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This study aims to analyze Islamic financial institutions’ (IFIs) current financial engineering and product development procedures.

The paper is quantitative in nature and the survey questionnaire were collected from managers and IF experts working for Islamic Banks, Takaful and other IFIs in Turkey, Malaysia and UAE. Two-stage structural equation modeling was used to test the hypothesis.

The findings highlighted that the Shari’ah Supervisory Board, Strategy and Planning of IFIs, Legal and Regulatory framework, pricing of a new product and financial performance positively impact the new product development (NPD) process. At the same time, Islamic values have no significant positive impact.

When generalizing the research results, data collection from the right departments was the main limitation of the current study. Future research may opt to collect data only from Product Development Departments.

The findings of this study will allow IFIs to reflect on their present methods, procedures and Shari’ah compliance framework for the NPD process.

Factors affecting the product development and financial engineering process are discussed in the literature. The findings of this study can be regarded as building blocks for future academic research on product development and financial engineering in Islamic finance.

Work-study programmes aim to support young adults transitioning from tertiary education to work and contribute to enhancing their employability. The purpose of this study is to provide a detailed analysis of the learning experiences of trainees in work-study programmes within the broad field of engineering.

The data gathering procedures used interviews with participants of a specific work-study programme and conducting thematic analyses to identify and understand the motivations of these adults for enrolling in work-study programmes and their associated workplace learning experiences. Fifty-two alumni of electrical and logistics engineering programmes completed 12 months of a separate work-study programme and consented to participate in an hour-long interview. They elaborated upon their work-learn experiences to explicate their work-learn needs and challenges in those interviews.

Findings include workplaces facilitated skills development through providing combinations of work tasks comprising both routine and novel work assignments; challenging circumstances at the workplace provided trainees with opportunities to develop adaptive capacities; and engaging in non-routine work processes fosters integration into the engineering workplace community.

The findings contribute to the existing literature by exemplifying how routine engineering activities are practical affordances through which engineering trainees construct knowledge and dispositions for engaging in challenging, non-routine engineering work. Such experiences are crucial in preparing trainees for advanced roles in logistics or electronic sectors.

This paper aims to highlight how the six sigma methods helped the medical equipment manufacturing company in finding and analysing the root causes that lead to the reduction in production rate, rejection rates, quality and other major causes that lead to the reduction in productivity of the blood bags manufacturing unit.

Given the critical nature of blood bag manufacturing Six Sigma was chosen as the primary methodology for this research since Six Sigma’s data-driven approach provides a structured framework to identify, analyse and rectify inefficiencies in the production processes. This study proposes the Six Sigma DMAIC (D-Define, M-Measure, A-Analyse, I-Improve, C-Control) encompassing rigorous problem definition, precise measurement, thorough analysis, improvement and vigilant control mechanisms for effectively attaining predetermined objectives.

The paper demonstrates how the Six Sigma principles were executed in a blood bag manufacturing unit. After a detailed and thorough data analysis, it was found that a total of 40 critical-to-quality factors under the five drivers such as Machine, Components, Inspection and Testing, People and Workspace were influential factors affecting the manufacturing of blood bags. From the study, it is identified that the drivers such as inspection and testing, components and machines contribute significantly to increasing productivity.

The paper offers valuable strategic insights into implementing Six Sigma methodologies within the specific context of a blood bag manufacturing unit. The Six Sigma tools and techniques used by the project team to solve issues within the blood bag manufacturing unit can be used for similar healthcare organizations to successfully deploy Six Sigma. The insights from this research might not be directly applicable to other manufacturing facilities or industries but can be used as a guiding reference for researchers and managers.

The current state of scholarly literature indicates a significant absence in the examination of Six Sigma methodologies designed specifically to improve production output in healthcare equipment manufacturing. This paper highlights the application of Six Sigma principles to enhance efficiency in the specific context of blood bag manufacturing.

This study aims to investigate the association between chief executive officer (CEO) educational backgrounds in science, technology, engineering and mathematics (STEM) and climate change disclosure within Indonesian companies.

Using data spanning from 2017 to 2022 from all publicly traded companies, the study uses ordinary least squares with fixed effects and robust standard error to evaluate the proposed hypothesis. In addition, a series of endogeneity tests are incorporated to bolster the robustness of the findings.

