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Water pipes degrade over time for a variety of pipe-related, soil-related, operational, and environmental factors. Hence, municipalities are necessitated to implement effective maintenance and rehabilitation strategies for water pipes based on reliable deterioration models and cost-effective inspection programs. In the light of foregoing, the paramount objective of this research study is to develop condition assessment and deterioration prediction models for saltwater pipes in Hong Kong.

As a perquisite to the development of condition assessment models, spherical fuzzy analytic hierarchy process (SFAHP) is harnessed to analyze the relative importance weights of deterioration factors. Afterward, the relative importance weights of deterioration factors coupled with their effective values are leveraged using the measurement of alternatives and ranking according to the compromise solution (MARCOS) algorithm to analyze the performance condition of water pipes. A condition rating system is then designed counting on the generalized entropy-based probabilistic fuzzy C means (GEPFCM) algorithm. A set of fourth order multiple regression functions are constructed to capture the degradation trends in condition of pipelines overtime covering their disparate characteristics.

Analytical results demonstrated that the top five influential deterioration factors comprise age, material, traffic, soil corrosivity and material. In addition, it was derived that developed deterioration models accomplished correlation coefficient, mean absolute error and root mean squared error of 0.8, 1.33 and 1.39, respectively.

It can be argued that generated deterioration models can assist municipalities in formulating accurate and cost-effective maintenance, repair and rehabilitation programs.

The unitized curtain wall system (UCWS), one of the prefabricated technologies, is increasingly attracting attention in the Hong Kong construction industry. However, this innovative technology still lacks on-site implementation in high-rise residential buildings. To promote its development, this study aims at identifying the influential factors of UCWS adoption in Hong Kong's high-rise residential buildings from a multi-stakeholder perspective.

Factors were first selected through an in-depth literature review and a semi-structured interview. Then the factors were validated through a questionnaire survey using Cronbach's Alpha Reliability Test. Next, the factors were ranked regarding their importance using mean-score ranking and standard deviation. Meanwhile, different stakeholders were clustered using an experimental factor analysis (EFA) model to find the shared preferences (namely common factors).

The result shows that reduction of construction time (B1) and insufficient site storage area (C1) are the most important factors. The six stakeholder groups were clustered into two segments. B1 and improved quality control are the shared interests. While C1 and the need of specification change are the common concerns.

There are two major breakthroughs in this study. First is the novelty of research objects. UCWS, particularly its application preference in high-rise residential buildings, has rarely been studied, yet it is urgently required. Second is the novel research perspective. The influential factors were studied from a multi-stakeholder perspective. Not only the significant factors for six specific stakeholders but also the shared preference for stakeholder groups was identified. The findings contribute to promoting UCWS more targeted, efficient and comprehensive, as well as demonstrating the collaborative possibilities of multi-stakeholders.

The purpose of this paper is to examine the evolution of the literature on technology transfer and culture, identifying the main contents of the current body of knowledge encompassing culture and technology transfer (TT), thus contributing to a better understanding of the relationship between TT and culture based on bibliometric and multivariate statistical analyses of the relevant body of literature.

Data for this study were collected from the Web of Science (WoS) Core Collection database. Based on a bibliometric analysis and in-depth empirical review of major TT subjects, supported by multivariate statistical analyses, over 200 articles were systematically reviewed. The use of these methods decreases biases since it adds rigor to the subjective evaluation of the relevant literature base.

The exploratory analysis of the articles shows that first, culture is an important topic for TT in the literature; second, the publication data demonstrate a great dynamism regarding the different contexts in which culture is covered in the TT literature and third, in the last couple of years the interest of stimulating a TT culture in the context of universities has continuously grown.

This study focuses on culture in the context of TT and identifies the main contents of the body of knowledge in the area. Based on this first insight, obtained through more detailed bibliometric and multivariate analyses, it is now important to develop and validate a theory on TT culture, emphasizing the dimensions of organizational culture, entrepreneurial culture and a culture of openness that fosters economic and societal spillovers, and to link those dimensions to the performance of TT activities.

From the practical point of view, managers in companies and universities should be aware of the importance of identifying those dimensions of culture that contribute most to the success of their TT activities.

Despite several literature reviews on the TT topic, no studies focusing specifically on culture in the context of TT have been developed. Therefore, given the multifaceted nature of the research field, this study aims to expand and to deepen the analysis of the TT literature by focusing on culture as an important and commonly cited element influencing TT performance.

Key performance indicators (KPIs) play a pivotal role in evaluating the level of success of an organization in achieving its business objectives. The objective of the current research is to identify and prioritize effective KPIs in branding products and construction projects, which contribute to the success of construction companies in a competitive environment.

The present research is of an inferential, descriptive and survey nature. In this study, we identified the influential key performance indicators of construction companies in branding products and construction projects for success in a competitive environment through a literature review and expert opinions. The data were collected using a questionnaire, and a combination of the one-sample t-test method with a 95% confidence level and the fuzzy multiple attribute decision-making (FMADM) method was employed for analysis.

The results indicate that the most influential key performance indicators for construction companies in branding products and construction projects for success in a competitive environment are, in order of significance, the following indices: “Marketing and Advertising,” “Financial,” “Creativity,” “Technical and Operational” and “Social and Political.”

The present research examines the importance of branding construction products and projects for the success of construction companies by improving their business objectives and utilizing key performance indicators throughout the product lifecycle (production and construction). This study provides solutions on how construction companies can increase their competitive advantage through branding and achieve long-term success in the global construction industry.

The purpose of this study is to find out what is the relationship between sustainability toward consumer behavior. Consumer behavior is the method of choosing, buying and using goods and services with an attachment to needs and wants. Now consumers are aware about sustainability, they make purchase decisions according to environmental safety, benefit to the society and increase economic growth.

