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Managing mega infrastructure projects (MIPs) is more complex because of time, size, social, environmental and financial implications. This study aims to address the management approaches, complexity and risk factors involved in MIPs. The study focuses on project success criteria and their individual effects on the success of MIPs.

To address the challenges and identify the most influencing factor for the success of MIPs, the study deployed a cross-sectional survey approach. Six hundred eighty-two usable samples were collected from the respondents to understand the impact of predetermined factors on the success of MIPs. The structural equation model and artificial neural network approach were used to derive the importance of factors affecting the success of MIPs.

The study's outcome confirms that all three influencing factors: feasibility studies, community engagements and contract selection, have a significant positive impact on the success of MIPs. Community engagement amongst all three has the most influential predictor for the success of MIPs.

The developed model will enable practitioners and policymakers from Indian construction companies and other emerging nations to concentrate on recognized risk reduction variables to enhance project success criteria and project management success, especially for MIPs.

The overarching goal of the study was to formulate an integrated research model to empirically demonstrate the complex interplay between heuristics, project characteristics, information system usage quality, empathy, and mindfulness in predicting users'/donors' donation behaviour and well-being in the context of donation-based crowdfunding (DBC) mobile apps.

The data were collected from 786 respondents and analysed using the multi-stage SEM-ANN-NCA (Structural equation modelling-artificial neural network-necessary condition analysis) method.

Increased perceived aesthetics, narrative structure, self-referencing, project popularity, project content quality, and initiator reputation would foster empathy. Empathy and mindfulness lead to donation behaviour, and, ultimately emotional well-being.

This study offers a clear framework by ranking the key contextual predictors and assessing the model’s necessity logic to facilitate crowdfunders' donation behaviour and well-being on DBC platforms. This research provides practical insights for bank marketers and further aids financial service providers in formulating an optimal DBC mobile app strategy.

Businesses embark on cause-related marketing (CRM) initiatives as a marketing strategy to fortify consumers' behavioural intentions. Prior research indicates that human values could be tapped to understand the consumers' responses to perceived organizational motives behind undertaking social cause initiatives. This research employs Schwartz's theory of human values to examine consumers' patronage intentions towards CRM-linked fashion products. Moreover, fashion leaders play a crucial role in the diffusion of the latest fashion and fashion trends. This research investigates by integrating human values and fashion leadership, offering insights into CRM-linked fashion consumption motives.

The overarching goal was to investigate the complex interplay between human values and female fashion leadership to predict CRM patronage intention (CPI). Hence, a large-scale research study on 2,050 samples was undertaken by adopting threefold partial least squares–multigroup analysis–artificial neural network (PLS-MGA-ANN) to establish and empirically test a comprehensive model.

This study is unique as it establishes and validates the relative or normalized importance placed on human values by fashion leaders, thereby predicting CPIs. The results revealed that women with high-fashion leadership and specific value types (benevolence, universalism, self-direction) are more likely to patronize CRM-linked fashion retailers. In addition, the findings validated that women with low-fashion leadership and specific value types (tradition, security, conformity) are more likely to patronize CRM-linked fashion stores.

The findings provide a valuable rationale to non-profit marketers, fashion marketing experts and practitioners to design customer value-based profiling and manage crucial CRM decisions.

Every nation celebrates their Independence Day as a grand celebration by the whole country together. The purpose of this study is to identify the people’s emotions and sentiments about their country at the time of Independence Day celebration through the analysis of social media messages.

To collect data from Twitter now which is known as “X”, this study used python computer programming language. Total 119,151 tweets were collected, and sentiment analysis was done based on the polarity score and Latent Dirichlet Allocation to understand the sentiments of public. The topic modelling was also done in this study, where topics and categories were created based on the words found through the Latent Dirichlet Allocation.

The result of this study revealed that people have the respect towards their country and are always ready to come together for the nation. They also feel proud to be a citizen of an independent nation. Therefore, they celebrate their country’s Independent Day every year with joy. Every nation needs a dynamic leader to lead the nation, whom people can trust easily and follow blindly.

The result of this study can be generalised as everyone supports and respects their nation and the national flag. This study is practically useful to other researchers to conduct similar study of sentiment analysis and topic modelling on other counties’ patriotism or other campaigns at national or global level.

As a result, this study contributes to understand people’s respect towards the nation. This study also showed people’s positive response at their leader’s request. There are many studies done on topic modelling and content analysis, but this is the first and original work attempted on the special subject on national patriotism campaign to the best of the authors’ knowledge.

Owing to the limitations of traditional infectious disease dynamic systems in accurately encapsulating the nuances of China’s dynamic epidemic prevention policies and considering the varying sensitivity of local governments to the unfolding of public health emergencies (PHEs), this paper introduces a novel infectious disease dynamic system.

This system, rooted in the distinct characteristics of infectious diseases and nuanced prevention and control measures, leverages a learning model for enhanced precision. It intricately incorporates factors such as the infectivity in sealed and controlled areas and the role of asymptomatic patients, thereby refining the dynamics of isolation, sealing, control and the transition from asymptomatic to confirmed cases. Employing the Markov Chain Monte Carlo (MCMC) parameter estimation approach significantly augments the accuracy in pinpointing the valid parameters of disease spread. Empirical analysis was meticulously carried out, using data from the Shanghai epidemic from 1 Mar 2022 to 1 Jul 2022.

This analysis not only illuminates the profound impact of control efforts on the trajectory of the epidemic but also underscores the pivotal role of social distancing in curbing the rapid transmission of infectious diseases. Furthermore, it reveals that an accelerated detection rate during the swift spread and peak of the epidemic paradoxically leads to a surge in confirmed cases and a consequent strain on medical resources, thereby impeding the pace of medical intervention.

