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This paper investigates the optimization of window design factors (WDFs) in hospital buildings, particularly in government hospitals within the arid climate of the Qassim region, with the aim of achieving a better cooling load reduction. Continuous monitoring of the hospital ward section is crucial due to patients' needs, requiring optimal indoor air quality and cooling load.

The study identifies the optimal conditions for WDF design to mitigate cooling load, including window-to-wall ratio (WWR), window orientation (WO), room size and U-value (thermal properties), effectively reduce energy consumption in terms of sensible cooling load (MWh/m2) and comply with local codes. Data collection involved a smart weather station, while the Integrated Environmental Solution Virtual Environment (IESVE) software facilitated the simulation process.

Key findings reveal that larger patient rooms were about 40% more energy-efficient than smaller rooms. The northern orientation showed lower energy consumption, and specific WWRs and glazing U-values significantly affected energy loads. In an analysis of U-value changes in energy performance based on the Saudi Building Code (SBC), the presented values did not meet the minimum energy consumption standards. For a valid 40% WWR with a thermal permeability of 2.89, 0.181 MWh/m2 was consumed, while for an invalid 100% WWR with the same permeability but facing the north, 0.156 MWh/m2 was consumed, which is considered an invalid practice. It is vital to follow prescribed standards to ensure energy efficiency and avoid unnecessary costs.

Focus lies in emphasizing the significance of adhering to prescribed standards, such as SBC, to guarantee energy efficiency and prevent unwarranted expenses. Additionally, the authors highlight the crucial role of optimizing glazing properties and allocating the WWR appropriately to achieve energy-efficient building design, accounting for diverse orientations and climatic conditions.

This study investigated the factors that influence customer satisfaction with AI-driven services by focusing on chatbot agents. The conceptual model included psychological and social factors, such as trust, perceived social presence, competence perception, social-oriented communication style, warmth perception, subjective norms and attachment anxiety.

A quantitative methodology was employed utilising a survey conducted among 525 consumers who interacted with chatbot services. The data were analysed using structural equation modelling (Smart-PLS 4.0) to test the proposed hypotheses.

The study revealed that social-oriented communication, perceptions of competence and warmth, trust and subjective norms significantly enhanced customer satisfaction with chatbots. Trust was critical in fostering satisfaction, whereas perceived social presence and attachment anxiety had minimal impact. The findings suggest that despite the emphasis on social presence, its influence on satisfaction may depend on contextual factors that were not captured in this study.

This study extended the Technology Acceptance Model and Stereotype Content Model by integrating factors such as perceived social presence, trust, competence perception, social-oriented communication style, warmth perception, subjective norm and attachment anxiety. Challenging conventional assumptions on the role of social presence and attachment anxiety, the study provides new insights into the complex dynamics of human–chatbot interactions, offering practical implications for improving chatbot design and enhancing user experience that emphasise the importance of trust, competence and social-oriented communication in customer satisfaction.

This study aims to examine the role of innovation and sustainability ethics on an organization’s reputation, and to explore how sustainability ethics can mediate the relationship between innovation and reputation. It also seeks to determine whether social media strengthens this relationship.

A cross-sectional technique was used to analyze 96 validated questionnaires. Data were collected from CEOs of manufacturing organizations using a simple random probability technique. Partial least squares and structural equation modeling statistical methods were applied to analyze the data.

The study found that sustainability ethics plays a mediating role between innovation and reputation. Furthermore, it was observed that innovation has a positive effect on sustainability ethics, which in turn has a significant impact on reputation. In addition, the study noted that social media moderates these relationships.

Despite its contributions, this paper has several limitations. It focuses solely on the manufacturing sector; future research could explore other sectors, such as services or finance, which may have distinct characteristics. In addition, this study proposes a single independent variable; future studies could incorporate additional variables, such as cultural factors or government support, to provide a broader understanding of the dynamics involved. Furthermore, the sample consists solely of CEOs, and responses may be biased. Future research could consider including employees as respondents to offer a more holistic perspective.

This paper deepens our understanding of the outcomes of sustainability ethics and highlights the crucial role of innovation in shaping reputation. It advises owners to reconsider their approach to innovation, as it impacts sustainability ethics and reputation. Cultivating an innovative culture influences employees to think creatively and share new ideas. Organizations should hire innovative and skilled workers and offer them social media marketing courses. It is recommended that leaders engage closely with clients and suppliers, encouraging communication through social media for valuable feedback. The integration of ethical considerations into sustainability ethics ensures that businesses contribute positively to society and the environment. This ethical dimension is essential for enterprises aiming to meet societal expectations.

This study contributes to signaling theory by extending its domain and providing empirical evidence with respect to sustainability, social media, innovation and reputation, as these factors have not been examined comprehensively.