This study validates the result through experts in textile industry by using the Decision-Making Trial and Evaluation Laboratory approach. This method has many benefits which provide decision makers and experts to understand the interdependence and influential relation between the criteria by hierarchical approach.

According to the results, green culture (F8) and green brand (F3) are the most influential (causal) factors and exert a substantial amount of influence over other factors for achieving organizational performance and sustainability. On the other hand, past experience (F14) and time pressure (F12) are the most influenced (effect) factors that are highly influenced by other factors.

The study conducted in Pakistan underscores the significance of maintaining a healthy and pristine environment for future generations. Both consumers and organizations play a vital role in this endeavor. It is imperative that they actively promote and support goods and services that advocate for sustainability.

Mangers should use long-term strategies that meet the high product value to enhance the organization’s reputation, so it will have positive consumer perception. If managers make policies to implement natural resources in their raw material, so this policy avoids conflicts and maintains a balance in our society.

This research delves into the complexities and subtleties associated with the identification and examination of the interconnections between the success factors of sustainability and consumer behavior.

This study aimed to identify and analyse the key factors influencing the adoption of e-government services and to discern their implications for various stakeholders, from policymakers to platform developers.

Through a comprehensive review of existing literature and detailed analysis of multiple studies, this research organised the influential factors based on their effect: highest, direct and indirect. The study also integrated findings to present a consolidated view of e-government adoption drivers.

The research found that users' behaviour, attitude, optimism bias and subjective norms significantly shape their approach to e-government platforms. Trust in e-Government (TEG) emerged as a critical determinant, with security perceptions being of paramount importance. Additionally, non-technical factors, such as cultural, religious and social influences, play a substantial role in e-government adoption decisions. The study also highlighted the importance of performance expectancy, effect expectancy and other determinants influencing e-government adoption.

While numerous studies have explored e-government adoption, this research offers a novel classification based on the relative effects of each determinant. Integrating findings from diverse studies and emphasising non-technical factors introduce an interdisciplinary approach, bridging the gap between information technology and fields like sociology, anthropology and behavioural sciences. This integrative lens provides a fresh perspective on the topic, encouraging more holistic strategies for enhancing e-government adoption globally.

Purchasing real estate is one of the most important and complex decisions in a life of an individual, which should take numerous factors into account. The purpose of this research is to identify which behavioral factors significantly affect the intention to buy real estate. Since the real estate market is continuously changing, along with other economic and life conditions, it is expected that different generations have different characteristics which affect their behavior; therefore, it is important to analyze generational influence on buyers' behavior.

A survey analysis was conducted on a sample of 434 respondents in Croatia. Partial least squares structural equation modeling was used to obtain the results. The moderating effect of generational affiliation was observed.

Overconfidence significantly affects intention to buy real estate, but it doesn't affect the level of importance individuals give to financial factors. On the other hand, herding significantly affects the level of importance given to financial factors, whereas it does not directly affect buying intention. A significant moderating effect of generational affiliation was found for the impact of overconfidence on financial factors, suggesting a negative effect for younger generations and a positive effect for older generations.

This research proposes a novel unique model with both behavioral and financial factors as predictors of the intention to buy real estate, together with generational differences in buyers' behavior. Understanding normal human behavior is crucial to determine how buyers' decisions and intentions change under the influence of certain biases or characteristics such as generational affiliation.

This study explores the adoption of Industry 4.0 in developing countries' export industries, focusing on factors influencing this adoption, the moderating role of market pressure and prioritizing key factors for sustainable growth.

Based on the “TOE theory” this study has proposed a research framework to identify the factors influencing the adoption and sustainable implementation of Industry 4.0 in the export industry. This study has collected valid datasets from 387 export-oriented industries and applied SEM-ANN dual-stage hybrid model to capture linear and nonlinear interaction between variables.

Results revealed that Technical Capabilities, System Flexibility, Software Infrastructure, Human Resource Competency and Market pressure significantly influence the Adoption of Industry 4.0. Higher market pressure as a moderator also improves the Industry 4.0 adoption process. Results also pointed out that system flexibility is a gray area in Industry 4.0 adoption, which can be enhanced in the export industry to maintain a sustainable adoption and implementation of Industry 4.0.

Minute information is available on the factors influencing the adoption of Industry 4.0 in export-oriented industries. This study has empirically explored the role of influential factors in Industry 4.0 and ranked them based on their normalized importance.

This study aims to identify an automated machine learning algorithm with high accuracy that sport practitioners can use to identify the specific factors for predicting Major League Baseball (MLB) attendance. Furthermore, by predicting spectators for each league (American League and National League) and division in MLB, the authors will identify the specific factors that increase accuracy, discuss them and provide implications for marketing strategies for academics and practitioners in sport.

This study used six years of daily MLB game data (2014–2019). All data were collected as predictors, such as game performance, weather and unemployment rate. Also, the attendance rate was obtained as an observation variable. The Random Forest, Lasso regression models and XGBoost were used to build the prediction model, and the analysis was conducted using Python 3.7.

The RMSE value was 0.14, and the R2 was 0.62 as a consequence of fine-tuning the tuning parameters of the XGBoost model, which had the best performance in forecasting the attendance rate. The most influential variables in the model are “Rank” of 0.247 and “Day of the week”, “Home team” and “Day/Night game” were shown as influential variables in order. The result was shown that the “Unemployment rate”, as a macroeconomic factor, has a value of 0.06 and weather factors were a total value of 0.147.

This research highlights unemployment rate as a determinant affecting MLB game attendance rates. Beyond contextual elements such as climate, the findings of this study underscore the significance of economic factors, particularly unemployment rates, necessitating further investigation into these factors to gain a more comprehensive understanding of game attendance.