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The construction of infrastructure projects for extracting natural resources is vital to the economies of countries and the strategies of mining companies. Project performance success (PJPF) means achieving the planned scope, cost, schedule and quality. This study aims to analyze if PJPF is influenced by the team’s psychological empowerment (PEMP) and structural empowerment (SEMP), the project manager’s transformational leadership (TLD) and shared leadership (SLD) styles and the cultural power distance (CPDT). The study also examined the mediating roles of TLD and CPDT.

This paper tested its hypotheses through confirmatory factor analysis and structural equation modeling in AMOS. Data were collected using the online survey platform SurveyMonkey. Owners, contractors and consultants from 24 countries across the Americas, Africa, Europe, Asia and Australia contributed a total of 222 responses. All participants were involved in construction projects owned by a mining company listed in the S&P 500.

PEMP has a positive impact on PJPF, SEMP and CPDT. PEMP fosters engaged and autonomous employees with agility and problem-solving skills. TLD mediates the relationship between PEMP and SLD. The results indicated that SEMP, TLD and SLD, on their own, do not directly contribute to project success. In contrast to prior studies, CPDT does not mediate the effects of PEMP on PJPF.

Although construction projects remain labor-intensive, research on measuring PEMP, SEMP, TLD, SLD and CPDT in this field is limited. This document is notable for incorporating the perspectives of owners, EPC contractors and consultants.

The purpose of this study was to address waste management in the food supply chain (FSC) through the integration of inspection processes in production and distribution centers under uncertain conditions, aiming to enhance sustainability across environmental, economic and social dimensions. The study introduces a sustainable forward and reverse FSC network using a closed-loop supply chain network approach to prevent the transfer of spoiled products, ultimately providing competitive advantages to stakeholders.

A robust multi-objective mathematical programming model is proposed, incorporating inspection processes to manage perishable products effectively. The model is solved using the Augmented Epsilon Constraint technique implemented in GAMS software, providing Pareto-optimal solutions tailored to decision-makers’ preferences. Furthermore, the methodology is applied in a real-world case study and solved with the Benders Decomposition algorithm to validate its practicality and effectiveness.

The proposed methodology effectively minimizes waste and enhances sustainability in the FSC by optimizing decision-making processes under uncertainty. The illustrative examples and real case study demonstrate the efficiency of the model and solution approach, highlighting the significant role of inspection in improving all three dimensions of sustainability.

The study offers valuable insights into and tools for food industry managers to make informed strategic and tactical decisions. By addressing waste management through advanced supply chain modeling, the research helps organizations reduce costs, improve sustainability and gain a competitive edge in the market.

This research is novel in its focus on integrating inspection into the FSC network and addressing uncertainty through robust mathematical modeling. It contributes to the existing literature by demonstrating the impact of inspection on sustainability in FSCs and providing practical solutions for real-world implementation.

Integrating project scheduling and material ordering problems is vital in realistically estimating project cost and duration. Also, the quality level of materials is important as one of the key project success factors.

In this paper, a three-objective mathematical model is presented for green project scheduling with materials ordering problems considering rental resources. The first objective is to minimize the total cost of the project site and logistics. The second objective is to minimize the environmental impacts of producing materials and the third objective is to maximize the total quality of materials. Since costs trigger several challenges in projects, cost constraints are considered in this model for the first time and also the cost of delay in supplying of materials by the suppliers has been deducted from the project costs. Subsequently, the model was implemented in a real case and solved by the Lagrangian Relaxation algorithm as an exact method on GAMS software for model validation.

Based on sensitivity analysis of some parameters, the findings indicate that the cost constraint and lead time have considerable effects on the project duration. Also, integrating project scheduling and material ordering improves the robustness of the project schedule.

The primary contributions of the present research can be stated as follows: considering the cost constraints in the project scheduling with material ordering problem, incorporating the rental resources and taking the quality levels of materials as well as the environmental impacts into account.

Untimely responses to emergency situations in urban areas contribute to a rising mortality rate and impact society's primary capital. The efficient dispatch and relocation of ambulances pose operational and momentary challenges, necessitating an optimal policy based on the system's real-time status. While previous studies have addressed these concerns, limited attention has been given to the optimal allocation of technicians to respond to emergency situation and minimize overall system costs.

In this paper, a bi-objective mathematical model is proposed to maximize system coverage and enable flexible movement across bases for location, dispatch and relocation of ambulances. Ambulances relocation involves two key decisions: (1) allocating ambulances to bases after completing services and (2) deciding to change the current ambulance location among existing bases to potentially improve response times to future emergencies. The model also considers the varying capabilities of technicians for proper allocation in emergency situations.

