Is Big data too Big for Swedish SMEs? : A quantitative study examining how the employees of small and medium-sized enterprises perceive Big data analytics

Sammanfattning: Background:  Marketing is evolving because of Big data, and there are a lot of possibilities as well as challenges associated with Big data, especially for small and medium-sized companies (SMEs), who face barriers that prevent them from taking advantage of Big data. For companies to analyze Big data, Big data analytics are used which helps companies analyze large amounts of data. However, previous research is lacking in regard to how SMEs can implement Big data analytics and how Big data analytics are perceived by SMEs. Purpose:  The purpose of this study is to investigate how the employees of Swedish SMEs perceive Big data analytics. Research Questions: How do employees of Swedish SMEs perceive Big data analytics in their current work environment? How do the barriers impact the perceptions of Big data analytics? Methodology: The research proposes a quantitative cross-sectional design as the source of empirical data. To gather the data, a survey was administered to the employees of Swedish companies that employed less than 250 people, these companies were regarded as SMEs. 139 answered the survey and out of those, the analysis was able to use 93 of the answers. The data was analyzed using previous theories, such as the Technology Acceptance Model (TAM). Findings: The research concluded that the employees had positive perceptions about Bigdata analytics. Further, the research concluded that two of the barriers (security and resources) analyzed impacted the perceptions of the employees, whereas privacy of personal data did not. Theoretical Implications: This study adds to the lacking Big data research and improves the understanding of Big data and Big data analytics. The study also adds to the existing gap in literature to provide a more comprehensive view of Big data. Limitations: The main limitation of the study was that previous literature has been vague and ambiguous and therefore may not be applicable. Practical Implications: The study helps SMEs understand how to better implement Big data analytics and what barriers need to be prioritized regarding Big data analytics. Originality: To the best of the author’s knowledge, there is a significant lack of academic literature regarding Big data, Big data analytics and Swedish SMEs, therefore this study could be one of the pioneer studies examining these topics which will significantly contribute to current research.