Purpose: – The purpose of this paper is to investigate the factors that influence Facebook usage among small and medium enterprises (SMEs). In addition, it examines the impact of Facebook usage on financial and non-financial performance of the SMEs. Design/methodology/approach: – Using integrated model, this study examined the influence of compatibility, cost effectiveness, interactivity and trust on Facebook usage and its subsequent impact on organizations performance. Statistical analyses were based on the data collected, through survey questionnaire from 259 SMEs in Malaysia. Partial Least Square (PLS) method was used to test the hypotheses. Findings: – The study revealed that Facebook usage has a strong positive impact on financial performance of SMEs; similarly it was also found that Facebook usage positively impacts the non-financial performance of SMEs in terms of cost reduction on marketing and customer service, improved customer relations and improved information accessibility. Additionally, factors such as compatibility, cost effectiveness and interactivity was identified as factors that influence Facebook usage among SMEs. Research limitations/implications: – This study is limited in selection of samples. The sample only covered one community of SME in Malaysia which limits generalizability of the findings. This study provided a clearer idea on the real importance of Facebook and its benefits. The results would motivate and guide organizations in the adoption of Facebook for business activities. The study also has various theoretical and practical contributions. Originality/value: – Very few empirical studies investigated the actual impact of Facebook usage among organizations. This study investigated the effect of Facebook usage on the financial performance of the organizations which is really important to study as it reveals the exact value of using Facebook for business activities.

Big data is an essential aspect of innovation which has recently gained major attention from both academics and practitioners. Considering the importance of the education sector, the current tendency is moving towards examining the role of big data in this sector. So far, many studies have been conducted to comprehend the application of big data in different fields for various purposes. However, a comprehensive review is still lacking in big data in education. Thus, this study aims to conduct a systematic review on big data in education in order to explore the trends, classify the research themes, and highlight the limitations and provide possible future directions in the domain. Following a systematic review procedure, 40 primary studies published from 2014 to 2019 were utilized and related information extracted. The findings showed that there is an increase in the number of studies that address big data in education during the last 2 years. It has been found that the current studies covered four main research themes under big data in education, mainly, learner’s behavior and performance, modelling and educational data warehouse, improvement in the educational system, and integration of big data into the curriculum. Most of the big data educational researches have focused on learner’s behavior and performances. Moreover, this study highlights research limitations and portrays the future directions. This study provides a guideline for future studies and highlights new insights and directions for the successful utilization of big data in education.

Advancements in information and communication technology, and online web users have given attention to the virtual representation of each user, which is crucial for effective service personalization. Meeting users need and preferences is an ongoing challenge in service personalization. This issue can be addressed through the building of a comprehensive user profile. A user profile is the summary of the user’s interests, characteristics, behaviours, and preferences, while user profiling is the system of collecting, organizing and inferring the user profile information. Many reviews on user profiling have been conducted but none focused on the effective profile modeling process. Hence, this article aims to provide a review of the recent state-of-the-art approach to user profiling. These include methods, description, characteristics, and taxonomy of the user profile. The study of the existing user profiling modeling in the aspect of data acquisition, feature extraction, profiling techniques, and profiling approaches (with the identification of their strengths and weaknesses) and the performance measures are also provided. In addition, the research challenges were also discussed with a focus on privacy, datasets, cold start issues, trust issues, and computational complexity. Moreover, the article identified an open research direction that serves as solutions to the identified challenges and motivation for further researchers in advancing user profiling. The findings showed that an effective modeling process enhances the construction of accurate user profile for service personalization.