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The purpose of this paper is to know the rate of loss of online citations used as references in scholarly journals. It also indented to recover the vanished online citations using Wayback Machine and also to calculate the half-life period of online citations.

The study selected three journals published by Emerald publication. All 389 articles published in these three scholarly journals were selected. A total of 15,211 citations were extracted of which 13,281 were print citations and only 1,930 were online citations. The online citations so extracted were then tested to determine whether they were active or missing on the Web. W3C Link Checker was used to check the existence of online citations. The online citations which got HTTP error message while testing for its accessibility were then entered in to the search box of the Wayback Machine to recover vanished online citations.

Study found that only 12.69 percent (1,930 out of 15,211) citations were online citations and the percentage of online citations varied from a low of 9.41 in the year 2011 to high of 17.52 in the year 2009. Another notable finding of the research was that 30.98 percent of online citations were not accessible (vanished) and remaining 69.02 percent of online citations were still accessible (active). The HTTP 404 error message – “page not found” was the overwhelming message encountered and represented 62.98 percent of all HTTP error message. It was found that the Wayback Machine had archived only 48.33 percent of the vanished web pages, leaving 51.67 percent still unavailable. The half-life of online citations was increased from 5.40 years to 11.73 years after recovering the vanished online citations.

This is a systematic and in-depth study on recovery of vanished online citations cited in journals articles spanning a period of five years. The findings of the study will be helpful to researchers, authors, publishers, and editorial staff to recover vanishing online citations using Wayback Machine.

The purpose of the present study is to identify the active and dead links of uniform resource locators (URLs) associated with web references and to compare the effectiveness of Chrome, Google and WayBack Machine in retrieving the dead URLs.

The web references of the Library Hi Tech from 2004 to 2008 were selected for analysis to fulfill the set objectives. The URLs were extracted from the articles to verify their accessibility in terms of persistence and decay. The URLs were then executed directly in the internet browser (Chrome), search engine (Google) and Internet Archive (WayBack Machine). The collected data were recorded in an excel file and presented in tables/diagrams for further analysis.

From the total of 1,083 web references, a maximum number was retrieved by the WayBack Machine (786; 72.6 per cent) followed by Google (501; 46.3 per cent) and the lowest by Chrome (402; 37.1 per cent). The study concludes that the WayBack Machine is more efficient, retrieves a maximum number of missing web citations and fulfills the mission of preservation of web sources to a larger extent.

A good number of studies have been conducted to analyze the persistence and decay of web-references; however, the present study is unique as it compared the dead URL retrieval effectiveness of internet explorer (Chrome), search engine giant (Google) and WayBack Machine of the Internet Archive.

The web references of a single journal, namely, Library Hi Tech, were analyzed for 5 years only. A major study across disciplines and sources may yield better results.

URL decay is becoming a major problem in the preservation and citation of web resources. The study has some healthy recommendations for authors, editors, publishers, librarians and web designers to improve the persistence of web references.

The present study compares the use of web citations as references in leading scholarly journals in Library and Information Science (LIS) and Communication and Media Studies (CMS). A total of 20 journals (each 10 from LIS and CMS) were selected based on the publishing history and reputation published between 2008 and 2017.

The present study compares the use of web citations as references in leading scholarly journals in LIS and CMS. A PHP script was used to crawl the Uniform Resource Locators (URLs) collected from the reference list. A total of 12,251 articles were downloaded and 555,428 references were extracted. Of the 555,428 references, 102,718 web citations were checked for their accessibility.

The research findings indicated that 76.90% URLs from LIS journals and 84.32% URLs from Communication and Media Studies journals were accessible and others were rotten. The majority of errors were due to HTTP 404 error code (not found) in both the disciplines. The study also tried to retrieve the rotten URLs through Time Travel, which revived 61.76% rotten URLs in LIS journal articles and 65.46% in CMS journal articles.

This is an in-depth and comprehensive comparative study on the availability of web citations in LIS and CMS journals articles spanning a period of 10 years. The findings of the study will be helpful to authors, publishers, and editorial staff to ensure that web citations will be accessible in the future.

This research work focuses on implementing this methodology in reducing the rejection rate of the turbocharger component problem that occurs during the manufacturing process. Using design, measure, analyze, improve and control (DMAIC) processes, it has been identified that clamping pressure on the component is one factor that affects quality. The impact of clamping pressure is studied to arrive at the ideal clamping pressure in which the rejection rate is the least.

