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The replenishment of construction materials heavily relies on the functioning of heavy machinery, which often leads to confusion and negotiations among construction work groups regarding the allocation rights of these materials. When multiple groups require the same construction materials, they often struggle to determine whether the delivered materials are intended for their own use or if they have encroached upon supplies designated for others. Such uncertainties and negotiations frequently result in delays in construction progress and have the potential to escalate into conflicts. To minimize misunderstandings among work groups and mitigate the risk of severe safety consequences, it is crucial to understand the decision-making processes involved in the interaction between work groups.

This paper adopts a game theory approach to examine the interactions among work groups from a safety perspective. Quantum response equilibrium (QRE), as a specialized form of game with incomplete information, is assumed to govern the behavior of work groups in this study. By conducting a questionnaire survey, interactive scenarios were simulated. A resource overlap scenario for high-altitude construction is established, with the key factors being the importance of construction materials, the time required to supplement materials, whether managers are present and the climate within the groups. The model parameters were estimated using the expectation–maximization algorithm. Additionally, individual traits and safety awareness are surveyed in the questionnaire, further explaining the results of the game.

The findings indicate that the likelihood of conflicts between work groups under resource overlap can be quantified. The radical behavior of construction work groups exhibits a positive correlation with the importance of construction materials and the time required for material replenishment. Furthermore, the presence of a safety climate and the oversight of management personnel play a significant role in maintaining the composure of construction work groups. The expanded results of the questionnaire demonstrate that there is considerable room for improvement in workers' safety awareness, and management approaches can be further enhanced to prevent unsafe behaviors from occurring.

A novel game theory model was developed to evaluate the behavior of construction groups in situations of resource overlap. This model offers practical suggestions to improve safety performance and efficiency in construction projects.

The production planning problem with fine-grained information has hardly been considered in practice. The purpose of this study is to investigate the data-driven production planning problem when a manufacturer can observe historical demand data with high-dimensional mixed-frequency features, which provides fine-grained information.

In this study, a two-step data-driven optimization model is proposed to examine production planning with the exploitation of mixed-frequency demand data is proposed. First, an Unrestricted MIxed DAta Sampling approach is proposed, which imposes Group LASSO Penalty (GP-U-MIDAS). The use of high frequency of massive demand information is analytically justified to significantly improve the predictive ability without sacrificing goodness-of-fit. Then, integrated with the GP-U-MIDAS approach, the authors develop a multiperiod production planning model with a rolling cycle. The performance is evaluated by forecasting outcomes, production planning decisions, service levels and total cost.

Numerical results show that the key variables influencing market demand can be completely recognized through the GP-U-MIDAS approach; in particular, the selected accuracy of crucial features exceeds 92%. Furthermore, the proposed approach performs well regarding both in-sample fitting and out-of-sample forecasting throughout most of the horizons. Taking the total cost and service level obtained under the actual demand as the benchmark, the mean values of both the service level and total cost differences are reduced. The mean deviations of the service level and total cost are reduced to less than 2.4%. This indicates that when faced with fluctuating demand, the manufacturer can adopt the proposed model to effectively manage total costs and experience an enhanced service level.

Compared with previous studies, the authors develop a two-step data-driven optimization model by directly incorporating a potentially large number of features; the model can help manufacturers effectively identify the key features of market demand, improve the accuracy of demand estimations and make informed production decisions. Moreover, demand forecasting and optimal production decisions behave robustly with shifting demand and different cost structures, which can provide manufacturers an excellent method for solving production planning problems under demand uncertainty.

Pakistan has experienced financial liberalization with rapid ups and downs in economic growth due to domestic issues during the last 2 decades. Motivated by inconclusive and conflicting time-driven findings about the performance of the business groups, this study examines the performance of business groups in Pakistan for a relatively long period from 2003 to 2018.

The study uses 3,821 firm-year observations from non-financial firms listed on the Pakistan Stock Exchange (PSX). For the estimation, pooled ordinary least squares (OLS) with industry- and year fixed effects and two-step system generalized methods of moments (GMM) are used.

