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Over the last three decades, construction projects have increasingly been delivered through alternative delivery methods. As a result, many owners have a range of delivery methods to choose from and aim to use the right one for each of their projects. Researchers have developed several tools and decision-support processes to facilitate this selection procedure. The purpose of this study is to review and discuss differences and common themes across selection tools developed by academic researchers and project owners.

The study reviews prominent selection processes and tools used for infrastructure projects by conducting an in-depth literature review and using the content analysis method to elicit findings on the methodologies and criteria presented in the literature.

This study presents three principal findings. First, findings show three common themes emerge within the selection criteria—characteristics, goals and risks. Second, while academic studies most commonly suggest employing multi-attribute analysis, this study reveals that, in practice, selection tools most frequently employ a staged or gated evaluation based on the type of criteria and their importance to the decision. Finally, this review further highlights the importance of institutional context in decision-making.

This work contributes to the body of knowledge by providing guidance to practitioners and opening new directions for researchers around the way selection criteria are categorized in the relevant literature and the institutional context considerations when structuring or evaluating a selection process or tool.

Classical unit root tests are known to suffer from potentially crippling size distortions, and a range of procedures have been proposed to attenuate this problem, including the use of bootstrap procedures. It is also known that the estimating equation’s functional form can affect the outcome of the test, and various model selection procedures have been proposed to overcome this limitation. In this chapter, the authors adopt a model averaging procedure to deal with model uncertainty at the testing stage. In addition, the authors leverage an automatic model-free dependent bootstrap procedure where the null is imposed by simple differencing (the block length is automatically determined using recent developments for bootstrapping dependent processes). Monte Carlo simulations indicate that this approach exhibits the lowest size distortions among its peers in settings that confound existing approaches, while it has superior power relative to those peers whose size distortions do not preclude their general use. The proposed approach is fully automatic, and there are no nuisance parameters that have to be set by the user, which ought to appeal to practitioners.

The purpose of this study was to prioritize the appropriate generic contractor selection criteria for Engineering–Procurement–Construction (EPC) projects in the construction industry.

Proceeding from a review of previous studies and validation by a small group of experts, a preliminary set of 16 criteria was first identified. This was followed by three rounds of Delphi surveys: firstly, with 64 experienced participants confirming the relevance of the 16 criteria; secondly, with a reduced subgroup of 47 more experienced participants scoring the importance of each; and finally, providing the opportunity for these 47 to revise their scores in the light of knowing the aggregated results of the previous round.

The results show the consensus view, of which the most important criteria are ranked as past performance, project understanding, technical attributes, key personnel, health and safety, past experience, time, management, financial, contractual and legal, quality, cost, relationships, environmental and sustainability, organizational and industrial relations, and geographic location.

The findings are useful for both practitioners and academics in making a significant contribution to the body of knowledge of the EPC process. This will assist in providing a better understanding of criteria importance and pave the way to developing an EPC contractor selection model involving the criteria most needed to objectively identify potential contractors and evaluate tenders.

While the Chinese securities market is booming, the phenomenon of listed companies falling into financial distress is also emerging, which affects the operation and development of enterprises and also jeopardizes the interests of investors. Therefore, it is important to understand how to accurately and reasonably predict the financial distress of enterprises.

In the present study, ensemble feature selection (EFS) and improved stacking were used for financial distress prediction (FDP). Mutual information, analysis of variance (ANOVA), random forest (RF), genetic algorithms, and recursive feature elimination (RFE) were chosen for EFS to select features. Since there may be missing information when feeding the results of the base learner directly into the meta-learner, the features with high importance were fed into the meta-learner together. A screening layer was added to select the meta-learner with better performance. Finally, Optima hyperparameters were used for parameter tuning by the learners.

An empirical study was conducted with a sample of A-share listed companies in China. The F1-score of the model constructed using the features screened by EFS reached 84.55%, representing an improvement of 4.37% compared to the original features. To verify the effectiveness of improved stacking, benchmark model comparison experiments were conducted. Compared to the original stacking model, the accuracy of the improved stacking model was improved by 0.44%, and the F1-score was improved by 0.51%. In addition, the improved stacking model had the highest area under the curve (AUC) value (0.905) among all the compared models.

Compared to previous models, the proposed FDP model has better performance, thus bridging the research gap of feature selection. The present study provides new ideas for stacking improvement research and a reference for subsequent research in this field.

Multi-criteria decision-making (MCDM) techniques are decision support systems that provide systematic approaches to solve hospitality and tourism (H&T) problems while minimizing the risk of failure. However, less is known about the application of MCDM techniques in H&T research. This study aims to systematically assess the use of MCDM techniques in H&T research to classify its current application and determine its application potential for H&T research.

