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Users' voluntary forwarding behavior opens a new avenue for companies to promote their brands and products on social networking sites (SNS). However, research on voluntary information disseminators is limited. This paper aims to bring an in-depth understanding of voluntary disseminators by answering the following questions: (1) What is the underlying mechanism by which some users are more enthusiastic to voluntarily forward content of interest? (2) How to identify them? We propose a theoretical model based on the Elaboration-Likelihood Model (ELM) and examine three types of factors that moderate the effect of preference matching on individual forwarding behavior, including personal characteristics, tweet characteristics and sender–receiver relationships.

Via Twitter API, we randomly crawled 1967 Twitter users' data to validate the conceptual framework. Each user’s original tweets and retweeted tweets, profile data such as the number of followers and followees and verification status were obtained. The final corpus contains 163,554 data points composed of 1,634 valid twitterers' retweeting behavior. Tweets produced by these core users' followees were also crawled. These data points constitute an unbalanced panel data and we employ different models — fixed-effects, random-effects and pooled logit models — to test the moderation effects. The robustness test shows consistency among these different models.

Preference matching significantly affects users' forwarding behavior, implying that SNS users are more likely to share contents that align with their preferences. In addition, we find that popular users with lots of followers, heavy SNS users who author tweets or forward other-sourced tweets more frequently and users who tend to produce longer original contents are more enthusiastic to disseminate contents of interest. Furthermore, interaction strength has a positive moderating effect on the relationship between preference matching and individuals' forwarding decisions, suggesting that users are more likely to disseminate content of interest when it comes from strong ties. However, the moderating effect of perceived affinity is significantly negative, indicating that an online community of individuals with many common friends is not an ideal place to engage individuals in sharing information.

This work brings about a deep understanding of users' voluntary forwarding behavior of content of interest. To the best of our knowledge, the current study is the first to examine (1) the underlying mechanism by which some users are more likely to voluntarily forward content of interest; and (2) how to identify these potential voluntary disseminators. By extending the ELM, we examine the moderating effect of tweet characteristics, sender–receiver relationships as well as personal characteristics. Our research findings provide practical guidelines for enterprises and government institutions to choose voluntary endorsers when trying to engage individuals in information dissemination on SNS.

This research investigates the impact of the COVID-19 pandemic on the use of transitional spaces in a Korean academic setting, to assess the impact of the pandemic on users' utilization of transitional spaces and evaluate any changes in their usage patterns. The research explores whether transitional spaces can function as social interactive spaces, aligning with Ray Oldenburg's “third-place” theory. The focus is on South Korean academic settings, aiming to create neutral and safe zones for users.

The adopted methodology involves reviewing the literature and employing design charrette as a major data collection tool. The design charrette provided a platform for users to share insights on current transitional spaces during the pandemic and envision these spaces as future social and interactive spaces.

The design charrette participants advocated for modifying the current transitional space design to transform these spaces into shared spaces for both visitors and regular users in the future. Restricting access for external users to the main building area until necessary. The significance of site amenities in determining transitional spaces as “third-places” was emphasized. While the nature of the building, its location and transitional space amenities are crucial aspects to consider, designers may prioritize user opinions and preferences, as the success or failure of the design ultimately centers on user behaviors.

The research focused on a specific university, hindered by limited access to other institutions during the pandemic. Restrictions on external users discouraged entry without proper permission, which was challenging to obtain. The conventional design charrette outlined in the research method was impossible due to pandemic-related limitations on gathering participants in one location. Therefore, the researcher modified the design charrette method to align with strict social distancing measures.

The results of the research are not limited to academic settings, but they can be implied in other environments where social interaction spaces are required and where there is a constant flow of visitors and regular users. The design charrette can be used as a methodology for interior spaces along with large-scale projects of urban planning.

The research analyzed transitional spaces during the pandemic, suggesting redesign to serve and act as buffer zones between private and public areas and become a common social gathering place for visitors and regular users within the built environment.

Our study examines how artificial intelligence (AI) can enhance decision-making processes to promote circular economy practices within the utility sector.

A unique case study of Alia Servizi Ambientali Spa, an Italian multi-utility company using AI for waste management, is analyzed using the Gioia method and semi-structured interviews.

Our study discovers the proactive role of the user in waste management processes, the importance of economic incentives to increase the usefulness of the technology and the role of AI in waste management transformation processes (e.g. glass waste).

The present study enhances the circular economy model (transformation, distribution and recovery), uncovering AI’s role in waste management. Finally, we inspire managers with algorithms used for data-driven decisions.

This study aims to minimize the cost and time of blood delivery in the whole blood supply chain network (BSCN) in disaster conditions. In other words, integrating all strategic, tactical and operational decisions of three levels of blood collection, processing and distribution leads to satisfying the demand at the right time.

