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This study aims to develop a predictive model using machine learning techniques to forecast construction dispute outcomes, thereby minimizing economic and social losses and promoting amicable settlements between parties.

This study develops a novel conceptual model incorporating project characteristics, root causes, and underlying causes to predict construction dispute outcomes. Utilizing a dataset of arbitration cases in Türkiye, the model was tested using five machine learning algorithms namely Logistic Regression, Support Vector Machines, Decision Trees, K-Nearest Neighbors, and Random Forest in a Python environment. The performance of each algorithm was evaluated to identify the most accurate predictive model.

The analysis revealed that the Support Vector Machine algorithm achieved the highest prediction accuracy at 71.65%. Twelve significant variables were identified for the best model namely, work type, root causes, delays from a contractor, extension of time, different site conditions, poorly written contracts, unit price determination, penalties, price adjustment, acceptances, delay of schedule, and extra payment claims. The study’s results surpass some existing models in the literature, highlighting the model’s robustness and practical applicability in forecasting construction dispute outcomes.

This study is unique in its consideration of various contract, dispute, and project attributes to predict construction dispute outcomes using machine learning techniques. It uses a fact-based dataset of arbitration cases from Türkiye, providing a robust and practical predictive model applicable across different regions and project types. It advances the literature by comparing multiple machine learning algorithms to achieve the highest prediction accuracy and offering a comprehensive tool for proactive dispute management.

Based on the reception of the principle of self-organization, the core of Heinz von Foerster's operational theories, I hypothesize how Heinz von Foerster's theory can be an orientation model for the epistemological problem of complexity. I have chosen this study to demonstrate complexity as an epistemological problem. This is because the question of how order arises - the core problem of complexity - is an epistemological question for which Heinz von Foerster developed an epistemology of self-organization. I do not present new research because HvF already had the complex organization of systems in mind. Rather, I build a critical approach to complexity on the research and work on operational epistemology in HvF.

This article aims to provide an orientation for a philosophical and epistemological understanding of complexity through a reading of Heinz von Foerster's operational theory. The article attempts to establish complexity as an epistemological phenomenon through the following method: (1) a conceptual description of the science of complexity based on the turn to thermodynamic time, (2) a genealogy of complexity going back to the systemic method, and (3) Heinz von Foerster's cybernetic approach to self-organization.

Based on the reception of the principle of self-organization, the core of Heinz von Foerster's operational theories, the conclusion is drawn that complexity as a description is based on language games.

The results present complexity not as an object of science, but as a description that stands for the understanding of complex description.

The hypothesis that complexity is a question of description or observation, i.e. of description for what language serves, has enormous social implications, in that the description of complexes and the recognition of their orders (patterns) cannot be left to algorithmic governmentality, but must be carried out by a social agency.

HvF's operational epistemology can serve as an epistemological model for critical complexity theory.

This study drawing upon opportunity-ability-motivation (OAM) theory focuses on digital entrepreneurship opportunities (DEOs), knowledge generation capabilities (EKGCs) and enterprise market-sensing capabilities (EMSC) affecting digital innovation in terms of exploitative and exploratory DIs.

Employing quantitative methodology on a sample of 352 software SMEs' managers, the study employed a hierarchical regression analysis to investigate whether and how digital entrepreneurship opportunities and knowledge generation capabilities support and nurture both DIs. Additionally, the moderated–mediation effect of market-sensing capabilities on the relationships among digital entrepreneurship opportunities, enterprise knowledge generation capabilities and DIs are considered.

The study findings reveal that digital entrepreneurship opportunities influence exploitative and exploratory DIs. Knowledge generation capabilities partially mediate the relationship between digital entrepreneurship opportunities and exploitative and exploratory DIs, respectively. Moreover, market-sensing capabilities strengthen not only the effect of digital entrepreneurship opportunities on knowledge generation capabilities but also the effects of knowledge generation capabilities on exploratory DI. The moderated–mediation outcomes reveal that the mediating role of knowledge generation capabilities on the relationship between digital entrepreneurship opportunities and both DIs is stronger when EMSC are high.

