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The purpose of this study is to present a systematic and comprehensive review of personalized, adaptive and semantic e-learning systems.

Preferred reporting items of systematic reviews and meta-analyses guidelines have been used for a thorough insight into associated aspects of e-learning that complement the e-learning pedagogies and processes. The aspects of e-learning systems have been reviewed comprehensively such as personalization and adaptivity, e-learning and semantics, learner profiling and learner categorization, which are handy in intelligent content recommendations for learners.

The adoption of semantic Web based technologies would complement the learner’s performance in terms of learning outcomes.

The evaluation of the proposed framework depends upon the yearly batch of learners and recording is a cumbersome/tedious process.

E-Learning systems may have diverse and positive impact on society including democratized learning and inclusivity regardless of socio-economic or geographic status.

A preliminary framework of an ontology-based e-learning system has been proposed at a modular level of granularity for implementation, along with evaluation metrics followed by a future roadmap.

Transportation-related pollution is expected to decrease when using battery electric cars. This will not only address energy and environmental issues but also promote reform and transformation in the zero-emission automotive industry. To craft policy interventions and promotional initiatives, manufacturers need to comprehend the techno-psychological perspectives of automotive users on the adoption of electric cars. Therefore, this study aims to test a “perception-attitude-intention” linking framework built upon the “Unified Theory of Technology Acceptance and Use” (UTAUT) and analyze the behavioral intentions of existing automobile users to embrace battery electric cars.

The conceptual model tests the underlying direct paths, the mediation of attitudes and the moderating gender effects in predicting users’ attitudes and behavioral intentions to adopt battery electric cars using a techno-psychological approach from UTAUT. “Structural equation modeling” is used to analyze the model using the 361 valid online responses received from conventional car owners.

The results show that behavioral intentions are directly predicted by UTAUT measures with attitudes and indirectly through its mediation and gender moderation. The results support the “Perceptions-Attitudes-Intentions” linkage model that explains the phenomenon of electric car adoption. However, the mediating and moderating paths between facilitating conditions and intentions do not support the model. In addition, the research corroborates that men have a stronger effect than women on behavioral intentions to prefer battery electric cars.

This work may assist manufacturers and regulators in developing marketing policies to encourage consumers’ adoption of battery electric cars and potentially improve their favorable perception of these vehicles.

This study contributes to the comprehension of how UTAUT constructs shape consumers’ attitudes and behavioral intentions regarding the adoption of battery cars equipped with emission-free technology. This study validates the grounded framework “perception-attitude-intention” linkage model, which also describes gender-wise differences toward electric car adoption in the backdrop of Indian sustainable transportation.

The purpose of this study is to identify and validate the key consumer-related factors influencing consumer attitude toward electric vehicles (EVs) and intention to purchase EVs. The role of consumer attitude in mediating consumer-related factors and intention to buy is also explored in this study. Consumer-related factors considered in this study include environmental orientation (EO), new technology orientation (NTO), social orientation (SO) and perceived monetary benefits (PMB).

Data for the study were collected from individuals who do not own an EV in Chennai, Tamil Nadu, India. Data were collected from 388 respondents using Google Forms. Convenience sampling technique was used to identify the respondents. Both descriptive and inferential statistics techniques were used to analyze data collected.

The study identified EO, SO and PMB as antecedents of attitude (ATT) toward EV and NTO, SO and PMB as potential drivers of EV purchase intention (PI). In addition, the authors also observe that EO is fully mediated by ATT toward consumer PI, whereas ATT partially mediates the relationship between the independent variables SO and PMB and the dependent variable PI.

EVs are at the forefront of automotive innovation. Automobile industries producing EVs showcase a commitment to embracing cutting-edge technology, positioning their companies as forward-thinking and technologically advanced. Creating awareness about EV technology, dispelling myths about range anxiety and charging times should be done methodologically. The lower operating costs of EVs and the government policies that reduce the total cost of ownership can lead to faster market penetration. The widespread adoption of EVs can contribute to a nation’s economic growth by adding new businesses in the industry, infrastructure development and job creation.

The reduced environmental impact of EVs, such as improved air quality and reduced greenhouse gas emissions, can lead to significant societal benefits in terms of better human health and mitigating the effects of climate change. Governments can leverage these benefits to achieve climate targets and improve public health.

