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The Elementary and Secondary School Emergency Relief (ESSER) requires districts to deliver “evidence-based interventions” to students impacted by the pandemic. The policy has created a unique opportunity for researchers and practitioners to engage with evidence to learn how recovery interventions work and under what conditions.

This study is part of a research-practice partnership (RPP) between Guilford County Schools, AIR-CALDER, Harvard University and NWEA to understand the impacts and implementation of ESSER-funded recovery programs. We use a case analysis approach and frameworks of evidence-use and RPPs to explain how researchers and Guilford leaders engage with evidence to improve and evaluate programs.

The RPP used evidence to inform Guilford leaders’ recovery approaches and strengthened researchers’ evaluations of programs. Conditions that enabled evidence engagement included the RPP’s goals, research activities and collaborative conditions such as boundary spanning activities, team meetings, relationships and trust. We also observed factors that hindered evidence engagement, including the RPP’s nascent stage, structure and breadth of goals, rapid policy timelines and other organizational conditions in Guilford.

Given the complexities of pandemic recovery, RPPs can help researchers evaluate programs in their local context, and present evidence in ways that are actionable to guide decision-making. District leaders can play a valuable role in co-designing research studies attuned to local priorities and context and facilitating research participation among internal stakeholders. However, newly formed RPPs with broad goals for impact will need more time and resources to build an improvement infrastructure for sustaining pandemic recovery.

The purpose of this paper is to examine the technical, social and moral aspects of accounting through the implementation of a novel balanced scorecard (BSC) that addresses the United Nations Sustainable Development Goal (UN SDG) 6 – Clean Water and Sanitation – within the Portuguese water utilities sector.

A novel research design is adopted, using actor network theory (ANT) as a broad approach to frame the study. ANT emphasizes the importance of ever-evolving networks of relationships and how concepts such as the BSC are just as important in structuring social practice. A set of expert interviews was conducted with stakeholders in the water utilities sector in Portugal, which led to the iterative development of a context-relevant BSC proposal and associated indicators.

A novel BSC architecture to achieve UN SDG 6 is proposed through a unique engagement between professionals and academics. The BSC, and the specific definition of indicators for an entire sector (water), contribute to bridging business processes with the common good to improve life and planetary conditions. Ultimately, the study discusses how the technical aspects of accounting can be enhanced to achieve social and moral imperatives. The paper also reflects on the limitations of broadening existing technical practices.

There is a burgeoning literature on how organizations are engaging with the UN SDG agenda. However, there is a dearth of studies on how management control systems are currently addressing, or can potentially contribute to measuring and managing specific UN SDGs such as Clean Water and Sanitation. This study makes a unique contribution to the literature by developing a novel BSC solution to SDG 6 measurement and management using a novel practitioner-led approach. Ultimately, our study highlights how accounting can be broadened to enhance technical practices while also serving a moral and social purpose.

Process mining (PM) has emerged as a leading technology for gaining data-based insights into organizations’ business processes. As processes increasingly cross-organizational boundaries, firms need to conduct PM jointly with multiple organizations to optimize their operations. However, current knowledge on cross-organizational process mining (coPM) is widely dispersed. Therefore, we synthesize current knowledge on coPM, identify challenges and enablers of coPM, and build a socio-technical framework and agenda for future research.

We conducted a literature review of 66 articles and summarized the findings according to the framework for Information Technology (IT)-enabled inter-organizational coordination (IOC) and the refined PM framework. The former states that within inter-organizational relationships, uncertainty sources determine information processing needs and coordination mechanisms determine information processing capabilities, while the fit between needs and capabilities determines the relationships’ performance. The latter distinguishes three categories of PM activities: cartography, auditing and navigation.

Past literature focused on coPM techniques, for example, algorithms for ensuring privacy and PM for cartography. Future research should focus on socio-technical aspects and follow four steps: First, determine uncertainty sources within coPM. Second, design, develop and evaluate coordination mechanisms. Third, investigate how the mechanisms assist with handling uncertainty. Fourth, analyze the impact on coPM performance. In addition, we present 18 challenges (e.g. integrating distributed data) and 9 enablers (e.g. aligning different strategies) for coPM application.

This is the first article to systematically investigate the status quo of coPM research and lay out a socio-technical research agenda building upon the well-established framework for IT-enabled IOC.

This work conducts a comprehensive analysis of how to incorporate resilience and sustainability into capacity expansion strategies for business-to-business (B2B) chemical supply chains. This study aims to guide both researchers and managers on ensuring profitability in B2B chemical supply chains while minimizing environmental impacts, complying with regulations and mitigating disruptions and risks.

A systematic literature review is conducted to analyze the interplay between sustainability and resilience in chemical B2B supply chains, specify the quantitative and qualitative methods used to tackle this challenge and identify the drivers and barriers concerning capacity expansion. In addition, a comprehensive conceptual framework is suggested to outline a compelling research agenda.

The findings emphasize the increasing importance of modeling and resolving decision-making challenges related to sustainable and resilient supply chains, particularly in capital-intensive chemical industries. Yet, there is no standardized strategy for addressing these challenges. The predominant solution methods are heuristic and metaheuristic, and the selection of performance metrics tends to be empirical and tailored to specific cases. The main barriers to achieving sustainability and resilience arise from resource limitations within the supply chain. Conversely, the key drivers of performance focus on enhancing efficiency, competitiveness, cost effectiveness and risk management.

This work offers practitioners a conceptual framework that synthesizes the knowledge and tackles the challenges of designing sustainable and resilient supply chains as well as managing their operations in the context of B2B chemical supply chains. Results provide a practical guide for navigating the complex interplay of sustainability, resilience and chemical supply chain expansion.

