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Megaproject's construction is essential for the development and economic growth of any country, especially in the developing world. In Kuwait, megaprojects are facing many restrictions that discourage their execution causing a significant delay in bidding, design, construction and operation phases with the execution quality being affected. The objective of this study is to develop a complexity measurement model using analytic hierarchy process (AHP) for megaprojects in Kuwait, with a focus on the New Kuwait University multi-billion campus Shadadiyah (College of Social Science, Sharia and Law (CSSL)) as a case study.

The study applies a hybrid fuzzy analytic hierarchy process (FAHP) method to compare the results with those obtained using the conventional AHP method. This can facilitate the project management activities during the different stages of construction. Data were collected based on the results of a two-round Delphi questionnaire completed by seniors and experts of the selected project.

It was found that project modeling methodology was responsible for complexity. It was grouped under several categories that include technological, goal, organizational, environmental and cultural complexities. The study compares complexity degrees assessed by AHP and FAHP methods. “Technological Complexity” scores highest in both methods, with FAHP reaching 7.46. “Goal Complexity” follows closely behind, with FAHP. “Cultural Complexity” ranks third, differing between methods, while “Organizational” and “Environmental Complexity” consistently score lower, with FAHP values slightly higher. These results show varying complexity levels across dimensions. Assessing and understanding such complexities were essential toward the completion of such megaprojects.

The contribution of this study is on providing the empirical evidential knowledge for the priority over construction complexities in a developing country (Kuwait) in the Middle East.

Income from investment properties can fluctuate depending on the state of the economy. The idea that there is always a potential exit (sale) value whenever the property stops performing at its optimum or deflation in the economy will always appeal to investors. To determine housing prices, investors would rely on a direct comparison approach (DCA) of recent substitute sales in the open market. Appraisers use this approach to develop an opinion of value when there is a plethora of recent sales to analyse.

The study was designed to establish the use of the analytical hierarchy process (AHP) approach as a support tool for deciding property appraisals. A case study of an industrial single-storey stand-alone building with grade-level parking in the south-east of Calgary, Canada, was investigated with the AHP approach. The result was cross-referenced with the DCA.

Using a consistency index of 0.077321 and a consistency ratio of 0.085912, the matrix multiplication was determined to be 0.456706. The average valuations derived from the adjusted price per square foot using the direct comparison method and the unadjusted price per square foot using the AHP were deemed the best value estimate in the light of available comparables. The implications of the findings suggest that AHP, as a quantitative technique, can support and validate the use of similar non-recent sale comparables when appraising investment properties with the DCA.

AHP is an alternative aid in quantitatively deciding the most significant value attribute for comparison before subjective adjustments. When intuitively applied in the DCA, these subjective adjustments almost always lead to an overvaluation of properties.

E-commerce companies use different types of dark patterns to manipulate choices and earn higher revenues. This study aims to evaluate and prioritize dark patterns used by e-commerce companies to determine which dark patterns are the most profitable and risky.

The analytic hierarchy process (AHP) prioritizes the observed categories of dark patterns based on the literature. Several corporate and academic specialists were consulted to create a comparison matrix to assess the elements of the detected dark pattern types.

Economic indicators are the most significant aspect of every business. Consequently, many companies use manipulative methods such as dark patterns to boost their revenue. The study revealed that the revenue generated by the types of dark patterns varies greatly. It was found that exigency, social proof, forced action and sneaking generate the highest revenues, whereas obstruction and misdirection create only marginal revenues for an e-commerce company.

The limitation of the AHP study is that the rating scale used in the analysis is conceptual. Consequentially, pairwise comparisons may induce bias in the results.

This paper suggests methodical and operational techniques to choose the priority of dark patterns to drive profits with minimum tradeoffs. The dark pattern ranking technique might be carried out by companies once a year to understand the implications of any new dark patterns used.

The advantages of understanding the trade-offs of implementing dark patterns are massive. E-commerce companies can optimize their spent time and resources by implementing the most beneficial dark patterns and avoiding the ones that drive marginal profits and annoy consumers.

This study focused on exploring the performance factors (PFs) that impact Infrastructure Sanitation Projects (ISSPs) in the construction sector. The aim was twofold: firstly, to identify these crucial PFs and secondly, to develop a robust performance model capable of effectively measuring and assessing the intricate interdependencies and correlations within ISSPs. By achieving these objectives, the study aimed to provide valuable insights into and tools for enhancing the efficiency and effectiveness of sanitation projects in the construction industry.