The research on sustainable business model innovation (SBMI) has seen a surge over the years. However, despite the surge, the literature on the antecedents of SBMI is still at a budding stage. Therefore, the purpose of this paper is to expand the literature on SBMI by investigating the influence of managerial ties on SBMI.

Data were collected from 200 SMEs in Saudi Arabia to test the conceptual model, which was analysed using structural equation modelling.

The findings highlighted the significance of managerial ties in promoting SBMI. Furthermore, the findings highlighted that managerial ties might not optimally result in SBMI uncles channelized through absorptive capacity. However, the findings could not confirm the moderating role of strategic flexibility.

The findings from this study have implications for SME owners, recommending that these firms develop managerial ties and absorptive capacities to realize the benefits of SBMI.

To the best of the authors’ knowledge, this study is one of the first to highlight the significance of managerial ties in promoting SBMI from an emerging market perspective.

Given the growing interest in modern construction techniques and the emergence of innovative technologies, construction site layout planning research has progressively been investigating approaches to adopt innovative concepts and incorporate renewed approaches to improve widespread efficiency. This research develops a decision-making tool that optimizes construction site layout plans. The developed model targets two main objectives: minimizing material transportation costs and maximizing safety by optimally placing facilities on construction sites.

A novel approach is devised based on the integration of Building Information Modeling and Generative Design (BIM-GD). This engine is used to optimize the multi-objective site layout problems to identify layout alternatives in the early project stages. Parametric modeling uses Dynamo to construct the model and explore constraints initially. Finally, the GD environment is utilized to create different design alternatives, and then the decision-making procedure selects the most appropriate design alternative. Additionally, a case study is applied to validate the effectiveness of the developed model.

The results indicate the effectiveness of the proposed GD tool and its potential for more complex applications. The GD engine examined optimal layout plans, balancing different objectives and adhering to appointed geometric constraints. A case study was conducted to assess the model's effectiveness and showcase its suitability. Construction Site Layout Planning (CSLP) is an essential step in design that can influence considerable aspects, such as material transportation expenses and different safety standards on the site. Employing visual programming for parametric modeling within Dynamo-Revit creates an expedient and user-friendly platform for planning engineers who may require more programming expertise to create and program algorithmic models visually. Utilizing GD in CSLP has proven to be a powerful tool with consequential prospects for improving applications and executing more models.

The findings from this framework are intended to help construction practitioners select the most appropriate site layout during early project stages while incorporating different safety criteria inside construction sites to alleviate actual safety risks.

A new approach is proposed that utilizes an integrated BIM-GD engine to optimize multi-objective site layout problems. This approach targets two main objectives: minimizing material transportation costs and maximizing safety by optimally placing facilities in construction sites.

The purpose of this study is to identify a critical pathway of the effect of big data analytics capabilities (BDACs) on strategic vigilance based on hierarchical process and a capability approach.

The researcher adopted a qualitative approach using interviews and a quantitative approach based on the interpretative structural modeling (ISM) fuzzy cross-impact matrix multiplication applied to classification (MICMAC) approach. A primary theoretical approach was also conducted to identify BDACs previously cited in the literature.

Four main subdivisions of BDACs were identified: management capabilities, infrastructure flexibility, talent capability and technology. Management capabilities followed by big data technical knowledge and associated with talent capabilities generate a flexible infrastructure to enhance SV. A dynamic capability perspective of knowledge and information is also required for SV.

Despite the opportunity of this research and the originality of results, some limitations have to be mentioned and can constitute further directives for future researchers, such as the problem of result generalization. First, this research was based in Saudi Arabia, and a comparative approach to defining BDAC on an international level can be more beneficial in providing an exhaustive list of these capabilities. Second, reliability issues, in this research can be addressed due to the use of qualitative data collection which is considered by many researchers as unspecified and can lack scientific rigor. Future studies can improve the number of interviews during the data collection process and data process using an advanced methodological approach. Third, the effect of BDAC in SV according to the hierarchical final modal is not quantified, future work can use this research model to appreciate each effect using a quantitative approach such as correlation and structural equation modeling while considering respondents with different profiles to take into account different point of view in this concern.

This research enriches the BDAC and MICMAC literature and contributes to this aspect in three main levels. First, by providing an additional empirical asset in this field, this study offers by the way a new case to the big data literature on the banking sector. Based on the limited knowledge as well as results collected from different databases and rigorously analyzed, this subject was not treated previously and the author could not find similar studies with the same approach dealing with the key BDACs in Saudi Arabia.

This research presents three main implications for policymakers and researchers interested in big data analytics (BDA) through a capability and strategic perspective. First, to attain SV, they should prioritize the development of interactive interfaces and open platforms as the primary step before collecting information and deconstructing it to guarantee the generation of knowledge and make decisions effectively. Second, policymakers must introduce organizational technologies in terms of technology management, technical knowledge and technology for decision-making. This requires simultaneous sharing and communication according to relational management. Third, the research conclusions have many critical managerial ramifications for banks in Saudi Arabia while considering the adoption of BDAC. The importance of BDACs (especially technical aspects) in shaping the decision-making to be strategically vigilant emphasizes policymakers’ orientation by paying close attention to these aspects and specific training programs to facilitate the use of such technologies and guarantee strong security measures. Moreover, findings support a balance between technical and functional BDAC.