Quality is the keyword in manufacturing where the production of a defect-free component is the most sought out objective. The definition of quality keeps getting refined throughout the years, from making products with no defects to minimizing rejection and scrap in the manufacturing process. Production facilities, to achieve this purpose, have adopted various methods and use of the DMAIC of Six Sigma methodology is one among them.

The study identified the fault causing the defect and suggested the methods to correct the fault. The suggestions would result in reducing the losses arising due to this and similar rejection causes.

With the adoption of DMAIC, it is found that misalignment of top and side clamp pressure is zero. When the side clamp pressure is at 75 PSI, and top clamp pressure is changed from 90 PSI to 95 PSI, the mean of responses is greater than the side clamp pressure of 80 PSI. Therefore, from the three-combination top clamp pressure of 100 PSI and the side clamp pressure of 75 PSI is the optimal condition.

The study aims to explore the dimensions of Quality 4.0 adoption, prioritization of these dimensions and the influential dimensions and their causal relationships that can guide the smooth adoption of Quality 4.0 to boost organizational performance.

The Quality 4.0 dimensions are explored from the extant literature. The qualitative data were captured from 12 highly experienced experts from diverse industries and academia through structured interview questions and group discussions in multiple phases. The inputs obtained from the experts were analyzed using Fuzzy-Technique for Order of Preference by Similarity to Ideal Solution for dimension priority, and Fuzzy-Decision-Making Trial and Evaluation Laboratory was employed to reveal the influential relationship between them.

The analysis reveals that quality scalability, quality culture and quality conformance are investigated as primary drivers of Quality 4.0 adoption. Data-driven analytical thinking and customer centricity emerge as dynamic dimensions that act as quality deliverable ends. Integrating these methodologies provides a robust framework for understanding and managing Quality 4.0 complexities, offering actionable insights for prioritizing initiatives and addressing interdependencies to ensure successful adoption and implementation.

The practical implications guide industries in creating strategic action plans tailored to their needs and fostering a quality-focused culture. The study also offers valuable insights into government policies, promoting sustainability, efficiency and a circular economy.

The study’s novelty lies in its prioritization and examination of the most influential causes and effects within the Quality 4.0 dimensions. This approach highlights core drivers and critical factors, providing a comprehensive framework for successful implementation.

This study evaluates the effect of the media image content of business to business (B2B) organizations to accelerate social media engagement. It highlights the importance of strategically designing image content for business marketing strategies.

This study designed a computation extensive research model based upon the stimulus-organism-response (SOR) theory using 39,139 Facebook posts of 125 organizations selected from Fortune 500 firms. Attributes from images and text were estimated using deep learning models. Subsequently, inferential analysis was established with ordinary least squares regression. Further machine learning algorithms, like support vector regression, k-nearest neighbour, decision tree and random forest, are used to analyze the significance and robustness of the proposed model for predicting engagement metrics.

The results indicate that the social media (SM) image content of B2B firms significantly impacts their social media engagement. The visual and linguistic attributes are extracted from the image using deep learning. The distinctive effect of each feature on social media engagement (SME) is empirically verified in this study.

This research presents practical insights formulated by embedding marketing, advertising, image processing and statistical knowledge of SM analytics. The findings of this study provide evidence for the stimulating effect of image content concerning SME. Based on the theoretical implications of this study, marketing and media content practitioners can enhance the efficacy of SM posts in engaging users.

This study aims to enhance the success of Sponsored Institutional R&D Projects (SIRPs) by investigating the influence of risk dimensions over four criteria Cost overrun, Time overrun, Schedule overrun, and Quality of projects. The study identifies risk dimensions and prioritizes and explores causal relationships to guide risk mitigation strategies during project execution.

This study was conducted with qualitative data obtained through a systematic questionnaire from 10 domain experts associated with SIRPs. Through an extensive literature review, the study identifies the ten risk dimensions, Fuzzy TOPSIS was employed for prioritizing the explored risk dimensions and Fuzzy DEMATEL explored the qualitative causal relationships among these dimensions. The sensitivity analysis was conducted to validate the robustness of findings by adjusting the weightage assigned to experts.

The findings reveal financial risk as the highest ranked and business risk as the least influential among identified dimensions. The study also highlights management risk as the most significant, followed by business risk and sensitivity analysis concludes that the findings are robust, lacking significant judgment bias.

This study contributes to risk management strategies for SIRPs, to ensure timely completion within the allocated budget. The findings of the study can be implemented in other such SIRPs. The study also informs funding agencies' policies and project monitoring mechanisms and advancing project management practices

Its originality lies in being the first to systematically identify and prioritize risk dimensions, adding significant value to SIRP management and academic discourse on project management and risk analysis.