To achieve the study's aim, the methodology for identifying the PFs for ISSPs involved several steps: extensive literature review, interviews with Egyptian industry experts, a questionnaire survey targeting industry practitioners and an analysis using the Relative Importance Index (RII), Pareto principle and analytic network process (ANP). The RII ranked factor importance,  and Pareto identified the top 20% for ANP, which determined connections and interdependencies among these factors.

The literature review identified 36 PFs, and an additional 13 were uncovered during interviews. The highest-ranked PF is PF5, while PF19 is the lowest-ranked. Pareto principle selected 11 PFs, representing the top 20% of factors. The ANP model produced an application for measuring ISSP effectiveness, validated through two case studies. Application results were 92.25% and 91.48%, compared to actual results of 95.77% and 97.37%, indicating its effectiveness and accuracy, respectively.

This study addresses a significant knowledge gap by identifying the critical PFs that influence ISSPs within the construction industry. Subsequently, it constructs a novel performance model, resulting in the development of a practical computer application aimed at measuring and evaluating the performance of these projects.

The COVID-19 pandemic has posed many challenges in almost all sectors around the globe. Because of the pandemic, government entities responsible for managing health-care resources face challenges in managing and distributing their limited and valuable health resources. In addition, severe outbreaks may occur in a small or large geographical area. Therefore, county-level preparation is crucial for officials and organizations who manage such disease outbreaks. However, most COVID-19-related research projects have focused on either state- or country-level. Only a few studies have considered county-level preparations, such as identifying high-risk counties of a particular state to fight against the COVID-19 pandemic. Therefore, the purpose of this research is to prioritize counties in a state based on their COVID-19-related risks to manage the COVID outbreak effectively.

In this research, the authors use a systematic hybrid approach that uses a clustering technique to group counties that share similar COVID conditions and use a multi-criteria decision-making approach – the analytic hierarchy process – to rank clusters with respect to the severity of the pandemic. The clustering was performed using two methods, k-means and fuzzy c-means, but only one of them was used at a time during the experiment.

The results of this study indicate that the proposed approach can effectively identify and rank the most vulnerable counties in a particular state. Hence, state health resources managing entities can identify counties in desperate need of more attention before they allocate their resources and better prepare those counties before another surge.

To the best of the authors’ knowledge, this study is the first to use both an unsupervised learning approach and the analytic hierarchy process to identify and rank state counties in accordance with the severity of COVID-19.

YouTube (YT) has become a trend among millennials, and thus, marketers are trying to harness the power of it to communicate with them. Global marketers need to understand the mechanism of communicating via YT advertising (YTAD), especially in India that consists of 440 million millennials to re-strategize their YT communications. Consequently, this study aims to examine the influence of YTAD on the cognitive attitude, namely, brand awareness (BA) and brand knowledge (BK) of Indian millennials. The study also tests the moderating impacts of gender, device used for YT watching (DEYTW), duration and frequency of YT watching (DUYTW and FEYTW) on BA and BK.

Generalized linear model – analysis of variance has been used to investigate the proposed relationships in the study. Responses of 294 Indian millennials who watch YTAD regularly have been used for the final analysis. Moderating effects were also tested using Bonferroni pairwise comparisons.

The results revealed that YTAD significantly improves the cognitive attitude (BA and BK). However, gender was not found to have any moderating effect in the relationship of YTAD with BA, whereas moderating effects of gender were observed in the relationship of YTAD with BK. Furthermore, other moderators including DEYTW, DUYTW and FEYTW were found to have significant moderating impacts in the above-mentioned relationships.

In a country like India, comprising the largest millennial population of the world who spends a significant portion of their time in watching YT, it becomes crucial for global marketers to understand how the cognitive attitude (i.e. BA and BK) of millennials improves by watching YT advertisements. As it will help them in strategizing their communications on YT to get favourable consumers’ responses like purchase intention and actual purchase that may happen only when people have favourable cognitive attitude towards advertised brands.

The study offers new perspective to the field of communication by investigating the impact of YTAD on the cognitive attitude (i.e. BA and BK) of Indian millennials.

The study explores the barriers to ESG adoption and develops a hierarchical relationship between identified barriers to determine the driving and dependence power.

Through literature and unstructured interviews nine barriers affecting the adoption of ESG reporting were identified and examined using the modified Total Interpretive Structural Modelling (m-TISM) approach. Further, MICMAC analysis is used to analyse the driver-dependence power amongst the barriers.

In this analysis, the variable lying at the lowest level of hierarchy are considered to be the critical factor and lack of clarity around regulatory and reporting standards is found to be very critical for ESG adoption. To address impediments to ESG adoption, the government should make ESG disclosure mandatory and clearly explain the rules, regulations and advantages associated with ESG implementation.

