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Purpose of this study is to design a compact gap coupled anchor shape patch antenna for wireless local area network/high performance radio local area network and worldwide interoperability for microwave access applications.

An anchor shape microstrip antenna is conceived, designed, simulated and measured. The anchor shape antenna is transformed to its rectangular equivalent by conserving the patch area. Modeling and simulation of the antenna is performed by Ansys high frequency structure simulator (HFSS) electromagnetic solver based on the concept of finite element method. The simulated results are experimentally verified by using Agilent E5071C vector network analyzer. Theoretical analysis of an electromagnetically gap coupled anchor shape microstrip patch antenna has been performed by obtaining the lumped element equivalent of the transformed antenna.

The proposed antenna has a compact conducting patch of dimension 0.26λ × 0.12λ mm² (λ is calculated at lower resonating frequency of 3.56 GHz) with impedance bandwidths of 100 and 140 MHz and antenna gains of 1.91 and 3.04 dB at lower resonating frequency of 3.56 GHz and upper resonating frequency of 5.4 GHz, with omni-directional radiation pattern.

In literature, one does not encounter anchor shape antenna using the concept of gap coupling and parasitic patches. The design has been optimized for wireless local area network/worldwide interoperability for microwave access applications with a relatively low patch area (291.12 mm²) as compared to other reported antennas for wireless local area network/worldwide interoperability for microwave access applications. Transformed antenna and the actual experimental antenna behavior varies, but the resonant frequencies of the transformed antenna as observed by theoretical analysis and simulated results (by high frequency structure simulator) are reasonably close, and the percentage difference between the resonant frequencies (both at lower and upper bands) is within the permissible limit of 1-2.5 per cent. Results confirm the theoretical proposition of transformation of shapes in antenna design, which allows a designer to adapt the design shape according to the application.

The purpose of this study was to develop a suitable module for digital conservation of traditional knowledge of medicinal plants (MPs) used by tribal communities living in the Anuppur district of Madhya Pradesh, Central India.

The research used a qualitative approach to gather the data of MPs through the use of literature review and field survey. Based on the acquired data, a prototype digital learning system was constructed and assessed. This study used digital learning technologies to assess the requirements for transmitting traditional knowledge of important MPs used by tribal communities so that people can absorb and conserve them.

Over time, the focus on the digital conservation of traditional MP’s knowledge has progressively increased globally. Despite the rise in this field of study, information technology methods to preserve and distribute traditional knowledge of MPs have remained a few. When adopting digital learning to maintain traditional knowledge of MPs, it was discovered that it would be necessary to engage with relevant knowledge keepers, use multimedia, and provide content in local languages.

This study helps in conservation of important MP species that are having biologically important therapeutic compounds meant for treating various ailments. Older generations of various tribal communities mainly hold traditional knowledge of important MPs, and unless it is preserved, it will perish along with its caretakers.

It is worth looking at a digital platform that can help future generations to maintain traditional knowledge of MPs, as it is a dynamic and ever-changing, it must involve a digital tool for its future conservation. Current methods for maintaining traditional knowledge of MPs were ineffective and constrained by space and time.

Additive manufacturing (AM) promises to reduce the weight of the component, it is required to be shown that the mechanical performance of AM parts meets stringent industrial design criteria. Very few studies are made on finite element analysis (FEA) of the component produced by AM for real-life workload conditions. This study is supposed to do FEA of the wheel hub, manufactured using metal three-dimensional (3D) printing, under static multi-load conditions and effect of infill pattern on maximum stress, deformation and factor of safety.

This study conducted FEA on wheel-hub using Ansys. The approach of Orthotropic properties is used to do static analysis of wheel-hub and compared results of different metal 3D printing material (Ti-6Al-4V and Al-Si10-Mg) with hexagonal and triangular infill patterns.

Ti-6Al-4V with Honeycomb patterns shows better results in all cases and can be replaced with standard conventional material.

Because of the chosen research approach, the research results may lack generalisability. Therefore, it is required to do an experimental study.

Metal components with applications across the automobile industry can be manufactured using AM technology. With the help of AM, components with high strength to weight ratio can be manufactured.

This paper fulfils the identified need of FEA of the component produced by AM for real-life workload conditions. This study is supposed to do FEA of the wheel hub, manufactured using metal 3D printing, under static multi-load conditions and Effect of infill pattern on maximum stress, deformation and factor of safety.

