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This paper presents a case study of a school district–university partnership to co-design a Children’s Cabinet, a cross-sector initiative bringing together institutional and community leaders to address youth well-being in the aftermath of the COVID-19 pandemic. In a vibrant, immigrant-serving community in the Northeast United States, the partnership was initiated by district leaders in Spring 2021 after pandemic disruptions led to a youth mental health crisis.

Our descriptive, qualitative case study focuses on the structure and emerging design principles of the research–practice partnership, which established a Children’s Cabinet comprised of educational, government and community leaders along with researchers. From Spring 2021 through Spring 2024, we collected and analyzed member and youth interviews, ethnographic observations and artifacts from all meetings and process interviews with key partners.

We describe the structure of the partnership, including how researchers and district leaders collaborated on meeting facilitation and how researchers conducted and shared applied research. We then discuss three design principles that guided the work, including centering relationships, sustaining focus on key goals and embedding applied research.

As embedded research partners, our team is uniquely situated to narrate the nature and structure of the partnership and reflect on the design of our cross-sector initiative. Increasingly, universities are partnering directly with districts on school improvement initiatives. Our work shows how engaging in RPPs to bring together school, community and research partners can facilitate local leadership and collaboration to address complex, cross-sector goals such as increasing youth well-being.

This chapter discusses the role of school leadership in the face of climate disasters and environmental injustices. These disruptions to schooling are emblematic of an increasing global uncertainty. School leaders play a pivotal role mitigating uncertainty following an environmental crisis or disaster through leadership activities that support their communities. However, preparing school leaders for unexpected disruptions to schooling has often been overlooked by preparation programs and professional development. The goal of this chapter is to equip school leaders with an essential understanding of both the influence of environmental injustice on schools and the tools to respond effectively to these events. First, the chapter contextualizes environmental injustice and inequality as a factor that influences school and student performance, especially for students living below the poverty line and students of color. Next, it synthesizes how school leaders have responded to prior instances of climate disasters and environmental injustices. Finally, it presents key considerations for school leaders confronting future occurrences.

In periods of economic distress, expectations for businesses change and there is a heightened need for reporting quality. This study investigates the impact of crises on earnings management in the real estate sector.

The data consisted of financial statements from 2005 to 2021 from real estate firms listed on 10 European stock exchanges. Estimated discretionary accruals from four standard accruals models were used as a proxy for earnings management, using cross-sectional industry and firm fixed effects models. The authors examined earnings management during three crises: the financial crisis (2008–2009), the debt crisis (2011–2012) and the COVID-19 pandemic (2020–2021).

The results showed less earnings management during the COVID-19 crisis and more earnings management during the financial crisis, though with slightly weaker evidence. The authors did not find significant evidence of earnings management related to the debt crisis. These results suggest that stakeholders in the real estate sector should be extra vigilant in crisis periods.

This study is the first to investigate earnings management in European real estate firms, focusing on the impact of crises.

The COVID-19 pandemic dramatically altered daily life for Gen Z. The purpose of this study was to examine parental perceptions of the pandemic’s effects on their children’s peer relationships. As children sought peer connection during the pandemic, technology usage soared. The second purpose of the current study was to assess how greater time on social media affected adjustment among Gen Z and whether this effect was mediated by experiences of cyber victimization.

In total, 250 U.S. parent-child dyads participated in the study. Parents reported on their children’s social media use and described how they believed the pandemic affected their children’s peer relationships. Child participants were transitioning to middle school and reported on cyber victimization and adjustment.

Thematic analysis of parental reflections revealed three themes: children spent more time online since the onset of the pandemic, there were negative implications of increased time online and there were positive and protective implications of being online. Analysis also indicated significant indirect effects of social media use on internalizing and externalizing problems through victimization.

Parents reported Gen Z continues to use electronic forms of communication and social media at high rates even after pandemic-related restrictions eased with some suggesting that their children prefer digital over face-to-face communication because they have become accustomed to this way of connecting and may find it easier than in-person interaction. Current findings highlight concerns about this increased time online as social media use negatively affected adjustment via cyber victimization.

The research paper examines the impact of perceived social media marketing activities (SMMAs) (interaction, entertainment, customization, trendiness and word of mouth (WOM)) on customer loyalty (CL) toward e-commerce providers. The study also explores the mediating role of customer relationship quality (CRQ) (commitment, trust and satisfaction) on the relationship between perceived SMMAs and CL.

The study is based on the S-O-R model, which states that characteristics of the environment (stimulus) arouse a cognitive state (organism) that results in positive or negative behavior (response). The present study proposes the characteristics of the e-commerce environment as stimuli (S), the inner state of customers as an organism (O) and consumer behavior as the response (R). This study investigated the responses of 487 social media users through structural equation modeling (SEM).

The results offer three crucial findings. First, the study validated that perceived SMMA comprises five dimensions (interaction, entertainment, customization, trendiness and WOM) in the Indian e-commerce context. Second, perceived SMMA significantly influences CRQ (commitment, trust and satisfaction). Third, CRQ significantly mediates the relationship between perceived SMMA and CL.

The study attempts to understand the effect of perceived SMMA on CL via CRQ in an e-commerce context, especially in an emerging economy like India. The present study argues that the SMMA of e-commerce is likely to be reflected in CL when the consumers experience CRQ through commitment, trust and satisfaction. Thus exploring the mediating role of CRQ is the authors' contribution.

Considering two types of subsidies for producers (supplier and manufacturer) and one for consumers based on product greenness and sales quantity, this study aims to formulate optimal supply chain green innovation and subsidy strategies, and to achieve this goal with the support of information systems.

