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The integration of relevant antecedents into TAM would lead to better understanding of the decision factors which act as enablers for the adoption of internet banking. The purpose of the paper is to determine the influence of the antecedents subjective norm, image, banks initiative, internet banking self-efficacy, internet usage efficacy, trust, perceived risk, trialability and government support on the existing constructs of the technology acceptance model (TAM) and to test measurement invariance and the moderating effect of the demographic variables on the relationship between the latent constructs used in this augmented TAM.

A survey questionnaire was administered on internet banking users and a total of 300 responses were collected. A two-step approach suggested by Hair et al. (2006) and Schumacker and Lomax (2004) was used in this study. The proposed model was assessed using the confirmatory factor analysis approach. The structural model was then tested in order to establish nomological validity. The data based on four demographic dimensions gender, age, income, education were divided into two groups for each of these demographic dimensions. The invariance test was first performed on the measurement model and then on the structural model. The measurement model and structural model were subjected to tests of equivalence of parameters across groups.

To a large extent the results of the study supports the proposed model and thereby contributes to understand the influence of subjective norm, image, banks initiative, internet banking self-efficacy, internet usage efficacy, trust, perceived risk and government support on internet banking adoption. The predictor variables in the augmented TAM were able to explain 29.9 per cent of the variance in the actual usage of internet banking as compared to the TAM which was able to explain only 26.5 per cent variance in the actual usage of internet banking. A significant difference in the relationship between the different constructs of the model was observed when the model was subjected to multi-group invariance testing.

The study suffers from the same limitations as most other studies involving TAM. In this study self-reported measures about the usage were taken as the actual usage. The findings of the study can be of use to marketers for target-specific marketing by customizing the marketing campaign focussing on the factors that were found to be strong influencers leading to the usage of internet banking for each target audience.

The main challenge in this study was to develop the conceptual model for the internet banking adoption by extending the TAM and to get a robust theoretical support from the extant literature for the relevant factors along with their relationship to uncover new insights about factors responsible for the internet banking adoption. The augmented model had an improved predictive capability and explanatory utility.

The main focus of the study was to validate the End User Computing Satisfaction (EUCS) model in the context of internet banking and to determine the major factors which contribute to level of satisfaction of internet banking users in India.

A survey questionnaire was administered on internet banking users and a total of 387 responses were collected. A factor analysis on the 12 items used in the EUCS model with oblique (non‐orthogonal) rotation and five fixed factors revealed the existence of the same latent constructs hypothesized in the original EUCS model. Confirmatory Factor Analysis (CFA) was then used to test and validate the four hypothesized models for model fit.

The current study confirms the evidence found in extant literature that EUCS is a second‐order construct. In this study the factor “Format” was found to have the highest loading and the factor “Content” had the lowest factor loading among all the five latent constructs in the EUCS model for internet banking. The factor “Accuracy” had the highest mean score and the factor “Content” had the lowest mean scores indicating that bank customers were satisfied with the accuracy of the information on the banks web site but were not comparatively satisfied by the information content on the web site.

This study was done based on a call by Pikkarainen et al. (2006) to validate the EUCS instrument for internet banking satisfaction in different countries to enhance the generalizability of the findings. This research is limited to samples from India and results of other countries need to be considered before a generalization of the findings can be made. This study will be useful to policy makers and banks to devise strategies for increasing the use of internet banking.

There is a myriad of literature on factors which lead to adoption of internet banking but extant literature on internet banking user satisfaction is limited particularly pertaining to banks operating in India. This study attempts to fill this gap in the literature.

The purpose of this paper is to address the shortcomings of limited research in forecasting the power of social media in India.

This paper uses sentiment analysis and prediction algorithms to analyze the performance of Indian movies based on data obtained from social media sites. The authors used Twitter4j Java API for extracting the tweets through authenticating connection with Twitter web sites and stored the extracted data in MySQL database and used the data for sentiment analysis. To perform sentiment analysis of Twitter data, the Probabilistic Latent Semantic Analysis classification model is used to find the sentiment score in the form of positive, negative and neutral. The data mining algorithm Fuzzy Inference System is used to implement sentiment analysis and predict movie performance that is classified into three categories: hit, flop and average.

In this study the authors found results of movie performance at the box office, which had been based on fuzzy interface system algorithm for prediction. The fuzzy interface system contains two factors, namely, sentiment score and actor rating to get the accurate result. By calculation of opening weekend collection, the authors found that that the predicted values were approximately same as the actual values. For the movie Singham Returns over method of prediction gave a box office collection as 84 crores and the actual collection turned out to be 88 crores.

The current study suffers from the limitation of not having enough computing resources to crawl the data. For predicting box office collection, there is no correct availability of ticket price information, total number of seats per screen and total number of shows per day on all screens. In the future work the authors can add several other inputs like budget of movie, Central Board of Film Certification rating, movie genre, target audience that will improve the accuracy and quality of the prediction.

The authors used different factors for predicting box office movie performance which had not been used in previous literature. This work is valuable for promoting of product and services of the firms.