This research integrates the opportunity-ability-motivation perspective to present a comprehensive framework that reveals the intricate interdependencies among digital entrepreneurship opportunity, knowledge generation and market-sensing capabilities in driving both exploratory and exploitative digital innovation in software SMEs. This approach significantly enhances our understanding of how software SMEs can strategically strengthen their internal skills and resources, ultimately leading to superior digital innovation outcomes.

Digitalization is changing the way entrepreneurs pursue opportunities. We have elaborated a conceptual framework to gain a better understanding of digital entrepreneurship. Using this framework, we have developed the Global Index of Digital Entrepreneurship Systems (GIDES), an analytical tool designed to measure and comprehend the impact of digitalization on entrepreneurship. This study aims to answer the research question of what specific bottlenecks are hindering the performance of digital entrepreneurial systems in the countries under investigation, with a particular focus on developing Asian economies.

GIDES is a composite indicator that evaluates the performance of digital entrepreneurial systems on a national scale. Unlike traditional entrepreneurship or most entrepreneurial ecosystem measures, GIDES adopts a systemic approach using the Penalty for Bottleneck (PFB) algorithm to spot bottlenecks that potentially degrade overall system performance.

GIDES assesses the digital entrepreneurship systems of 113 countries, with a specific focus on 21 developing Asian economies. Singapore is ranked first among developing Asian countries globally. However, most developing Asian economies have significant room for improvement. While developing Asia excels in terms of physical infrastructure, it needs to work on its culture and informal institutions.

Digital transformation is not happening in isolation. Instead, it is closely linked to and happens within the context of entrepreneurship. The level of digitalization of the economy, described by digital framework conditions, impacts entrepreneurial activity through their influence on national-level general and systemic framework conditions. Thus, GIDES monitors all the general, structural and digital frameworks that support digital entrepreneurship. Consequently, it offers a deeper understanding of how digitalization impacts entrepreneurship.

This research compares consultants' and contractors' professional practices and perspectives on the use of specifications in the engineering of built environment projects.

The study initiates with a review of literature, toward realizing (1) the need for inclusion of specifications, throughout the projects' stages (design, tender, construction and handing over), as well as the (2) causes and effects of defective specifications in design and execution of engineered projects. Additionally (3) the study investigates the challenges and causes of specification variations to projects' design and execution. The systematic methodology adopted in this research utilizes frequency indicators, to rank and correlate the perspectives of 60 professionals, who are experienced in consulting and contracting of mega public and private projects.

The research considers the uncertainties and differences that could be present in engineering developments and use of design specifications for projects' execution and handing over. The two groups of experts' (consultants and contractors) perspectives are usually observed as conflicting, in practice. Practices comparison of their experiences with engineering projects' specifications usability provides unique empirical findings. The analysis and assessment of the experts' knowledge, this research addresses gaps identified in the literature, providing valuable insights.

The study provides in-depth recommendations and discusses possible trends and methods for enhancing specifications' usability in design engineering and execution of built environment projects. The analysis and recommendations present and assess experts' knowledge while bridge gaps identified from the literature.

The aim of this study is to investigate the application of advanced language models, particularly ChatGPT-4, in identifying and utilizing industrial symbiosis opportunities within the circular economy. It examines how the model can aid in promoting sustainable industrial practices by processing data from the MAESTRI project database, which includes various symbiotic relationships, as well as randomly selected waste codes not included in the database. The research involves structured queries related to industrial symbiosis, circular economy, waste codes and potential opportunities. By assessing the model’s accuracy in response generation, the study seeks to uncover both the capabilities and limitations of the language model in resource efficiency and waste reduction, emphasizing the need for ongoing refinement and expert oversight.

The study adopts a mixed-methods approach, combining qualitative and quantitative analyses to explore the potential of ChatGPT-4 in identifying industrial symbiosis opportunities. Data from the EU-funded MAESTRI project database, which includes existing symbiotic relationships, as well as randomly selected waste codes not included in the database, are used as the primary sources. The language model is queried with structured questions on industrial symbiosis, circular economy and specific waste codes utilizing the model’s advanced functions such as file upload. Responses are evaluated by comparing them with the MAESTRI database and official European Waste Catalogue (EWC) codes.