This study introduces a composite green-product supply chain where suppliers focus on green innovation for component greenness and manufacturers focus on green innovation for manufacturing process greenness. Game theory modeling is applied to investigate the differences of product greenness, supply chain members’ profit and social welfare under different government subsidy strategies.

Increasing the unit greenness subsidy coefficient can boost product greenness and supply chain members’ profits, but does not always raise social welfare. When the government exclusively offers subsidies to producers, subsidies should be allocated to suppliers when there is a significant disparity in supply chain green innovation costs. Conversely, it is more beneficial to subsidize manufacturers. Consumer subsidies have the potential to enhance both environmental and economic performance in the supply chain compared with producer-exclusive subsidies, but may not always maximize social welfare when supply chain members have low unit costs associated with green innovation.

This study examines the optimal decisions for green supply chain innovation and government subsidy strategies. Supply chain members and the government can use the information system to collect and evaluate the cost of upstream and downstream green innovation, and then develop reasonable collaborative green innovation and subsidy strategies.

The primary objective of this research is to conceptualize the integration of enterprise risk management (ERM) as a mechanism to enhance the connection between corporate sustainability (CS) reporting and financial performance. This study suggests that future researchers should validate the proposed conceptualization by conducting a comprehensive content analysis of sustainability reports of Malaysian oil and gas companies. This analysis will allow for the collection of pertinent data regarding CS reporting and ERM implementation. The present study takes a comprehensive approach by integrating legitimacy, stakeholder, and resource-based view (RBV) theories, proposing a robust conceptual design that emphasizes the role of ERM in the connection between CS reporting and firm performance. Drawing on theoretical foundations, this study proposes that CS reporting will have a direct effect on financial performance. Moreover, the integration of ERM serves to strengthen the nexus between CS reporting and financial performance. This study offers valuable insights for stakeholders in the oil and gas sector by providing strategic guidance to enhance financial performance not only through CS reporting but also by implementing ERM. Moreover, the framework proposed in this study is expected to bring tangible and intangible benefits to corporations, including reducing information asymmetry, improving the quality of disclosure, and creating value within the field of CS. The proposed conceptual framework holds great significance as it enhances the applicability of legitimacy, stakeholder, and RBV theories, while also creating value for stakeholders through CS reporting and the adoption of risk management practices to enhance financial performance.

Online portfolio selection sequentially allocates wealth among a set of assets and aims to maximize the investor’s cumulative return in the long run. Various existing algorithms in the finance and accounting area adopt an indirect approach to exploit one asset characteristic through the channel of assets’ expected return and thus cannot fully leverage the power of various asset characteristics found in the literature. This study aims to propose new algorithms to overcome this issue to enhance investment performance.

We propose a parameterized portfolio selection (PPS) framework, which directly incorporates multiple asset characteristics into portfolio weights. This framework can update parameters timely based on final performance without intermediate steps and produce efficient portfolios. We further append L1 regularization to constrain the number of active asset characteristics. Solving the PPS formulation numerically, we design two online portfolio selection (OLPS) algorithms via gradient descent and alternating direction method of multipliers.

Empirical results on five real market datasets show that the proposed algorithms outperform the state of the arts in cumulative returns, Sharpe ratios, winning ratios, etc. Besides, short-term characteristics are more important than long-term characteristics, and the highest return category is the most important characteristic to improve portfolio performance.

The proposed PPS algorithms are new end-to-end online learning approaches, which directly optimize portfolios by asset characteristics. Such approaches thus differ from existing studies, which first predict returns and then optimize portfolios. This paper provides a new algorithmic framework for investors’ OLPS.

The standard method to estimate a stochastic frontier (SF) model is the maximum likelihood (ML) approach with the distribution assumptions of a symmetric two-sided stochastic error v and a one-sided inefficiency random component u. When v or u has a nonstandard distribution, such as v follows a generalized t distribution or u has a χ2 distribution, the likelihood function can be complicated or untractable. This chapter introduces using indirect inference to estimate the SF models, where only least squares estimation is used. There is no need to derive the density or likelihood function, thus it is easier to handle a model with complicated distributions in practice. The author examines the finite sample performance of the proposed estimator and also compare it with the standard ML estimator as well as the maximum simulated likelihood (MSL) estimator using Monte Carlo simulations. The author found that the indirect inference estimator performs quite well in finite samples.

Living walls (LWs), vegetated walls with an integrated growth layer behind, are being increasingly incorporated in buildings. Examining plant characteristics’ comparative impacts on LWs’ energy efficiency-related thermal behavior was aimed, considering that studies on their relative effects are limited. LWs of varying leaf albedo, leaf transmittance and leaf area index (LAI) were studied for Antalya, Turkey for typical days of four seasons.

Dynamic simulations run by Envi-met were used to assess the plant characteristics’ influence on seasonal and orientation-based heat fluxes. After model calibration, a sensitivity analysis was conducted through 112 simulations. The minimum, mean and maximum values were investigated for each plant characteristic. Energy need (regardless of orientation), temperature and heat flux results were compared among different scenarios, including a building without LW, to evaluate energy efficiency and variables’ impacts.

LWs reduced annual energy consumption in Antalya, despite increasing energy needs in winter. South and west facades were particularly advantageous for energy efficiency. The impacts of leaf albedo and transmittance were more significant (44–46%) than LAI (10%) in determining LWs’ effectiveness. The changes in plant characteristics changed the energy needs up to ca 1%.

This study can potentially contribute to generating guiding principles for architects considering LW use in their designs in hot-humid climates.

The plant characteristics’ relative impacts on energy efficiency, which cannot be easily determined by experimental studies, were examined using parametric simulation results regarding three plant characteristics.