Search results

Recently, the popularity of software has grown significantly in the market. Enhancement of software is needed to decrease the burden of getting high-quality and reliable software. To achieve this, the software is upgraded by adding new features to the previous version. Therefore, adding new features in the last version to be consistent with the earlier version is challenging. This paper aims to discuss the optimal software enhancement and customer growth model.

This paper discusses a model when new features are added to the software, and the customers' adoption of the software is presented as a customer growth model. An optimal control problem is introduced to maximize the profit obtained from the software system over the system's lifetime period. Total gain is calculated by the value generated from selling the software over the total expenditure during the software development process. The closed-form solution and some theoretical results are presented using the optimal control-theoretic approach. The theoretical results are supported by a numerical example.

This paper gives several substantive insights during sensitivity analysis and provides essential results. The results discussed here are compatible with the actual scenario and useful in software enhancement.

The authors have proposed a new feature growth and customer growth model to maximize the total profit using optimal control theory.

Currently, the design, technological features of voices, and their analysis of various applications are being simulated with the requirement to communicate at a greater distance or more discreetly. The purpose of this study is to explore how voices and their analyses are used in modern literature to generate a variety of solutions, of which only a few successful models exist.

The mel-frequency cepstral coefficient (MFCC), average magnitude difference function, cepstrum analysis and other voice characteristics are effectively modeled and implemented using mathematical modeling with variable weights parametric for each algorithm, which can be used with or without noises. Improvising the design characteristics and their weights with different supervised algorithms that regulate the design model simulation.

Different data models have been influenced by the parametric range and solution analysis in different space parameters, such as frequency or time model, with features such as without, with and after noise reduction. The frequency response of the current design can be analyzed through the Windowing techniques.

A new model and its implementation scenario with pervasive computational algorithms’ (PCA) (such as the hybrid PCA with AdaBoost (HPCA), PCA with bag of features and improved PCA with bag of features) relating the different features such as MFCC, power spectrum, pitch, Window techniques, etc. are calculated using the HPCA. The features are accumulated on the matrix formulations and govern the design feature comparison and its feature classification for improved performance parameters, as mentioned in the results.

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.

Artificial intelligence (AI) is increasingly applied by businesses to optimize their processes and decision-making, develop effective and efficient strategies, and positively influence customer behaviors. Businesses use AI to generate behaviors such as customer retention (CR). The existing literature on “AI and CR” is vastly scattered. The paper aims to review the present research on AI in CR systematically and suggest future research directions to further develop the field.

The Scopus database is used to collect the data for systematic review and bibliometric analysis using the VOSviewer tool. The paper performs the following analysis: (1) year-wise publications and citations, (2) co-authorship analysis of authors, countries, and affiliations, (3) citation analysis of articles and journals, (4) co-occurrence visualization of binding terms, and (5) bibliographic coupling of articles.

Five research themes are identified, namely, (1) AI and customer churn prediction in CR, (2) AI and customer service experience in CR, (3) AI and customer sentiment analysis in CR, (4) AI and customer (big data) analytics in CR, and (5) AI privacy and ethical concerns in CR. Based on the research themes, fifteen future research objectives and a future research framework are suggested.

The paper has important implications for researchers and managers as it reveals vital insights into the latest trends and paths in AI-CR research and practices. It focuses on privacy and ethical issues of AI; hence, it will help the government develop policies for sustainable AI adoption for CR.

To the author's best knowledge, this paper is the first attempt to comprehensively review the existing research on “AI and CR” using bibliometric analysis.

The new technology aspects of Industry 4.0 (I4.0), such as digital technologies including artificial intelligence (AI), block chain, big data analysis and the internet of things (IoT) as a digital cosmos, have the potential to fundamentally transform the future of business and supply chain management. By augmenting the functional components of the food supply chain (FSC), these technologies can transform it into an intelligent food supply chain (iFSC). The purpose of this study is to identify the I4.0 utilization for FSC to become an iFSC. Additionally, it suggests future research agendas to bridge the academic knowledge gaps.

This study utilizes the bibliometric analysis methodology to investigate the techno-functional components of iFSC in the context of I4.0. The study followed steps of bibliometric analysis to assess existing components’ knowledge in the area of intelligent food supply chain management. It further reviews the selected articles to explore the need for I4.0 technologies’ adoption as well as its barriers and challenges for iFSC.

This study examines the integration of emerging technologies in FSC and concludes that the main emphasis is on the adoption of blockchain and internet of things technology. To convert it into iFSC, it should be integrated with I4.0 and AI-driven FSC systems. In addition to traditional responsibilities, emerging technologies are acknowledged that are relatively uncommon but possess significant potential for implementation in FSC. This study further outlines the challenges and barriers to the adoption of new technologies and presents a comprehensive research plan or collection of topics for future investigations on the transition from FSC to iFSC. Utilizing artificial intelligence techniques to enhance performance, decision-making, risk evaluation, real-time safety, and quality analysis, and prioritizing the elimination of barriers for new technologies.