This paper proposes an integrated BSCN in disaster conditions to consider four categories of facilities, including temporary blood collection centers, field hospitals, main blood processing centers and medical centers, to optimize demand response time appropriately. The proposed model applies the location of all permanent and emergency facilities in three levels: blood collection, processing and distribution. Other essential decisions, including multipurpose facilities, emergency transportation, inventory and allocation, were also used in the model. The LP metric method is applied to solve the proposed bi-objective mathematical model for the BSCN.

The findings show that this model clarifies its efficiency in the total cost and blood delivery time reduction, which results in a low carbon transmission of the blood supply chain.

The researchers proposed an integrated BSCN in disaster conditions to minimize the cost and time of blood delivery. They considered multipurpose capabilities for facilities (e.g. field hospitals are responsible for the three purposes of blood collection, processing and distribution), and so locating permanent and emergency facilities at three levels of blood collection, processing and distribution, support facilities, emergency transportation and traffic on the route with pollution were used to present a new model.

Surgical services are settings where resilient performance (RP) is necessary to cope with a wide range of variabilities. Although RP can benefit from a supportive built environment (BE), prior studies have focused on the operating room, giving scant attention to support areas. This study takes a broader perspective, aiming at developing BE design knowledge supportive of RP at the surgical service as a whole.

Seven BE design prescriptions developed in a previous work in the context of internal logistics of hospitals, and thus addressing interactions between workspaces, were used as a point of departure. The prescriptions were used as a data analysis framework in a case study of the surgical service of a medium-sized private hospital. The scope of the study included surgical and support areas, in addition to workflows involving patients and family members, staff, equipment, sterile instruments and materials, supplies, and waste. Data collection included document analysis, observations, interviews, and meetings with hospital staff.

Results identified 60 examples of using the prescriptions, 77% of which were related to areas other than the operating rooms. The developed design knowledge is framed as a set of prescriptions, examples, and their association to workflows and areas, indicating where it should be applied.

The design knowledge is new in surgical services and offers guidance to both BE and logistics designers.

Technology advancement has changed how banks operate. Modernizing technology has, on the one hand, made it simpler for banks to do their daily business, but it has also increased cyberattacks. The purpose of the study is to to determine the factors that have the most effects on online fraud detection and to evaluate the advantages of AI and human psychology research in preventing online transaction fraud. Artificial intelligence has been used to create new techniques for both detecting and preventing cybercrimes. Fraud has also been facilitated in some organizations via employee participation.

The main objective of the research approach is to guide the researcher at every stage to realize the main objectives of the study. This quantitative study used a survey-based methodology. Because it allows for both unbiased analysis of the relationship between components and prediction, a quantitative approach was adopted. The study of the body of literature, the design of research questions and the development of instruments and procedures for data collection, analysis and modeling are all part of the research process. The study evaluated the data using Matlab and a structured model analysis method. For reliability analysis and descriptive statistics, IBM SPSS Statistics was used. Reliability and validity were assessed using the measurement model, and the postulated relationship was investigated using the structural model.

There is a risk in scaling at a fast pace, 3D secure is used payer authentication has a maximum mean of 3.830 with SD of 0.7587 and 0.7638, and (CE2).

This study focused on investigating the benefits of artificial intelligence and human personality study in online transaction fraud and to determine the factors that affect something most strongly on online fraud detection. Artificial intelligence and human personality in the Indian banking industry have been emphasized by the current research. The study revealed the benefits of artificial intelligence and human personality like awareness, subjective norms, faster and more efficient detection and cost-effectiveness significantly impact (accept) online fraud detection in the Indian banking industry. Also, security measures and better prediction do not significantly impact (reject) online fraud detection in the Indian banking industry.

Indian railways (IR) is one of the largest railway networks in the world. As a part of its strategic development initiative, demand forecasting can be one of the indispensable activities, as it may provide basic inputs for planning and control of various activities such as coach production, planning new trains, coach augmentation and quota redistribution. The purpose of this study is to suggest an approach to demand forecasting for IR management.

A case study is carried out, wherein several models i.e. automated autoregressive integrated moving average (auto-ARIMA), trigonometric regressors (TBATS), Holt–Winters additive model, Holt–Winters multiplicative model, simple exponential smoothing and simple moving average methods have been tested. As per requirements of IR management, the adopted research methodology is predominantly discursive, and the passenger reservation patterns over a five-year period covering a most representative train service for the past five years have been employed. The relative error matrix and the Akaike information criterion have been used to compare the performance of various models. The Diebold–Mariano test was conducted to examine the accuracy of models.