Mobile phones have become one of the most favored devices to maintain social connections as well as logging digital information about personal lives. The privacy of the metadata being generated in this process has been a topic of intense debate over the last few years, but most of the debate has been focused on stonewalling such data. At the same time, such metadata is already being used to automatically infer a user’s preferences for commercial products, media, or political agencies. The purpose of this paper is to understand the predictive power of phone usage features on individual privacy attitudes.

The present study uses a mixed-method approach, involving analysis of mobile phone metadata, self-reported survey on privacy attitudes and semi-structured interviews. This paper analyzes the interconnections between user’s social and behavioral data as obtained via their phone with their self-reported privacy attitudes and interprets them based on the semi-structured interviews.

The findings from the study suggest that an analysis of mobile phone metadata reveals vital clues to a person’s privacy attitudes. This study finds that multiple phone signals have significant predictive power on an individual’s privacy attitudes. The results motivate a newer direction of automatically inferring a user’s privacy attitudes by leveraging their phone usage information.

An ability to automatically infer a user’s privacy attitudes could allow users to utilize their own phone metadata to get automatic recommendations for privacy settings appropriate for them. This study offers information scientists, government agencies and mobile app developers, an understanding of user privacy needs, helping them create apps that take these traits into account.

The primary value of this paper lies in providing a better understanding of the predictive power of phone usage features on individual privacy attitudes.

The study examines the effect of increased Economic Policy uncertainty on analyst optimism in the Indian market. The study also explores whether the SEBI Research Analyst Regulation, 2014, has effectively contained the optimistic nature of analysts.

The study is based on firms in the Indian market. The sample period is 2003–2020. It runs a linear panel regression to measure the impact of Economic Policy uncertainty on the optimism level of analysts' forecasts and recommendations, controlling for firm fixed effects. Further, the impact of the SEBI Research Analyst Regulation, 2014, has been assessed with the help of the difference-in-difference approach.

The Economic Policy uncertainty is significantly and positively related to the analyst optimism, reflected in the forecast bias and recommendation in the Indian context. The experience of analysts and the age of the firm positively drive optimism. However, introducing the Research Analyst Regulation by SEBI led to a decline in analyst optimism. The regulation decoupled the analysts' compensation from brokerage service transactions. Thus, the results suggest that the regulation has effectively curbed the incentive to produce optimistic output.

This is the first study in the Indian market to assess the impact of uncertainty on analyst output. It also investigates the effectiveness of the first analyst-specific regulation in India, i.e. The Research Analyst Regulation, 2014.

The purpose of this paper is to add an important new dimension to the earnings management literature by establishing a link between idiosyncratic risk and the degree of accrual management.

Based on a comprehensive sample of 44,599 firm-year observations during the period spanning 1987-2009, the study offers robust empirical evidence of the importance of firm-specific idiosyncratic volatility as a determinant of earnings manipulation. The authors use standard measures of earnings management and idiosyncratic volatility. The authors test the hypotheses with robust econometrics techniques.

The authors document a strong positive relationship between idiosyncratic risk and accruals management. Further, the authors find a positive association between residual volatility and discretionary accruals whether accruals are income inflationary or income deflationary. The findings are robust to alternate idiosyncratic risk proxies and variables associated with earnings management.

Overall, the knowledge derived from this study provides additional tools to assess the degree of earnings management by firms, and hence the quality of the financial reporting. Thus the findings will enable standard setters, financial market regulators, analysts, and investors to make more informed legislative, regulatory, resource allocation, and investment decisions.

The year 2010 was coming to a close, and Kapil, Marketing Manager of GEF India Private Limited (GEF), was thinking about the future. He had drafted a brief (see Exhibit 1) on conducting market research to assess the health of the brand Freedom Refined Sunflower Oil, which GEF had launched in the southern Indian state of Andhra Pradesh (AP) in February of that year.1 Kapil was very happy to note that the brand had achieved good sales, in fact, significantly higher sales than the target set for the launch. This had been achieved thanks to a well-thought-out launch plan that had included considerably more above the line (ATL) marketing expenditure than any of the competing brands in the market. He was interested in finding out whether and where exactly the brand had taken root in the minds of consumers. Another important purpose of the proposed market research was to assess the effectiveness of the launch plan. Above all, he felt it would provide valuable insights when he set out to prepare a marketing plan for the coming year.