The study finds that group-affiliated firms outperform independent firms in accounting performance, while underperform in market performance. The outperformance is mainly driven by medium-sized business groups, while underperformance is driven by small and large business groups. Further, the study documents that the underperformance in terms of market performance of firms affiliated with small and large groups is greater before the economic downturn, while outperformance in terms of the accounting measure of firms affiliated with medium-sized groups is greater during the economic downturn. These findings support our time-driven concerns. Overall, the authors' findings are consistent with institutional and transaction cost theories.

Business groups are important channels to reduce market inefficiencies. Business groups may enhance the affiliated firms' resources and resistance capacity through active utilization of the internal capital market, specifically when market conditions are not ideal for affiliates. However, effective utilization of internal capital markets depends on group size. Therefore, investors should deliberate on the size of business groups and diversification within business groups.

The authors extend the literature by providing fresh evidence related to the performance of business groups in the Pakistani context while accounting for the role of the size of business groups.

The benefits of integrating immersive technologies (ImTs) within a construction safety context are acknowledged within the literature; however, its practical application on construction sites remains low. Whilst research into the integration of ImTs within the construction industry is underway, most have viewed this from a broader adoption context or within educational settings, and not specifically from a practical on-site safety perspective. Therefore, the purpose of this study is to address the contributing factors to its integration within on-site safety processes, using the experiences of active construction professionals.

This study adopts a qualitative approach. Data was collected through online focus groups involving UK based construction professionals with experience in using ImTs, recruited using selective sampling. Data sets were subsequently analysed using inductive thematic analysis and are presented within key themes.

The results showed that among the experienced construction professionals, the use of ImTs specifically for on-site safety applications (including inductions/training/workshops) was rare on projects. The findings identified various contributing factors related to the integration of ImTs, including the potential improvements in on-site safety practices such as enhanced communication of hazards, safety planning, engagement during training and more accurate risk assessment. Critical challenges, concerns and frustrations included a lack of engagement from senior level management, inadequate leadership, limited investment, a need for digital expertise, fear of complacency and the acceptance of ImTs within existing safety processes from the wider project team.

This study provides a fresh perspective to this field by using practical accounts from active and experienced on-site construction professionals. This study supports the integration of ImTs within the construction industry, presenting key contributing factors influencing its integration within on-site safety processes. These factors can be considered by industry adopters, and includes the rationale, challenges and potential on-site benefits of ImTs.

Risk perception and ethnocentrism are recognized as significant psychological factors influencing tourism behaviors. However, the impact of tourist ethnocentrism (TE) on tourism and hospitality-related behaviors has mainly been overlooked in previous research. Hence, the objective of the present study is to propose a comprehensive TE model by exploring the influence of TE and risk perception on the domestic hospitality and tourism industry in Israel.

A convenience sample of 204 Israeli respondents 18 years of age or older took part in the study. Structural equation modeling (SEM) was employed to assess hypothesized relationships in the proposed model.

The data confirmed five out of the eight hypotheses tested. The study findings suggest that TE is unrelated to willingness to pay (WTP) a price premium for local travel or dine in local restaurants. Similarly, the authors found no association between risk perception and willingness to dine in local restaurants.

The current investigation contributes to the literature by proposing a model conceptualizing the influence of both TE and risk perception on hospitality (i.e. dine in a local restaurant) and tourism (i.e. domestic travel). The present research findings contribute to the tourism ethnocentrism literature and shed new light on social identity theory (SIT) by pointing to the importance of considering value for money in future conceptualizations and suggest monetary considerations may overshadow other in-group considerations as conceptualized in SIT.

This research work has developed an integrated fuzzy Delphi and neutrosophic best–worst framework for selecting the sustailient (sustainable and resilient) supplier for an additive manufacturing (AM)-enabled industry.

An integrated fuzzy Delphi method (FDM) and neutrosophic best–worst method (N-BWM) approach is developed. 34 supplier evaluation criteria falling under 4 groups, that is, traditional, sustainable, resilient, and AM specific, are identified and validated using the FDM. Afterward, the weights of each criterion are measured by N-BWM. Later on, the performance evaluation is carried out to determine the best-suited supplier. Finally, sensitivity analysis is performed to know the stability and robustness of the proposed framework.

The outcome indicates the high performance of the suggested decision-making framework. The analysis reveals that supplier 4 (S4) is selected as the most appropriate for a given firm based on the FDM and N-BWM method.