The current study is very few among examining the barriers to ESG adoption and steppingstone towards the mandatory implementation of ESG disclosures in current scenario.

This study aims to identify, analyze and rank the critical success factors (CSFs) of Lean Six Sigma (LSS) implementation in Indian manufacturing sector based micro, small and medium enterprises (MSMEs). This study provides critical insight for managers and researchers aspiring for successful implementation of LSS in Indian manufacturing MSMEs.

The CSFs were extracted from literature followed by a questionnaire-based survey from 120 industry professionals with extensive knowledge and experience about LSS working in Indian manufacturing MSMEs. Further, the CSFs were grouped based on their fundamental relevance and ranked using best worst method (BWM) approach using inputs from LSS experts.

This study provides insights on success factors that have helped Indian manufacturing MSMEs to implement LSS. The findings signify that “Strategy based CSFs” were ranked as the top most important factors, followed by two other category factors namely “Bottom-Line CSFs” and “Supplier based and other category-based CSFs”.

The proposed research is specifically relevant to the context of MSMEs in the Indian manufacturing sector. In the future, the same approach can be extended to a global context, encompassing service sector-based MSMEs in healthcare and finance.

This study provides valuable inputs for managers, decision-makers, industrial practitioners and researchers about Indian manufacturing MSMEs. The identified CSFs and their prioritization offer a roadmap for successful adoption of LSS. Managers can allocate resources, and make strategic decisions based on the prioritized CSFs. Decision-makers can align their initiatives with the identified CSFs. Industrial practitioners gain insights to enhance their LSS initiatives, and researchers can focus their efforts on areas critical to LSS implementation in Indian MSMEs. Furthermore, the structured approach employed in this study can be adopted by various MSME sectors globally, thereby broadening the comprehension of LSS implementation.

This study contributes to the existing body of knowledge by addressing the gaps in literature on CSFs related to LSS adoption within Indian manufacturing MSMEs. While LSS has been widely studied, there is limited focus on its adoption in the context of Indian MSMEs. The combination of extensive literature review, questionnaire-based survey and the application of the BWM approach for prioritizing CSFs adds originality to the research.

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.

This paper aims to explore the prevalence of depression, anxiety symptoms and suicidal ideation among the Romani population in Ukraine and their connections with various social health determinants: age, gender, household characteristics, employment and living conditions.

For measuring mental health conditions, GAD-7 and PHQ-9 were used. Individual interviews were conducted by trained volunteers of the International Charitable Organization “Roma Women’s Foundation Chirikli”. Data were gathered from January to March 2020.

The overall level of depression found in the sample was 8.08, while the mean for anxiety was 7.22. In general, 32.7% of respondents scored positively for signs of depression and 29.6% for anxiety. The two-week prevalence of suicidal ideations was 26.9%. Compared to the general population, the prevalence of depression among the Romani research participants was twofold higher, and anxiety was 2.5-fold higher. Signs of depression and anxiety in women were significantly higher (36% vs 28.6% for depression and 33.9% vs 24.2% for anxiety) than in men. Signs of depression and anxiety were higher for people without education than for university students (9.32 vs 3.04 for depression and 8.26 vs 3.00 for anxiety). The lowest levels of depression, anxiety and suicidal ideation were among officially married persons (6.61, 6.36 and 0.23, respectively). Significant small positive correlations were found between all measurements and the number of household members (0.149 for depression, 0.124 for suicidal ideation and 0.175 for anxiety; p < 0.001) and the number of children (0.303 for depression, 0.224 for suicidal ideation and 0.243 for anxiety; p < 0.001). In terms of employment, the highest scores for depression, anxiety and suicidal ideation were found among those who are employed seasonally (9.06, 8.25 and 0.61) or irregularly (9.09, 8.12 and 0.57) in contrast with self-employed (4.88, 4.90 and 0.19) and full-time employees (5.86, 5.51 and 0.18). Living place (city, village or camp) showed no relation with mental health, except for suicidal ideation: those living in villages had higher levels of suicidal ideation than those living in cities (0.49 vs 0.31).

The study has some limitations. Data were gathered from January to March 2020, and since then, the situation in Ukraine has drastically changed due to the full-scale Russian invasion. While this study’s data and conclusions might serve as a baseline for further research, they do not represent the real-time situation. While many social factors were analysed, the effects found for them do not necessarily represent causality, given the statistical methods used. Interactions among factors were not studied; therefore, no firm conclusions can be made about the effects of those interactions on mental health.

To the best of the authors’ knowledge, this paper is original in terms of its topic, as the first-ever in Ukraine quantitative study of mental health and social determinants of mental health of the Romani population.