The COVID-19 pandemic has had far-reaching effects on society and will continue to be a subject of study for researchers in the years to come. Businesses have implemented technologies that reduce reliance on physical currencies, such as e-commerce sites and contactless payments. This study aims to examine the users’ attitudes and behaviors toward mobile payments. The focus is on identifying the most effective techniques and approaches that businesses can use to encourage user adoption of mobile payments.

This study uses survey data from 396 active mobile payment users across the mid-west region of the USA to test the proposed hypothesis. The snowball sampling approach is used to sample the participants for the data collection. This study uses partial least squares structural equation modeling to test the ten hypotheses proposed in this study.

This study finds that organizational commitment and privacy customization can significantly overcome users’ protective attitudes toward mobile payments during the pandemic. In addition, providing users with privacy customization options can significantly encourage self-disclosure, which is crucial for transaction authentication and fraud detection.

Envisioned in the backdrop of the COVID pandemic, this is one of the earliest studies investigating the role of privacy customization, self-disclosure and organizational commitment on mobile payment adoption.

This paper aims to explore the impact of the data observation period on the computation of altmetric measures like velocity index (VI) and half-life. Furthermore, it also attempts to determine whether article-level computations are better than computations on the whole of the data for computing such measures.

The complete publication records for the year 2016 indexed in Web of Science and their altmetric data (original tweets) obtained from PlumX are obtained and analysed. The creation date of articles is taken from Crossref. Two time-dependent variables, namely, half-life and VI are computed. The altmetric measures are computed for all articles at different observation points, and by using whole group as well as article-level averaging.

The results show that use of longer observation period significantly changes the values of different altmetric measures computed. Furthermore, use of article-level delineation is advocated for computing different measures for a more accurate representation of the true values for the article distribution.

The analytical results show that using different observation periods change the measured values of the time-related altmetric measures. It is suggested that longer observation period should be used for appropriate measurement of altmetric measures. Furthermore, the use of article-level delineation for computing the measures is advocated as a more accurate method to capture the true values of such measures.

The research work suggests that altmetric mentions accrue for a longer period than the commonly believed short life span and therefore the altmetric measurements should not be limited to observation of early accrued data only.

The present study indicates that use of altmetric measures for research evaluation or other purposes should be based on data for a longer observation period and article-level delineation may be preferred. It contradicts the common belief that tweet accumulation about scholarly articles decay quickly.

Several studies have shown that altmetric data correlate well with citations and hence early altmetric counts can be used to predict future citations. Inspired by these findings, majority of such monitoring and measuring exercises have focused mainly on capturing immediate altmetric event data for articles just after the publication of the paper. This paper demonstrates the impact of the observation period and article-level aggregation on such computations and suggests to use a longer observation period and article-level delineation. To the best of the authors’ knowledge, this is the first such study of its kind and presents novel findings.

The main purpose of this study is to explore and validate the question “whether altmetric mentions can predict citations to scholarly articles”. The paper attempts to explore the nature and degree of correlation between altmetrics (from ResearchGate and three social media platforms) and citations.

A large size data sample of scholarly articles published from India for the year 2016 is obtained from the Web of Science database and the corresponding altmetric data are obtained from ResearchGate and three social media platforms (Twitter, Facebook and blog through Altmetric.com aggregator). Correlations are computed between early altmetric mentions and later citation counts, for data grouped in different disciplinary groups.

Results show that the correlation between altmetric mentions and citation counts are positive, but weak. Correlations are relatively higher in the case of data from ResearchGate as compared to the data from the three social media platforms. Further, significant disciplinary differences are observed in the degree of correlations between altmetrics and citations.

The results support the idea that altmetrics do not necessarily reflect the same kind of impact as citations. However, articles that get higher altmetric attention early may actually have a slight citation advantage. Further, altmetrics from academic social networks like ResearchGate are more correlated with citations, as compared to social media platforms.

The paper has novelty in two respects. First, it takes altmetric data for a window of about 1–1.5 years after the article publication and citation counts for a longer citation window of about 3–4 years after the publication of article. Second, it is one of the first studies to analyze data from the ResearchGate platform, a popular academic social network, to understand the type and degree of correlations.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-11-2019-0364