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We propose a machine learning based methodology to deal with data collected from a mobile application asking users their opinion regarding fashion products. Based on different machine learning techniques, the proposed approach relies on the data value chain principle to enrich data into knowledge, insights and learning experience.

Online interaction and the usage of social media have dramatically altered both consumers’ behaviors and business practices. Companies invest in social media platforms and digital marketing in order to increase their brand awareness and boost their sales. Especially for fashion retailers, understanding consumers’ behavior before launching a new collection is crucial to reduce overstock situations. In this study, we aim at providing retailers better understand consumers’ different assessments of newly introduced products.

By creating new product-related and user-related attributes, the proposed prediction model attends an average of 70.15% accuracy when evaluating the potential success of new future products during the design process of the collection. Results showed that by harnessing artificial intelligence techniques, along with social media data and mobile apps, new ways of interacting with clients and understanding their preferences are established.

From a practical point of view, the proposed approach helps businesses better target their marketing campaigns, localize their potential clients and adjust manufactured quantities.

The originality of the proposed approach lies in (1) the implementation of the data value chain principle to enhance the information of raw data collected from mobile apps and improve the prediction model performances, and (2) the combination consumer and product attributes to provide an accurate prediction of new fashion, products.

This study aims to analyze the factors affecting tea-producing farmers' intention to use e-commerce exchanges (ECEs) to sell their products, combining the technology acceptance model (TAM) theory and barrier factors.

The authors use the generalized structural equation modeling (GSEM) to analyze the intermediate model that is built on TAM.

Research results show that perceived usefulness (PU) and perceived ease of use (PEU) significantly influence farmers' intention to use ECEs to sell their products. However, knowledge and information barriers hinder farmers' intention to use such ECEs.

Encouraging farmers to use ECEs is the most helpful solution for agricultural economic development in the context of the Covid-19 pandemic.

From an academic perspective, this is the first study that combines the TAM theory of Davis (1989) and barrier factors to analyze farmers' intention to use ECEs. The findings are valuable references for policymakers to propose strategies for agricultural economic development during the current pandemic. At the same time, the empirical results obtained from this study provide good orientations for agricultural economic development 4.0 in the future.

Digital supply chains require nascent technologies like blockchain and Internet of Things (IoT). There is a need to develop a roadmap for the implementation of these technologies, as they require a huge amount of resources and infrastructure. The purpose of this paper is to analyze the challenges of implementing blockchain-IoT integrated architecture in the green supply chain and develop strategies for the same.

After a thorough literature survey of Scopus-indexed journals and books, 37 barriers were identified, which were then brought down to 15 barriers after confirming with industry and academic experts using the Delphi method. Using the total interpretive structural modeling (TISM) method and cross-impact matrix multiplication applied to classification (MICMAC) analysis, the barriers were modeled, and finally, strategies were formulated using a concept map to handle the barriers in the blockchain-IoT integrated architecture for a green supply chain.

This paper presents the research on barriers that can be considered for incorporating blockchain and IoT in the green supply chain. It was found from the TISM model that environmental concerns are Level-1 barriers and need to be addressed by developing appropriate technology and allocating funds for the same. An integrated ecosystem with blockchain and IoT is developed.

The focus of this study was on the challenges of blockchain and IoT; hence, it is required to extend the research and find challenges for different industries and also analyze the criteria using other multi-criteria decision-making (MCDM) methods. Further research is required for the integration of blockchain-IoT with supply chain functions.

The transformation of a traditional supply chain into a green supply chain is possible with the integration of technologies. This research work and the strategies developed are useful to managers and practitioners working on technology implementation. Planning resources and addressing key barriers is possible with the concept maps and architecture developed.

Green supply chain management (SCM) is gaining importance in industry as well as the academic sector due to government Policies and norms worldwide for reducing emissions and encouraging environment-friendly production systems. Incorporating blockchain and IoT in a green supply chain will further digitize and increase transparency in supply chains.

We have done a categorization of all barriers based on the expert survey by academicians and industry experts from industries in India. The concept map helps in identifying possible solutions for the challenges and initiatives to be taken for the smooth integration of technologies in the green supply chain.

The purpose of this study is to introduce a new type of sensor which uses microwave metamaterials and direct-coupled split-ring resonators (DC-SRRs) to measure the dielectric properties of solid materials in real time. The sensor uses a transmission line with a bridge-type structure to measure the differential frequency, which can be used to calculate the dielectric constant of the material being tested. The study aims to establish an empirical relationship between the dielectric properties of the material and the frequency measurements obtained from the sensor.

In the proposed design, the opposite arm of the bridge transmission line is loaded by DC-SRRs, and the distance between DC-SRRs is optimized to minimize the mutual coupling between them. The DC-SRRs are loaded with the material under test (MUT) to perform differential permittivity sensing. When identical MUT is placed on both resonators, a single transmission zero (notch) is obtained, but non-identical MUTs exhibit two split notches. For the design of differential sensors and comparators based on symmetry disruption, frequency splitting is highly useful.

