Abstract: Though some research studies about big data have been reported in literature recently, few studies about applying big data analytics to analyze firms’ performance are performed and reported in existing business literature. Empowered by a novel framework of big data analytics, this paper illustrates our preliminary work of apply the proposed big data analytics framework to predict firms’ performance in stock market. In particular, the proposed framework leverages big data and probabilistic language modeling to analyze the stock performance of firms in near real-time. Our empirical test demonstrates the merits of the proposed framework. The practical implication of our research work is that firms can apply our big data analytics framework to analyze their internal and external performance, and hence develop more effective business strategies in advance.

• Publication Date: 13-Mar-2016
• DOI: 10.15224/978-1-63248-089-7-39
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Abstract: This paper investigates the correlations between wage and productivity in United States fast food restaurants. The research examines whether or not the industry’s expansion is based on the labor productivity improvement. The author selects four major fast food firms, and compares the representative firm’s performances with its group mean. Using data from Bureau of Labor Statics, the wages are lower generally than the economic sector average; but the productivity is not lower than the National average. The representative firm, McDonald, does not show significant change in productivity, rather productivity has followed the stable trend over time. The research found that the wage in the fast food industry was lower than economic sector average, but productivity was not lower.

• Publication Date: 13-Mar-2016
• DOI: 10.15224/978-1-63248-089-7-71
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• Downloads: 0