The uniqueness of this study lies in the provision of an up-to-date review of the food supply chain. In doing so, the authors have expanded the current knowledge base on the utilization of all I4.0 technologies in FSC. The review of designated publications yield a distinctive contribution by highlighting hurdles and challenges for iFSC. This information is valuable for operations managers and policymakers to consider.

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.

Circularity has acted as an essential phenomenon for small and medium enterprises (SMEs) in emerging economies, pressuring entrepreneurs to its adoption in their businesses. During the adoption and implementation of circularity, entrepreneurs or circular entrepreneurs (to be precise) are facing various challenges to its effective functioning. However, the scholarly literature has offered limited research into this phenomenon. Thus, the purpose of this research is to identify the various barriers and sub-barriers for circular entrepreneurs to adopt circularity in SMEs of emerging economies.

A combined qualitative and quantitative approach was employed to achieve the objectives of the study. In the first stage, through an extensive literature review, a list of barriers was identified and in the second stage, a deductive approach was employed to finalize the barriers. Finally, Best-Worst Method (BWM), a multi-criteria decision-making (MCDM) method, was used to analyse the significant importance of the barriers.

The findings of the study suggested the “financial barrier” as the first-ranked barrier in the adoption of Circular Business Models (CBMs), followed by the “regulatory and operational barrier” as the top second and third barriers. In terms of sub-barriers, “lack of access to funding and capital” has been identified as the top sub-barrier in the adoption of CBM, followed by “excessive regulations and red tape” and “challenges due to ambiguity of the concept”.

To transition from a circular to a linear business approach considerably quicker and smoother, entrepreneurs may utilize the findings of this study as a blueprint for the steps to overcome the barriers in a linear to a circular transition.

This research differentiates from other studies due to solicited input directly from the people who are most familiar with the challenges of making the transition from linear to CBM, i.e. the entrepreneurs themselves.

Using bibliometric, this work aims to evaluate the current status of the body of research on the subject of supply chain resilience (SCR) in developing nations and to provide recommendations for potential lines of inquiry for additional research.

Three hundred and thirty-six articles published between 2011 and 2021 were retrieved in Scopus for this bibliometric analysis. This analysis focuses on SCR research conducted in developing countries, highlighting its key authors, countries, institutions, journals, articles and themes.

This bibliometric review seeks to enrich the discourse on SCR in developing nations through a comprehensive and detailed review of 336 articles covering 138 Journals, 73 countries, 877 authors, 743 organizations and 1,145 author keywords. The United Kingdom, India, and the United States provided a substantial share of the publications. Publication-wise, Hong Kong Polytechnic University and The University of Hong Kong play key roles. The author found that supply chain risk management, sustainability, agile management, artificial intelligence and blockchain are trending topics. Additionally, the author identified eight themes by page rank analysis.

This study's importance lies primarily in its examination of the current information about SCR in developing countries and significant cluster areas (themes). In the paths, it recommends for further study, which academics may take, and industry professionals should apply in their businesses to create a more resilient and sustainable supply chain.

Based on published studies, this study gives exploratory data on SCR in developing countries context. This is first of its kind bibliometric study that focuses on developing countries.

Using the lens of the natural resource-based view (NRBV) theory, this study investigates the effect of green supply chain management (GSCM) practices such as green manufacturing (GM), eco-design (ED), green purchasing (GP) and investment recovery (IR) on the carbon-neutral supply chain (CNSC) performance of firms through the mediating influence of logistics eco-centricity (LE).

A conceptual framework that hypothesizes the relationship between GSCM practices, LE and the CNSC performance of firms is developed. Key GSCM practices are then identified using experts’ opinions. Furthermore, we collected responses from logistics companies to validate the conceptual framework using the partial least squares structural equation modeling (PLS-SEM) method.

Through this study, we found that GSCM practices significantly improve a firm's CNSC performance, and the relationships between GSCM practices and CNSC performance are positively mediated by LE.

The implications of the study suggest that logistics managers can benefit from the findings of this study to comprehend the impact of various GSCM techniques on LE and CNSC from the viewpoint of the NRBV paradigm.

This research provides valuable perspectives for managers and supply chain (SC) practitioners in their quest for sustainable and environmentally responsible SC operations through an extensive and novel analysis of the connection between GSCM practices, LE and CNSC performance.

Artificial intelligence (AI) can enhance human resource resiliency (HRR) by providing the insights and resources needed to adapt to unexpected changes and disruptions. Therefore, the present research attempts to develop a framework for future researchers to gain insights into the actions of AI to enable HRR.

The present study used a systematic literature review, bibliometric analysis, and network analysis followed by content analysis. In doing so, we reviewed the literature to explore the present state of research in AI and HRR. A total of 98 articles were included, extracted from the Scopus database in the selected field of research.

The authors found that AI or AI-associated techniques help deliver various HRR-oriented outcomes, such as enhancing employee competency, performance management and risk management; enhancing leadership competencies and employee well-being measures; and developing effective compensation and reward management.

The present research has certain implications, such as increasing the HR team's proficiency, addressing the problem of job loss and how to fix it, improving working conditions and improving decision-making in HR.

The present research explores the role of AI in HRR following the COVID-19 pandemic, which has not been explored extensively.