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For ranking aggregation in crowdsourcing task, the key issue is how to select the optimal working group with a given number of workers to optimize the performance of their aggregation. Performance prediction for ranking aggregation can solve this issue effectively. However, the performance prediction effect for ranking aggregation varies greatly due to the different influencing factors selected. Although questions on why and how data fusion methods perform well have been thoroughly discussed in the past, there is a lack of insight about how to select influencing factors to predict the performance and how much can be improved of.

In this paper, performance prediction of multivariable linear regression based on the optimal influencing factors for ranking aggregation in crowdsourcing task is studied. An influencing factor optimization selection method based on stepwise regression (IFOS-SR) is proposed to screen the optimal influencing factors. A working group selection model based on the optimal influencing factors is built to select the optimal working group with a given number of workers.

The proposed approach can identify the optimal influencing factors of ranking aggregation, predict the aggregation performance more accurately than the state-of-the-art methods and select the optimal working group with a given number of workers.

To find out under which condition data fusion method may lead to performance improvement for ranking aggregation in crowdsourcing task, the optimal influencing factors are identified by the IFOS-SR method. This paper presents an analysis of the behavior of the linear combination method and the CombSUM method based on the optimal influencing factors, and optimizes the task assignment with a given number of workers by the optimal working group selection method.

This paper aims to “identify,” “analyse” and “categorise” the readiness factors of lean sustainability in health-care organisation using total interpretive structural modelling (TISM).

To obtain the data, a closed-ended questionnaire was used in addition to a scheduled interview. To identify how the factors interact, the TISM approach was used, and the matriced’ impacts croise’s multiplication applique’e a UN classement (MICMAC) analysis was used to rank and categorise the lean sustainability readiness factors.

This study identified ten lean sustainability readiness factors for health-care organisation. The identified factors are resources utilization practice (F1), management commitment and leadership (F2), operational flexibility (F3), workforce engagement and time commitment (F4), sustainability motivational factors (F5), awareness of lean and sustainable practice (F6), hospital design (F7), energy efficiency practices in hospitals (F8), responsible autonomy (F9) and new system adoptability training (F10). The key/driving factors are identified in this study are operational flexibility, sustainability motivational factors, management commitment and leadership, new system adoptability training.

The study focussed primarily on lean sustainability factors for the health-care sector.

This research will aid key stakeholders and academics in the better understanding the readiness factors that influence lean sustainability in health-care organisation. This study emphasises the factors that must be considered when applying lean sustainable practices in health care as a real-world application in a health-care organisation. These readiness factors for lean sustainability can be used by an organization to comprehend more about the concept and the components that contribute to health-care lean sustainability.

This study proposes the TISM technique for health care, which is a novel attempt in the subject of lean sustainability in this sector.

Integrating the Chat Generative Pre-Trained Transformer-type (ChatGPT-type) model with government services has great development prospects. Applying this model improves service efficiency but has certain risks, thus having a dual impact on the public. For a responsible and democratic government, it is necessary to fully understand the factors influencing public acceptance and their causal relationships to truly encourage the public to accept and use government ChatGPT-type services.

This study used the Latent Dirichlet allocation (LDA) model to analyze comment texts and summarize 15 factors that affect public acceptance. Multiple-related matrices were established using the grey decision-making trial and evaluation laboratory (grey-DEMATEL) method to reveal causal relationships among factors. From the two opposite extraction rules of result priority and cause priority, the authors obtained an antagonistic topological model with comprehensive influence values using the total adversarial interpretive structure model (TAISM).

Fifteen factors were categorized in terms of cause and effect, and the antagonistic topological model with comprehensive influence values was also analyzed. The analysis showed that perceived risk, trust and meeting demand were the three most critical factors of public acceptance. Meanwhile, perceived risk and trust directly affected public acceptance and were affected by other factors. Supervision and accountability had the highest driving power and acted as the causal factor to influence other factors.

This study identified the factors affecting public acceptance of integrating the ChatGPT-type model with government services. It analyzed the relationship between the factors to provide a reference for decision-makers. This study introduced TAISM to form the LDA-grey-DEMATEL-TAISM method to provide an analytical paradigm for studying similar influencing factors.

This study aims to investigate factors that influence e-government adoption among public sector departments with the view to determine how such factors may be used to better facilitate e-government adoption across United Arab Emirates (UAE) public sectors. The use of e-government is advocated for the central government in the UAE.

