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Lack of trust in online transactions has been dted, by scholars in the past, as the main reason for the dislike of online shopping. The objective of this paper is to develop a framework for studying the influence of website characteristics on Trust in online travel portals and empirically validate it. In the first phase, a causal model is developed in which the relative importance attached to the different website characteristics, to generate trust in online travel portals, are identified. In the next phase, a set of models has been proposed, that focus on the customers' personal variables, ie., demographic and psychographic-that moderate the relationship between these antecedents of trust, and trust. Our empirical model offers insights into the relative importance of the website characteristics contributing to trust in travel portals across customers of varying psychographic and demographic values in India.

Online recommender systems are an integral part of e-commerce. There are a plethora of algorithms following different approaches. However, most of the approaches except the singular value decomposition (SVD), do not provide any insight into the underlying patterns/concepts used in item rating. SVD used underlying features of movies but are computationally resource-heavy and performs poorly when there is data sparsity. In this article, we perform a comparative study among several pre-processing algorithms on SVD. In the experiments, we have used the MovieLens 1M dataset to compare the performance of these algorithms. KNN-based approach was used to find out K-nearest neighbors of users and their ratings were then used to impute the missing values. Experiments were conducted using different distance measures, such as Jaccard and Euclidian. We found that when the missing values were imputed using the mean of similar users and the distance measure was Euclidean, the KNN-based (K-Nearest Neighbour) approach of pre-processing the SVD was performing the best. Based on our comparative study, data managers can choose to employ the algorithm best suited for their business.

Innovation plays a critical role in the growth of developing economies like India. The primary purpose of the current study is to investigate the role of research and development (R&D) and Information and communication technology (ICT) in firms’ innovation outcomes. The study uses data from firm-level surveys in India to examine the linkages of R&D and ICT on innovation outcomes and establishes linkages with policy elements to develop innovation ecosystems. The study uses a set of tobit regression models, generalized structural equations model and explorative content analysis to examine innovation outcomes of firms. The results indicate that both R&D and ICT parameters play a significant role in influencing innovation outcomes though they are moderated by the size of the firm and sector. The study adds to the existing literature on the resource-based view of the firm and also the literature on innovation management in the context of emerging economies. The authors examine the role of capabilities around R&D and ICT in influencing firm-level innovations.