Product and Process Innovation: Antecedents and Performance outcomes in small IT firms in India

Innovation is a strategic necessity for the survival of today’s organizations. The wide recognition of innovation as a competitive necessity, particularly in dynamic market environments, makes it an evergreen domain for research. This dissertation deals with innovation in small Information Technology (IT) firms in India. The IT industry in India has been a phenomenal success story of the last three decades, and is today facing a crucial phase in its history characterized by the need for fundamental changes in strategies, driven by innovation. This study, while motivated by the dynamics of changing times, importantly addresses the research gap on small firm innovation in Indian IT.This study addresses three main objectives: (a) drivers of innovation in small IT firms in India (b) impact of innovation on firm performance (c) variation in the extent of innovation adoption in small firms. Product and process innovation were identified as the two most contextually relevant types of innovation for small IT firms. The antecedents of innovation were identified as Intellectual Capital, Creative Capability, Top Management Support, Organization Learning Capability, Customer Involvement, External Networking and Employee Involvement.Survey method was adopted for data collection and the study unit was the firm. Surveys were conducted in 2014 across five South Indian cities. Small firm was defined as one with 10-499 employees. Responses from 205 firms were chosen for analysis. Rigorous statistical analysis was done to generate meaningful insights. The set of drivers of product innovation (Intellectual Capital, Creative Capability, Top Management Support, Customer Involvement, External Networking, and Employee Involvement)were different from that of process innovation (Creative Capability, Organization Learning Capability, External Networking, and Employee Involvement). Both product and process innovation had strong impact on firm performance. It was found that firms that adopted a combination of product innovation and process innovation had the highest levels of firm performance. Product innovation and process innovation fully mediated the relationship between all the seven antecedents and firm performance The results of this study have several important theoretical and practical implications. To the best of the researcher’s knowledge, this is the first time that an empirical study of firm level innovation of this kind has been undertaken in India. A measurement model for product and process innovation was developed, and the drivers of innovation were established statistically. Customer Involvement, External Networking and Employee Involvement are elements of Open Innovation, and all three had strong association with product innovation, and the latter twohad strong association with process innovation. The results showed that proclivity for Open Innovation is healthy in the Indian context. Practical implications have been outlined along how firms can organize themselves for innovation, the human talent for innovation, the right culture for innovation and for open innovation. While some specific examples of possible future studies have been recommended, the researcher believes that the study provides numerous opportunities to further this line of enquiry.

Prediction of Key Symptoms of Learning Disabilities in School-Age Children Using Rough Sets

This paper highlights the prediction of learning disabilities (LD) in school-age children using rough set theory (RST) with an emphasis on application of data mining. In rough sets, data analysis start from a data table called an information system, which contains data about objects of interest, characterized in terms of attributes. These attributes consist of the properties of learning disabilities. By finding the relationship between these attributes, the redundant attributes can be eliminated and core attributes determined. Also, rule mining is performed in rough sets using the algorithm LEM1. The prediction of LD is accurately done by using Rosetta, the rough set tool kit for analysis of data. The result obtained from this study is compared with the output of a similar study conducted by us using Support Vector Machine (SVM) with Sequential Minimal Optimisation (SMO) algorithm. It is found that, using the concepts of reduct and global covering, we can easily predict the learning disabilities in children

Prediction of Learning Disabilities in School Age Children using SVM and Decision Tree

This paper highlights the prediction of Learning Disabilities (LD) in school-age children using two classification methods, Support Vector Machine (SVM) and Decision Tree (DT), with an emphasis on applications of data mining. About 10% of children enrolled in school have a learning disability. Learning disability prediction in school age children is a very complicated task because it tends to be identified in elementary school where there is no one sign to be identified. By using any of the two classification methods, SVM and DT, we can easily and accurately predict LD in any child. Also, we can determine the merits and demerits of these two classifiers and the best one can be selected for the use in the relevant field. In this study, Sequential Minimal Optimization (SMO) algorithm is used in performing SVM and J48 algorithm is used in constructing decision trees.

Using Neural Network Classifier Support Vector Machine Regression for the prediction of Melting Point of Drug – like compounds

In our study we use a kernel based classification technique, Support Vector Machine Regression for predicting the Melting Point of Drug – like compounds in terms of Topological Descriptors, Topological Charge Indices, Connectivity Indices and 2D Auto Correlations. The Machine Learning model was designed, trained and tested using a dataset of 100 compounds and it was found that an SVMReg model with RBF Kernel could predict the Melting Point with a mean absolute error 15.5854 and Root Mean Squared Error 19.7576