AN EMPIRICAL STUDY ON WHETHER FACEBOOK PROMOTION CAN DELIVER VALUE TO INDIAN START-UPS?

Startups face challenges in investing large amounts of money in business promotion due to resource constraints and operational size. Hence, business promotion strategies like advertising, public relations, sales promotions, billboards, and event sponsorship are generally avoided by startup companies. Instead, Startups tend to employ social media strategies to run promotional campaigns on platforms like Facebook, Twitter, Instagram, and YouTube, among others. However, there is a paucity of research to understand whether such promotional campaigns are delivering the desired value and able to achieve the objectives set by the startups. The present study makes an endeavor to explore whether promotions through social media are effective in delivering significant outcome to startups. Therefore, this study aims to empirically investigate whether Facebook Promotion is delivering value to Indian startups. To conduct the research, cross-sectional data has been collected from 100 startup located in different cities in India. The data has been analyzed following the “Partial Least Squared-Structural Equation Modelling (PLS-SEM)” technique. The findings from this study may contribute towards the literature on understanding the importance of social media strategies in marketing campaigns for startups. Furthermore, this study may enhance clarity about the value obtained by startups, particularly during the early stage of their existence. Consequently, the study findings may help startups plan their investments in promoting their products and services on social media, in general, and Facebook, in particular

Farmers' voices on the Spoken Web

A network of voice recordings, accessible from any telephone, provides information to farmers in India. Villagers can add their own VoiceSites to promote their business

Farmers' voices on the Spoken Web

A network of voice recordings, accessible from any telephone, provides information to farmers in India. Villagers can add their own VoiceSites to promote their business

AUGMENTED REALITY VIRTUAL MIRRORS-THE INNOVATIVE DRIVERS FOR CONSUMER PURCHASE DECISION-MAKING

This paper emphasizes the importance of Augmented Reality (AR) and its impact on customer purchasing decisions through in-store Virtual Mirrors (VMs) built through real-time applications using digitalization processes. The paper contributes to the emerging topic of Consumer Purchase Decision-Making by examining international brands that use VM Technology to create immersive customer experiences and how it affects consumer decision-making. The study takes into consideration, as a case study, iconic American Brand Tommy Hilfiger, which has a multi-level store on the prestigious Regent Street in London opened in 2006. The study's data was gathered from reports made by 20 British nationals residing in London who utilized the publicly available VMs at the specified retailer. The process commenced with the editing and organization of responder reports, selected according to the level of saturation in several VM scenarios. The observations and conclusions yielded a lot of empirical data to analyze. This level identified 870 conceptual categories related to Virtual Mirrors using content and sentiment analysis. In the respondents' remarks, 1,279 instances pertinent to the study's topic were identified, along with the most utilized terms

SENTIMENT ANALYSIS USING NOVEL DEEP LEARNING METHODS

In the current digital era, the humongous amount of data being generated has been impacting public lives in one or the other ways. Sentiment analysis, also known as opinion mining, is related to contextual mining of texts which helps in identification and extraction of subjective information from the source material. Sentiment analysis is being used for brand monitoring and reputation management across different market segments. It helps to understand how the public perceive a particular brand, product or service that is highly useful for different tech companies, marketing agencies, media organizations, fashion brands etc. In today’s scenario we have been suffering with data overload which makes it impossible to analyze public sentiments without any sort of error or bias. Sentiment analysis provides better insights into the public reviews as it can be automated which ultimately helps in decision making. There are various deep learning and machine learning methods and models as well as natural language processing tools which help in examining and analyzing public opinions with low time complexity. However, deep learning methods have become highly popular in recent times as these models provide high efficiency and accuracy. In this review paper we have provided a complete overview of the common deep learning frameworks being employed for sentiment classification and analysis. This paper discusses various learning models, evaluation, text representations and other metrics in deep learning architectures. The key findings of different authors have been discussed in detail. This paper will help other researchers in understanding the deep learning techniques being used for sentiment analysis

Spoken Web : la parole aux paysans

Depuis un téléphone, les paysans indiens peuvent accéder à des informations vocales enregistrées et ajouter leurs propres VoiceSites pour promouvoir leur exploitatio

Low Power Reduction Techniques Implementation and Analysis in Sense Amplifier Circuit Configurations

MTCMOS (Multi-Threshold CMOS), sleepy stack, sleepy keeper, and footer stack are examples of low power saving techniques incorporated into the core gpdk 90nm technology papers used in the proposed study using Cadence. The main focus of these tests is the power consumption of various sense amplifier circuits. The simulation results show that the charge-transfer sense amplifier uses much less energy than voltage and current sense amplifiers. The present mode detecting amplifier’s power consumption can be decreased by up to 98 percent by using MTCMOS technology.</jats:p

A Nonlinear Anisotropic Diffusion Equation for Image Restoration with Forward-backward Diffusivities

Background: In the image processing area, deblurring and denoising are the most challenging hurdles. The deblurring image by a spatially invariant kernel is a frequent problem in the field of image processing. Methods: For deblurring and denoising, the total variation (TV norm) and nonlinear anisotropic diffusion models are powerful tools. In this paper, nonlinear anisotropic diffusion models for image denoising and deblurring are proposed. The models are developed in the following manner: first multiplying the magnitude of the gradient in the anisotropic diffusion model, and then apply priori smoothness on the solution image by Gaussian smoothing kernel. Results: The finite difference method is used to discretize anisotropic diffusion models with forward- backward diffusivities. Conclusion: The results of the proposed model are given in terms of the improvement. </jats:sec