorithm enables the mining of rules containing specific attribute labels.

4. Residents' attitudes toward energy use are largely unrelated to the stability of electricity use.

The stability of electricity consumption is important to the stable operation of the grid.

An improved FP-growth algorithm is employed to explore the influencing factors.

The improved algorithm enables the mining of rules containing specific attribute labels.

Residents' attitudes toward energy use are largely unrelated to the stability of electricity use.

The purpose of this study is to determine the competency characteristics required for the employment of master’s degree students in educational technology.

A combined qualitative and quantitative method was used to consult multiple experts through a modified Delphi method. Competency characteristics were extracted from Chinese recruitment apps, national recruitment websites and university training programs. Ten senior teacher experts who teach educational technology master’s students were consulted through a questionnaire consultation to validate the proposed competency model. The weights of competency characteristics were determined through a combination of the analytic hierarchy process and entropy method.

The results show that when recruiting educational technology master’s students, more emphasis is placed on operational skills. The majority of companies tend to assess practical abilities rather than theoretical knowledge. Relevant knowledge of educational technology, psychology, computer science and education is considered to be the basic knowledge components of educational technology master’s students, while professional skills are the core skills required for their positions. Therefore, universities need to focus on training, educational technology graduate students in these areas of competence. The study also found that professional qualities (such as physical and mental fitness) and personality traits (interpersonal communication and interaction) receive more attention from companies and are essential competencies for educational technology master’s students.

A competence model for educational technology master’s students is proposed, which includes aspects such as knowledge, personal skills/abilities, professional qualities and personality traits. The competence elements included in this model can serve as reference indicators for universities to cultivate the competence of educational technology master’s students, as well as reference points for recruiting units to help them select talents. This represents a new dimension in research related to the employment of educational technology master’s students. The study enriches the research objects and competence dictionary in the field of competence research.