Amidst the era of e-economy, one of the difficulties from the standpoint of the information systems manager is, among others, the forecast of memory needs for the organization. In particular, the manager is often confronted with maintaining a certain threshold amount of memory for a prolonged period of time. However, this constraint requires more than technical and managerial resolutions, encompassing knowledge management for the group, eliciting tacit knowledge from the end users, and pattern and time series analyses of utilization for various applications. This paper proposes a framework for building an automated intelligent agent for memory management under the client-server architecture. The emphasis is on collecting the needs of the organization and acquiring the application usage patterns for each client involved in real time. Due to the dynamic nature of the tasks, incorporation of a neural network architecture with tacit knowledge base is suggested. Considerations for future work associated with technical matters comprising platform independence, portability, and modularity are discussed.

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Display technology is reshaping the consumer, business, government, and even not-for-profit markets in the midst of the digital convergence, coupled with recent smart phones led by Apple, Inc.First-Generation (1G) display technology was dominated by the Cathode Ray Tubes, followed by Liquid Crystal Display and Plasma in 2G. A radically innovative shift as a disruptive technology is expected to follow in 3G to utilize virtually any transparent material, which wirelessly connects to portable access points. This paper studies the feasibility of the 3G Display Technology (DT) with Technology S-Curves, and presents possible business models and technology strategies which may be generated from it. Additional subsets of business models may be derived for a wide range of industry applications.

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This paper investigates the data analytics between consumer purchase decisions relative to the on-line reviews. The multi-attributes associated with purchase decisions are comprised of nationalism and consumer preference to be correlated with online reviews using big data analytics. By far, a small fraction of meaningful studies have sought to correlate nationalism and ethnocentrism with big data analytics to date. Globally accepted generic products are selected to expedite the process of data engineering. Two sets were arranged: passenger automobiles for transportation with an estimated $9 trillion global market and the smart phone, boosting its market size of approximately $5 billion. Both products provide minimized cultural, linguistic, gender, age, and/or custom barriers of entry for prospective digital consumers, thereby allowing relatively unrestricted engagement with online reviews and purchases. A series of hypothesis tests indicate that there is a positive correlation between nationalism and automobiles. As to smart cell phones, however, nationalism had nominal control factors. Multi-variate analytics were performed by using R and Tableau Public.