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Despite the benefits provided by the implementation of the lean philosophy, the most recent research discusses whether the high degrees of efficiency obtained with the implementation this philosophy could contribute positively or negatively in current pandemic scenario. This study aims to analyze how resilient construction industry companies in Brazil were in the face of the COVID-19 pandemic, comparing the performance of construction companies with different implementation levels of lean construction.

Three case studies were carried out in construction companies in Brazil with different application levels of the lean philosophy.

Among the results obtained, greater resilience to face COVID-19 was verified in those with a higher implementation level of the lean philosophy. Additionally, it was found that the larger the size of the companies surveyed, the greater the level of implementation of the lean philosophy.

Due to the state of pandemic, and the work carried out in the home office, contact with companies was restricted. In addition, few companies actually adopt the lean philosophy in the construction sector in Brazil. Most of companies use only a few lean construction tools in specific sectors. For the few others who actually implement the philosophy, the acceptance to participate in the research was low.

The lean construction is something very new and innovative for the construction sector in Brazil, and there is little evidence of its use. Few companies adopt the philosophy in the country, and many of them also did not experience such an impactful moment in their entire existence. Thus, the analysis of the relationship between lean construction and resilience in the civil construction sector in Brazil is something innovative.

This study aims to develop a regression-based machine learning model to predict housing price, determine and interpret factors that contribute to housing prices using different data sets available publicly. The significant determinants that affect housing prices will be first identified by using multinomial logistics regression (MLR) based on the level of relative importance. A comprehensive study is then conducted by using SHapley Additive exPlanations (SHAP) analysis to examine the features that cause the major changes in housing prices.

Predictive analytics is an effective way to deal with uncertainties in process modelling and improve decision-making for housing price prediction. The focus of this paper is two-fold; the authors first apply regression analysis to investigate how well the housing independent variables contribute to the housing price prediction. Two data sets are used for this study, namely, Ames Housing dataset and Melbourne Housing dataset. For both the data sets, random forest regression performs the best by achieving an average R² of 86% for the Ames dataset and 85% for the Melbourne dataset, respectively. Second, multinomial logistic regression is adopted to investigate and identify the factor determinants of housing sales price. For the Ames dataset, the authors find that the top three most significant factor variables to determine the housing price is the general living area, basement size and age of remodelling. As for the Melbourne dataset, properties having more rooms/bathrooms, larger land size and closer distance to central business district (CBD) are higher priced. This is followed by a comprehensive analysis on how these determinants contribute to the predictability of the selected regression model by using explainable SHAP values. These prominent factors can be used to determine the optimal price range of a property which are useful for decision-making for both buyers and sellers.

By using the combination of MLR and SHAP analysis, it is noticeable that general living area, basement size and age of remodelling are the top three most important variables in determining the house’s price in the Ames dataset, while properties with more rooms/bathrooms, larger land area and closer proximity to the CBD or to the South of Melbourne are more expensive in the Melbourne dataset. These important factors can be used to estimate the best price range for a housing property for better decision-making.

A limitation of this study is that the distribution of the housing prices is highly skewed. Although it is normal that the properties’ price is normally cluttered at the lower side and only a few houses are highly price. As mentioned before, MLR can effectively help in evaluating the likelihood ratio of each variable towards these categories. However, housing price is originally continuous, and there is a need to convert the price to categorical type. Nonetheless, the most effective method to categorize the data is still questionable.

The key point of this paper is the use of explainable machine learning approach to identify the prominent factors of housing price determination, which could be used to determine the optimal price range of a property which are useful for decision-making for both the buyers and sellers.

The purpose of this study is to propose a new consumer value segmentation method for low-dimensional dense market datasets to quickly detect and cluster the most profitable customers for the enterprises.

In this study, the comprehensive segmentation bases (CSB) with richer meanings were obtained by introducing the weighted recency-frequency-monetary (RFM) model into the common segmentation bases (SB). Further, a new market segmentation method, the CSB-MBK algorithm was proposed by integrating the CSB model and the mini-batch k-means (MBK) clustering algorithm.