The study reveals that CEOs with a STEM educational background are more inclined to participate in corporate climate change disclosure compared to their counterparts with a non-STEM background. These results emphasize the significant role CEO educational backgrounds play in shaping a company’s approach to sustainability, specifically in the realm of climate change disclosure. The insights gleaned from this research hold valuable implications for various stakeholders, including top management and investors aiming to enhance corporate sustainability. Recognizing the influence of CEO characteristics, particularly a STEM educational background, proves pivotal in improving corporate climate change disclosure. Stakeholders can leverage this understanding to formulate and implement effective strategies toward realizing a company’s sustainability vision.

Notably, this study stands out as it was conducted within the context of Indonesia, a nation actively encouraging nonsocial graduates to assume crucial positions within the Republic of Indonesia.

The purpose of this paper is to understand if and how industrialized house building (IHB) could support productivity developments for housebuilding on project and industry levels. The take is that fragmentation of construction is one explanation for the lack of productivity growth, and that IHB could be an integrating method of overcoming horizontal and vertical fragmentation.

Singe-factor productivity measures are calculated based on data reported by IHB companies and compared to official produced and published research data. The survey covers the years 2013–2020 for IHB companies building multi-storey houses in timber. Generalization is sought through descriptive statistics by contrasting the data samples to the used means to control vertical and horizontal fragmentation formulated as three theoretical propositions.

According to the results, IHB in timber is on average more productive than conventional housebuilding at the company level, project level, in absolute and in growth terms over the eight-year period. On the company level, the labour productivity was on average 10% higher for IHB compared to general construction and positioned between general construction and general manufacturing. On the project level, IHB displayed an average cost productivity growth of 19% for an employed prefabrication degree of about 45%.

Empirical evidence is presented quantifying so far perceived advantages of IHB. By providing analysis of actual cost and project data derived from IHB companies, the article quantifies previous research that IHB is not only about prefabrication. The observed positive productivity growth in relation to the employed prefabrication degree indicates that off-site production is not a sufficient mean for reaching high productivity and productivity growth. Instead, the capabilities to integrate the operative logic of conventional housebuilding together with logic of IHB platform development and use is a probable explanation of the observed positive productivity growth.

The paper aims to analyse the construct of business model innovation (BMI) in the digital and sustainable landscape, investigating the key role of boundary strategies. The paper advances a comprehensive framework aimed at further understanding the overlap among digitalization, sustainability and BMI development, by a “boundary approach”.

The paper follows a theoretical approach based on an in-depth review of relevant literature on BMI, digitalization and sustainability as relevant megatrends and, boundary management. By critically integrating the literature, a framework is developed with the objective of supporting firms in the current transformation challenges.

The paper highlights the interplay among BMIs, megatrends and boundary management. The pressures and opportunities driven by the technological changes have made even more relevant the management of resources placed in the boundary area. Our study shows how firms can rethink their BMs in the digital and sustainable landscape by providing a boundary-based framework.

The framework offers insights and guidelines to help practitioners manage the change processes dictated by digitalization and sustainability. The authors encourage a focus on boundary resources/capabilities to increase the effective management of the digitalization and sustainability processes, to grasp the external stimuli driven by these two megatrends and to develop new/renewed BMIs.

This study emphasizes the importance of developing new BMIs in the current digital and sustainable landscape starting from the analysis of firm’s boundaries. The paper enriches the BMI literature supporting the enhancement of boundary management, leading firms to overcome challenges in the digital and sustainable landscape.

Clause 5 [Design] outlines the Contractor's general design obligations, the process for preparing the Contractor's Documents, applicable technical standards (and how to manage the impact of changes therein during project execution) and other provisions relating to design, documents and training.

Prior research has emphasised the importance of the early phases of construction projects, as well as the difficulties of procuring engineering services – especially due to the uncertainties. Despite that, studies on the public procurement of engineering services are scarce. Although scholars have shown that uncertainty may affect the choice of control modes, the level of uncertainty that characterises services is not addressed by the two task characteristics: knowledge of the transformation process and output measurability. The purpose is to investigate organisational control in public procurement of engineering services.

