The use of Web 2.0 technologies, in particular, Social Media tools, are rapidly increasing. Accordingly, the distinction between personal and professional lives is blurring, as users incorporate the advantages of SM into their work environment. Companies have begun to integrate Web 2.0 technologies into their business activities, as the expectation from employees, customers and partners increases. One of the ways to aid integration with these technologies is to use a stages of growth framework as a guideline for future implementation. The purpose of this paper is to investigate the appropriateness of Earl's [1] model, as companies evolve using Web 2.0 technologies. Earl states that when companies reach the full transformation stage of the e-business model it is necessary to revisit the six stages as technologies evolve and try to adapt to the latest demands from customers, suppliers, partners and employees.
The growth of mobile technologies and smartphones is reshaping the individual and organisational behaviour which affect the business environment. One of the key challenges of mobile payment is how to understand and manage user expectations and technology acceptance. Therefore, to better understand mobile payment use and acceptance, we need to analyse the factors and barriers that influence technology use. The investigation uses Technology Acceptance Model in conjunction with Organisational Semiotics, a socio-technical method of design, to overcome possible limitations addressed in research. This approach offers methods that can help to develop a research model for mobile payment use focusing on technical and social aspects.
In this article, we revisit the caf e story first introduced in 2011. Recent data from this caf e run by business students at a Midwestern public university are explored and analyzed. The data were collected using a point-of-sale system over a 3-month period during the spring semester of 2015. These data can be used in introductory statistics courses to illustrate the use of time series and forecasting, applications of data mining and visualization, as well as sampling, confidence intervals, and inference using ANOVA and chisquare tests for independence. Since the data pertain to a student-run business, we believe that statistics students, especially those in business disciplines, will find the data's context to be relevant and interesting. In addition to the technical exercises, we provide background and context for several managerial issues that these data can be used to address, thus emphasizing the importance of data-driven decision making.
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