This study used bibliometric analysis to examine all published MCDM studies focused on H&T industries, since 1997. In addition, topic modelling was used to discover key concepts. Finally, top cited studies in terms of total citations per year and total citations were qualitatively reviewed for more insights.

The findings revealed an ongoing interest in applying MCDM techniques in H&T research. Specifically, the extension of fuzzy theory in MCDM techniques is burgeoning among H&T researchers. However, a certain number of MCDM techniques seem to be ignored in this field with a repetitive application of MCDM techniques in particular areas.

The data for the current research was solely retrieved from Scopus and other databases were not included. Therefore, future research is called for to re-examine the study by considering data from various databases.

This study contributes to extant H&T literature by identifying the most prolific and influential countries, journals, publications and trends by applying MCDM techniques in H&T research, and elucidating the implications and characteristics of MCDM techniques in H&T research.

The purpose of the study is to provide insights into the process of decision making. Indian Railways as an organization has to use the available resources based on thorough analysis and proper application of available evaluation methodologies.

This study uses an integrated group discussion – analytical hierarchy process (AHP) to prioritize capital-intensive Indian Railways projects.

Through an email-based survey, six important criteria for ranking upcoming domestic freight corridors were selected, and weights for these criteria were calculated through AHP. The sensitivity analysis of the decision model suggests that the results of this study are significant, reliable and robust.

This study lays a foundation for the decision-makers of Indian Railways to consider a scientific approach while finalizing the big investment projects. This paper provides essential insights on prioritizing capital-intensive transport projects and is readily applicable to any case study.

Innovative design and execution approaches are employed in infrastructure sectors and planning to enhance the integrated project delivery system, assure the sustainability of infrastructure projects, and meet the demands of the dynamic, changing environment. Delivery methods must incorporate new technologies. By combining digital technology, teamwork, and mass manufacturing, a greater degree of exceptional quality, sustainability, and resilience in the environment will be generated. As a result, a new approach does not rely on the reaction policy, but instead considers alternative scenarios and employs a simulation model to determine the best course of action.

In the paper, the system dynamics approach to construction management is validated in light of pertinent research. Additionally, it describes the difficulties facing the infrastructure projects' delivery system. Additionally, the strategy for system dynamics creation is described. This strategy includes a causal loop diagram, generates a stock-flow diagram, and simulates forecasts of model behavior over time. Next, the optimization model's validation process is used to create a system dynamics model for choosing the best infrastructure project delivery system project and controlling it to maximize sustainability, mass production, digital integration, and team integration. The dynamic complexity of project management is growing.

The primary goal is to present a system dynamics (SD) simulation to look at how well infrastructure projects perform in terms of choosing the best method for delivering infrastructure projects. One of the most ideal methods for delivering projects is integrated project delivery. An effective methodology for making strategic decisions on the choice of the best project delivery method. In order to enhance certain infrastructure project delivery system metrics for sustainability, mass production, digital integration, and team integration, the model included building strategy and sophisticated system dynamics simulation. According to the construction strategy, the outcomes have been satisfactory.

System dynamics research has been done to replicate the idea of contemporary construction in order to determine the best approach for delivering infrastructure. The government and decision-makers would benefit from understanding this research as they decide on the best delivery method for boosting the sustainability and productivity of infrastructure projects in Egypt.

The post-pandemic crisis has reshaped work dynamics across industries, leading to a widespread reliance on technology for remote work and business continuity. Operations have shifted to the digital space, altering job requirements and creating new career opportunities. The expansion of the digital industry has generated numerous career choices. The purpose of this study is to determine the relationships between self-efficacy, social media, career outcome expectations and career choices among fresh graduates in Malaysia amid the pandemic crisis.

Research data were collected from a sample of 318 fresh graduates from both public and private universities in Malaysia. Partial least squares structural equation modeling (PLS-SEM) was used to analyze the data in this study.

The empirical findings revealed significant correlations between self-efficacy, social media usage and career outcome expectations and the career choices of Malaysian fresh graduates.

The present study offers an empirical framework to explain career choices among fresh graduates in Malaysia during the pandemic crisis, based on a review of related literature on careers. This research contributes to the body of knowledge on career choices among Generation Z fresh graduates and provides practical implications for organizations and individual employees. It suggests developing relevant Human Resource Development (HRD) interventions to retain the young workforce within organizations.

This study enriches the existing literature on self-efficacy, social media and career outcome expectations in the context of a pandemic crisis. It offers a new interpretation of how individual and contextual factors impact career choices, shaping the career management attitudes of fresh graduates in the post-pandemic era. The empirical findings also give valuable insights into higher education institutions, organizations and government authorities in Malaysia to develop relevant interventions to assist undergraduate students in their career choice exploration.