The Augmented Epsilon-Constrained (AEC) method is employed to solve the proposed model for small-sized problem. Due to the NP-Hardness of the model, the NSGA-II and MOPSO metaheuristic algorithms are utilized to obtain efficient solutions for large-sized problems. The findings demonstrate the superiority of the MOPSO algorithm.

This study can be useful for emergency medical centers and healthcare companies in providing more effective responses to emergency situations by sending technicians and ambulances.

In this study, a two-objective mathematical model is developed for ambulance location and dispatch and solved by using the AEC method as well as the NSGA-II and MOPSO metaheuristic algorithms. The mathematical model encompasses three primary types of decision-making: (1) Allocating ambulances to bases after completing their service, (2) deciding to relocate the current ambulance among existing bases to potentially enhance response times to future emergencies and (3) considering the diverse abilities of technicians for accurate allocation to emergency situations.

Earned value management (EVM)–based models for estimating project actual duration (AD) and cost at completion using various methods are continuously developed to improve the accuracy and actualization of predicted values. This study primarily aimed to examine natural gradient boosting (NGBoost-2020) with the classification and regression trees (CART) base model (base learner). To the best of the authors' knowledge, this concept has never been applied to EVM AD forecasting problem. Consequently, the authors compared this method to the single K-nearest neighbor (KNN) method, the ensemble method of extreme gradient boosting (XGBoost-2016) with the CART base model and the optimal equation of EVM, the earned schedule (ES) equation with the performance factor equal to 1 (ES1). The paper also sought to determine the extent to which the World Bank's two legal factors affect countries and how the two legal causes of delay (related to institutional flaws) influence AD prediction models.

In this paper, data from 30 construction projects of various building types in Iran, Pakistan, India, Turkey, Malaysia and Nigeria (due to the high number of delayed projects and the detrimental effects of these delays in these countries) were used to develop three models. The target variable of the models was a dimensionless output, the ratio of estimated duration to completion (ETC(t)) to planned duration (PD). Furthermore, 426 tracking periods were used to build the three models, with 353 samples and 23 projects in the training set, 73 patterns (17% of the total) and six projects (21% of the total) in the testing set. Furthermore, 17 dimensionless input variables were used, including ten variables based on the main variables and performance indices of EVM and several other variables detailed in the study. The three models were subsequently created using Python and several GitHub-hosted codes.

For the testing set of the optimal model (NGBoost), the better percentage mean (better%) of the prediction error (based on projects with a lower error percentage) of the NGBoost compared to two KNN and ES1 single models, as well as the total mean absolute percentage error (MAPE) and mean lags (MeLa) (indicating model stability) were 100, 83.33, 5.62 and 3.17%, respectively. Notably, the total MAPE and MeLa for the NGBoost model testing set, which had ten EVM-based input variables, were 6.74 and 5.20%, respectively. The ensemble artificial intelligence (AI) models exhibited a much lower MAPE than ES1. Additionally, ES1 was less stable in prediction than NGBoost. The possibility of excessive and unusual MAPE and MeLa values occurred only in the two single models. However, on some data sets, ES1 outperformed AI models. NGBoost also outperformed other models, especially single models for most developing countries, and was more accurate than previously presented optimized models. In addition, sensitivity analysis was conducted on the NGBoost predicted outputs of 30 projects using the SHapley Additive exPlanations (SHAP) method. All variables demonstrated an effect on ETC(t)/PD. The results revealed that the most influential input variables in order of importance were actual time (AT) to PD, regulatory quality (RQ), earned duration (ED) to PD, schedule cost index (SCI), planned complete percentage, rule of law (RL), actual complete percentage (ACP) and ETC(t) of the ES optimal equation to PD. The probabilistic hybrid model was selected based on the outputs predicted by the NGBoost and XGBoost models and the MAPE values from three AI models. The 95% prediction interval of the NGBoost–XGBoost model revealed that 96.10 and 98.60% of the actual output values of the testing and training sets are within this interval, respectively.

Due to the use of projects performed in different countries, it was not possible to distribute the questionnaire to the managers and stakeholders of 30 projects in six developing countries. Due to the low number of EVM-based projects in various references, it was unfeasible to utilize other types of projects. Future prospects include evaluating the accuracy and stability of NGBoost for timely and non-fluctuating projects (mostly in developed countries), considering a greater number of legal/institutional variables as input, using legal/institutional/internal/inflation inputs for complex projects with extremely high uncertainty (such as bridge and road construction) and integrating these inputs and NGBoost with new technologies (such as blockchain, radio frequency identification (RFID) systems, building information modeling (BIM) and Internet of things (IoT)).

