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This research explores the application of machine learning to optimize pricing strategies in the aftermarket sector, particularly focusing on parts with no assigned values and the detection of outliers. The study emphasizes the need to incorporate technical features to improve pricing accuracy and decision-making.

The methodology involves data collection from web scraping and backend sources, followed by data preprocessing, feature engineering and model selection to capture the technical attributes of parts. A Random Forest Regressor model is chosen and trained to predict prices, achieving a 76.14% accuracy rate.

The model demonstrates accurate price prediction for parts with no assigned values while remaining within an acceptable price range. Additionally, outliers representing extreme pricing scenarios are successfully identified and predicted within the acceptable range.

This research bridges the gap between industry practice and academic research by demonstrating the effectiveness of machine learning for aftermarket pricing optimization. It offers an approach to address the challenges of pricing parts without assigned values and identifying outliers, potentially leading to increased revenue, sharper pricing tactics and a competitive advantage for aftermarket companies.

Globally, the landscape of corporate carbon disclosures (CCD) is continually evolving as societal, environmental and regulatory expectations change over time. The goal of this study is to examine the challenges faced by Indian firms’ corporate carbon reporting (CCR). The literature recognized the hurdles to reaching net zero emissions and decarbonization, which are equally applicable to carbon disclosure (CD).

The scope 3 emission disclosure barriers (S3EDBs) identified from the literature were ranked, and their relationships were discovered using the “Grey-based decision-making trial and evaluation laboratory” (Grey- DEMATEL) technique.

The key findings are the S3EDBs, the most prominent barriers, their interrelationships and important insights for managers of organizations in prioritizing the action area for scope 3 CD. Eight S3EDBs were categorized in terms of cause and effect, threshold value is calculated as 0.78. “Quality, and reliability of data,” “Government policies and statutory requirement on emission disclosure” and “Traceability and managing supply chain partners” are the most prominent S3EDBs.

The results will help industry people in countries with emerging economies that have significant scope 3 carbon footprints. The managers can plan to deal with top S3EDBs as a step towards decarbonization and ultimately fighting climate change (CC).

This study is one of the first to rank these barriers to CD so that industry practitioners can prioritize their actions. The core contribution of this research is to detect the most significant S3EDBs and their interdependencies.

In an era of rapid telemedicine expansion, patient loyalty is paramount for effective health-care delivery. This study aims to understand loyalty behaviours in telemedicine to refine services. The primary objectives are to elucidate the current state of scholarly inquiry concerning loyalty within the telemedicine sphere and to address existing research deficiencies within this domain. This exploration seeks to provide valuable insights and contribute to the advancement of knowledge in this critical area of inquiry.

This study uses a bibliometric analysis to investigate patient loyalty in telemedicine. By reviewing existing literature and analysing bibliometric data, the research identifies key deficiencies and addresses pertinent research questions within the telemedicine loyalty domain. This methodological approach aims to offer a comprehensive understanding of the current state of research and highlight areas requiring further investigation.

This study reveals significant gaps in existing research on telemedicine loyalty, identifying a need for more focused studies on patient loyalty behaviours. Through a bibliometric analysis, the findings highlight critical areas for improvement and potential strategies for enhancing patient loyalty in telemedicine. These insights are crucial for refining telemedicine services and ensuring effective health-care delivery.

The findings may not capture all dimensions of patient loyalty in telemedicine, requiring further empirical studies. Future research should expand on these limitations by incorporating diverse methodologies and broader data sets to validate and extend the study’s insights.

The insights from this study can help health-care providers refine their telemedicine services to enhance patient loyalty. By understanding loyalty behaviours, providers can develop targeted strategies to improve patient satisfaction and retention. These practical implications are essential for the continuous improvement of telemedicine services, ensuring they meet patient needs and expectations effectively.

Enhancing patient loyalty in telemedicine leads to significant societal benefits, particularly by improving health-care access for underserved populations in rural or economically disadvantaged areas. Continuous and trusted care helps reduce health-care disparities and fosters health equity, positively impacting quality of life through timely medical consultations. In the context of medical tourism, telemedicine facilitates reliable remote consultations, boosting confidence in health-care systems abroad and benefiting local economies. In addition, tourists can access health-care services while travelling, enhancing their sense of safety and well-being. Overall, these advancements highlight telemedicine’s potential to create a more equitable and accessible health-care landscape.

This study fills a critical gap in telemedicine research by focusing on patient loyalty, an area often overlooked in existing literature. The bibliometric analysis offers a novel approach to understanding and addressing loyalty behaviours. The findings contribute valuable knowledge, advancing the discourse on telemedicine loyalty and providing a foundation for future research and service improvements.

