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Contemporary construction techniques provide benefits of speed and cost savings on a large scale, and is viable in urban regions with exorbitant housing demand. In rural areas, where scale and access to technology are unavailable, locally prevalent vernacular architecture and methods are more suitable. Although vernacular construction techniques have historically proven more sustainable and climate-sensitive, the lack of skilled labour and lack of versatility in material selection limits its application on large-scale projects. This study explores the choice of building design and technology, from the context of embodied energy, carbon and other life cycle impacts for housing construction.

Life cycle assessment (LCA) that evaluates impacts due to the products/processes is used to analyse different construction techniques. Further a detailed estimation of embodied carbon and embodied energy is done for both “vernacular” and “contemporary” choices of construction methodology for a case study project.

The building constructed using vernacular techniques has lower embodied carbon and energy by over 30% compared to the other clusters designed using contemporary confined masonry techniques. However, with a few external interventions the contemporary methods can be implemented with improved sustainability.

The limitation of the study is that it presents a case study-based exploration into comparing construction techniques to provide a practical understanding of making sustainable design choices and, hence, is limited to two construction methods. However, the same method could be extended to compare other construction techniques. Furthermore, it does not present a whole building LCA since the operating phase impacts are assumed to be fairly constant for such housing type, irrespective of the chosen method. Similarly, the demolition phase or the potential of reuse of the waste generated, water consumption and cultural and social heritage are not investigated in comparing the alternatives. Nevertheless, future studies could perform extensive exploratory and modelling studies on the operation phase and demolition phase to understand these impacts further.

In mass housing projects that belong to the so-called “affordable housing” or low-income housing category, sustainability concerns are not yet at the forefront of the decision-making process. Therefore, this study emphasizes the importance of incorporating sustainability into building design and construction and making sustainability accessible to even low-income communities. Adequate planning, social awareness initiatives and imparting skills and knowledge of sustainability to these communities are of utmost importance. The choice of design and materials should be encouraged by keeping in mind lower upfront costs as well as low maintenance and operational costs.

The primary implications of the study are that the vernacular technologies are much superior in terms of sustainability in comparison to conventional construction of RCC framed structures as well as contemporary construction methods such as confined masonry. However, the implementation of such techniques presents significant challenges such as a lack of skilled forces, increased maintenance and lack of flexibility to minor modifications. Hence, although being a sustainable choice its acceptance and execution present practical difficulties. Therefore, this study primarily aims to reinforce the belief in vernacular architecture and techniques to build sustainable and resilient communities while highlighting the challenges of the modern world in implementing them.

Most studies advocate using construction methods based on their ease of implementation, maintenance or cost. However, this study highlights the importance of considering the aspect of sustainability in the context of the choice of methods for housing construction in urban and semi-urban areas. This study also addresses the need not to overlook vernacular construction technologies while selecting technology for housing for low-income communities.

System dynamics has several applications in the built environment, and few studies indicate that it has potential in evaluating sustainability. Sustainability in the built environment involves numerous entities and multiple trade-offs. Hence, a Multi-Criteria Decision-Making (MCDM) method is ideal for promoting sustainability-based decision-making in the built environment. Therefore, this study integrates system dynamics with an MCDM method to enable the sustainability assessment by capturing the time-induced dynamic changes affecting long time sustainability performance of buildings.

Conventional sustainability assessment tools in the built environment lack a comprehensive evaluation that balances the needs of the society, economy and environment. This study develops a system dynamics-based framework to enable sustainability-conscious decision-making and policy analysis in the built environment.

Various material, technology and water-related policies specific to the buildings are investigated for a case study building. It is found that the effect of penetration of renewable energy technology to the tune of 80% and above in the energy mix is a much superior policy in sustainability improvement in comparison to material and water-related policies. The study also demonstrates the effect of weights assigned for the different indicators on sustainability-based decisions.

The study provides a methodological framework for a sustainability-based decision support system for the built environment that enables dynamic performance evaluation by coupling system dynamics with the MCDM. This coupling further strengthens system dynamics as a decision-making and policy analysis for sustainability evaluation in the built environment.

Sexual harassment and discrimination are continuing and chronic workplace problems (Quick & McFadyen, 2017) that affect the health, well-being and socio-economic future of victim/survivors (Blau & Winkler, 2018). Despite this, management and leadership education have been primarily addressing this workplace issue from a legal responsibility perspective and using preventative strategies such as promoting the value of equity, diversity, inclusion and belongingness and explaining the importance of safe, healthy and respectful workplaces. While the establishment of policies, human rights training and disciplinary procedures are undeniably important, rarely do business educators prepare future managers to engage with employees in trauma-informed, compassionate and respectful ways. The co-authors have used a collective restorying process to engage in co-designing a workshop for early career managers and students of management and leadership. The workshop includes iterative exploration of the language and authentic performativity of unbiased compassion while engaging in collective reflexivity. The basis of the workshop centres the research proposition that to support a claimant the manager must performatively lead with authentic compassion while using unbiased language in order to assure procedural justice while mitigating procedural trauma. Early career managers, and hence their organizations, are ill-equipped to deal with workplace investigations of sexual harassment and discrimination. By collectively exploring and practicing unbiased compassion, managers will not only be more prepared to respond to a claim of sexual harassment or discrimination, but they will also reduce employee's felt sense of procedural trauma and increase the organization's likelihood of due diligence.

