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The study aims to pioneer an innovative approach for the evaluation of government portal websites (GPWs) by introducing an eye-tracking-based method. The research meticulously pinpoints and analyses the distinct usability issues and challenges that users encounter while navigating and interacting with GPWs.

This study devises an eye-tracking-based GPW usability evaluation approach, which focuses on the major functions (i.e. government information disclosure, government services and interactive responses) of GPWs. An Entropy Weighted Technique for Order Preference by Similarity to an Ideal Solution (EW-TOPSIS) method is employed to process eye-tracking indicator results for deriving GPW usability results.

The proposed approach is demonstrated to assess the usability of 12 GPWs in pilot smart cities in China, and it is found that most GPWs have lower-than-average usability. GPWs with low usability require more cognitive load that exhibit increased fixation and saccade. The comparisons among the GPW usability results from (1) the eye-tracking experiment, (2) questionnaire surveys and (3) the ready-made performance evaluation report validate the effectiveness of eye-tracking-based GPW usability evaluation.

The work contributes to shifting the GPW usability evaluation approach from a subjective judgment paradigm to an objective paradigm, as well as provides implications for enhancing GPW usability, including improving search function, reducing website complexity and prioritizing user needs.

Demand forecasts are a key component of planning efforts and are crucial for managing core operations. This study aims to evaluate the use of several machine learning (ML) algorithms to forecast demand for residential construction in Jordan.

The identification and selection of variables and ML algorithms that are related to the demand for residential construction are indicated using a literature review. Feature selection was done by using a stepwise backward elimination. The developed algorithm’s accuracy has been demonstrated by comparing the ML predictions with real residual values and compared based on the coefficient of determination.

Nine economic indicators were selected to develop the demand models. Elastic-Net showed the highest accuracy of (0.838) versus artificial neural networkwith an accuracy of (0.727), followed by Eureqa with an accuracy of (0.715) and the Extra Trees with an accuracy of (0.703). According to the results of the best-performing model forecast, Jordan’s 2023 first-quarter demand for residential construction is anticipated to rise by 11.5% from the same quarter of the year 2022.

The results of this study extend to the existing body of knowledge through the identification of the most influential variables in the Jordanian residential construction industry. In addition, the models developed will enable users in the fields of construction engineering to make reliable demand forecasts while also assisting in effective financial decision-making.

The purpose of this paper is to develop a bi-objective optimization framework to select prefabricated construction service composition. An improved algorithm-genetic simulated annealing algorithm (GSA) is employed to demonstrate the application of the framework.

The weighted aggregate multi-dimensional collaborative relationship is used to quantitatively evaluate the synergistic effect. The quality of service is measured using the same method. The research proposed a service combination selection framework of prefabricated construction that comprehensively considers the quality of service and synergistic effect. The framework is demonstrated by using a GSA that can accept poor solutions with a certain probability. Furthermore, GSA is compared with the genetic algorithm (GA), simulated annealing algorithm (SA) and particle swarm optimization algorithm (PSO) to validate the performance.

The results indicated that GSA has the largest optimal fitness value and synergistic effect compared with other algorithms, and the convergence time and convergence iteration of the improved algorithm are generally at a low level.

The contribution of this study is that the proposed framework enables project managers to clarify the interactions of the prefabricated construction process and provides guidance for project collaborative management. In addition, GSA helps to improve the probability of successful collaboration between potential partners, therefore enhancing client satisfaction.

Exact solution of time–cost trade-off problem (TCTP) by the state-of-the-art meta-heuristic algorithms can be obtained for small- and medium-scale problems, while satisfactory results cannot be obtained for large construction projects. In this study, a hybrid heuristic meta-heuristic algorithm that adapts the search domain is developed to solve the large-scale discrete TCTP more efficiently.

Minimum cost slope–based heuristic network analysis algorithm (NAA), which eliminates the unfeasible search domain, is embedded into differential evolution meta-heuristic algorithm. Heuristic NAA narrows the search domain at the initial phase of the optimization. Moreover, activities with float durations higher than the predetermined threshold value are eliminated and then the meta-heuristic algorithm starts and searches the global optimum through the narrowed search space. However, narrowing the search space may increase the probability of obtaining a local optimum. Therefore, adaptive search domain approach is employed to make reintroduction of the eliminated activities to the design variable set possible, which reduces the possibility of converging into local minima.

