Search results

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.

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.

The changes in tensile behavior of polycrystalline nanocopper lattice with changes in temperature, average grain size (AGS) and strain rate, have been explored. The existence of a critical AGS has also been observed which shows that the Hall–Petch relationship behaves inversely.

Nanoscale deformation of polycrystalline nanocopper has been done in this study with the help of an embedded atom method (EAM) potential. Voronoi construction method has been employed for creating four polycrystals of nanocopper with different sizes. Statistical analysis has been used to examine the observations with emphasis on the polycrystal size effect on melting point temperature.

The study has found that the key stress values (i.e. elastic modulus, yield stress and ultimate tensile stress) are significantly influenced by the considered parameters. The increase in strain rate is observed to have an increasing impact on mechanical properties, whereas the increase in temperature degrades the mechanical properties. In-depth analysis of the deformation mechanism has been studied to deliver real-time visualization of grain boundary motion.

This study provides the relationship between required grain size variations for consecutive possible variations in mechanical properties and may help to reduce the trial processes in the synthesis of polycrystalline copper based on different temperatures and strain rates.

To understand the role of the unorganized sector in the push toward a circular economy (CE), the authors consider the case of the unorganized tire retreading industry in India and examine the barriers it faces in contributing to a circular tire supply chain in India.

The authors used grounded theory methodology (GTM) to understand the barriers to realizing the CE in the Indian unorganized tire retreading industry. This methodology facilitates the acquisition of new insights into an existing phenomenon or in studying emerging areas that require investigation.

Through the analysis, the authors tease out ten critical barriers that impede the Indian unorganized tire retreading industry. The two most vital barriers are the lack of effective promotional methods and the poor implementation of standards.

This study emphasizes the importance of further investigating the potential role of the unorganized sector in fostering the transition to a CE in emerging economies.

The research provides useful policy prescriptions to regulators and insights to original tire manufacturers (OTMs) that enable the unorganized tire retreaders in India to contribute to the movement toward a circular supply chain (CSC).

This study is the first to systematically examine the unorganized sector to understand the barriers to CE. This study provides an original theoretical contribution by expanding the scope of stakeholder and institutional theories.

In experiments utilising sweat solution and distilled water, seamed ensembles performed less thermally efficiently than unseamed fabrics.

Water may not accurately reflect perspiration when testing multi-layered clothes for thermal comfort in wet state. Most researchers used water or sodium chloride (NaCl) to measure wet state thermal comfort. However, human perspiration is an extremely complex mixture of aqueous chemicals, including minerals, salts, lipids, urea and lactic acid. This study compares the effects of simulated sweat solution to distilled water on the thermal behaviour of a multi-layered fabric assembly with different seam patterns.

Experiment results show that stitching decreases thermal resistance and thermal conductivity. Seam pattern of 10 cm diagonal spacing is more thermally resistant than 2.5 cm diagonal spacing. In comparison to that of simulated sweat, fabric that has been moistened with distilled water exhibits increased thermal conductivity. Hollow polyester wadding or micro polyester wadding as the intermediate layer exhibits greater thermal resistance than multi-layered construction with spacer fabric as middle layer.

This study considers human perspiration while designing protective clothing for wet thermal comfort.

Substantial pressure from civil society and investors has forced governments around the world to take climate neutrality initiatives. Several countries have pledged their nationally determined contributions towards net-zero. However, there exist various obstacles to achieving the same and the agriculture sector is one of them. Thus, this study identifies and models the critical barriers to achieving climate neutrality in the agriculture food supply chain (AFSC).

Sixteen barriers are identified through a literature survey and are validated by the questionnaire survey. Furthermore, the interactions amongst the barriers are estimated through the application of the “weighted influence non-linear gauge system (WINGS)” method which considers the both intensity of influence and the strength of the barrier. To mitigate these barriers, a framework based on green, resilient and inclusive development (GRID) is proposed.

The obtained results reveal that lack of collaboration amongst AFSC stakeholders, lack of information and education awareness, and lack of technical expertise obtained a higher rank (amongst the top five) in three indicators of the WINGS method and thus are the most significant barriers.

This paper is the first attempt in modelling the climate neutrality barriers for the Indian AFSC. Additionally, the mitigating strategies are prepared using the GRID framework.

Explainable artificial intelligence (XAI) has importance in several industrial applications. The study aims to provide a comparison of two important methods used for explainable AI algorithms.

