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This paper investigates the interactional relationships between sustainable human resource management (SHRM) and organizational performance (OP). Sustainable HRM is an approach that links HRM and sustainability. These studies focused on integrating HR with sustainable developments, such as economic and social aspects, in favour of focusing on the environmental aspect. Organizational change is an ongoing process that has to be managed effectively to keep the change in place for a long time.

A framework was offered to estimate the cause-and-effect relation of the SHRM and OP factors. Data is gathered from professionals from various pharmaceutical industries. This study applied two methods, Fuzzy AHP and DEMATEL Type II. These techniques are used to understand the cause-and-effect factors and their interactions.

It was observed from the findings that the factor of SHRM, such as Social Justice (F2), Green Job Design (F5), Green Training (F6) and Implementation of Green Policy (F8), was the most critical for the pharmaceutical sector that effects Financial performance (F13), Customer Satisfaction (F15) and Market performance (F14). Pharmaceutical firms ought to coordinate public health advocacy efforts, engage in healthcare initiatives and provide financial support for environmentally friendly efforts that improve social and economic conditions.

For this sustainability, managers concentrate on creating an environment that is healthy and acceptable, and they work hard to mitigate the impact of natural factors and repair damage done to the environment; it is essential to move towards sustainable development to resolve environmental problems. Improving HR efficiency is among essential HRM responsibilities, as they expand the knowledge base of the workforce, enhance human capital, and eventually create valuable intangible assets and promote and encourage sustainable pharmaceutical products for some years.

This research paper has presented exclusive worth to the SHRM and organizational performance literature as it employs fuzzy FAHP and DEMATEL type 2. There is less research on SHRM in the pharmaceutical sector with these factors. In addition, FAHP and TYPE 2 DEMATEL are used in very few researches on SHRM approaches.

Global construction has been affected by COVID-19 unprecedently. The construction sectors in the least developed countries are considered as vulnerable, but the covid made the countries experience the worst situation ever. To minimize the losses by effective measures, there needs to assess the COVID-19 impacts on the construction sector. So, the aim of this study is to investigate the most critical impacts of COVID-19 on construction in the least developed countries by considering the case study of Bangladesh.

The authors adopted multistep research methods, including (1) literature analysis and discussion with experts to establish a comprehensive list of COVID-19 impacts; (2) through a questionnaire survey, data were collected from 217 construction professionals by email, Google Form and Skype for quantifying the significance of covid impacts; (3) reliability of the survey checked by the Cronbach Alpha test; (4) Relative Importance Index (RII) to determine the ranks of the impacts based on their significance; (5) Interpretive Structural Model (ISM) to explore the corelations and the hierarchical structure; and (6) cross-impact matrix multiplication applied to classification (MICMAC) analysis to classify the COVID-19 impacts.

The study identified a total of 18 COVID-19 impacts on the construction sector. Among them, the job cuts, schedule delays, project suspension, cost overrun and effects on mental health are more influential and significant than others. Further, this study found that unpaid leave and job cuts are the two most fundamental impacts which influence other succeeding significant impacts. And ultimately all the impacts lead to hampering the national economy and development. Finally, MICMAC analysis suggested that unpaid leave and job cuts should be addressed first to resolve and effects on the national economy and development should be later.

This study does not consider all the COVID-19 impacts due to the relevant context and simplicity of the ISM method. Also, the respondent's attitude might be slightly different during the post-mass vaccination period.

This study will help the company's management, employees and government to develop effective strategies to understand the insight of their interrelations and ultimately overcome the identified covid effects. This will must contribute to the industry, its employees, the government and society by ensuring the national economy and development, construction operations, investment, employment and social security.

This study will contribute to the knowledge body (practitioners and researchers) by providing the list of significant covid impacts and insight into their interrelations for further deep analysis of the pandemic effects. This will also help the authorities and stakeholders in developing policies and strategies to minimize or avoid these effects and avoid future consequences due to any pandemic like covid.

This study aims to develop cause-and-effect relationships among the critical success factors (CSFs) of fintech adoption and rank these CSFs based on their importance in the model.

To achieve the objectives of the study, the Fuzzy Decision-Making Trial and Evaluation Laboratory (FDEMATEL) approach was used. The data was collected from 16 experts using a questionnaire.

The findings demonstrated the interrelationships among the CSFs. In total, 16 critical factors were recognized as causal factors, and the remaining eight were considered effect factors. The CSFs were ranked based on their importance in fintech adoption.

This study is novel as it investigates CSFs of fintech adoption using FDEMATEL, and it contributes to understanding the nature of these factors and how they affect fintech adoption. The findings propose a significant basis to deepen fintech adoption and deliver a clue to design a practical framework for fintech adoption.

This study aimed to design a user-participatory methodology to investigate the post-occupancy sustainability of reused historical buildings and to apply it to a case study.