The key concepts and dimensions associated with capacity expansion planning for a resilient and sustainable chemical supply chain are identified through structured and comprehensive analyses of existing literature. A conceptual framework is proposed for delineating the intersections among sustainability, resilience and chemical supply chain expansions. This mapping endeavor aims to facilitate a future characterized by the deployment of a nexus of resilience and sustainability in chemical supply chains. To this end, a promising future research agenda is accordingly outlined.

This work aims to integrate the concepts generated by a systematic literature review on patient flows in emergency departments (ED) to serve as a basis for developing a generic process model for ED.

A systematic literature review was conducted using PRISMA guidelines, considering Lean Healthcare interventions describing ED patients’ flows. The initial search found 141 articles and 18 were included in the systematic analysis. The literature analysis served as the basis for developing a generic process model for ED.

ED processes have been represented using different notations, such as value stream mapping and workflows. The main alternatives for starting events are arrival by ambulance or walk-in. The Manchester Triage Scale (MTS) was the most common protocol referred to in the literature. The most common end events are admission to a hospital, transfer to other facilities or admission to an ambulatory care system. The literature analysis allowed the development of a generic process model for emergency departments. Nevertheless, considering that several factors influence the process of an emergency department, such as pathologies, infrastructure, available teams and local regulations, modelling alternatives and challenges in each step of the process should be analysed according to the local context.

A generic business process model was developed using BPMN that can be used by practitioners and researchers to reduce the effort in the initial stages of design or improvement projects. Moreover, it’s a first step toward the development of generalizable and replicable solutions for emergency departments.

Text classification is a widely accepted and adopted technique in organizations to mine and analyze unstructured and semi-structured data. With advancement of technological computing, deep learning has become more popular among academicians and professionals to perform mining and analytical operations. In this work, the authors study the research carried out in field of text classification using deep learning techniques to identify gaps and opportunities for doing research.

The authors adopted bibliometric-based approach in conjunction with visualization techniques to uncover new insights and findings. The authors collected data of two decades from Scopus global database to perform this study. The authors discuss business applications of deep learning techniques for text classification.

The study provides overview of various publication sources in field of text classification and deep learning together. The study also presents list of prominent authors and their countries working in this field. The authors also presented list of most cited articles based on citations and country of research. Various visualization techniques such as word cloud, network diagram and thematic map were used to identify collaboration network.

The study performed in this paper helped to understand research gaps that is original contribution to body of literature. To best of the authors' knowledge, in-depth study in the field of text classification and deep learning has not been performed in detail. The study provides high value to scholars and professionals by providing them opportunities of research in this area.

The purpose of this study is to provide a detailed demonstration of how artificial intelligence (AI) can be used to potentially generate valuable insights and recommendations regarding the role of accounting in addressing key sustainability-related issues.

The study offers a novel method for leveraging AI tools to augment traditional scoping study techniques. The method was used to show how the authors can produce recommendations for potentially enhancing organisational accountability pertaining to seasonal workers.

Through the use of AI and informed by the knowledge base that the authors created, the authors have developed prescriptions that have the potential to advance the interests of seasonal workers. In doing so, the authors have focussed on developing a useful and detailed guide to assist their colleagues to apply AI to various research questions.

This study demonstrates the ability of AI to assist researchers in efficiently finding solutions to social problems. By augmenting traditional scoping study techniques with AI tools, the authors present a framework to assist future research in such areas as accounting and accountability.

Artificial intelligence (AI) can enhance human resource resiliency (HRR) by providing the insights and resources needed to adapt to unexpected changes and disruptions. Therefore, the present research attempts to develop a framework for future researchers to gain insights into the actions of AI to enable HRR.

The present study used a systematic literature review, bibliometric analysis, and network analysis followed by content analysis. In doing so, we reviewed the literature to explore the present state of research in AI and HRR. A total of 98 articles were included, extracted from the Scopus database in the selected field of research.

The authors found that AI or AI-associated techniques help deliver various HRR-oriented outcomes, such as enhancing employee competency, performance management and risk management; enhancing leadership competencies and employee well-being measures; and developing effective compensation and reward management.

The present research has certain implications, such as increasing the HR team's proficiency, addressing the problem of job loss and how to fix it, improving working conditions and improving decision-making in HR.

The present research explores the role of AI in HRR following the COVID-19 pandemic, which has not been explored extensively.

This research paper aims to address the scarcity of social sustainability (SS) in construction by developing a Social Sustainability Assessment Tool (SocSAT), further demonstrating the tool's applicability in measuring SS practices across the construction project life cycle.

Various research methods were used to establish the assessment criteria, including literature review, focused group discussions and structured interviews. Subsequently, the SocSAT was developed using proprietary spreadsheet software, which was further demonstrated its applicability and validity using ten project case studies.

The SocSAT was built on 19 indicators for pre-construction, 25 indicators for construction, and 4 indicators for post-construction phases. The SocSAT offers a comprehensive means for practitioners to assess and monitor SS practices across project phases. The findings indicate that the SocSAT is a straightforward and user-friendly assessment tool that enables practitioners to self-diagnose and better comprehend the present SS practices across the project life cycle.

Limitations include the context-specific nature of validating the SocSAT.

SocSAT enables construction practitioners to monitor and improve their SS practices effectively, facilitating informed decision-making and promoting sustainable construction. The tool is a valuable guide for stakeholders to incorporate SS considerations, ensuring transparent communication and project success.

This research fills a gap in SS research within the construction industry. The SocSAT offers theoretical advancements with practical value, providing a standardized approach to objectively measure the SS dimension across construction project life cycles.