The aim of this study is to evaluate Big Data Analytics (BDA) drivers in the context of food supply chains (FSC) for transition to a Circular Economy (CE) and Sustainable Operations Management (SOM).

Ten different BDA drivers in FSC are examined for transition to CE; these are Supply Chains (SC) Visibility, Operations Efficiency, Information Management and Technology, Collaborations between SC partners, Data-driven innovation, Demand management and Production Planning, Talent Management, Organizational Commitment, Management Team Capability and Governmental Incentive. An interpretive structural modelling (ISM) methodology is used to indicate the relationships between identified drivers to stimulate transition to CE and SOM. Drivers and pair-wise interactions between these drivers are developed by semi-structured interviews with a number of experts from industry and academia.

The results show that Information Management and Technology, Governmental Incentive and Management Team Capability drivers are classified as independent factors; Organizational Commitment and Operations Efficiency are categorized as dependent factors. SC Visibility, Data-driven innovation, Demand management and Production Planning, Talent Management and Collaborations between SC partners can be classified as linkage factors. It can be concluded that Governmental Incentive is the most fundamental driver to achieve BDA applications in FSC transition from linearity to CE and SOM. In addition, Operations Efficiency, Collaborations between SC partners and Organizational Commitment are key BDA drivers in FSC for transition to CE and SOM.

The interactions between these drivers will provide benefits to both industry and academia in prioritizing and understanding these drivers more thoroughly when implementing BDA based on a range of factors. This study will provide valuable insights. The results from this study will help in drawing up regulations to prevent food fraud, implementing laws concerning government incentives, reducing food loss and waste, increasing tracing and traceability, providing training activities to improve knowledge about BDA and focusing more on data analytics.

The main contribution of the study is to analyze BDA drivers in the context of FSC for transition to CE and SOM. This study is unique in examining these BDA drivers based on FSC. We hope to find sustainable solutions to minimize losses or other negative impacts on these SC.

In past decades, artificial neural network (ANN) models have revolutionised various stock market operations due to their superior ability to deal with nonlinear data and garnered considerable attention from researchers worldwide. The present study aims to synthesize the research field concerning ANN applications in the stock market to a) systematically map the research trends, key contributors, scientific collaborations, and knowledge structure, and b) uncover the challenges and future research areas in the field.

To provide a comprehensive appraisal of the extant literature, the study adopted the mixed approach of quantitative (bibliometric analysis) and qualitative (intensive review of influential articles) assessment to analyse 1,483 articles published in the Scopus and Web of Science indexed journals during 1992–2022. The bibliographic data was processed and analysed using VOSviewer and R software.

The results revealed the proliferation of articles since 2018, with China as the dominant country, Wang J as the most prolific author, “Expert Systems with Applications” as the leading journal, “computer science” as the dominant subject area, and “stock price forecasting” as the predominantly explored research theme in the field. Furthermore, “portfolio optimization”, “sentiment analysis”, “algorithmic trading”, and “crisis prediction” are found as recently emerged research areas.

To the best of the authors’ knowledge, the current study is a novel attempt that holistically assesses the existing literature on ANN applications throughout the entire domain of stock market. The main contribution of the current study lies in discussing the challenges along with the viable methodological solutions and providing application area-wise knowledge gaps for future studies.

The purpose of this study is to investigate the impact of psychological ownership (PO) on residents’ destination advocacy (DA) behaviour in the context of emerging tourist destinations and to assess the role of attitude as a mediator in the relationship.

A quantitative methodology was used and primary data was collected via an online survey to a sample of 333 residents from emerging tourist destinations in India. This study used the partial least squares (PLS) method to test the hypotheses.

The results indicate that residents’ knowledge about their hometown positively influences PO which in turn affects DA behaviour. Furthermore, PO influences attitude which in turn significantly influences DA behaviour. Additionally, the findings reveal the mediating role of attitude between PO and residents’ advocacy behavioural outcomes.

This study advances the concept that residents are important stakeholders who can promote a destination. Local authorities should prioritise residents over tourists and incorporate their image, identity, personality, style and values into destination promotion. They can also improve destination services to boost residents' positive attitudes.

The uniqueness of the study lies in associating PO and outcome as DA behaviour. The model suggests that enhancing PO of their hometown among the residents can have significant advantages for tourism development.

The study aims to highlight the barriers faced by the entrepreneurs toward achieving sustainability in business and innovation cultivation by offering solutions for academicians, practitioners and policymakers. The study uses the resource-based view (RBV) theory to discuss how an organization’s resources and capabilities influence the competitive ambience and barriers faced by entrepreneurs.

