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Family–school partnerships are an essential component of the special education process for children with disabilities. Notably, recent legislative reauthorizations of IDEA (2004) have focused on increasing parent involvement. For many parents, participation occurs primarily through the individualized education program (IEP) meetings. Parent involvement often includes parent advocating for their children. However, many parents face barriers when advocating to obtain appropriate special education services for their children with disabilities. Culturally and linguistically diverse families face greater systemic barriers (e.g., language and cultural differences) to access services for their own children with disabilities. School professionals can foster opportunities to help families be active members of the IEP process. For example, school professionals should connect families with resources to learn about their special education rights. Specifically, school personnel can encourage families to reach out to their local Parent Training and Information (PTI) Center to be educated and empowered to advocate for services. In addition, parents can be encouraged to attend parent advocacy programs to help increase knowledge, advocacy, and empowerment to access and advocate for services for their own children. Advancing the values of working with parents of students with special education needs is discussed.

This study aims to identify an automated machine learning algorithm with high accuracy that sport practitioners can use to identify the specific factors for predicting Major League Baseball (MLB) attendance. Furthermore, by predicting spectators for each league (American League and National League) and division in MLB, the authors will identify the specific factors that increase accuracy, discuss them and provide implications for marketing strategies for academics and practitioners in sport.

This study used six years of daily MLB game data (2014–2019). All data were collected as predictors, such as game performance, weather and unemployment rate. Also, the attendance rate was obtained as an observation variable. The Random Forest, Lasso regression models and XGBoost were used to build the prediction model, and the analysis was conducted using Python 3.7.

The RMSE value was 0.14, and the R2 was 0.62 as a consequence of fine-tuning the tuning parameters of the XGBoost model, which had the best performance in forecasting the attendance rate. The most influential variables in the model are “Rank” of 0.247 and “Day of the week”, “Home team” and “Day/Night game” were shown as influential variables in order. The result was shown that the “Unemployment rate”, as a macroeconomic factor, has a value of 0.06 and weather factors were a total value of 0.147.

This research highlights unemployment rate as a determinant affecting MLB game attendance rates. Beyond contextual elements such as climate, the findings of this study underscore the significance of economic factors, particularly unemployment rates, necessitating further investigation into these factors to gain a more comprehensive understanding of game attendance.

The role of chief executive officer (CEO) personal characteristics in shaping corporate policies has attracted increasing academic attention in the past two decades. In this review, the authors synthesize extant research on CEO attributes by reviewing 232 articles published in 29 journals from the accounting, finance and management literature. This review provides an overview of existing findings, highlights current trends and interdisciplinary differences in research approaches and identifies potential avenues for future research.

To review the literature on CEO attributes, the authors manually collected peer-reviewed articles in accounting, finance and management journals from 2000 to 2021. The authors conducted in-depth analysis of each paper and manually recorded the theories, data sources, country of study, study period, measures of CEO attributes and dependent variables. This procedure helped the authors group the selected articles into themes and sub-themes. The authors compared the findings in various disciplines and provided direction for future research.

The authors highlight the role of CEO personal attributes in influencing corporate decision-making and firm outcomes. The authors categorize studies of CEO traits into three main research themes: (1) demographic attributes and experience (including age, gender, culture, experience, education); (2) CEO interactions with others (social and political networks) and (3) underlying attributes (including personality, values and ideology). The evidence shows that CEO characteristics significantly affect a wide range of specific corporate policies that serve as mechanisms through which individual CEOs determine firm success and performance.

CEO selection is one of the most crucial decisions made by corporations. The study findings provide valuable insights to corporate executives, boards, investors and practitioners into how CEOs’ personal characteristics can impact future firm decisions and outcomes that can, in turn, inform the high-stake process of CEO recruitment and selection. The study findings have significant practical implications for corporations, such as contributing to executive training programs, to assist executives and directors attain a greater level of self-awareness.

