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The purpose of this study was to examine if team composition based on adaption-innovation (A-I) problem-solving styles is related to the teamwork quality and team effectiveness (TE) of student project teams participating in a [state-gifted program (SGP)].

A correlational design was conducted with a sample of 72 (SGP) participants, consisting of 15 project teams (n = 15), which formed three groups: (1) the homogeneous adaptive group, which consists of five homogeneous adaptive teams (n = 5); (2) the homogeneous innovative group, which consists of five homogeneous innovative teams (n = 5), and (3) the heterogeneous group (i.e. a mix of innovative and adaptive individuals), which consists of five heterogeneous teams (n = 5).

A one-way ANOVA and post-hoc test revealed that team composition based on problem-solving styles is related to teamwork quality and TE. Regarding TE, both homogeneous groups (i.e. all adaptive or all innovative individuals) were more effective than the heterogeneous group. However, regarding teamwork quality, only the adaptive group had significantly higher teamwork quality than the heterogeneous group.

We recommend that leadership educators utilize Kirton’s adaption-innovation inventory (KAI) as a tool for building effective student project teams. KAI can be used by leadership educators in two major ways: to assign students to groups (as done in the current study) or for team building, where team members share their KAI scores to better understand their problem-solving preferences.

The findings add to the literature by specifying the type of homogeneous groups (i.e. homogeneous adaptive), which may offer an advantage over heterogeneous groups regarding teamwork quality.

This study aimed to explore students’ higher-order thinking skills development when they engaged in pattern blocks learning activities using Blended Learning Classroom teaching approaches through the Thailand Lesson Study Incorporated Open Approach model.

The researchers utilized a multiple case research design for 47 higher education students from the Non-degree Higher-order Thinking in Mathematics Program using a purposive sampling technique. The researchers used the On-Demand teaching approach for two days and three hours of a combination of Virtual Live and Face-to-Face teaching approaches, making up a total of 70 and 30 percent of student learning time respectively.

The quantitative results from the online survey questionnaire indicated that the majority of the participants perceived positively the phenomena of the Blended Learning Classroom teaching approach ranging from a mean score of 4.44–4.65. The qualitative results revealed that participants used different interpretations, and utilized their creative, imaginative thinking to connect various concepts while they were solving the problem situations using pattern blocks.

The discussions were limited to a study representative of 47 Non-degree Higher-order Thinking in Mathematics Program students.

The use of the pattern blocks in developing students’ higher-order thinking skills was found to strengthen the effectiveness of Blended Learning Classroom teaching approaches.

This study adds new insight to the literature on Higher-order Thinking Skills development.

This study aims to develop and apply a process model for prioritizing and selecting basic research projects in developing countries.

Basic research is mainly funded by governments and since, unlike technological research, it does not have clear business goals, its prioritization is one of the complicated issues in formulating science and technology policy. Adopting a design science research methodology, the authors chose a general framework for project portfolio selection as an appropriate artifact for solving this problem. By customizing it for two specific features of this study, i.e. national scale of the problem and the basic nature of research proposals, the authors developed the proposed framework for solving the problem of priority setting.

The process for selecting basic research proposals consists of several steps, which can be categorized into eight steps including strategic decisions, preparation, pre-screening, evaluating individual proposals, screening, portfolio selection and monitoring. This study emphasizes the necessity of defining goals that can be evaluated for the national basic research portfolio, as a key strategic decision. Evaluating individual proposals is a peer-review-based process. In contrast, portfolio selection is done through a zero-one linear programming model. The validity of the proposed framework has been confirmed based on the data obtained from the Iran National Science Foundation.

To the best of the authors’ knowledge, in this research, for the first time, a mathematical model for prioritizing basic research at the national level has been presented, which effectively contributes to policymaking regarding the development of an optimum national research portfolio.

The purpose of this study is to formulate a new class of vehicle routing problem with an objective to minimise the total cost of raw material collection and derive a new approach to solve optimization problems. This study can help to select the optimum number of suppliers based on cost.

To model the raw material vehicle routing problem, a mixed integer linear programming (MILP) problem is formulated. An interesting phenomenon added to the proposed problem is that there is no compulsion to visit all suppliers. To guarantee the demand of semiconductor industry, all visited suppliers should reach a given raw material capacity requirement. To solve the proposed model, the authors developed a novel hybrid approach that is a combination of block and edge recombination approaches. To avoid bias, the authors compare the results of the proposed methodology with other known approaches, such as genetic algorithms (GAs) and ant colony optimisation (ACO).