The results show that our proposed CSB model can reflect consumers' contributions to a market, as well as improve the clustering performance. Moreover, the proposed CSB-MBK algorithm is demonstrably superior to the SB-MBK, CSB-KMA and CSB-Chameleon algorithms with respect to the Silhouette Coefficient (SC), the Calinski-Harabasz (CH) Index , the average running time and superior to the SB-MBK, RFM-MBK and WRFM-MBK algorithms in terms of the inter-market value and characteristic differentiation.

This paper provides a tool for decision-makers and marketers to segment a market quickly, which can help them grasp consumers' activity, loyalty, purchasing power and other characteristics in a target market timely and achieve the precision marketing.

This study is the first to introduce the CSB-MBK algorithm for identifying valuable customers through the comprehensive consideration of the clustering quality, consumer value and segmentation speed. Moreover, the CSB-MBK algorithm can be considered for applications in other markets.

Preservice teachers (PSTs) typically have few opportunities to observe social studies instruction in PSTs' elementary field placements. To practice effective integration as a pathway to include more social studies instruction, PSTs developed a unit plan based on inquiry during an undergraduate methods course. The purpose of this study was to explore what scaffolds were effective in PSTs' development of social studies inquiry projects.

The author used a multiple case study approach, examining initial submissions, feedback and resubmissions as PSTs developed PSTs' inquiry projects. The data were analyzed with an eye on PSTs' ability to plan a unit according to the four dimensions of the C3 Framework's inquiry arc (National Council for the Social Studies [NCSS], 2013).

The author analyzed data related to PSTs’ area of highest need from PSTs' initial submissions: staging the task, formative tasks and resources. PSTs were able to develop inquiry projects after being supported in their (1) organization, (2) clarity, (3) alignment and (4) developmental appropriateness.

This study shows that novice teachers can create high-quality social studies learning experiences in elementary school when provided appropriate supports (in this case, feedback about organization, clarity, alignment and developmental appropriateness). With the tools to develop such projects, new teachers may be able to increase the amount of social studies taught in elementary classrooms.

In the Fall of 2020, three teacher educators in Ohio collaborated on a three-month long online professional development series on how to write Focused Inquiries, a la the Inquiry Design Model (IDM).

The authors detail the contents of the six group professional development (PD) sessions and share the lessons that the authors learned as a result of leading this training.

Given this study’s mixed results, the authors often came back to the questions of “maybe it was us? Maybe it was the pandemic? Maybe there wasn’t enough training? Or maybe IDM creation isn’t a skill that all teachers possess and maybe that’s ok?” The authors share the struggles with these questions and situate all of this within the current culture wars raging around schools today.

Finally, the authors offer the current approach to inquiry training for teachers that situates inquiry creation later in the process after significant structured introductory work.

With the rapid-growing house market in the past decade, the purpose of this paper is to study the important issue of house price information flows among 12 major cities in China, including Shanghai, Beijing, Xiamen, Shenzhen, Guangzhou, Hangzhou, Ningbo, Nanjing, Zhuhai, Fuzhou, Suzhou and Dongguan, during the period of June 2010 to May 2019.

The authors approach this issue in both time and frequency domains, latter of which is facilitated through wavelet analysis and by exploring both linear and nonlinear causality under the vector autoregressive framework.

The main findings are threefold. First, in the long run of the time domain and for timescales beyond 16 months of the frequency domain, house prices of all cities significantly affect each other. For timescales up to 16 months, linear causality is weaker and is most often identified for the scale of four to eight months. Second, while nonlinear causality is seldom determined in the time domain and is never found for timescales up to four months, it is identified for scales beyond four months and particularly for those beyond 32 months. Third, nonlinear causality found in the frequency domain is partly explained by the volatility spillover effect.

Results here should be of use to policymakers in certain policy analysis.

This study aims to progress scholarly understanding of the staycation phenomenon by examining customer segments and documenting local customers’ attribute preferences.

A stated choice experiment is used to examine customer preferences for staycation package attributes. Latent class discrete choice modeling is deployed to classify customers into market segments based on their preferences. The profile of each segment is enhanced by documenting customer characteristics and consumption styles.

Six prominent market segments are identified using a combination of sociodemographics, consumption styles and staycation attribute preferences. The findings draw on consumer experiences during the COVID-19 pandemic to generate theoretical insights into preferred staycation packages. Empirically, the estimation results from the research framework and choice experimental method demonstrate that staycation market segments exhibit distinct preference structures.