The proposed structure is demonstrated using electromagnetic simulation, and a prototype of the proposed sensor is fabricated and experimentally validated to prove the differential sensing principle. Here, the sensor is analyzed for sensitivity by using different MUTs with relative permittivity ranges from 1.006 to 10 and with a fixed dimension of 9 mm × 10 mm ×1.2 mm. It shows a very good average frequency deviation per unit change in permittivity of the MUTs, which is around 743 MHz, and it also exhibits a very high average relative sensitivity and quality factor of around 11.5% and 323, respectively.

The proposed sensor can be used for differential characterization of permittivity and also as a comparator to test the purity of solid dielectric samples. This sensor most importantly strengthens robustness to environmental conditions that cause cross-sensitivity or miscalibration. The accuracy of the measurement is enhanced as compared to conventional single- and double-notch metamaterial-based sensors.

Collaboration and preference for teamwork play a fundamental role in strengthening practical completion of team tasks. An organizational culture should facilitate learning systems where knowledge creation occurs through socialization. The purpose of this study is to develop a moderated mediation model, investigating the conditional indirect effect of co-worker support on the relationship between preference for teamwork and communities of practice.

Questionnaire survey was conducted via Google Forms to collect data from 210 employees working in the private and public sector in India. Hayes PROCESS macro models were used for analyzing the mediation of personal interaction and moderation of co-worker support.

This study showed evidence regarding the mediating role of personal interaction on the relationship between preference for teamwork and communities of practice. Co-worker support moderated the relationship between personal interaction and communities of practice. It also moderated the conditional indirect effect.

The results approve the substantial role of preference for teamwork in influencing personal interaction and communities of practice. The mediating role of personal interaction on preference for teamwork and communities of practice can lead to creation and sustenance of communities of practice. Furthermore, the moderating role of co-worker support as a conditional indirect effect shows that social support and exchange can lead to social learning.

Theoretical explanations and analytical approaches provide insights into the relationship between the preference for teamwork and communities of practice through a conditional indirect effect, a one of its kind of a study.

One of the sources for the increase in the carbon footprint on the earth is the manufacturing of cement, which causes a severer environmental impact. Abundant research is going on to diminish CO₂ content in the atmosphere by appropriate utilization of waste by-products of industries. Alkali-activated slag concrete (AASC) is an innovative green new concrete made by complete replacement of cement various supplementary cementitious raw materials. Concrete is a versatile material used in different fields of structures, so it is very important to study the durability in different exposures along with the strength. The purpose of this paper is to study the performance of AASC by incorporating quartz sand as fine aggregate under different exposure conditions.

The materials for this innovative AASC are selected based on preliminary studies and literature surveys. Based on numerous trials a better performance mix proportion of AASC with quartz sand is developed with 1:2:4 mix proportion, 0.8 alkali Binder ratio, 19 M of NaOH and 50% concentration of Na₂SiO₃. Subsequently, AASC cubes are prepared and exposed for 3, 7, 14, 28, 56, 90, 112, 180, 252 and 365 days in ambient, acid, alkaline, sulfate, chloride and seawater and tested for compressive strength. In addition, to study the microstructural characteristics, scanning electron microscope (SEM), energy dispersive X-ray analysis and X-ray diffraction analysis was also performed.

Long-term performance of AASC developed with quartz sand is very good in the ambient, alkaline environment of 5% NaOH and seawater with the highest compressive strength values of 51.8, 50.83 and 64.46, respectively. A decrease in compressive strengths was observed after the age of 14, 56 and 112 days for acid, chloride and sulfate exposure conditions, respectively. SEM image shows a denser microstructure of AASC matrix for ambient, alkaline of 5% NaOH and seawater.

The proposed AASC is prepared with a mix proportion of 1:2:4, so the other proportions of AASC need to verify. In general plain, AASC is not used in practice except in few applications, in this work the effect of reinforced AASC is not checked. The real environmental exposure in fields may not create for AASC, as it was tested in different exposure conditions in the laboratory.

The developed AASC is recommended in practical applications where early strength is required, where the climate is hot, where water is scarce for curing, offshore and onshore constructions exposed to the marine environment and alkaline environment industries like breweries, distilleries and sewage treatment plants. As AASC is recommended for ambient air and in other exposures, its implementation as a construction material will reduce the carbon footprint.

The developed AASC mix proportion 1:2:4 is an economical mix, because of low binder content, but it exhibits a higher early age compressive strength value of 45.6 MPa at the age of 3 days. The compressive strength increases linearly with age from 3 to 365 days when exposed to seawater and ambient air. The performance of AASC is very good in the ambient, alkaline environment and seawater compared to other exposure conditions.

This paper aims to propose a decision-making framework by investigating the impact of perceived risk and computer self-efficacy on the intention to use online stock trading. Furthermore, it demonstrates the mediation effect of attitude and perceived risk as well as the moderating effect of financial literacy.

An integration of two popular models, technology acceptance model (TAM) and theory of planned behaviour (TPB), is used to provide a sound theoretical base and enhance the understanding of investors’ behaviour towards online trading platforms. The proposed hypothesised model was examined using structural equation modelling.