The study finds that ChatGPT-4 possesses a solid understanding of fundamental concepts related to industrial symbiosis and the circular economy. However, it encounters challenges in accurately describing EWC codes, with a notable portion of descriptions found to be incorrect. Despite these inaccuracies, the model shows potential in suggesting symbiotic opportunities, although its effectiveness is limited. Interestingly, the study reveals that the model can occasionally identify correct symbiotic relationships even with initial inaccuracies. These findings highlight the need for expert oversight and further development of the language model to improve its utility in complex, regulated fields like industrial symbiosis.

This study’s originality lies in its exploration of advanced language models, particularly ChatGPT-4, for identifying industrial symbiosis opportunities within the circular economy framework. Unlike previous research, which primarily focuses on specific sectors and AI’s role in general resource efficiency, this study specifically examines the capabilities and limitations of the language model in handling specialized and regulated information, such as EWC codes across various sectors. It employs a novel approach by comparing AI-generated responses with an established symbiosis database, which is comprehensive and spans all sectors rather than being limited to a single industry, as well as with randomly selected waste codes not included in the database. The study contributes to understanding how AI tools can support sustainable industrial practices, emphasizing the importance of refining these models for practical applications in environmental and industrial contexts.

This paper systematically presents a critical review of data envelopment analysis (DEA) for performance measurement in the construction field.

The review approach consists of a systematic literature search, validation analysis and content analysis. The paper systematically reviews previous papers according to the year of publication, journal, authors, region, research keywords, performance measurement indicators and methodology framework.

A total of 192 journal papers from the first year of publication to 2022 are identified. DEA applications have increased over the years, particularly after 2020. All of the top five journals which published the most identified papers are Q1 journals. Around 74 primary indicators of performance measurement are recognised for the construction industry, company and project levels. A total of 21 top keywords are identified and then divided into five clusters using VOSviewer. DEA has been widely utilised to measure construction performance and benchmark technologies, particularly where sustainable development performance has become a popular topic recently.

How to effectively distinguish performance indicators, collect research data, build DEA models and deeply analyse DEA results are illustrated for future practitioners. The performance measurement and improvement cycle based on DEA is provided. Research directions and method recommendations are developed for future scholars using DEA.

This is the first comprehensive review that has initially presented various performance indicators and a methodology framework for developing DEA models to investigate performance measurement in the construction field. The methodology framework of DEA is developed, including data collection, model construction and further analysis of DEA results.

The study aims to investigate the factors influencing consumer buying behaviour towards online shopping in Ghana.

A web survey was employed to test the concept mode. Perceived ease of use (PEOU), perceived usefulness (PU), perceived hedonic value (PHV) and perceived trust (PT) served as the independent variables, while consumer online buying behaviour (ConOBB) served as the dependent variable. The convenience sampling method was adopted to solicit data from 437 respondents. SPSS 26 and STATA 17 were the analytical tools used for the study. The analysis of the data includes a structural equation model (SEM) to assess the measurement and the influence of PEOU, PU, PHV and PT on ConOBB.

The study revealed that PEOU (Beta = 0.262), PU (Beta = 0.216), PHV (Beta = 0.354) and PT (Beta = 0.630) all had a positive relationship with ConOBB. The study also established that there are significant relationships between the factors (PEOU, PU, PHV and PT) and ConOBB in Ghana.

The study focuses on Ghanaian consumers and emphasises mainly online shopping factors, which are PEOU, PU, PHV and PT. E-commerce businesses face fierce competition due to the increased availability of websites and other online platforms. To attract potential customers, companies must streamline processes, ensure user-friendliness and create a user-friendly experience.