Practitioners and policymakers can incorporate the findings of this study in designing and/or assessing staycation packages. This can ensure differentiated products for defined segments that resonate within local communities through positive word of mouth, thus offering prospective spillovers to visiting friends and relatives.

This is a pioneering study on preference heterogeneity from the customer perspective, with a focus on staycation markets. The findings can encourage and assist hotel sector leaders to capitalize on local market developments to achieve a more resilient hospitality business model.

This study aims to analyze how mixes of COVID-19 policy responses are shaping the context in which companies will compete in the following years, defining how the crisis might impact firms’ ability to keep their commitments to sustainable practices.

European country-performance data for the years 2019 and 2020 were grouped into indicators of macro sustainability, then cross-analyzed against the policies adopted during the period (also grouped based on their impacts on sustainability pillars), using correlations, factor analysis and clustering.

The influence of traditional sustainability determinants was reframed according to the novel context shaped by the policy responses to the pandemic crisis. The social and digitalization aspects gained the most relevance and appeared interconnected, with digitalization of employment attaining overall more traction. Moreover, changes in the leadership within sustainability domains were observed for each identified country-cluster, due to newly implemented emergency policies. In fact, environmental innovation, digitalization and social support policies appeared to be the main variables to be impacted by the intensity of the policy efforts.

Businesses monitoring the developments of sustainability policies closely, will observe novel trends in technological applications.

Policymakers and researchers may gauge the efficacy of policies against the COVID-19 crisis in the domain of sustainable development and resilience.

This paper provides a cross-analysis of quantitative macroeconomic and quantified policy responses to the 2020 pandemic crisis, linking each indicator to the pillars of sustainability that were relevant for companies between the crucial pandemic outbreak years 2019 and 2020.

This article unpacks the construction of authority in architectural trade journals as multimodal disciplinary communication and how librarians can use these journals to engage student's critical thinking in information and visual literacy instruction.

An analysis of project articles was done in two consecutive issues of ten architecture print trade journals including tracking details about the building types, geographic locations, firms represented, visual coverage, and visual categorizes and conventions.

The projects represented in the analyzed trade journals were predominately public buildings built by established firms in Europe, North America and Asia. The journals employed various methods for crediting and captioning visuals, showing marked differences in conferring authority on architectural photographers and descriptive versus analytical analysis of visual communications. Overall, visuals in architecture trade journals dominate the article space, with photographs being the most prominent type; however, individual journals differ in disciplinary conventions such as presence of people, use of color and indications of scale and compass direction.

These findings strengthen the case for library print subscriptions to trade journals as useful when facilitating student exploration of disciplinary communication to identify markers of authority, examine bias and apply disciplinary conventions in their own scholarly output.

By interrogating the value of print journals in architecture, findings of this study may influence further research into the significance of print journals in other disciplines and a larger professional discussion about the implications of library trends to providing digital-only journal access.

This study aims to develop a taxonomy of organizations according to business process orientation (BPO) maturity and investigate the difference between clusters in terms of performance outcome.

A survey of various organizations in Indonesia is conducted. The main variables are critical practices (CPs) as the measurement variables of BPO maturity and performance outcome. Cluster analysis is performed to obtain an empirical taxonomy of the organizations. ANOVA test is used to test if there are statistically different performance outcomes across different clusters.

Cluster analysis resulted in six archetypes labeled according to their characteristics: Beginners, Non-technical, Domestics, IT laggards, Excellers, and Champions. The ANOVA test results show that the archetypes with high CPs tend to have high perceived performance results.

This study is limited because the authors use a single dataset from organizations in Indonesia. Further study involving more organizations will be beneficial to validate and enrich the taxonomy of organizational archetypes.

Results of the study can be used as a benchmarking tool by organizations to identify their positions against other organizations and set their areas for improvement. It can also help them identify a roadmap for improvement that will benefit their organization.

Using the CPs as a measure of BPO enables the authors to identify supplier orientation and information and technology (IT) implementation as the primary differentiators within the taxonomy. The use of IT differentiates the bottom, middle and top clusters.