This article aims to model the challenges of implementing artificial intelligence and machine earning (AI-ML) for moderating the impacts of COVID-19, considering the agricultural supply chain (ASC) in the Indian context.

20 critical challenges were modeled based on a comprehensive literature review and consultation with experts. The hybrid approach of “Delphi interpretive structural modeling (ISM)-Fuzzy Matrice d' Impacts Croises Multiplication Applique'e à un Classement (MICMAC) − analytical network process (ANP)” was used.

The study's outcome indicates that “lack of central and state regulations and rules” and “lack of data security and privacy” are the crucial challenges of AI-ML implementation in the ASC. Furthermore, AI-ML in the ASC is a powerful enabler of accurate prediction to minimize uncertainties.

This study will help stakeholders, policymakers, government and service providers understand and formulate appropriate strategies to enhance AI-ML implementation in ASCs. Also, it provides valuable insights into the COVID-19 impacts from an ASC perspective. Besides, as the study was conducted in India, decision-makers and practitioners from other geographies and economies must extrapolate the results with due care.

This study is one of the first that investigates the potential of AI-ML in the ASC during COVID-19 by employing a hybrid approach using Delphi-ISM-Fuzzy-MICMAC-ANP.

Development or upgradation of airplanes requires many different analyses, e.g. thermal, aerodynamic, structural and safety. Similar studies were performed during configuration change design of commuter category aircraft equipped with pusher turboprop engines. In this paper, thermo-fluid analyses of interactions of the new propulsion system in tractor configuration with selected elements of airplane skin are carried out. This study aims to check the airplane skin material, and its geometry, including the Plexiglas passenger window material degradation, due to hot exhaust gas plume impingement. The impact of change in exhaust stub angle and asymmetric inboard-outboard stubs on the jet thrust at various flight operating conditions like minimum off-route altitude and cruise performance is assessed.

Commercial software-based numerical models were developed. In the first stage, heat and fluid flow analysis was performed over a twin-engine airplane’s nacelle, wing and center fuselage with its powerplant mounted in the high wing configuration. Subsequently, numerical simulations of thermal interactions between the hot exhaust gases, which leave the exhaust system close to the nacelle, flaps and the center fuselage, were estimated for various combinations of exhaust stub angles with asymmetry between inboard-outboard stubs at different airplane configurations and operating conditions.

The results of the simulations are used to recommend modifications to the design of the considered airplane in terms of material selection and/or special coatings. The importance and impact of exhaust jet thrust on the overall aircraft performance are investigated.

The advanced numerical model for the exhaust jet-airplane skin thermal interaction was developed to estimate the temperature effects on the propeller blades and aircraft fuselage surfaces during different flight operating conditions with multiple combinations of stub orientations.

The study aims to assess how e-agri supply chain coordination (SCC), supply chain integration (SCI) and competitive capabilities (COC) impact both market performance (MO) and operational performance (OP). It particularly emphasizes the critical role of information flow for the benefit of farmers, intermediaries and end-consumers, shedding light on the broader implications of these factors within the agricultural supply chain.

Data are collected online from farmers, intermediaries engaged in buying/supplying agri-products and consumers using a semi-structured questionnaire of 25 items adapted from the extant literature measuring SCC, SCI, COC, OP and MP. The survey instrument is validated for its reliability, convergent and discriminant validity tests. Purposive and convenient sampling is used for data collection. Finally, 280 responses were analyzed using SEM to conclude the study.

Findings underscore information flow’s significance in the e-agri supply chain, addressing various stakeholders’ needs. Technical excellence, featuring robust transaction capabilities and cost-effective maintenance is pivotal. Enhanced supply chain coordination fosters integration and efficient information sharing, enhancing agricultural market performance and sectoral efficiency.

The study explores technology adoption, understands information flow and coordination impact, enhances efficiencies, empowers farmers and promotes transparency, sustainability and consumer benefits.

The study promotes efficient information flow, digital adoption, collaborative planning, tech investment and enhanced responsiveness for agricultural sector managers.

The study bridges the rural-urban divide, empowering farmers, providing fair pricing, sustainable practices, transparency, informed consumers, responsible consumption and income distribution.

The novelty of this study lies in its comprehensive information flow study, new theoretical model and e-agri-specific sub-constructs that define coordination and integration, aiding efficiency and competitiveness.