The substantial growth in online social networks has vastly expanded the potential impact of electronic word of mouth (eWOM) on consumer purchasing decisions. A critical literature review exposed that there is limited research on the impact of online consumer reviews on online purchasing decisions of Saudi Arabian consumers. This research reports on results of a study on the effects of online reviews on Saudi citizens' online purchasing decisions. The results show that Saudi Internet shoppers are very much influenced by eWOM, and that a larger percentage of them are dependent on such online forums when making decisions to purchase products through the Internet.

Inductive learning enables the system to recognize patterns and regularities in previous knowledge or training data and extract the general rules from them. In literature there are proposed two main categories of inductive learning methods and techniques. Divide-and-Conquer algorithms also called decision Tree algorithms and Separate-and-Conquer algorithms known as covering algorithms. This paper first briefly describe the concept of decision trees followed by a review of the well known existing decision tree algorithms including description of ID3, C4.5 and CART algorithms. A well known example of covering algorithms is RULe Extraction System (RULES) family. An up to date overview of RULES algorithms, and Rule Extractor-1 algorithm, their solidity as well as shortage are explained and discussed. Finally few application domains of inductive learning are presented.