The legal/intuitive recommendations made to governments are strict control of prices, adequate supervision, removal of additional rules, removal of unfair regulations, clarification of the future trend of a law change, strict monitoring of property rights, simplification of the processes for obtaining permits and elimination of unnecessary changes particularly in developing countries and at the onset of irregular projects with limited information and numerous uncertainties. Furthermore, the managers and stakeholders of this group of projects were informed of the significance of seven construction variables (institutional/legal external risks, internal factors and inflation) at an early stage, using time series (dynamic) models to predict AD, accurate calculation of progress percentage variables, the effectiveness of building type in non-residential projects, regular updating inflation during implementation, effectiveness of employer type in the early stage of public projects in addition to the late stage of private projects, and allocating reserve duration (buffer) in order to respond to institutional/legal risks.

Ensemble methods were optimized in 70% of references. To the authors' knowledge, NGBoost from the set of ensemble methods was not used to estimate construction project duration and delays. NGBoost is an effective method for considering uncertainties in irregular projects and is often implemented in developing countries. Furthermore, AD estimation models do fail to incorporate RQ and RL from the World Bank's worldwide governance indicators (WGI) as risk-based inputs. In addition, the various WGI, EVM and inflation variables are not combined with substantial degrees of delay institutional risks as inputs. Consequently, due to the existence of critical and complex risks in different countries, it is vital to consider legal and institutional factors. This is especially recommended if an in-depth, accurate and reality-based method like SHAP is used for analysis.

This paper addresses a crucial research need by examining the influence of compatibility, a pivotal design element for hotel concierge apps, on the socio-psychological dynamics of digital hotel guests. While prior research has examined the constructs, their application to digital concierge apps introduces a unique context. We posit that compatibility significantly influences central variables rooted in theory of planned behaviors (TPBs) and technology acceptance model (TAM), fostering positive usage intentions.

Analyzing data from 668 four-star hotel guests through PLS-SEM substantiates compatibility’s role, endorsing the theoretical amalgamation of affordance, TPB, and TAM frameworks.

Compatibility positively affected perceived ease of use, perceived usefulness, and attitude toward behavior. Besides, usage intention positively affected willingness to pay a price premium and revisit intention.

This paper adopts compatibility as a unifying force for integrating TPB and TAM; the predictive ability of digital concierges' usage intentions on revisit intentions to upscale hotels. Further, this paper is the first attempt to highlight employing compatibility as a pivotal design factor for digital concierge apps in the hospitality setting.

In traditional Bayesian belief networks (BBNs), a large amount of data are required to complete network parameters, which makes it impractical. In addition, no systematic method has been used to create the structure of the BBN. The aims of this study are to: (1) decrease the number of questions and time and effort required for completing the parameters of the BBN and (2) present a simple and apprehensible method for creating the BBN structure based on the expert knowledge.

In this study, by combining the decision-making trial and evaluation laboratory (DEMATEL), interpretive structural modeling (ISM) and BBN, a model is introduced that can form the project risk network and analyze the impact of risk factors on project cost quantitatively based on the expert knowledge. The ranked node method (RNM) is then used to complete the parametric part of the BBN using the same data obtained from the experts to analyze DEMATEL.

Compared to the traditional BBN, the proposed method will significantly reduce the time and effort required to elicit network parameters and makes it easy to create a BBN structure. The results obtained from the implementation of the model on a mass housing project showed that considering the identified risk factors, the cost overruns relating to material, equipment, workforce and overhead cost were 37.6, 39.5, 42 and 40.1%, respectively.

Compared to the traditional BBN, the proposed method will significantly reduce the time and effort required to elicit network parameters and makes it easy to create a BBN structure. The results obtained from the implementation of the model on a mass housing project showed that considering the identified risk factors, the cost overruns relating to material, equipment, workforce and overhead cost were 37.6, 39.5, 42 and 40.1%, respectively. The obtained results are based on a single case study project and may not be readily generalizable.

The presented framework makes the BBN more practical for quantitatively assessing the impact of risk on project costs. This helps to manage financial issues, which is one of the main reasons for project bankruptcy.

The study aims to identify a model of commercializing entrepreneurial ideas in information and communication technology (ICT) knowledge-based companies.

A mixed method has been used in the research. The participants of the qualitative part were 15 key informants selected by sampling method purposefully and theoretically, while a sample of 205 experts was randomly chosen for the quantitative part. Data collection was completed through a semistructured interview in the qualitative part and by a researcher-made questionnaire in the quantitative part. The reliability of the research was confirmed by Cronbach’s alpha. The validity of the qualitative and quantitative parts was approved, respectively, by the criteria of Corbin and Strauss (2008) and by the content validity. Data analysis was done in the qualitative part through open, axial and selective coding, while in the quantitative part through partial least squares structural equation modeling (PLS-SEM) and adaptive neuro-fuzzy inference systems (ANFIS).

