PurposeThe purpose of this paper is twofold: first, to develop a performance evaluation framework for construction and demolition waste management (CDWM); second, to investigate feasible and effective strategies to improve the CDWM performance.Design/methodology/approachA review of the literature highlighted a lack of comprehensive research to evaluate CDWM performance of key project stakeholders, like owners, contractors and consultants. After the identification of 22 performance variables through a pilot study, a first questionnaire survey was conducted to investigate the views of respondents toward CDWM performance. The 132 responses were analyzed using factor analysis to determine specific CDWM performance factors, which formed a conceptual performance evaluation framework of CDWM. Furthermore, a practical index (PI) was proposed to integrate the feasibility and effectiveness of CDWM strategies. The values of PI were employed to prioritize CDWM strategies from data collected in a second questionnaire survey.FindingsThe validated results from factor analysis revealed that the conceptual performance evaluation framework of CDWM consists of six factors; and the attitude toward CDWM emerged as the foremost critical factor. The prioritization of PI values indicated that raising CDWM awareness among construction stakeholders was the most feasible and effective strategy for CDWM.Originality/valueThis CDWM performance evaluation framework is one of the first to holistically evaluate CDWM from key stakeholder perspectives. In addition, the PI firstly enables quantitative integration of the feasibility and effectiveness of CDWM strategies.

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PurposeThe purpose of this study is to identify driving factors and a quantitative model for implementing public-private partnership (PPP) projects in Ethiopia as a case study in emerging economies.Design/methodology/approachA review of the literature and semi-structured interviews were carried out to identify driving factors affecting the implementation of PPP projects in the Ethiopian context. Data were collected through a questionnaire survey within three months, with 59 validated responses; mean score technique and factor analysis were conducted. The fuzzy synthetic evaluation (FSE) method was applied to develop a driving index (DI) for implementing infrastructure PPP projects. Finally, a comparative analysis of top-five drivers was conducted between four emerging economies.FindingsMean values show that all driving variables are important. Through factor analysis, 22 identified driving variables were grouped into six factors, namely, benefit for public and private sectors, attention of private sector, social development, cost reduction, management ability of public sector and ability of private sector. The FSE method constructs a DI and shows that benefit for public and private sectors is the most crucial factor for PPP implementation in the context of Ethiopia. Apart from this, most driving forces for adopting PPP projects in these countries related to financial problems.Originality/valueThis study is one of the first integrate driving factors for PPP implementation. The index provides the decision-makers with a comprehensive tool to assess the needs of PPP implementation.

PurposeThis paper aims to propose a comprehensive framework for prioritizing complexity criteria. The framework was validated by applying in infrastructure international development (ID) project as a case study.Design/methodology/approachA literature review highlighted the limitations of existing complexity prioritization methods. Then, a combination of the fuzzy decision-making trial and evaluation laboratory (DEMATEL) and fuzzy analytic network process (ANP) was employed as a foundation to develop a three-stage complexity prioritization framework. Focus group discussion and questionnaire surveys were used to practically test the framework in the infrastructure ID projects.FindingsThe three-stage complexity prioritization framework was validated to be reliable and feasible. The findings showed ability of consultants, scope uncertainties, site compensation and clearance, communication between stakeholders, administrative procedure and project duration were the most significant complexity criteria of ID projects in the Vietnamese context.Practical implicationsThe framework is a robust tool that enables the researchers to grasp the interaction of complexity criteria for complexity prioritization. Later studies can apply the proposed framework, with some minor revisions, to assess the interaction of criteria in other research topics in, and beyond, project complexity. Results of the case study suggest project stakeholders focusing on complex interactions among criteria to reduce project complexity.Originality/valueThis study contributes to the body of knowledge by providing a comprehensive complexity prioritization framework that grasps the interrelationship of complexity criteria. For stakeholders of ID projects, the findings provide insightful perspectives to understand complexity, which can help to enhance project performance.

Employee retention is becoming a major concern in organizational management. To maintain business’ competitive advantages, companies need to keep employees working for their organizations. Thus, many firms are trying to find out how to retain their employees. This study aims to investigate determinants of employee retention of South Korean construction employees. From the review of the literature and discussions with industrial practitioners, eight significant determinants affecting employee retention in South Korean construction firms are identified. The fuzzy technique for order of preference by similarity to ideal solution (TOPSIS) is employed to prioritize the identified determinants. The fuzzy TOPSIS analysis shows that personal characteristics, personal development, promotion opportunities, and work-life balance are the four most critical determinants. Construction firms are suggested to focus on these determinants to improve employee retention rates within their companies and achieve sustainable development.

We introduce Trankit, a light-weight Transformer-based Toolkit for multilingual Natural Language Processing (NLP). It provides a trainable pipeline for fundamental NLP tasks over 100 languages, and 90 pretrained pipelines for 56 languages. Built on a state-of-the-art pretrained language model, Trankit significantly outperforms prior multilingual NLP pipelines over sentence segmentation, part-of-speech tagging, morphological feature tagging, and dependency parsing while maintaining competitive performance for tokenization, multi-word token expansion, and lemmatization over 90 Universal Dependencies treebanks. Despite the use of a large pretrained transformer, our toolkit is still efficient in memory usage and speed. This is achieved by our novel plugand-play mechanism with Adapters where a multilingual pretrained transformer is shared across pipelines for different languages. Our toolkit along with pretrained models and code are publicly available at: https: //github.com/nlp-uoregon/trankit.

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