The adoption of a knowledge-based dynamic capabilities (KBDCs) view to analyze the interaction between different BDACs in banks in Saudi Arabia to be strategically vigilant using a mixed approach.

In today’s intricate and dynamic construction sector, traditional project management techniques, which view projects in isolation, are no longer sufficient. Project Portfolio Management (PPM) has proven to be an efficient alternative solution for handling multiple construction projects. As such, based on a Machine Learning (ML) approach, this study aims to explore the Critical Success Factors (CSFs) influencing the adoption of PPM, aiming to enhance PPM implementation in construction projects.

A questionnaire based on CSFs gathered from prior studies was developed and validated by experts. Afterward, exploratory data analysis is conducted to understand CSF–PPM relationships. Preprocessing techniques ensure uniformity in variable magnitudes. Lastly, ML techniques, namely Linear Discriminant Analysis (LDA), Logistic Regression (LR) and Extra Trees Classifier (ETC) are developed to model and investigate CSFs' impact on PPM adoption.

The findings pointed out that the ETC model marginally outperforms other ML models with a classification accuracy of 93%. Also, the project size, utilized PPM tool and resource allocation-related factors are the most significant CSFs that influenced the PPM performance by about 48.5%.

This study contributes to the existing body of knowledge by raising awareness among construction companies and other project stakeholders about the pivotal CSFs that must be considered when prioritizing projects and designing an optimal PPM approach.

This paper aims to explore the importance of knowledge-oriented leadership in the context of business model innovation (BMI) and open innovation. This study also seeks to explore the existence of a mediating effect of absorptive capacity.

Data were collected using a survey, from the information technology industry in India. The relationships proposed in the conceptual framework were tested with structural equation modeling (SEM) using Smart partial least squares (PLS) 3.

The findings highlight a positive influence of knowledge-oriented leadership on BMI and open innovation. Results also revealed that open innovation can facilitate BMI. Additionally, findings highlighted the mediating role of absorptive capacity in these relationships.

This study suggests that businesses should employ and value leaders who can create, transform, store and use knowledge resources. Companies that employ such leaders encourage the creation and exchange of new knowledge, which consequently would lead to the successful exploration of innovative approaches. A culture of open innovation would help managers break down barriers, overcome inertia and continuously test new business models to generate revenues, increase value, enhance organizational effectiveness and ultimately gain competitive advantage.

This study is one of the first to empirically contribute to the role of knowledge-oriented leadership and BMI from the perspective of open innovation.

The purpose of this paper is to develop and test an integrated model to examine the relationship between work engagement and three facets of perceived job performance (PJP). The authors argue that work engagement might not optimally improve PJP unless it is channelized through information and communication technology orientation.

Data for the present research were collected from higher educational institutes in the northern region of India by using a convenient sampling technique. Results of structural equation modeling (SEM) through AMOS 20 revealed that work engagement facilitates all three facets i.e. task performance, contextual performance and adaptive performance of teaching professionals. Furthermore, SEM results established the partial mediating effect of information and communication technology orientation between work engagement, task performance, contextual performance and adaptive performance.

Findings from present research contribute theoretically as well as practically to job performance and work engagement literature by giving insights to administrators and practitioners on how to improve the overall job performance of teaching professionals by enhancing their engagement and addressing their need for digital know-how.

To the best of the authors’ knowledge, this study is one of the first to study the impact of work engagement and information and communication technology on the three facets of PJP using a diverse sample of 1030 teachers from universities in North India.

Formwork design and construction for reinforced concrete buildings take significant time, effort and money. Construction procedures are time-consuming for designers and costly for the contractor. Poor engineering decisions have led to several workplace accidents in the construction industry. This paper aims to present an integrated building information modeling – genetic algorithm (BIM-GA) model to automate formwork design, 3D visualization and optimization.

Data are precisely extracted from a 3D structural model and used to optimize formwork design based on available formwork components and prices. Optimization models are made using GA approach. A library of 3D formwork components was modeled and stored using Revit. The optimized design solution thereafter would be visualized automatically in Revit to readily acquire formwork quantities schedules and shop drawings.

A case study illustrating the proposed approach demonstrated that using BIM will reduce formwork design, quantification and drawing time by more than 50% of the traditional approach with safer design and accurate results due to process automation and optimize cost for the given data.

This research introduces an innovative integrated BIM and GA model for the optimization and automation of slab formwork design, which has significantly benefited the construction industry. The utilization of GA in the optimization process allows for the attainment of an optimal formwork design, ultimately leading to a reduction in construction cost and time.