The uptake of online shopping in Ghana is relatively slow compared with other countries, particularly in Africa. Online shoppers and service providers in Ghana are keen to sustain the industry. However, there is currently a scarcity of empirical studies in Ghana that focus on specific factors that influence consumer online buying behaviour. This study offers a new model that offers novel insights into the specific factors that aid in consumer online shopping behaviour in Ghana. The measuring instrument, which was found to be reliable and valid, also serves as an added value that this study offers. Both the model and measuring instrument can assist managers offering online shopping opportunities to be able to measure and formulate strategies that would enhance consumer online shopping experiences. By adding perceived hedonic value and trust to the model, this study offers a unique extension of the technology acceptance model. Thus, the findings add to the body of knowledge in the field of online shopping, particularly in the context of Ghana.

Researchers from different disciplines have advocated for the incorporation of Systems Thinking in higher education in various fields such as environmental studies, engineering, economics and management. Nevertheless, educational experiences that seek to integrate the systems approach and citizenship education are not numerous and have not been sufficiently documented. This research paper presents classroom research aimed at understanding how Systems Thinking and citizenship education are articulated within a general education course.

The research was designed using Grounded Theory within an exploratory-descriptive approach. To collect data, the following techniques were used: participant observation, focus group and documentary analysis. The data analysis was carried out through the constant comparative method in its open, axial, selective and theoretical contrast coding phases.

The data analysis highlights the potential of Systems Thinking within the context of Citizenship Education as a comprehensive set of cognitive, affective and methodological tools. Moreover, it sheds light on the pedagogical practices, the teaching strategies and the type of socioemotional interactions in the classroom that are key elements in promoting citizenship attitudes, ideas and behaviors.

The research underscores the importance of a systemic vision of education that explicitly incorporates social-emotional learning and experiences of citizen participation beyond the classroom where students can build new knowledge from first-hand interactions with processes of social transformation.

The paper explores the potential value of urban assemblage theory as a conceptual framework for understanding the role heritage has in social sustainable urban placemaking. The authors conceptualise urban placemaking as a dynamic and complex social assemblage. Heritage is one of the many dimensions of such a complex and dynamic urban assembly. Based on the approach to urban assemblage theory, the authors aim to uncover how postindustrial city-making unfolds. When approaching the case studies, the authors ask the following: Whose city for which citizens are visible through the selected case studies? How is social sustainability achieved through heritage in urban placemaking?

The main research material is derived from theoretical literature and the testing of an assemblage methodological approach through three Norwegian urban regeneration case studies where heritage partake in urban placemaking. The three case studies are the Tukthus wall (what is left of an 19th century old prison), the Vulkan neighbourhood (an 19th century industrial working area) and Sørengkaia (an 19th century industrial harbour area) in Oslo, Norway. The three case studies are representing urban regeneration projects which are common worldwide, and not at least in a European context.

The paper reveals the dynamic factors and processes at play in urban placemaking, which has its own distinct character by the uses of heritage in each of the case study areas. Placemaking could produce “closed” systems which are stable in accordance with its original functions, or they could be “open” systems affected by the various drivers of change. The paper shows how these forces are depending on two sets of binary forces at play in urban placemaking: forces of “assemblages” co-creating a place versus destabilising forces of “disassembly” which is redefining the place as a process affected by reassembled placemaking.

For research, the authors focus on the implications this paper has for the field of urban heritage studies as it provides a useful framework to capture the dynamic complexity of urban heritage areas.

For practice, the authors state that the paper can provide a useful platform for dialogue and critical thinking on strategies being planned.

For society, the paper promotes the significance in terms of fostering an inclusive way of thinking and planning for urban heritage futures.

The paper outlines dynamics of urban regeneration through heritage which are significant for understanding urban transformation as value for offering practical solutions to social problems in urban planning. The assemblage methodological approach (1) makes awareness of the dynamic processes at play in urban placemaking and makes the ground for mapping issue at stake in urban placemaking; (2) becomes a source for modelling urban regeneration through heritage by defining a conceptual framework of dynamic interactions in urban placemaking; and (3) defines a critically reflexive tool for evaluating good versus bad (heritage-led) urban development projects.