The implementation of new technology in the workplace has produced some negative responses from employers. Two field surveys investigating the prevalence of repetitive VDU work in the UK are discussed and responses from four categories of workers – data entry operators, administrators, programmers and supervisors – are analysed. Critical psychological variables show that in all cases data entry operators gave more extreme (worse) responses than any other group. It is concluded that there appears to be widespread evidence of more routine menial tasks having been created as a result of the introduction of new technology. Guidelines regarding the use of VDUs, especially over protracted periods, are advocated, with the introduction of timely rest breaks.

The number of English learners (ELs) has increased dramatically in the public schools across the nation. In 2000, 3.8 m students, or approximately 8% of the US public school student population, were identified as ELs. In 2016, the number reached 4.9 m or 9.6% of the general student population. In some states, the rise in the EL population has been even more pronounced. Their increased presence in content classes poses opportunities as well as great challenges for teachers. As these learners are learning fundamental language skills, they are also responsible for learning content and skills associated with various disciplines. In social studies, it includes developing a foundational understanding of US history, geography and government, while also developing the ability to comprehend, analyze and evaluate complex informational texts, which include contextualized language, low-incidence concepts and discrete facts.

One way schools are adapting practice for ELs is through collaboration between general education and EL resource teachers. This article reports findings from interviews with a high school social studies teacher and an English as a second language (ESL) colleague.

The collaboration between these two teachers illustrated how specific strategies thoughtfully designed, yet sensibly implemented, can help teachers feel effective and empowered in their social studies classes. Their story also highlighted ways they have worked together in spite of the system confines.

Together, the authors hope their experiences inform teachers, teacher educators and school leaders about the importance of adapting practices for ELs in social studies and the power of collaboration to make those efforts successful.

The purpose of this paper is to develop an artificial neural network (ANN) based prediction model via integration with data envelopment analysis (DEA) to provide the means of predicting incremental performance goals. The findings confirm the usefulness of the herein developed prediction approach, based on the results of analyses of time series data from the smartphone industry.

A two-stage hybrid model was developed, incorporating sequential measurement and prediction capability. In the first stage, a Chames, Cooper, and Rhodes DEA model is the preprocessor, generating efficiency scores (ES) of decision-making units (DMUs). In the second or follow-on stage, the ANN prediction module utilizes knowledge variables and ES to predict the change in performance needed for a desired level of improvement.

This combined approach effectively captured the information contained in the industry’s turbulent characteristics, and subsequently demonstrated an adaptive prediction capability. The back propagating neural network successfully predicted the incremental performance targets of DMUs, which translated the desired improvement levels into actionable performance goals, e.g., revenue and operating income.

This paper presents an incremental prediction approach that supports better practice benchmarking. This study differentiates itself from previous research by introducing an adaptive prediction method which generates relevant quantity outputs based upon desired improvement levels. The proposed modeling approach integrates performance measurement with a prediction framework and advances benchmarking practices to enable better performance prediction.

The purpose of this paper is to design an innovative performance modeling system by jointly using data envelopment analysis (DEA) and artificial neural network (ANN). The hybrid DEA-ANN model integrates performance measurement and prediction frameworks and serves as an adaptive decision support tool in pursuit of best performance benchmarking and stepwise improvement.

Advantages of combining DEA and ANN methods into an optimal performance prediction model are explored. DEA is used as a preprocessor to measure relative performance of decision-making units (DMUs) and to generate test inputs for subsequent ANN prediction modules. For this sequential process, Charnes, Cooper, and Rhodes and Banker, Chames and Cooper DEA models and back propagation neural network (BPNN) are used. The proposed methodology is empirically supported using longitudinal data of Japanese electronics manufacturing firms.

The combined modeling approach proves effective through sequential processes by streamlining DEA analysis and BPNN predictions. The DEA model captures notable characteristics and efficiency trends of the Japanese electronics manufacturing industry and extends its utility as a preprocessor to neural network prediction modules. BPNN, in conjunction with DEA, demonstrates promising estimation capability in predicting efficiency scores and best performance benchmarks for DMUs under evaluation.

Integration of adaptive prediction capacity into the measurement model is a practical necessity in the benchmarking arena. The proposed framework has the potential to recalibrate benchmarks for firms through longitudinal data analysis.

This research paper proposes an innovative approach of performance measurement and prediction in line with superiority-driven best performance modeling. Adaptive prediction capabilities embedded in the proposed model enhances managerial flexibilities in setting performance goals and monitoring progress during pursuit of improvement initiatives. This paper fills the research void through methodological breakthrough and the resulting model can serve as an adaptive decision support system.