Studies about EV diffusion and adoption in India are yet to gain momentum considering the slow penetration of EVs in the Indian automobile market. The Indian Government is enforcing policies and incentives to accelerate the adoption of EVs. However, without understanding the drivers of consumer EV adoption, it is difficult for policymakers to accelerate the growth of the EV market. The present study is one step toward understanding the same in its original sense.

The purpose of this study is to identify the structural determinants of e-returns service interactions, examine their impact on online shoppers' loyalty and propose returns service interventions from an interactive marketing perspective to facilitate consumer interaction and repeat purchase intentions with e-retailers.

This study empirically tests the research hypotheses based on cross-sectional survey data collected from Chinese online consumers who experienced interactions during the product returns process.

E-return service interaction includes three determinants: access support, friendly interaction and communication support. These interactions positively impact ease of return, returns satisfaction and customer loyalty. Returns satisfaction is a key mediator in the relationship between return service interaction and loyalty. Access support and friendly interaction have both direct and indirect effects on loyalty, while communication support has only an indirect effect.

This study contributes to understanding e-returns service interaction by analyzing its structural determinants, providing a robust scale foundation and analytical framework for future empirical research. Additionally, this study explores the driving role of e-returns service interaction in forming e-customer loyalty, offering a theoretical basis for the service recovery function of e-returns service interaction. It enriches the application of service recovery theory and relationship marketing theory in the field of interactive marketing.

Even after establishing their business successfully, many business owners get demotivated, and it leads to unwillingness to grow. This study aims to propose a comprehensive model that represents interrelationships among various personal factors affecting “unwillingness to grow.”

The personal factors for unwillingness to grow were identified by extant literature, and expert interviews were conducted to establish the contextual relationships among these factors. The interrelationships among the filtered variables have been done using interpretive structural modeling (ISM) and MICMAC analysis was done to determine the importance of each factor in influencing “unwillingness to grow.”

In total, 30 personal attributes were identified from previous literature, out of which 15 were selected for the final study. The result identifies 7 variables having a strong impact on “unwillingness to grow.” These attributes are “absence of strong network,” “lack of vision,” “lack of proactiveness,” “reluctance to involve external consultants,” “absence of/small founding team,” “lack of ambition” and “improper attitude.”

The research attempts to create a bricolage of all the important personal factors affecting “unwillingness to grow.” Previous researches have used few attributes, but with the help of ISM, a graphical modeling technique, it became possible to draw interrelationship between 15 attributes. Further, with the help of MICMAC, the importance of each attribute was determined.

In today's fast developing era, the volume of data is increasing day by day. The traditional methods are lagging for efficiently managing the huge amount of data. The adoption of machine learning techniques helps in efficient management of data and draws relevant patterns from that data. The main aim of this research paper is to provide brief information about the proposed adoption of machine learning techniques in different sectors of manufacturing supply chain.

This research paper has done rigorous systematic literature review of adoption of machine learning techniques in manufacturing supply chain from year 2015 to 2023. Out of 511 papers, 74 papers are shortlisted for detailed analysis.

The papers are subcategorised into 8 sections which helps in scrutinizing the work done in manufacturing supply chain. This paper helps in finding out the contribution of application of machine learning techniques in manufacturing field mostly in automotive sector.

The research is limited to papers published from year 2015 to year 2023. The limitation of the current research that book chapters, unpublished work, white papers and conference papers are not considered for study. Only English language articles and review papers are studied in brief. This study helps in adoption of machine learning techniques in manufacturing supply chain.

This study is one of the few studies which investigate machine learning techniques in manufacturing sector and supply chain through systematic literature survey.

1. A comprehensive understanding of Machine Learning techniques is presented.

2. The state of art of adoption of Machine Learning techniques are investigated.

3. The methodology of (SLR) is proposed.

4. An innovative study of Machine Learning techniques in manufacturing supply chain.

A comprehensive understanding of Machine Learning techniques is presented.

The state of art of adoption of Machine Learning techniques are investigated.

The methodology of (SLR) is proposed.

An innovative study of Machine Learning techniques in manufacturing supply chain.

