In today's world the data is considered as an extremely valued asset and its volume is increasing exponentially every day. This voluminous data is also known as Big Data. The Big Data can be described by 3Vs: the extreme Volume of data, the wide Variety of data types, and the Velocity required processing the data. Business companies across the globe, from multinationals to small and medium enterprises (SMEs), are discovering avenues to use this data for their business growth. In order to bring significant change in businesses growth the use of Big Data is foremost important. Nowadays, mostly business organization, small or big, wishes valuable and accurate information in decision-making process. Big data can help SMEs to anticipate their target audience and customer preferences and needs. Simply, there is a dire necessity for SMEs to seriously consider big data adoption. This study focusses on SMEs due to the fact that SMEs are backbone of any economy and have ability and flexibility for quicker adaptation to changes towards productivity. The big data holds different contentious issues such as; suitable computing infrastructure for storage, processing and producing functional information from it, and security and privacy issues. The objective of this study is to survey the main potentials & threats to Big Data and propose the best practices of Big Data usage in SMEs to improve their business process.

Customer purchase intention in online shopping stores can be influenced by electronic word of mouth (eWom) communication generated by the comments of consumers on social networking sites. However, the adoption of eWom information by consumers is influenced by various factors. This study investigates the mechanism through eWom antecedents influence eWom adoption and consumer purchase intention. The study also examines how eWom adoption mediates the impact of antecedents of eWom adoption (Quality, Consumer Attitude, Credibility, Usefulness, Needs, and Adoption) on customer’s purchase intention. Using the hypothetic-deductive approach, the current study used a cross-sectional self-administered survey to collect data from a convenience sample of university students residing in Karachi. The SmartPLS software was used to analyze the collected data. Study findings reveal that all predictors of eWom adoption are significant. It was also found that eWOM adoption mediates the impact of eWom antecedents on consumer purchase intention. The results provide significant implications for website designers and digital marketers. For marketers working with social media, the findings of this study are encouraging. Marketers can use these findings to develop viral marketing campaigns and encourage customers to contribute useful and credible eWom that could improve the customers’ purchase intention.