Building on the theoretical foundation of upper echelons theory, the authors offer an integrated theoretical framework to consolidate existing empirical research on the impacts of CEO personal attributes on firm outcomes across accounting and finance (A&F) and management literature. The study findings provide a roadmap for scholars to bridge the interdisciplinary divide between A&F and management research. The authors advocate a more holistic and multifaceted approach to examining CEOs, each of whom embodies a myriad of personal characteristics that comprise their unique identity. The study findings encourage future researchers to expand the investigation of the boundary conditions that magnify or moderate the impacts of CEO idiosyncrasies.

The purpose of this study is to examine the effect of manager attributes and psychological factors on the adoption of sustainability reporting (SR) among small and medium-sized enterprises (SMEs) in Ghana.

The study is based on a cross-sectional data gathered using questionnaires administered to managers of SMEs in Ghana. The data is analyzed using structural equation modeling.

The results reveal that SME managers with requisite educational qualifications and knowledge about sustainability accounting adopt SR. The attitudes, subjective norms and perceived behavioral control of managers of SMEs on issues of sustainability also affect the adoption of SR. However, SMEs with old and long-serving managers do not adopt SR. SMEs with manager attributes such as professional education, gender and religious affiliation do not appear to adopt SR.

There is the need for regulators and other stakeholders to sensitize, persuade and provide awareness, training and educational certification to support managers of SMEs to enable them to adopt SR.

This study contributes to the literature on SR by offering a clear understanding of how manager attributes and psychological factors influence the adoption of SR by SMEs in developing countries.

This study aims to examine the impact of chief executive officers’ (CEOs’) personal characteristics on firms’ risk taking and the moderating role of family ownership on this relationship.

This study used 2,647 firm-year observations of non-financial firms listed on Pakistan Stock Exchange over the period 2013–2021. To test the hypotheses, the authors used ordinary least squares regression and, to resolve the possible endogeneity problem, the authors used system generalized method of moments technique.

Drawing insights first from upper echelons theory, the authors report that CEOs with business, economics, finance and/or management educational background and female CEOs reduce firms’ risk-taking behaviour. Further, using insights from social and organizational identity theoretical perspectives, the results indicate that due to strong family affiliation and organizational identity, family owners exhibit risk aversion behaviour and moderate this relationship.

This study provides novel evidence of risk averse behaviour of CEOs with business, economics, finance and/or management educational background and female CEOs along with moderating impact of family ownership on this relationship in an emerging economy. Overall, the results extend empirical support for upper echelons and social identity theories in an emerging market context.

Although businesses continue to take up artificial intelligence (AI), concerns remain that companies are not realising the full value of their investments. The study aims to provide insights into how AI creates business value by investigating the mediating role of Business Process Management (BPM) capabilities.

The integrative model of IT Business Value was contextualised, and structural equation modelling was applied to validate the proposed serial multiple mediation model using a sample of 448 organisations based in the EU.

The results validate the proposed serial multiple mediation model according to which AI adoption increases organisational performance through decision-making and business process performance. Process automation, organisational learning and process innovation are significant complementary partial mediators, thereby shedding light on how AI creates business value.

In pursuing a complex nomological framework, multiple perspectives on realising business value from AI investments were incorporated. Several moderators presenting complementary organisational resources (e.g. culture, digital maturity, BPM maturity) could be included to identify behaviour in more complex relationships. The ethical and moral issues surrounding AI and its use could also be examined.

The provided insights can help guide organisations towards the most promising AI activities of process automation with AI-enabled decision-making, organisational learning and process innovation to yield business value.

While previous research assumed a moderated relationship, this study extends the growing literature on AI business value by empirically investigating a comprehensive nomological network that links AI adoption to organisational performance in a BPM setting.

Integrated reporting (IR) is the latest development in corporate reporting. It is a tool capable of better representing the ability of companies to create value over time. The purpose of this paper is to examine the relationship between the CEO’s characteristics (age, gender, education and experience) and firm performance through a mediating variable, namely, IR.

This study is a quantitative research and used panel data. Based on a sample of 449 UK firms or using a sample of 449 UK companies between 2010 and 2020 on STATA17 and structural equation model was used to analyze data and test hypotheses.