The developed algorithm is compared with plain meta-heuristic algorithm with two separate analyses. In the first analysis, both algorithms have the same computational demand, and in the latter analysis, the meta-heuristic algorithm has fivefold computational demand. The tests on case study problems reveal that the developed algorithm presents lower total project costs according to the dependent t-test for paired samples with α = 0.0005.

In this study, TCTP is solved without considering quality or restrictions on the resources.

The proposed method enables to adapt the number of parameters, that is, the search domain and provides the opportunity of obtaining significant improvements on the meta-heuristic algorithms for other engineering optimization problems, which is the theoretical contribution of this study. The proposed approach reduces the total construction cost of the large-scale projects, which can be the practical benefit of this study.

The quality liability of prefabricated components (PCs) is a major issue among key stakeholders. The blockchain-based quality tracking systems are supposed to support a more transparent and trusting quality control process. However, many factors affect the stakeholders' willingness toward the adoption of such quality tracking systems. The purpose of this research is to investigate the key factors that influence the stakeholders' adoption decisions toward the application of the quality tracking system in PCs and develop coping strategies.

An evolutionary game model is established that includes the manufacturer, constructor and developer. Four scenarios of equilibriums and the game's evolutionary stable strategies are analyzed, and the corresponding stability conditions are then obtained. Based on the tripartite game model, two representative projects are used as case studies to simulate how different factors affect the stakeholders' decisions.

First, trade-offs between cost and benefits were the most prominent factor in the adoption decision-making. Second, the advancement of technologies would compensate for their immaturity. Third, subsidy and penalty provision of the developer and high-level trust both incentivize the stakeholders to adopt the quality tracking systems.

This research investigates the influence of technology, environment and participant related factors on the adoption decisions of the quality tracking system for PCs and discovered that technology maturity and advancement played an essential role. It is expected that the research findings would be of value to policy makers and project management personnel for better quality control of prefabricated construction.

Inclination angle has been reported to have an enhancing effect on the thermal-hydraulic characteristics and entropy of some thermal systems. Therefore, this paper aims to numerically investigate the effects of inclination angle, volume concentration and Reynolds number on the thermal and hydraulic characteristics and entropy generation rates of water-based Al₂O₃ nanofluids through a smooth circular aluminum pipe in a turbulent flow.

A constant heat flux of 2,000 Watts is applied to the circular surface of the tube. Reynolds number is varied between 4,000 and 20,000 for different volume concentrations of alumina nanoparticles of 0.5%, 1.0% and 2.0% for tube inclination angles of ±90o, ±60o, ±45o, ±30o and 0o, respectively. The simulation is performed in an ANSYS Fluent environment using the realizable kinetic energy–epsilon turbulent model.

Results show that +45o tube orientation possesses the largest thermal deviations of 0.006% for 0.5% and 1.0% vol. concentrations for Reynolds numbers 4,000 and 12,000. −45o gives a maximum pressure deviation of −0.06% for the same condition. The heat transfer coefficient and pressure drop give maximum deviations of −0.35% and −0.39%, respectively, for 2.0% vol. concentration for Reynolds number of 20,000 and angle ±90o. A 95%–99.8% and 95%–98% increase in the heat transfer and total entropy generation rates, respectively, is observed for 2.0% volume concentration as tube orientation changes from the horizontal position upward or downward.

Research investigating the effect of inclination angle on thermal-hydraulic performance and entropy generation rates in-tube turbulent flow of nanofluid is very scarce in the literature.

This study's main goal is to expand the theoretical perspective and discuss the unique influence of age and tenure on R&D teams' incremental innovation outcomes. We answer scholars call for additional research on age-related processes by testing pathways through which older employees can benefit organizational performance. The current study advances the literature by relating to the context-related process of cohesion and investigating its moderating influence on the relationship between team antecedents (i.e. age and tenure) and incremental innovation.

This research sample consists of 108 R&D teams operating in six mature high-tech organizations located in Israel. The participating entities design and manufacture state-of-the-art innovations in the semiconductors, communications and information technology sectors. The number of teams in each participating entity was 35, 21, 21, 19, 7 and 5, respectively. The sample consisted of 443 R&D employees and 212 team leaders/managers. The total sample comprised 655 participants. Team members filled out questionnaires to assess the independent variables. The dependent-variable questionnaire focusing on the team's incremental innovation accomplishments was completed by two managers for each team.