A stage-wise dissection of the Shanghai epidemic and comparative analyses against the evolution profiles in ASEAN countries elucidates the five stages of PHE risk evolution in alignment with the crisis lifecycle theory. These stages encompass hidden transmission, multi-point dissemination, multi-chain parallelism, rapid spread, fluctuation rebound and multi-community spread, each presenting unique challenges and dynamics in the control and management of the epidemic.

The study aims to examine the conceptualization of online abuse (OA) and identifies theories, countries of research, top-cited articles, methodologies, antecedents, mediators, outcomes and moderators of OA and future research opportunities. Two research questions are addressed. How have the past studies on OA progressed regarding theories, context, characteristics and methodology? What future research opportunities can be done in this area?

This study systematically reviews, synthesizes and integrates OA literature using the well-recommended preferred reporting items for systematic reviews and meta-analyses (PRISMA) rules. The literature on OA was synthesized based on the Theory–Context–Characteristics–Methodologies (TCCM) framework given by Paul and Rosado-Serrano.

Through an examination of TCCM used in OA research, the review presents an all-inclusive and up-to-date overview of the research in this arena and sets a future research agenda to spur scholarly research. This systematic literature review has analyzed top-quality sample papers, published in the past decade. As a result, it contributes to a better understanding of this relationship by analyzing the different types of use and the value added to the shopping experience.

This study provides groundwork for researchers and promotes a deeper understanding of OA.

The need to match highly variable demand for healthcare services with existing capacity has been a significant health system challenge experienced by the COVID-19 pandemic. Despite mounting research on various COVID-19 responses in the recent literature, there has not been a structured review examining key determinants of health system volume flexibility. The focus of the present research is to review recent literature based on a conceptual framework developed based on the volume flexibility literature on COVID-19. The review also provides a meaningful way to guide practice and future research in the area.

A literature review was performed guided by the theoretical frameworks developed in the previous volume of flexibility literature. Selected research papers were reviewed and analyzed to identify key determinants of health system volume flexibility.

Seven determinants of health system volume flexibility under three domains (demand management, capacity management, and performance) included demand management strategies, integration, capacity management strategies, workforce management, technology, quality of disease outcomes, and health system knowledge.

The research provides both health systems researchers as well as practitioners with a foundational conceptual framework and knowledge related to health system volume flexibility. In addition, this research identifies future research areas to expand knowledge relevant to health system volume flexibility.

The purpose of this study is to investigate the influence of credibility and information quality (IQ) of online food vloggers on consumer attitude and purchase intention towards street food consumption. It also examines the relationship between consumer attitude and purchase intention influenced by online food vlogger reviews in North India.

In order to evaluate the framework, primary data were gathered from 389 street food consumers located in Jammu, Chandigarh and Delhi – cities situated in northern India. The collected data were then subjected to analysis using the partial least squares-structural equation modeling (PLS-SEM) techniques.

The results indicate that the perceived value of street food, influenced by the credibility of food vloggers and the quality of information provided, has a positive impact on consumer attitude and purchase intention towards street food consumption. A positive impact of consumer attitude on the purchase and consumption of street food was also observed.

This research offers a thorough investigation into the elements that influence consumers' opinions regarding vloggers endorsements. The findings reveal that consumer's attitudes towards vloggers recommendations are mainly influenced by the quality of information provided, followed by credibility and the intention to make a purchase. Furthermore, this research is of significance to practitioners and academics interested in comprehending consumer behavior in the realms of tourism and food-related endeavors.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-02-2024-0158

This paper aims to provide a full introduction, new classification, comparison and investigation of the challenges as well as applications of layerless 3D printing, which is one of the industry 4.0 pioneers.

Given the significance and novelty of uniform 3D printing, more than 250 publications were collected and reviewed in an unbiased and clear manner.

As a result, the majority of uniform parts printed in polymer form are known up to this point. In a novel division for better researchers’ comprehension, uniform printing systems were classified into three categories: oxygen inhibition (OI), liquid lubrication (LL) and photon penetration (PP), and each was thoroughly investigated. Furthermore, these three approaches were evaluated in terms of printing speed, precision and accuracy, manufacturing scale and cost.

The parameters of each approach were compared independently, and then a practical comparison was conducted among these three approaches. Finally, a variety of technologies, opportunities, challenges and advantages of each significant method, as well as a future outlook for layerless rapid prototyping, are presented.

In the digital age, the spread of online behavior and real-world information leads to social contagion. This study aims to investigate the contagion phenomenon of online physician choice and then discuss its potential influence on the sub-specialization process in the healthcare service industry. In specific, this study aims to propose the basic mechanism of infection and immunity as follows – exposure to antigen may lead to an immune response, and the success of the immune response may depend on the provision of appropriate immune signaling.

Data collected from haodf.com including 4 disease types and 247 physicians from 2008 to 2015 were used to test the proposed hypotheses. Panel vector autoregression method was utilized to analyze the panel data.

The obtained result shows that social contagion of physician choice over disease type is salient on e-consultation platforms, indicating that physicians associated with/on haodf.com are concentrating on an even narrower type of disease. Disclosing more simple signals (physician history orders) results in more disease concentration for that physician in the future. In contrast, disclosing more detailed signals (physician-contributed knowledge or physician reviews) leads to less disease concentration.

This finding implies that physician-contributed knowledge and physician reviews may act as immune signal which will tend to trigger a success immune response. This study not only suggests managers should be careful about the double-edged sword effect of online physician choice contagion but also provides the useful approaches to promote or restrain such a contagion in a flexible way.