The present research uses grey-causal modelling (GSC) to analyse the barriers against successful entrepreneurship.

The research focuses on the usefulness of dynamic capabilities, managing and cooperating resources in the entrepreneurship setting. The paper highlights the importance of resource gathering and nurturing as a method to combat scarcity. This research further identifies that financial limitations, regulatory obstacles, challenges to sourcing qualified labour, poor infrastructure and technology, limited mentorship opportunities, lack of scalability, low initial cost barriers in product development and risk-averse attitudes are the major factors hindering entrepreneurs from obtaining sustainable business and innovation.

The contribution of this research to the literature is that it assesses RBV theory within the realm of entrepreneurship, providing a different perspective on resources and capabilities as well as the challenges faced by entrepreneurs. The systematic approach to the analysis and prioritization of various barriers is innovative, and it adds knowledge in this area.

The purpose of this study is to identify Offshore Wind Energy (OWE) as a key technology that could drive countries toward achieving climate goals. However, there are multiple challenges that this sector faces.

This study aims to identify the challenges faced by the sector globally by systematically reviewing the existing literature in global context and portraying it in the Indian context. Factors are identified using content analysis.

Results suggest high levelized cost of energy as the most discussed challenge for the growth of OWE. Insufficient financial support and policy, initial capital and inadequate technology formed the second, third and fourth most discussed challenges respectively.

To reduce the cost of OWE, the distribution companies in India could adopt feed-in tariffs (FiTs) in the early stages of development and make OWE procurement mandatory. The renewable purchase obligation (RPO) in India is specific to solar and non-solar; policy should accommodate offshore wind-specific RPO targets for each state to reach the 2030 target of 30 GW from OWE.

To the best of the authors’ knowledge, this is the first attempt to study the challenges of OWE development from a global perspective and portray these major challenges in the Indian context and uses content analysis from the existing literature to ascertain the major roadblocks for the development of OWE.

The study identifies the unexplored gap in literature that includes futuristic challenges for OWE from climate change. Future studies can explore the possibilities of forecasting based on climate change scenarios and rank the challenges based on their relevance caused by possible damages.

This study aims to extend and explore patterns and trends of research in the linkage of big data and knowledge management (KM) by identifying growth in terms of numbers of papers and current and emerging themes and to propose areas of future research.

The study was conducted by systematically extracting, analysing and synthesizing the literature related to linkage between big data and KM published in top-tier journals in Web of Science (WOS) and Scopus databases by exploiting bibliometric techniques along with theory, context, characteristics, methodology (TCCM) analysis.

The study unfolds four major themes of linkage between big data and KM research, namely (1) conceptual understanding of big data as an enabler for KM, (2) big data–based models and frameworks for KM, (3) big data as a predictor variable in KM context and (4) big data applications and capabilities. It also highlights TCCM of big data and KM research through which it integrates a few previously reported themes and suggests some new themes.

This study extends advances in the previous reviews by adding a new time line, identifying new themes and helping in the understanding of complex and emerging field of linkage between big data and KM. The study outlines a holistic view of the research area and suggests future directions for flourishing in this research area.

This study highlights the role of big data in KM context resulting in enhancement of organizational performance and efficiency. A summary of existing literature and future avenues in this direction will help, guide and motivate managers to think beyond traditional data and incorporate big data into organizational knowledge infrastructure in order to get competitive advantage.

To the best of authors’ knowledge, the present study is the first study to go deeper into understanding of big data and KM research using bibliometric and TCCM analysis and thus adds a new theoretical perspective to existing literature.

Current research in the field of behavioural finance has attempted to discover behavioural biases and their characteristics in individual investors’ irrational decision-making. This study aims to find out how biases in information based on knowledge affect decisions about investments.

In step one, through existing research and consultation with specialists, 13 relevant items covering major aspects of bias were determined. In the second step, multiple linear regression and artificial neural network were used to analyse the data of 337 retail investors.

The investment choice was heavily impacted by regret aversion, followed by loss aversion, overconfidence and the Barnum effect. It was observed that the Barnum effect has a statistically significant negative link with investing choices. The research also found that investors’ fear of making mistakes and their tendency to be too sure of themselves were the most significant factors in their decisions about where to put their money.

This research contributes to the expansion of the knowledge base in behavioural finance theory by highlighting the significance of cognitive psychological traits in how leading investors end up making irrational decisions. Portfolio managers, financial institutions and investors in developing markets may all significantly benefit from the information offered.

This research is a one-of-a-kind study, as it analyses the emotional biases along with the cognitive biases of investor decision-making. Investor decisions generally consider the shadowy side of knowledge management.