The results obtained from this study indicate that all variables, except subjective norms, had a significant impact on investors’ intention to trade online. Perceived risk was found to be a partial mediator between computer self-efficacy and the intention of investors. Finally, financial literacy was also found as a significant moderator for online trading intention of investors.

This study shows the significance of using the TAM and TPB together to provide a comprehensive understanding of the factors that influence an investor’s behaviour in adopting and using technology for online trading. The hybrid approach of TAM and TPB could be considered for a more nuanced and complete understanding of technology adoption and usage in risky affairs like investment decisions. Again, the significant moderating role of financial literacy provides a lance to look into the scope for improvements in investment decision-makings.

The paper develops an assessment framework for analysing the variables based on the hybrid approach for online trading intention in the context of a developing country.

This study explores how titanium oxide (TiO₂) filler influences the specific wear rate (SWR) in flax fiber-reinforced epoxy composites (FFRCs) through a Taguchi approach. It aims to boost abrasive wear resistance by incorporating TiO₂ filler, promoting sustainable and eco-friendly materials.

This study fabricates epoxy/flax composites with TiO₂ particles (0–8 wt%) using hand layup. Composites were tested for wear following American Society for Testing and Materials (ASTM) G99-05. Statistical analysis used Taguchi design of experiments (DOE), with ANOVA identifying key factors affecting SWR in abrasive sliding conditions.

The study illuminates how integrating TiO2 filler particles into epoxy/flax composites enhances abrasive wear properties. Statistical analysis of SWR highlights abrasive grit size (grit) as the most influential factor, followed by normal load, wt% of TiO₂ and sliding distance. Grit size has the highest effect at 43.78%, and wt% TiO₂ filler contributes 15.61% to SWR according to ANOVA. Notably, the Taguchi predictive model closely aligns with experimental results, validating its reliability.

This paper integrates TiO₂ filler and flax fibers to form a novel hybrid composite with enhanced tribological properties in epoxy composites. The use of Taguchi DOE and ANOVA offers valuable insights for optimizing control variables, particularly in natural fiber-reinforced composites (NFRCs).

The purpose of this research is to introduce the Layar augmented reality (AR) application among library users and to understand the user’s satisfaction towards the information services provided by the Layar application with the help of the structural equation model (SEM).

According to Thomas (2016), action research is mainly undertaken to develop new skills or new approaches and to solve issues and problems with direct application to any applied setting. The present study helps to develop new skills and approaches to repackaging information using AR applications. Researchers have identified the question of what could be done to increase the awareness of Layar AR applications among students. Because the Layar augmented application is one of the new tools for an academic library to repackage information for mass accessibility. Therefore, in the present action research approach, researchers encompass two activities action and research. Researchers have used participatory action research methods by collecting data from 17 MBA institute libraries affiliated with Savitribai Phule Pune University. Researchers have systematically used the Layar application in the library by obtaining permission from each higher authority. Researchers have designed a Layar satisfaction model using the SEM with AMOS and SPSS.

The researcher found that the relationship between experience, performance and service quality is positively significant. The user’s experience is satisfied with the Layar application, but users are not satisfied with the service quality and performance of the Layar application.

This study tested Layar AR application in MBA libraries affiliated with Savitribai Phule Pune University in the Pune and Pimpri Chinchwad areas.

The Layar app helps the academic library to convert selected print collections into an AR feel for library users. This is an additional method of providing information services to users through mobile devices. A total of 157 students downloaded the Layar application from their handsets and provided feedback through a questionnaire. Researchers have found that the relationships between users and Layar experience, performance and service quality are positively significant. The user experience is satisfied with the Layar application, but users are not satisfied with the service quality and performance of the Layar application.

This study examined the performance, service quality and user experience of Layar applications. Structural equation and Modelling theories were used to examine the relationship between user satisfaction and information services using the Layar application.

This paper aims to understand the emotional patterns of senior citizens when they digitally transact online and how the personality trait of neuroticism can be a vital part of being susceptible to fraud. The authors identify with the cognitive aspect of fear as a vista of neuroscience and behavioural finance in digital banking in today’s banking 5.0 where consumer centricity stands as one of the pillars of the Digital Payment Index of RBI.

This study is empirical and investigative in nature. Primary data has been collected through online questionnaires (via Google Forms) and the interview method to understand the phenomenology of fear and incidents related to becoming victims of fraud and its propensity.

The results exhibit that fear and emotional patterns do affect vulnerability and have a long-lasting psychological impact and susceptivity towards digital frauds.

Fear as an emotion is used to understand the emotional expressive patterns of senior citizens as consumers of digital banking. The OCEAN model is one of the widely used personality models at the global level. This research study helps in highlighting the nuances linked to the behavioural and cognitive part of fear in digital crime.

This research will be beneficial to reduce the susceptibility towards fraud from a behavioural perspective in the usage of digital banking and evaluate solutions for senior citizens to mitigate and cope up with the pressures and perils associated with digital frauds.