The commercialization model of ICT entrepreneurial ideas was depicted by the paradigmatic version of Corbin and Strauss (2008). The model has been consisted of six sectors as follows: causal conditions (including stimuli of science and technology parks, interests and motivation of managers of ICT knowledge-based company and environmental stimuli), contextual conditions (including skills and abilities of managers of ICT knowledge-based company, status of ICT knowledge-based company and enabling and facilitating legal framework), intervening conditions (including the complex nature of the ICT industry, science and technology parks’ support of companies, facilities and equipment for commercialization of ICT entrepreneurial ideas and economic system stability), strategies (including marketing research, planning and feasibility study of ICT entrepreneurial idea, design and production of ICT product and release and supply of ICT product), consequences (successful commercialization of ICT entrepreneurial ideas in the post-COVID-19 era) and the central phenomenon (ICT entrepreneurial ideas: commercialization in the post-COVID-19 era). Then, the main factors were confirmed through PLS-SEM and ANFIS. Among the factors, interests and motivation of managers of ICT knowledge-based companies, status of ICT knowledge-based companies, facilities and equipment for commercialization of ICT entrepreneurial ideas and release and supply of ICT products were identified as the most influential factors.

The model can help solve the challenges of managers and policymakers to commercialize ICT entrepreneurial ideas. Therefore, innovative production will increase, value will be created for the beneficiaries and economic, social and political growth will occur in the post-Corona era.

Commercialization of ICT entrepreneurial ideas has the potential to affect many aspects of economic and societal activities in the society such as GDP growth, employment, productivity, poverty alleviation, quality of life and education.

The research includes innovation in presenting a multidimensional commercialization model based on an entrepreneurial perspective in the special field of ICT with a mixed approach including grounded theory, PLS-SEM and ANFIS in ICT knowledge-based companies. But the most important innovation of the study is related to the findings. The main categories, subcategories and concepts of the research have been presented in the form of a theory entitled “ICT entrepreneurial ideas: commercialization in the post-COVID-19 era.”

This study aims to address the key issues concerning supplier selection in traditional construction procurement by proposing an innovative, novel, state-of-the-art prototype plugin building information modelling – supplier search and ranking (BIM-SSR) and an associated conceptual framework. It enhances building information modelling (BIM) capabilities through web crawling and analytical hierarchy processes (AHP). It uses the World Wide Web to procure construction material suppliers.

Prevalent issues in traditional procurement of material suppliers have been identified through a rigorous literature review. Field experts vetted these issues. A framework has been presented to address these issues based on integrated web crawling and AHP as a multi-criteria decision-making (MCDM) method. A BIM prototype (BIM-SSR) has been developed using Python and plugged into Autodesk Revit to automate the search and evaluation of material suppliers based on precise material specifications from the BIM design. The BIM-SSR prototype is tested through a case study and validated by field professionals for its efficiency in tackling the identified issues.

Thirteen key issues have been identified concerning traditional construction procurement pertinent to supplier selection. Best-value procurement was encouraged by identifying supplier selection criteria such as cost, delivery time, experience, compliance with quality management standards, warranties, and claim period. The presented BIM-SSR prototype has an efficiency of 80%–95% in addressing the issues identified in this study and 97.5% effectiveness in improving the overall procurement management process.

The BIM-SSR prototype developed in this study is a novel and innovative addition to the body of knowledge that has been integrated into Autodesk Revit as a Plugin. Automation of supplier search and selection through digital technologies, including web crawling and integration of traditionally accepted MCDM methods such as AHP in BIM, is another innovation in the current study. Overall, this study presents a holistic, innovative system, from conceptual design to practical implementation and demonstration. This is one of the steps to help the traditional construction procurement process evolve into a more modern and digital procurement.

The lack of a direct link between business value and sustainability is a critical roadblock to truly embedding sustainability in business strategies. Before launching the sustainability journey, every organisation should answer the question: “What value would this strategy offer our organisation?” Conversely, when organisations are opportunistic toward quick profits, the negative consequences of one domain spill over to another. The desire to produce more may result in overproduction, overconsumption or environmental pollution.

To give a complete analysis of sustainable capabilities, this study combines current theoretical understanding from past literature, followed by exploratory interviews and a thorough case study. The case study ventured into uncharted territories, unveiling an exciting new sphere of value catalysed by the mechanisms of sustainable co-creation. Additionally, it exposed thought-provoking motives driving supply chain actors’ unwavering commitment to ethical decision-making, even amidst towering challenges.

Our empirical lens reveals the hidden mechanics of resource sharing and the genesis of newfound value, illuminating previously obscure corners of the sustainability field. Moreover, it sheds light on retailers striving to cultivate green retail supply chains. It delivers an actionable framework that bolsters business sustainability and fuels competitive edge, which is vital in the rapidly evolving landscapes of emerging economies.

This study offers insights into the sustainable value-creation mechanism in ALPHA, a Malaysian retailer, uncovering how supply chain actors’ business activities generate economic, social and environmental performance.