This study was designed in four stages. In the first stage, the sustainability parameters and sub-parameters were determined in the reused historical buildings based on the literature. The second stage included a field study in which the current situation of the study area was analysed, and the users were reached using the survey technique. In the third stage, the data obtained from the user participation were analysed with importance performance analysis (IPA) and an IPA matrix was created. The fourth stage included an evaluation of the results of the analysis and the development of recommendations.

IPA is a supportive method for ensuring the sustainable use of historic buildings. According to the data obtained from the IPA, it was seen that the functional sustainability of the building was achieved to a great extent. At the same time, there were deficiencies in technical and environmental sustainability. In terms of aesthetic sustainability, it was observed that the importance and performance values given by the users were generally consistent with each other.

The originality of this study is that the performance of the reused historical buildings in the process of use was monitored with appropriate parameters, and a user-participated method was proposed that allows improvement suggestions to be developed in line with the results obtained.

This study examines the underlying relationships between the critical factors of building information modeling (BIM) implementation and the factors' groupings among architecture, engineering and construction (AEC) organizations in Saudi Arabia. The objectives of the study are to (1) identify the critical factors for BIM implementation, (2) analyze the interrelationships between the critical factors and (3) compare the critical factors between the different organizational characteristics.

First, potential factors were identified through a systematic literature review and interviews with AEC professionals. Then, a questionnaire survey was sent to AEC professionals and the collected data were analyzed using the following techniques and tests: mean score ranking, standard deviation, normalized value, factor analysis (FA), analysis of variance (ANOVA) and post-hoc Tukey test.

The analyses show that 14 factors are critical for BIM implementation in Saudi Arabia. The top critical factors include the existence of standard contracts on data security and user confidentiality, consistent views on BIM among stakeholders and the availability of guidelines for implementing BIM. Of the 14 critical factors, 9 can be grouped into 4 underlying factors: environmental, governmental, legal and organizational. The analysis shows that the criticality of the most critical factors grouped by the FA varies between different levels of BIM competency. Finally, the presence of public–private partnerships (PPPs) in realizing BIM projects is a new and emerging critical factor for BIM implementation in Saudi Arabia.

This study differs from prior works on BIM implementation in Saudi Arabia by using FA to explore the underlying relationships among factors of BIM implementation and the factors' groupings. Based on the FA results, a roadmap for implementing the BIM was developed. These findings will help to purposefully and efficiently customize BIM implementation strategies and initiatives to ensure successful BIM implementation in Saudi Arabia.

This study aimed to use enrollment information including demographic, family background and financial status, which can be gathered before the first semester starts, to construct early prediction models (EPMs) and extract crucial factors associated with first-year student dropout probability.

The real-world samples comprised the enrolled records of 2,412 first-year students of a private university (UNI) in Taiwan. This work utilized decision trees (DT), multilayer perceptron (MLP) and logistic regression (LR) algorithms for constructing EPMs; under-sampling, random oversampling and synthetic minority over sampling technique (SMOTE) methods for solving data imbalance problems; accuracy, precision, recall, F1-score, receiver operator characteristic (ROC) curve and area under ROC curve (AUC) for evaluating constructed EPMs.

DT outperformed MLP and LR with accuracy (97.59%), precision (98%), recall (97%), F1_score (97%), and ROC-AUC (98%). The top-ranking factors comprised “student loan,” “dad occupations,” “mom educational level,” “department,” “mom occupations,” “admission type,” “school fee waiver” and “main sources of living.”

This work only used enrollment information to identify dropout students and crucial factors associated with dropout probability as soon as students enter universities. The extracted rules could be utilized to enhance student retention.

Although first-year student dropouts have gained non-stop attention from researchers in educational practices and theories worldwide, diverse previous studies utilized while-and/or post-semester factors, and/or questionnaires for predicting. These methods failed to offer universities early warning systems (EWS) and/or assist them in providing in-time assistance to dropouts, who face economic difficulties. This work provided universities with an EWS and extracted rules for early dropout prevention and intervention.

The nature of construction works has a negative impact on physical, mental and emotional well-being and makes it difficult for the construction industry to attack and retain its workforce. The current study seeks to integrate the current knowledge focused on work–life balance (WLB) in the industry into an understandable whole.

An interpretivist philosophical approach was adopted using a bibliometric review and a narrative review of existing studies from both Scopus and Google databases. The Visualisation of Similarities viewer (VOSviewer) was used to prepare co-occurrence maps from the bibliographic data garnered.

The study reveals that the prominent factors influencing the WLB of the construction workforce are organizational culture, salary earned, heavy workload, long working hours and inflexible working time. The recent WLB discourse is on organisational commitment, job satisfaction and workplace dynamics. While WLB areas for further exploration are job stress, safety performance, employee attrition and an ageing workforce. Meanwhile, Africa and South America are still lagging in WLB research.