The results show that IR has only indirect mediation on the relationship between CEO’s characteristics and firm performance but mediates the relationship between CEO experience and performance in a complementary manner.

This article is motivated by the low number of works in the context about the corporate social responsibility and sustainability issues. It makes an important contribution to the academic literature by adding to the limited body of research on CEO’s characteristics, IR and firm performance. This study focuses primarily on the importance of integrated reporting in UK.

Although scholars in strategic management have identified innovating and exit as firms’ two sequential strategic responses to long-run crisis, the potential interdependency has yet remained implicit. Specifically, in the context of Chinese Privately Owned Enterprises (POEs), this study investigates the interrelationship of these two strategic responses during long-run crisis. Building on resource redeployment perspective, the authors propose that firms tend to simultaneously leverage innovating and exit responses.

The authors use the data from the 2010 Chinese POEs survey to verify how firms in the long-term crisis made strategic responses after the 2008 financial crisis. Besides, the authors utilize Probit regressions as the basic analysis and further employ bivariate Probit regressions to conduct robustness tests.

This study provides empirical evidence confirming that firms in the long-run period of the crisis tend to adopt both exit and innovating strategies at the same time, that is, the strategy of resource redeployment. Moreover, this study further finds that government subsidies, the degree of marketization and firm’s organizational capability could all accentuate the decision-making of firms’ resource redeployment.

The authors thus contribute to the study of strategic responses to crisis in strategic management by dynamically find out the interdependency of two responses and enrich the research on resource redeployment perspective by identifying three influential positive antecedents, adding to the ongoing investigation on positive drivers of resource redeployment.

Entrepreneurship is one of the significant drivers of economic growth, development and job generation in several countries worldwide. Realizing its significant contribution to the nation’s development, policymakers and educators have also drawn attention to fostering entrepreneurship among the youth. Researchers attempted to comprehend the dynamics and investigate the factors influencing entrepreneurial intention (EI). As is true for other abilities and response tendencies, individual differences exist for EI also. This study aims to explore the relationship of emotional intelligence (EIn) and cognitive flexibility (CF) with EI and mediating effect of entrepreneurial self-efficacy (ESE) in the relationship between CF, EIn and EI.

The cross-sectional survey was conducted to gather responses from 635 individuals aged 17–26 years (M = 19.2, SD = 1.49). The hypotheses were tested using correlation, regression and mediation analysis.

The findings indicated that EIn and CF were significantly and positively related to EI. Furthermore, ESE was found to be a partial mediator between EIn and EI and a full mediator between CF and EI.

Results reflected the critical significance of ESE and implied that EI might be strengthened by intervening in ESE through various sources.

This study adds to the existing literature by incorporating less studied individual factors (EIn and CF) to better understand EI by explaining the mediation mechanism through ESE.

No study has investigated the effects of different parameters on publication bias in meta-analyses using a machine learning approach. Therefore, this study aims to evaluate the impact of various factors on publication bias in meta-analyses.

An electronic questionnaire was created according to some factors extracted from the Cochrane Handbook and AMSTAR-2 tool to identify factors affecting publication bias. Twelve experts were consulted to determine their opinion on the importance of each factor. Each component was evaluated based on its content validity ratio (CVR). In total, 616 meta-analyses comprising 1893 outcomes from PubMed that assessed the presence of publication bias in their reported outcomes were randomly selected to extract their data. The multilayer perceptron (MLP) technique was used in IBM SPSS Modeler 18.0 to construct a prediction model. 70, 15 and 15% of the data were used for the model's training, testing and validation partitions.

There was a publication bias in 968 (51.14%) outcomes. The established model had an accuracy rate of 86.1%, and all pre-selected nine variables were included in the model. The results showed that the number of databases searched was the most important predictive variable (0.26), followed by the number of searches in the grey literature (0.24), search in Medline (0.17) and advanced search with numerous operators (0.13).

The results of this study can help clinical researchers minimize publication bias in their studies, leading to improved evidence-based medicine.

To the best of the author’s knowledge, this is the first study to model publication bias using machine learning.