The findings indicate that the proposed model can be useful in industries, where multiple suppliers are used. The proposed hybrid approach provides a better sequence of suppliers compared to other heuristic techniques.

The data used in the proposed model is generated based on previous literature. The problem derives from the assumption that semiconductor industries use a variety of raw materials.

This study provides a new model and approach that can help practitioners and policymakers select suppliers based on their logistics costs.

This study provides two important contributions in the context of the supply chain. First, it provides a new variant of the vehicle routing problem in consideration of raw material collection; and second, it provides a new approach to solving optimisation problems.

This research aims to propose a model for the complex decision-making involved in the ecological restoration of mega-infrastructure (e.g. railway engineering). This model is based on multi-source heterogeneous data and will enable stakeholders to solve practical problems in decision-making processes and prevent delayed responses to the demand for ecological restoration.

Based on the principle of complexity degradation, this research collects and brings together multi-source heterogeneous data, including meteorological station data, remote sensing image data, railway engineering ecological risk text data and ecological restoration text data. Further, this research establishes an ecological restoration plan library to form input feature vectors. Random forest is used for classification decisions. The ecological restoration technologies and restoration plant species suitable for different regions are generated.

This research can effectively assist managers of mega-infrastructure projects in making ecological restoration decisions. The accuracy of the model reaches 0.83. Based on the natural environment and construction disturbances in different regions, this model can determine suitable types of trees, shrubs and herbs for planting, as well as the corresponding ecological restoration technologies needed.

Managers should pay attention to the multiple types of data generated in different stages of megaproject and identify the internal relationships between these multi-source heterogeneous data, which provides a decision-making basis for complex management decisions. The coupling between ecological restoration technologies and restoration plant species is also an important factor in improving the efficiency of ecological compensation.

Unlike previous studies, which have selected a typical section of a railway for specialized analysis, the complex decision-making model for ecological restoration proposed in this research has wider geographical applicability and can better meet the diverse ecological restoration needs of railway projects that span large regions.

The adaptive performance of architects as a key professional in project delivery teams has become important for developing strategies, skills and cognitive behaviours for sustainability of working systems. However, the understanding and knowledge of adaptive performance of architects is lacking in the current literature. Thus, this study fills this gap by primarily assessing the adaptive performance of architects in project teams in project delivery.

By adopting the widely used eight-dimension attributes of adaptive performance, a questionnaire survey was conducted among team participants and stakeholders who directly or indirectly work on projects with architects in the public and private sectors project delivery supply chain in Ghana. A total of 42 responses were subsequently used in a fuzzy set theory analysis being facilitated by a set of linguistic terms.

From the assessment, the overall adaptive performance of architects from the eight-dimension attributes emerged to be fairly high. Additionally, the architects’ performance in the individual eight-dimensions showed varied results. High performance was registered in architects’ ability to handling work stress and cultural adaptability. Also, architects demonstrated a fairly high performance in dealing with uncertain or unpredictable work situations. However, in the cases of learning work tasks, technologies and procedures, interpersonal adaptability and handling crisis and emergency situations, architects were deemed to have low and fairly low adaptive performance among project teams.

Given the vagueness and complexities in understanding adaptability among teams and its assessment, through the use of fuzzy set theory based on a suitable set of linguistics terms, the study presents a novel understanding of the level of architects’ adaptive performance in project teams in project delivery. The findings are extremely useful in helping architects adapt and cope with changing competitive work environment by developing the right cognitive behaviours for task functions and organizational roles, disruptions and aiding their ability to self-regulate.

Most of the existing time-cost-quality-environmental impact trade-off (TCQET) analysis models have focused on solving a simple project representation without taking typical activity and project characteristics into account. This study aims to present a novel approach called the “hybrid opposition learning-based Aquila Optimizer” (HOLAO) for optimizing TCQET decisions in generalized construction projects.

In this paper, a HOLAO algorithm is designed, incorporating the quasi-opposition-based learning (QOBL) and quasi-reflection-based learning (QRBL) strategies in the initial population and generation jumping phases, respectively. The crowded distance rank (CDR) mechanism is utilized to rank the optimal Pareto-front solutions to assist decision-makers (DMs) in achieving a single compromise solution.

The efficacy of the proposed methodology is evaluated by examining TCQET problems, involving 69 and 290 activities, respectively. Results indicate that the HOLAO provides competitive solutions for TCQET problems in construction projects. It is observed that the algorithm surpasses multiple objective social group optimization (MOSGO), plain Aquila Optimization (AO), QRBL and QOBL algorithms in terms of both number of function evaluations (NFE) and hypervolume (HV) indicator.