In this study multiple criteria has been used to compare between explainable Ranked Area Integrals (xRAI) and integrated gradient (IG) methods for the explainability of AI algorithms, based on a multimethod phase-wise analysis research design.

The theoretical part includes the comparison of frameworks of two methods. In contrast, the methods have been compared across five dimensions like functional, operational, usability, safety and validation, from a practical point of view.

A comparison has been made by combining criteria from theoretical and practical points of view, which demonstrates tradeoffs in terms of choices for the user.

Our results show that the xRAI method performs better from a theoretical point of view. However, the IG method shows a good result with both model accuracy and prediction quality.

The purpose of this study is not only to develop an integrated model of subjective well-being (SWB) by using confirmatory factor analysis (CFA) and structural equation modeling (SEM) but also to prioritize the subfactors of personality and SWB that affect the contextual performance (CP) of an employee by using a fuzzy-analytical hierarchy process (FAHP), which has not been done in the past.

The data were collected from 15 field experts and 412 employees of information technology (IT) companies operating in India. The data analysis was performed in two stages. The first stage includes CFA and SEM. The second stage includes prioritizing the factor through FAHP.

The results of SEM analysis manifested that all the dimensions of personality have a significant impact on CP. However, in the case of SWB, only two components (satisfaction with life and positive affect) have a significant impact on CP. Results of FAHP show that SWB is more important in predicting CP than the Big Five personality dimensions.

This research is a novel attempt to test and prioritize the factors affecting the CP of IT employees. The findings of the research will be useful for managers in increasing the performance of their employees. Further, the findings of the research will contribute to the literature on the factors affecting CP.

Purpose: Entrepreneurship is one of the best ways towards self-sufficiency and poverty alleviation for women in a country without guaranteed employment. The research contributes and provides reasons for supporting women’s entrepreneurship, as although it is well-documented that women’s entrepreneurship increases economic growth, other approaches need attention, especially those related to sustainability.

Need for the study: Women have superior emotional intelligence, resilience, and capacity to maintain a healthy work–life balance; women make better business and entrepreneurial leaders. Many businesses see encouraging women to join the workforce as a key to achieving sustainability goals. This study will provide valuable insights into the role of female entrepreneurs in driving sustainable business practices in the USA and India – rural India and urban India.

Methodology: The chapter adopts a descriptive research design methodology in conducting a thorough literature review on the evolution of women entrepreneurship in India. The case-based approach has been adopted to depict women entrepreneurs and their growth stories. The analysis is based on qualitative analysis of secondary data and primary data. This research proposes exciting revelations regarding the trend of women entrepreneurship in India and, from a global perspective, challenges women entrepreneurs face in organised and unorganised sectors in India and policies in different countries that promote women entrepreneurship towards sustainability.

Findings: Women business owners in India help the economy thrive, reduce poverty, and have a greater chance of shaping India’s destiny. This chapter looks at how entrepreneurial actions affect cultural morals and values. Women’s business growth rates vary from country to country, and this variation is influenced by the fact that countries can be broadly categorised as either developed or developing.

Aim/Purpose: This chapter supplies an overview of extant literature investigating consumer preferences and purchase behaviour towards Electric vehicles in India. The present research applied the predictive and prescriptive analysis methods to understand the consumer purchase intentions evolved with the advancement of technologies. The study considers the security and privacy of data for companies and consumers.

Design/Methodology/Approach: The conceptual model is analysed via path analysis using interviews with a set of people, and online survey data is collected from Indian users and prospects. Secondary data research methods are used to make interpretations.

Findings: The research found that consumer preferences and purchase behaviour about electric vehicles have a wide scope in marketing decision-making. The findings support linking social cognitive perception and attitude intent with mitigating socio-demographic variables and mediating attitudes towards Battery Electric Vehicles (BEV).

Research limitations/implications: The present chapter provides implications for future interdisciplinary research addressing consumer behaviour with mentioned technologies. This study proposes some theoretical and policy implications and supplies guidance to industry-specific consumers, suppliers, policymakers and business professionals to encourage the adoption of BEVs in the emerging transportation industry. The research explores the future possibility and extent to which the consumer prefers and buys EVs.

Originality/Value: This study is based on the ‘perception-attitude-intention’ socio-psychological linkage framework. Earlier studies overlooked the effects of socio-psychological characteristics and socio-demographic moderators when considering the adoption of BEVs.