The coach production strategy has been proposed on the most suitable auto-ARIMA model. Around 6,000 railway coaches per year have been produced in the past 3 years by IR. As per the coach production plan for the year 2023–2024, a tentative 6551 coaches of various types have been planned for production. The insights gained from this paper may facilitate need-based coach manufacturing and optimum utilization of the inventory.

This study contributes to the literature on rail ticket demand forecasting and adds value to the process of rolling stock management. The proposed model can be a comprehensive decision-making tool to plan for new train services and assess the rolling stock production requirement on any railway system. The analysis may help in making demand predictions for the busy season, and the management can make important decisions about the pricing of services.

As China’s e-commerce and cross-border e-commerce rapidly develop, the cross-border e-commerce supply chain exhibits characteristics of globalized development scale, collaborative multiparty participation, streamlined management processes, digitalized production and trade and flexible strategic choices. It tends toward data-driven intelligence, interoperable information collaboration, personalized order responses, sustainable supply chain management and secure blockchain technology. These characteristics and trends provide critical references for businesses, governments and investors.

In response to issues such as inconsistent legal regulations, imbalanced logistics and transportation, imperfect payment settlements and opaque supply chains.

It is recommended to take measures to strengthen cooperation and communication, optimize logistics, reduce customs clearance difficulties, reinforce safeguard measures and promote sustainable development, collectively fostering the healthy growth of cross-border e-commerce.

With the rapid development of cross-border e-commerce, green and low-carbon initiatives have become a significant trend in this sector. The cross-border e-commerce supply chain refers to the mechanism that reduces environmental impacts and enhances resource efficiency from manufacturers to consumers. It primarily involves manufacturers, e-commerce platforms, logistics companies and payment and settlement processes. The cross-border e-commerce supply chain is gradually becoming a highlight in China’s foreign trade, supporting the concept of “buying globally and selling globally” and connecting the “world’s factory” with the “world’s market.”

Buoyed by the increasing demand for improved productivity and environmentally conscious manufacturing, research in the area of lean production and green manufacturing has experienced significant growth since Dües et al. (2013). Taking the latter as the point of reference, a review of recent developments in the complementary and conflicting areas between lean production and green manufacturing that has been missing is presented.

A systematic search was done to identify articles on lean production and green manufacturing from Scopus, Web of Science and Google Scholar. The population-intervention-outcome format was used to develop and answer the research questions. ATLAS.ti 22 was used to analyse 141 qualifying papers and identify the research themes.

Lean production and green manufacturing have strong synergy, and when integrated, they tend to deliver superior organisational performance than their individual implementations. This is consistent with the pre-2013 results, and other areas of synergy and divergence were also identified.

The study considers only papers published in the manufacturing sector after Dües et al. (2013). A review of lean production and green manufacturing in integrated product-service systems may also be relevant, especially due to the continuing trend since its introduction.

Any new adopter of lean production should consider implementing it simultaneously with green manufacturing.

This study establishes the persistence of the pre-2013 patterns of synergy and divergence between lean production and green manufacturing, and identifies new considerations for their joint implementation.

This study aims to analyze how conventional Bedouin weaving techniques have changed through the history of Israel, offering knowledge on the craft’s cultural relevance and historical development among the Bedouin people and how their weaving and embroidery differ based on the three main geographic characteristics. It tries to comprehend the causes of the transition from organic to synthetic materials and the part played by the Lakiya Negev Bedouin Weaving women’s cooperative in maintaining this legacy.

The main goal of this study is to trace the emergence of Bedouin weaving traditions in the Negev Desert using a qualitative research methodology that combines historical analysis and ethnographic investigation. A thorough grasp of the subject’s significance is provided through the data gathering, which consists of interviews, archival research and field observations.

Through the years, Bedouin weaving techniques have significantly shifted away from using traditional organic materials in favor of synthetic replacements, according to the research. It emphasizes the crucial part played by the Lakiya Negev Bedouin Weaving women’s organization in safeguarding this traditional legacy and giving Bedouin women access to economic prospects.

The limitation of the study includes its emphasis on the Negev region and the Israeli Bedouin community, which may not accurately reflect all Bedouin weaving techniques. Greater regional settings may be explored in future studies.

The investigation emphasizes the value of investing in initiatives for cultural preservation and the empowerment of underprivileged groups through economic possibilities.

By preserving ancient weaving techniques, this research enables Bedouin women in the Negev Desert to maintain their cultural identity and socioeconomic well-being.

By emphasizing the socio-cultural dimensions and the organization’s role in preserving traditional craftsmanship in a changing socio-economic environment, this research presents a unique investigation of the evolution of Bedouin weaving techniques in Israel.