The applicability of this framework is demonstrated through an industrial case of a 3D-printed trinket manufacturer. The proposed research helps AM decision-makers better understand resiliency, sustainability, and AM-related attributes. With this, the practitioners working in AM business can prioritize the supplier selection criteria.

This is the primitive study to undertake the most critical aspect of supplier selection for AM-enabled firms. Apart from this, an integrated FDM-N-BWM framework is a novel contribution to the literature on supplier selection.

Until now, a lot of research has been done and applied to provide security and original data from one user to another, such as third-party auditing and several schemes for securing the data, such as the generation of the key with the help of encryption algorithms like Rivest–Shamir–Adleman and others. Here are some of the related works that have been done previously. Remote damage control resuscitation (RDCR) scheme by Yan et al. (2017) is proposed based on the minimum bandwidth. By enabling the third party to perform the verification of public integrity. Although it supports the repair management for the corrupt data and tries to recover the original data, in practicality it fails to do so, and thus it takes more computation and communication cost than our proposed system. In a paper by Chen et al. (2015), using broadcast encryption, an idea for cloud storage data sharing has been developed. This technique aims to accomplish both broadcast data and dynamic sharing, allowing users to join and leave a group without affecting the electronic press kit (EPK). In this case, the theoretical notion was true and new, but the system’s practicality and efficiency were not acceptable, and the system’s security was also jeopardised because it proposed adding a member without altering any keys. In this research, an identity-based encryption strategy for data sharing was investigated, as well as key management and metadata techniques to improve model security (Jiang and Guo, 2017). The forward and reverse ciphertext security is supplied here. However, it is more difficult to put into practice, and one of its limitations is that it can only be used for very large amounts of cloud storage. Here, it extends support for dynamic data modification by batch auditing. The important feature of the secure and efficient privacy preserving provable data possession in cloud storage scheme was to support every important feature which includes data dynamics, privacy preservation, batch auditing and blockers verification for an untrusted and an outsourced storage model (Pathare and Chouragadec, 2017). A homomorphic signature mechanism was devised to prevent the usage of the public key certificate, which was based on the new id. This signature system was shown to be resistant to the id attack on the random oracle model and the assault of forged message (Nayak and Tripathy, 2018; Lin et al., 2017). When storing data in a public cloud, one issue is that the data owner must give an enormous number of keys to the users in order for them to access the files. At this place, the knowledge assisted software engineering (KASE) plan was publicly unveiled for the first time. While sharing a huge number of documents, the data owner simply has to supply the specific key to the user, and the user only needs to provide the single trapdoor. Although the concept is innovative, the KASE technique does not apply to the increasingly common manufactured cloud. Cui et al. (2016) claim that as the amount of data grows, distribution management system (DMS) will be unable to handle it. As a result, various proven data possession (PDP) schemes have been developed, and practically all data lacks security. So, here in these certificates, PDP was introduced, which was based on bilinear pairing. Because of its feature of being robust as well as efficient, this is mostly applicable in DMS. The main purpose of this research is to design and implement a secure cloud infrastructure for sharing group data. This research provides an efficient and secure protocol for multiple user data in the cloud, allowing many users to easily share data.

The methodology and contribution of this paper is given as follows. The major goal of this study is to design and implement a secure cloud infrastructure for sharing group data. This study provides an efficient and secure protocol for multiple user data in cloud, allowing several users to share data without difficulty. The primary purpose of this research is to design and implement a secure cloud infrastructure for sharing group data. This research develops an efficient and secure protocol for multiple user data in the cloud, allowing numerous users to exchange data without difficulty. Selection scheme design (SSD) comprises two algorithms; first algorithm is designed for limited users and algorithm 2 is redesigned for the multiple users. Further, the authors design SSD-security protocol which comprises a three-phase model, namely, Phase 1, Phase 2 and Phase 3. Phase 1 generates the parameters and distributes the private key, the second phase generates the general key for all the users that are available and third phase is designed to prevent the dishonest user to entertain in data sharing.

Data sharing in cloud computing provides unlimited computational resources and storage to enterprise and individuals; moreover, cloud computing leads to several privacy and security concerns such as fault tolerance, reliability, confidentiality and data integrity. Furthermore, the key consensus mechanism is fundamental cryptographic primitive for secure communication; moreover, motivated by this phenomenon, the authors developed SSDmechanismwhich embraces the multiple users in the data-sharing model.