Using random sampling, a total of 172 participants from ten departments and organisations in Dubai and Sharjah completed the online survey for this pilot study.

The authors found that performance expectancy and facilitating conditions have positive effects on e-government adoption. Furthermore, this study revealed the factors that encourage more e-government adoption between government organisations in the UAE. This study reveals three facilitating conditions may encourage e-government adoption in UAE public sector organisations when short- and long-term performances have positive effects on e-government usage.

This study provides middle managers clarity on factors that would influence government-to-government (G2G) uptake in more government organisations across the country. For uniformity and consistency, middle managers are now better informed as a result of this study to determine how best to use the six factors to motivate subordinates for more effective G2G.

The scope and results of this study is a contribution to e-government studies because it identifies the factors that positively influence G2G adoption. This scope exceeds the studies by Chan et al. (2021) and Habib et al. (2020) which focuses on the use of e-government for citizens or the public. This study focuses on the use of e-government within the government and between government departments.

The introduction of quality management Internet of things (QM IoT) can help food supply chain members to enhance real-time visibility, quality, safety and efficiency of products and processes. Current literature indicates three main research gaps, including a lack of studies in QM IoT in the food supply chain, the vagueness of integrative adoption of new technology framework and deficient research covering both adoption attitude and intention in the same model. This study aims to propose an analysis model based on the technological–organizational–environmental (TOE) framework and reinforced by the collaborative structure to capture the importance of the supply chain network.

The partial least square-structural equation modeling (PLS-SEM) was applied to test the impacts of the adoption factors on QM IoT adoption attitude and intention among 197 respondents in food manufacturing in Thailand.

The results indicated that compatibility, trialability, adaptive capacity, innovative capability, executive support, value chain partner pressure, presence of service provider and information sharing significantly impacted the attitude toward QM IoT adoption, while adaptive capability, innovative capability and information sharing directly influenced the QM IoT adoption intention. Furthermore, the attitude toward QM IoT adoption positively impacted the QM IoT adoption intention.

This study contributed to academicians by proposing a more solid adoption framework for QM IoT area. In addition, the business practitioners could actively prepare themselves for the QM IoT adoption, whereas the service providers could provide better and suitable service.

This research contributes to the building of a more solid framework and indicates significant factors that impact the attitude toward QM IoT adoption and adoption intention.

When coping with uncertainties, three characteristics distinguish firms: agility, adaptability and alignment (triple-A). Based on significant field research, the triple-A highlights the significance of coordinating agility, adaptability and alignment. Start-ups are facing a lot of challenges in this turbulent environment. However, this sector is undergoing a major transformation. Agility, adaptability and alignment concepts have had a major influence on the supply chain, but their implementation in start-ups has been less visible. This paper aims to identify, analyze and categorize the enablers for agility, adaptability and alignment in start-ups using the total interpretive structural modeling (TISM) approach.

In addition to the scheduled interview, a closed-ended questionnaire was used to collect data. To identify how the factors interact, the TISM technique is used, and the Matriced’Impacts Croises-Multipication Applique’ and Classment method is used to rank and categorize the agility, adaptability and alignment enablers.

This study identified ten agility, adaptability and alignment factors for start-ups. It has been found that the key importance should be given to management involvement, conflict management, collaboration and information integration.

This study primarily focused on the agility, adaptability and alignment factors in start-ups.

This study will help academics and key stakeholders understand the aspects that lead to agility, adaptability and alignment in start-ups.

Agility, adaptability and alignment concepts have had a major influence on the supply chain, but their implementation in start-ups has been less visible. Therefore, this is a novel attempt in this industry’s agility, adaptability and alignment.

This study aims to analyze the interaction and key factors within the network of factors influencing the success of green highway projects.

Through literature review and interviews with experienced project managers, this study identified 33 influencing factors from the perspectives of stakeholders and life cycle. The interaction between these influencing factors was determined by surveying different experts, most of whom have participated in green highway projects in China. Then, social network analysis (SNA) was used to explore the impact and control ability of different factors.

According to the prioritization of these factors, the results showed that some key factors were identified, especially incremental cost, lack of standards and regulations, development of innovative technologies and materials, public awareness and environmental protection behavior. Finally, some meaningful suggestions were put forward for different influencing factors of green highway projects.