This paper introduces a novel concept called hybrid opposition-based learning (HOL), which incorporates two opposition strategies: QOBL as an explorative opposition and QRBL as an exploitative opposition. Achieving an effective balance between exploration and exploitation is crucial for the success of any algorithm. To this end, QOBL and QRBL are developed to ensure a proper equilibrium between the exploration and exploitation phases of the basic AO algorithm. The third contribution is to provide TCQET resource utilizations (construction plans) to evaluate the impact of these resources on the construction project performance.

This paper aims to extend insights about the relationship between inter-organizational collaboration and approaches to control from the perspective of decision-makers. We investigate the relationship between approaches to control and intended forms of integration between actors responsible for solving the complex problem of integrated person-centered care for elderly with diverse and significant needs.

The empirical study is based on a content analysis of contractual agreements. We have analyzed a total of 118 collaboration agreements and associative documents between all Swedish regions and municipalities.

The study shows that intended integration is subject to remarkable variation in intended forms of inter-organizational collaboration in this Swedish case. The paper illustrates that decision-makers’ intentions with proposed collaboration in each given context are important for the chosen approach to control. Regardless of intended forms of integration, our study suggests that an imminent soft approach to control is expressed alongside limited signs of hard control. Various forms of intended integration can be managed by the two approaches simultaneously insofar as the agreements appear to have a two-sided purpose.

Our paper proposes an empirically driven taxonomy of intended forms of integration initiatives. The taxonomy provides resources for studies about how collaboration can be managed when it is stipulated by national legislation but local self-governance gives actors considerable freedom to decide on how to organize and manage services. By presenting the taxonomy and relating this to approaches of control, our iterative study builds on and adds to a recent stream of research arguing that the relationship between collaboration and approaches to control may by fuzzier and more complex than originally thought.

The evolution of digital construction within Industrial Revolution 5.0 emphasises the harmonious integration of humans and technology, value-driven technology implementation, adaptive leadership and user-centric design solutions. This shift necessitates architects to enhance their soft skills to navigate the transition effectively. Failure to demonstrate these skills may confine architects to passive roles as technology consumers rather than innovative leaders. While existing research predominantly focuses on the technical aspects of digital construction, this study investigates how architects effectively manifest their soft skills within such dynamic contexts, addressing a critical gap in the current literature.

Employing an interpretive phenomenological approach, the research engaged 14 individual architects practicing in architectural consultancy practices (ACPs) in Malaysia through semi-structured interviews and written exercises to unveil their experiences.

Thematic analysis revealed six primary themes: “analytical and critical thinking,” “digital communication,” “diversity in leadership styles,” “emotional intelligence,” “ethics and professionalism,” and “curiosity and agility.”

Furthermore, the developed thematic map serves as a foundational resource for human resource practices aiming to design upskilling and reskilling programs that focus on nurturing soft skills. Emphasising soft skills, these programs are designed to thrive in the digital construction era and beyond.

These shared experiences significantly contribute to the theoretical understanding of soft skills within the digital construction context, providing novel insights into their practical application when addressing real-world implications.

This study explores how problem-based learning (PBL) programs can address Sustainable Development Goals (SDGs) via the higher education (HE) curriculum, teaching materials and relevant assessments, supporting learning at scale for HE institutions.

Employing SDGs and their indicators as the coding framework, our two-phase study evaluates the curriculum and teaching materials of seven PBL programs at a leading higher education institution (HEI). The first phase involved a content analysis to assess the degree of sustainability integration in 156 relevant courses. The second phase applied a semi-automated mapping protocol to analyze learning and teaching materials in 120 relevant courses.

The school aligns with 17 SDGs (100%), covering 94 indicators (55.62%). On average, each program within the school addresses over ten of these goals and incorporates more than 24 associated indicators. However, the study reveals an imbalance in the incorporation of SDGs, with some goals not yet deeply and comprehensively embedded in the curriculum. While there is a substantial focus on sustainability theories, the practical implications of SDGs in emerging countries, particularly through case studies and assessments, require significant enhancement.

Mapping SDGs allows HEIs to identify strengths and gaps in SDG integration, thereby improving the PBL approach to enhance student work readiness in sustainability-focused careers.

Through the lens of transformative learning theory, this study provides evidence of SDG integration into PBL curricula. It highlights a mapping methodology that enables HEIs to evaluate their sustainability readiness in curriculum, teaching materials and relevant assessments.