The findings reported here will assist stakeholders in identifying appropriate WLB initiatives that can be used to improve the well-being of the construction workforce. Also, the gaps in knowledge for further research were highlighted.

The findings reveal current trends and a road map for future studies on WLB in construction. It also reveals prominent factors influencing the WLB of workers in the construction industry.

This study aims to compare and evaluate the application of commonly used machine learning (ML) algorithms used to develop models for assessing energy efficiency of buildings.

This study foremostly combined building energy efficiency ratings from several data sources and used them to create predictive models using a variety of ML methods. Secondly, to test the hypothesis of ensemble techniques, this study designed a hybrid stacking ensemble approach based on the best performing bagging and boosting ensemble methods generated from its predictive analytics.

Based on performance evaluation metrics scores, the extra trees model was shown to be the best predictive model. More importantly, this study demonstrated that the cumulative result of ensemble ML algorithms is usually always better in terms of predicted accuracy than a single method. Finally, it was discovered that stacking is a superior ensemble approach for analysing building energy efficiency than bagging and boosting.

While the proposed contemporary method of analysis is assumed to be applicable in assessing energy efficiency of buildings within the sector, the unique data transformation used in this study may not, as typical of any data driven model, be transferable to the data from other regions other than the UK.

This study aids in the initial selection of appropriate and high-performing ML algorithms for future analysis. This study also assists building managers, residents, government agencies and other stakeholders in better understanding contributing factors and making better decisions about building energy performance. Furthermore, this study will assist the general public in proactively identifying buildings with high energy demands, potentially lowering energy costs by promoting avoidance behaviour and assisting government agencies in making informed decisions about energy tariffs when this novel model is integrated into an energy monitoring system.

This study fills a gap in the lack of a reason for selecting appropriate ML algorithms for assessing building energy efficiency. More importantly, this study demonstrated that the cumulative result of ensemble ML algorithms is usually always better in terms of predicted accuracy than a single method.

The main purpose of this paper is to explore whether the nature of innovation is accumulative or radical and to what extent past year accumulation of technology stock can predict future innovation. More importantly, the authors are concerned with whether a change of policy regime or a variance in the quality of technology will moderate the nature of innovation.

The authors examined a dataset of 3.6 million Chinese patents during 1985–2015 and constructed more than 5 million citation pairs across 8 sections and 128 classes to track knowledge spillover across technology fields. The authors used this citation dataset to calculate the technology innovation network. The authors constructed a measure of upstream invention, interacting the pre-existing technology innovation network with historical patent growth in each technology field, and estimated measure's impact on future innovation since 2005. The authors also constructed three sets of metrics – technology dependence, centrality and scientific value – to identify innovation quality and a policy dummy to consider the impact of policy on innovation.

Innovation growth is built upon past year accumulation and technology spillover. Innovation grows faster for technologies that are more central and grows more slowly for more valuable technologies. A pro-innovation and pro-intellectual property right (IPR) policy plays a positive and significant role in driving technical progress. The authors also found that for technologies that have faster access to new information or larger power to control knowledge flow, the upstream and downstream innovation linkage is stronger. However, this linkage is weaker for technologies that are more novel or general. On most occasions, the nature of innovation was less responsive to policy shock.

This paper contributes to the debate on the nature of innovation by determining whether upstream innovation has strong predictive power on future innovation. The authors develop the assumption used in the technology spillover literature by considering a time-variant, directional and asymmetric matrix to model technology diffusion. For the first time, the authors answer how the nature of innovation will vary depending on the technology network configurations and policy environment. In addition to contributing to the academic debate, the authors' study has important implications for economic growth and industrial or innovation management policies.

Quality is a sensitive and high-priority issue in the global construction including in Bangladesh. This research is intended to provide necessary information to stakeholders and authorities for better management of the construction quality in Bangladesh. Therefore, this study seeks to find and prioritize the factors affecting the construction quality in Bangladesh.

In total 65 factors were extracted and categorized from the literature and expert panel discussion. Subsequently, these factors were designed in a questionnaire under 13 major groups for a survey where 176 construction professionals participated and returned their completed survey form. Collected data were tested by the Cronbach Alpha to check the reliability before proceeding to the Relative Importance Index (RII) analysis for determining the relative ranks of identified factors.

Statistical analysis of survey data represents that the most significant factors are: lack of management commitment, lack of technical skill and experience of the consultant, delays in progress investigation, political interference and contractor's desire for unrealistic profit. The most crucial major groups of factors influencing the construction quality are management, material, consultant, cost and time and contract-related major groups.

It will contribute to the body of knowledge, as it points out the impact of factors affecting quality in Bangladeshi construction. Authorities and stakeholders can be helped by the overview of the high and low ranks factors, understanding the diverse characteristics of factors and making more aware the industry about the quality issues which need to be a top concern to solve. Other developing countries that share the same socio-economic context as Bangladesh can be benefit from the results of this study to control quality issues in construction.