While the key influencing factors of the green highway projects have been identified by considering the interrelationships between different factors, the specific influencing paths and levels of different factors are not analyzed, more studies and methods should be conducted on this area in the future.

This finding of factors influencing the success of green highway projects which is useful for managers to overcome various obstacles encountered in similar projects. Moreover, considering the interaction among the influencing factors enables managers to make systematic and efficient decisions.

Developing green highways have been perceived as a major innovation to help achieve the synergy of environmental protection, economic development and social responsibility. Studying the key factors influencing the success of green highway projects and putting forward targeted suggestions are crucial for promoting the environmental protection transformation of highway construction.

Compared with most studies on the identification of key influencing factors of construction projects, this research emphasized the interaction between different factors within the system in the analysis process. The findings could provide useful references to promote the successful implementation of green highway projects.

This study intends to build up a thorough understanding of social factors that largely influence students’ decision-making to opt institution for higher studies in India.

This descriptive research follows two sequential phases consisting of the literature review to identify social factors and validate the factors through the questionnaire method. Factor analysis was applied to identify the various factors that influence the student’s institution choice.

The research work explores and identified four factors and their associated attributes that impact students’ decision-making to opt institution for higher studies. It was found that the career advisor influence variable has the highest level of variance, followed by societal norms, social platform and cohort influence.

The present study is limited to social factors only. Therefore, many other determinants which may influence the student’s decision-making to opt the institute for higher studies remain unaddressed in this study.

The findings of the study can guide the institutions' admission management in underpinning the acceptance of social factors to observe their influence on student’s choice of an institution. An important implication is the identification of career advisor influence as the strongest social factor which may bridge the student's career fit in the institution and social platform influence which may help higher education institutions to redesign their marketing strategies to augment students’ enrolment.

This study provides insight into the important role of social factors that impact the student's decision-making regarding institution choice in India.

Blockchain technology (BT) is setting world-shattering standard in all type of transactions in business. BT has the prospective to drastically transform supply chain (SC). The main challenge is to enhance trust among the SC stakeholders. This paper aims to identify and prioritize the factors and its challenges that influence the adoptability of BT in SC. The prioritization of these factors will be helpful to the practitioners to decide the strategy of implementing the BT in SC.

The factors influencing adoption of BT are identified from the review of literature and expert opinion is used to rank the factors influencing the adoptability of BT in SC using grey relational analysis (GRA).

We identified and prioritized key factors: inter-organizational trust and relational governance as organizational challenge, data transparency and data immutability as technological challenge, interoperability and product type as operational challenge and social influence and behavioral intention as social challenge that influences adoptability of BT in SC.

The priority of these factors will guide future researchers and industry practitioners to plan rational and financial strategy for implementing BT in SC.

The purpose of this paper is to explore the key factors influencing the service innovation of mobile social networks (MSNs), figure out the mechanism of all factors in different stages of service innovation and help mobile social application developers promote better service innovation.

From previous research, this paper adopted nine initial factors that influence the service innovation of MSNs, and divide the service innovation process into three stages (i.e. demand analysis, service design and innovation implementation). On that basis, the authors constructed a model, and then collected data from 184 managers from 20 leading MSN corporates in China through questionnaires to examine the model. Furthermore, factor analysis was used to extract key factors influencing the service innovation of MSNs, correlation analysis was employed to discuss the relationship among factors and regression analysis was applied to explore their specific roles in different stages in the service innovation process.

The empirical results show that the service innovation of MSNs is mainly influenced by five key factors: user, developer, market environment, social environment and technology. The authors found that different factors played remarkably different roles in the three stages. In specific, all factors but technology are important in the demand analysis stage; all factors but social environment are critical to service design; and all factors but user contribute to the implementation of service innovation.

The results of this study can help mobile social application developers and mobile social service providers in China to better understand the driving force of service innovation and what should be emphasized in different stages, and then find the optimal path to implement service innovation, improve their service quality and user experience and facilitate the development of Chinese MSNs.

This is the first research that comprehensively explores factors influencing the service innovation of Chinese MSNs from multi-dimensional perspectives, which provides profound theoretical guidance to the practice of service innovation in China. Also, it contributes to the development of innovation theory of traditional web services.