The existing control model was adjusted in this study by conceptually adding uncertainty as a third aspect to the two task characteristics. A single case study of the Swedish Transport Administration was used. The empirical data, comprising 14 interviews with managers from the client and engineering consulting companies, were analysed using flexible pattern matching and visual mapping approaches and then illustrated using the model.

The public client did not base its choice of control modes on uncertainty, but rather on the other two task characteristics. Consequently, the service providers argued that the chosen control modes reduced their creativity, increased their financial risks and caused unclear responsibilities. This study therefore shows that uncertainty is an important factor to consider in the choice of control modes, both from a theoretical perspective and from the service providers' point of view. The developed model may therefore be useful for researchers as well as practitioners.

This study is the first attempt to add uncertainty as a task characteristic when choosing control modes. The results contribute to the scarce control literature regarding the procurement of engineering services for construction projects and the procurement of other services with high uncertainty.

The term knowledge refers to the part of the world investigated by a specific discipline and that includes a specific taxonomy, vocabulary, concepts, theories, research methods and standards of justification. Our approach uses domain knowledge to improve the quality of business process models (BPMs) by exploiting the domain knowledge provided by large language models (LLMs). Among these models, ChatGPT stands out as a notable example of an LLM capable of providing in-depth domain knowledge. The lack of coverage presents a limitation in each approach, as it hinders the ability to fully capture and represent the domain’s knowledge. To solve such limitations, we aim to exploit GPT-3.5 knowledge. Our approach does not ask GPT-3.5 to create a visual representation; instead, it needs to suggest missing concepts, thus helping the modeler improve his/her model. The GPT-3.5 may need to refine its suggestions based on feedback from the modeler.

We initiate our semantic quality enhancement process of a BPM by first extracting crucial elements including pools, lanes, activities and artifacts, along with their corresponding relationships such as lanes being associated with pools, activities belonging to each lane and artifacts associated with each activity. These data are systematically gathered and structured into ArrayLists, a form of organized collection that allows for efficient data manipulation and retrieval. Once we have this structured data, our methodology involves creating a series of prompts based on each data element. We adopt three approaches to prompting: zero-shot, few-shot and chain of thoughts (CoT) prompts. Each type of prompting is specifically designed to interact with the OpenAI language model in a unique way, aiming to elicit a diverse array of suggestions. As we apply these prompting techniques, the OpenAI model processes each prompt and returns a list of suggestions tailored to that specific element of the BPM. Our approach operates independently of any specific notation and offers semi-automation, allowing modelers to select from a range of suggested options.

This study demonstrates the significant potential of prompt engineering techniques in enhancing the semantic quality of BPMs when integrated with LLMs like ChatGPT. Our analysis of model activity richness and model artifact richness across different prompt techniques and input configurations reveals that carefully tailored prompts can lead to more complete BPMs. This research is a step forward for further exploration into the optimization of LLMs in BPM development.

The limitation is the domain ontology that we are relying on to evaluate the semantic completeness of the new BPM. In our future work, the modeler will have the option to ask for synonyms, hyponyms, hypernyms or keywords. This feature will facilitate the replacement of existing concepts to improve not only the completeness of the BPM but also the clarity and specificity of concepts in BPMs.

To demonstrate our methodology, we take the “Hospitalization” process as an illustrative example. In the scope of our research, we have presented a select set of instructions pertinent to the “chain of thought” and “few-shot prompting.” Due to constraints in presentation and the extensive nature of the instructions, we have not included every detail within the body of this paper. However, they can be found in the previous GitHub link. Two appendices are given at the end. Appendix 1 describes the different prompt instructions. Appendix 2 presents the application of the instructions in our example.

In our research, we rely on the domain application knowledge provided by ChatGPT-3 to enhance the semantic quality of BPMs. Typically, the semantic quality of BPMs may suffer due to the modeler's lack of domain knowledge. To address this issue, our approach employs three prompt engineering methods designed to extract accurate domain knowledge. By utilizing these methods, we can identify and propose missing concepts, such as activities and artifacts. This not only ensures a more comprehensive representation of the business process but also contributes to the overall improvement of the model's semantic quality, leading to more effective and accurate business process management.

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.