We found a negative association between team members' age and incremental innovation. Hypothesis 2, which predicted a positive association between team members' tenure and incremental innovation, was marginally supported. The interaction between team members' age and team members' tenure on incremental innovation was marginally supported. Hypothesis 4, which predicted that the negative association between team members' age and incremental innovation would be mitigated when the level of team cohesion is low, was supported. Hypothesis 5, which assumed that the positive association between team members' tenure and incremental innovation would be stronger when the level of cohesion is high, was supported.

This research's results regarding the negative influence of R&D employees' age on incremental innovation are crucial for managers and team leaders in the high-tech industry. Following the age stereotype, many of them avoid recruiting and assigning older employees to R&D teams dealing in innovation creation and development. They should expand their perspective and consider additional attributes in order to assign the employees that best match the team's mission. The results show that R&D teams produce high and similar levels of incremental innovation when the level of team members' tenure is high, regardless of their age.

This study benefited from a relatively high number of respondents and teams from leading high-tech organizations, a high response rate and a research design that made it possible to establish a linkage between data on the independent variables and data on incremental innovation collected from separate independent sources. The data on the dependent variable—incremental innovation—was based on independent assessments made by two managers for each team. The study's measurements were based on leading studies on innovation.

This study investigates the validity of Ferreira et al.’s (2020) Organizational Bullshit Perception Scale by examining its distinctiveness from similar constructs (perceptions of organizational politics, organizational cynicism, procedural justice) and its predictive validity through its relations with important organizational attitudes (organizational identification) and behaviors (counterproductive work behavior and organizational citizenship behavior). This study also examines the moderating effects of honesty–humility on the relations between organizational bullshit perception and the outcomes of counterproductive work behavior, organizational citizenship behavior and organizational identification. Finally, this study examines the incremental validity of organizational bullshit perception in predicting counterproductive work behavior, organizational citizenship behavior and organizational identification above and beyond similar constructs in an exploratory fashion.

Survey data were collected from a sample of working adults online via Amazon’s Mechanical Turk platform across two waves (final N = 323 for wave 1 and 174 for wave 2), one month apart.

The results indicate that organizational bullshit perception, as measured by Ferreira et al.’s (2020) scale, represents a distinct construct that has statistically significant relations with counterproductive work behavior, organizational citizenship behavior and organizational identification, even after controlling for procedural justice, organizational cynicism and perceptions of organizational politics. The results, however, showed no support for honesty–humility as a moderator.

These findings suggest that organizations can benefit from assessing and working to alleviate their employees’ perceptions of organizational bullshit. This construct predicts behaviors and attitudes important for organizational functioning.

This study adds to Ferreira et al.’s (2020) original work by demonstrating organizational bullshit perception’s distinctiveness from existing constructs in the literature and its implications for organizations and their employees.

This study aims to examine the relationship between cultural intelligence and organisational commitment and its effect on learning goal orientation and turnover intention within the expatriate society of the United Arab Emirates (UAE).

A survey instrument was developed to collect data from 173 non-management expatriates employed by multinational corporations located in Dubai, UAE. SmartPLS bootstrap software was used to analyse the path coefficients and test the research hypotheses.

The results demonstrate that cultural intelligence enhances both learning goal orientation and turnover intention of expatriates. Moreover, organisational commitment partially mediates the relationship between cultural intelligence and turnover intention/learning goal orientation.

This study contributes by advancing extant knowledge with regard to cultural intelligence and organisational commitment effects on turnover intention and learning goal orientation of expatriates within a context of high cultural heterogeneity.

Integrating individual and relational centric voice literature, the authors draw on self-presentation theory to analyse the role of status pursuit in employee voice. Status pursuit is believed to be ubiquitous as it is linked to access to scarce resources and social order pecking.

The authors present a cross-level conceptual model outlining relational nuances of employee status pursuit that drive upward voice.

The model integrates status pursuit with peer- and leader-related facets, focusing on three targets of voice: immediate leader (supervisor), diagonal leader (supervisor of another team/unit) and co-workers. The model highlights how employee voice can be directed to diverse targets, and depending on interpersonal attributes, how it serves as underlying links for upward voice.

While employee voice can help to address important workplace concerns, it can also be used to advance employees' self-interest. Though there is a wealth of research on the importance of employee voice to organisational performance and individual wellbeing, especially through collective representation such as trade unions, there is a lack of literature on how employees navigate the social-relational work setting to promote their interests and develop status.