After the COVID-19 pandemic, companies from a variety of sectors began repurposing their product development and manufacturing activities. To be successful, repurposing requires a framework that illustrates Agile New Product Development (ANPD) and Industry 4.0 practices. The current study aims to focus on developing a framework that managers and decision-makers can use to successfully adopt ANPD-Industry 4.0 practices and decision-making activities.

Initially, a literature review is conducted to identify practices related to ANPD and Industry 4.0. Similarly, performance metrics are identified through a review of the literature. To compute the weights of the shortlisted practices, the Pythagorean fuzzy Analytical Hierarchy Process is used and the Pythagorean fuzzy Combined Compromise Solution (PFCoCoSo) method is used to rank the shortlisted performance metrics.

According to the findings, ANPD practices (ADP) are the most prominent among shortlisted practices. Following that are Technology Adoption Practices, Organizational Management Practices (OMP), Human Resource Management Practices and System Integration Practices. Customer requirement analysis, for example, is an ADP practice that has a significant impact on the successful repurposing of product development activities.

The identified practices can make a significant contribution during repurposing product development activities. Practices that promote sustainable product development, as well as the use of advanced technologies, will be beneficial in improving organizational performance. Managers can evaluate performance using performance metrics that have been prioritized.

After the COVID-19 pandemic, this could be the first of its kind to develop an RPD framework.

In vitro millifluidic cultures with perfusion are essential tools to analyse and understand the interactions between cells, their matrix and multi-cell populations. The purpose of this paper is to focus on the design and development of a 3D-printed template that can be used for fabrication of a clear view poly (dimethyl siloxane) (PDMS) device. The major objective is to obtain a transparent device prototype that allows perfusion culture of two cell types for multiple days that can be imaged using laser scanning confocal microscopy.

The authors used a two-step approach for achieving the final geometric structure at a faster timeline and lower cost. The first part focuses on comparing the fidelity of the printing templates using fused deposition modelling (FDM) and stereolithography (SLA) printers for a range of dimensions. They then show that the complex geometry chip with connection chambers can be printed using low resolution low cost FormLab SLA printer. The final optimized design was then printed using high-resolution Projet 6000 SLA printer to obtain smoother structures.

In this work, the authors have shown that the FormLab SLA printer yields significantly lower error for printing complex design geometries as compared to FDM printer. Result shows that FormLab printer can be used to achieve a minimum dimension of 0.5 mm. They then use the printer to optimize the device dimension for the culture chip which requires several iterations of printing and experimenting. They showed the two-step protocol of printing the optimized template in a high-resolution SLA printer and further fabricating a clear view millifluidic PDMS device that is compatible confocal microscopy imaging. They used this culture chip for perfusion culture of two cell type, and the controlled fluidic exchange between the two chambers led to the formation of neuroglia junction.

One of the major bottlenecks for obtaining complex geometry in mili/microfluidic device by 3D printing is the need of multiple iterations on printing. This makes the tuning of dimension significantly expensive. Another challenge is to obtain a smooth surface of PDMS that leads to a leak proof clear view device compatible for laser based confocal imaging. The combination of two printers plays a crucial role for the rapid prototyping of the imaging device with flow control. The proposed approach lowers the cost for prototyping of in vitro culture chip with complex geometries to improve on biological research demanding multi-chamber fluidic device.

This research article explores the intricate relationship between self-help groups (SHGs) and destination sustainability in the context of tourism. SHGs, typically formed by individuals with shared interests and objectives, have gained prominence as a means to promote economic, social and environmental sustainability at tourism destinations. This study investigates the impact of SHGs on destination sustainability, focusing on various dimensions, including economic empowerment, cultural preservation, environmental conservation and social development. After conducting an extensive review of the literature and supported by empirical case studies, this chapter undertakes a comprehensive analysis of the role played by SHGs in promoting the sustainability of tourism destinations. Furthermore, it examines the challenges encountered by SHGs in their pursuit of sustainable tourism objectives. The findings of this research contribute to a better understanding of the potential of SHGs in fostering destination sustainability and provide valuable insights for policymakers, destination managers and stakeholders interested in harnessing the power of community-driven initiatives for sustainable tourism.