For decades, scholars have debated which mode of education is superior. Some argue that online is superior and others argue that online is less effective than traditional face-to-face courses. Still others suggest that the hybrid mode (e.g., online blended with face-to-face lectures) is the most desired and productive content delivery method for students. However, students' perceptions towards online learning as compared to traditional face-to-face learning have largely been overlooked. This paper intends to fill this void in the literature and explore minority students' perceptions towards online learning versus traditional face-to-face modes of education in higher education.
Purpose - The paper seeks to categorize mission and vision statements into clusters and demonstrate how these clusters can be profiled in the context of Globalization, Innovation and Strategy Centric features for assessment of strategic alignment, positioning and direction. Based on text mining methodology, mission and vision statements of the top 772 Fortune companies were analyzed to understand: 1) How mission and vision statements can be meaningfully categorized into clusters, 2) How attributes of each cluster can be meaningfully evaluated in the context of the degree to which Globalization, Innovation and Strategy Centric Mission and Vision statements are discovered. Clustering Toolkit (CLUTO) software was used for text mining the data collected from two websites. A recursive bisection approach has been followed to reach the desired number of six clusters, which were further analyzed through Wordle software for visual representation. The study clustered the companies in the dataset into groups in which globalization, innovation, and strategy issues were dominant. The epistemological contribution of this research includes how text mining can be used to meaningfully categorize a large dataset consisting of mission and vision statements of 772 Fortune corporations, how knowledge contained in a large dataset can be managed through the use of text mining in analyzing cluster attributes, and how these clusters can be profiled in the context of Globalization, Innovation and Strategy Centric features for assessment of strategic alignment, positioning, and direction.
Mission and vision statements are critical to a company’s success both from a company’s long-term goals and appearance to potential customers. We analyze a collection of 772 mission and vision statements from companies via natural language processing. This data is hand annotated into 15 industry types. We show the distinctiveness and connectiveness of each industry via text processing and machine learning techniques. The extracted features of each industry are a telling and guiding indicator of what that industry embraces. We show high predictive power via machine learning to determine an industry by looking only at the mission and vision statements
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