Files shared in the cloud should be encrypted for security purpose; later these files are decrypted for the users to access the file. Furthermore, the key consensus process is a crucial cryptographic primitive for secure communication; additionally, the authors devised the SSD mechanism, which incorporates numerous users in the data-sharing model, as a result of this phenomena. For evaluation of the SSD method, the authors have considered the ideal environment of the system, that is, the authors have used java as a programming language and eclipse as the integrated drive electronics tool for the proposed model evaluation. Hardware configuration of the model is such that it is packed with 4 GB RAM and i7 processor, the authors have used the PBC library for the pairing operations (PBC Library, 2022). Furthermore, in the following section of this paper, the number of users is varied to compare with the existing methodology RDIC (Li et al., 2020). For the purposes of the SSD-security protocol, a prime number is chosen as the number of users in this work.

This work examines the returns to education for workers who pursue additional education after time out of the labor force. It compares those who remain in the labor force during additional education with those who drop out of the labor force during additional education. It compares two cohorts of the National Longitudinal Survey of Youth (NLSY).

This work utilizes a difference equation to estimate the returns to education for workers who pursue additional education after time spent out of school and in the labor force.

The results indicate a sheepskin return of approximately 14% for those who remain in the labor force and a return of approximately 9% to years of additional education for those who drop out of the labor force. This contrasting pattern of returns is robust to sample selection correction and a variety of checks.

This work does not fully account for all threats to causation. Further research could pursue these and make use of data from more clearly defined periods of education.

This work finds key differences between the internal labor market faced by those remaining in the labor force and the external labor market faced by those dropping out of the labor force. A policy focused on re-training workers should account for these differences.

This is the first work to compare workers who pursue additional education while remaining in the labor force to workers who pursue additional education and drop out of the labor force.

Political risk has been identified as a major impediment to the success of foreign invested projects, in developing countries. Infrastructure projects are especially sensitive to host-country political climates. Governance in emerging economies can be unstable, which adversely impacts infrastructure projects, given their high capital-intensity, long operational periods and high asset specificity. While the detrimental impact of political risk is well documented, the mitigation of such impacts on infrastructure projects remains largely unexamined. This study, therefore, addresses this by exploring the available identified political risk management (PRM) strategies based on resilience theory and evaluating their effectiveness.

A mixed-method approach was employed to identify PRM strategies. Firstly, a comprehensive literature review identified 40 potential PRM strategies. However, the applicability of those 40 strategies was uncertain due to the scarcity of PRM studies. Thus, expert interviews, drawing on the insights of Chinese infrastructure industry professionals with experience in FII, were applied to review the identified strategies. This process reduced the pool of applicable strategies to 34. Subsequently, 356 questionnaires were sent out to investors from China, Australia and Singapore, with 218 valid responses returned. Based on the data collected from the surveys, statistical analysis was used to evaluate and classify applicable PRM strategies.

Results reveal the most effective top five strategies for offsetting the detrimental effects of political risk on foreign infrastructure investment to be: (1) selection of suitable markets and projects; (2) maintaining good relationship with government; (3) purchasing political insurance; (4) utilizing capable contractors from both host country and home country; and (5) adopting an appropriate entry mode. The 34 strategies were further consolidated into four meta-strategies through factor analysis, resulting in the formulation of a strategy selection matrix.

The findings of this study offer a rational means by which infrastructure investment practitioners considering projects in developing countries, may arrive at an optimal political risk mitigation strategy. The findings also offer government of host countries directives to improving the political environment in order to attract foreign investment flows into local infrastructure projects.

This paper aims to introduce a novel structure for the physical internet (PI)–enabled sustainable supplier selection and inventory management problem under uncertain environments.

To address hybrid uncertainty both in the objective function and constraints, a novel interactive hybrid multi-objective optimization solution approach combining Me-based fuzzy possibilistic programming and interval programming approaches is tailored.

Various numerical experiments are introduced to validate the feasibility of the established model and the proposed solution method.

Due to its interconnectedness, the PI has the opportunity to support firms in addressing sustainability challenges and reducing initial impact. The sustainable supplier selection and inventory management have become critical operational challenges in PI-enabled supply chain problems. This is the first attempt on this issue, which uses the presented novel interactive possibilistic programming method.