Showing 4 results for Framework
Peyman Akhavan, Reza Hosnavi , Sanjaghi Mohammad ,
Volume 20, Issue 3 (9-2009)
Abstract
This paper is to develop a knowledge management (KM) model in some Iranian academic research centers (ARC) based on KM critical success factors. General KM critical success factors (CSF) were identified through literature review. Then the research procedure led to the identification of KM critical success factors in Iranian ARCs including 16 different factors. It was done through first stage survey by about 300 sample targets. Then, these 16 factors were surveyed separately again by experts through a Delphi panel. The experts suggested their practical solutions for exploiting the 16 factors in ARCs through a KM framework based on a KM cycle. This 2 years research has been done during 2006 to 2008.
Mohammad Ali Shafia, Arnoosh Shakeri,
Volume 20, Issue 4 (4-2010)
Abstract
This paper aims at emphasizing the importance of establishing a Project Management (PM) system in Technology Transfer (TT) processes and developing a conceptual framework for it. TT is an important process in Technology Management affairs for all enterprises. Most of the time, lack of a particular concentration on technical, commercial and legal aspects of TT process, leads to mismanagement of other aspects of transferring project, like Time and Project Integration. This situation may lead to failure and loss of many opportunities in transfer process. To overcome this problem, inputs, outputs and activities of a typical TT processes are identified and based on these components, a conceptual framework for managing this project & prevent the loss is developed using Project Management models and methodologies.
K.v.k Sasikanth, K. Samatha, N. Deshai, B. V. D. S. Sekhar, S. Venkatramana,
Volume 31, Issue 3 (9-2020)
Abstract
The Today’s interconnected world generates huge digital data, while millions of users share their opinions, feelings on various topics through popular applications such as social media, different micro blogging sites, and various review sites on every day. Nowadays Sentiment Analysis on Twitter Data which is considered as a very important problem particularly for various organizations or companies who want to know the customers feelings and opinions about their products and services. Because of the data nature, variety and enormous size, it is very practical for several applications, range from choice and decision creation to product assessment. Tweets are being used to convey the sentiment of a tweeter on a specific topic. Those companies keeping survey millions of tweets on some kind of subjects to evaluate actual opinion and to know the customer feelings. This paper major goal would be to significantly collect, recognize, filter, reduce and analyze all such relevant opinions, emotions, and feelings of people on different product or service could be categorized into positive, negative or neutral because such categorization improves sales growth about a company's products or films, etc. We initiate that the Naïve Bayes classifier be the mainly utilized machine learning method for mining feelings from large data like twitter and popular social network because of its more accuracy rates. In this paper, we scrutinize sentiment polarity analysis on Twitter data in a distributed environment, known as Apache Spark.
Hasbullah Hasbullah, Zulfa Fitri Ikatrinasari, Humiras Hardi Purba,
Volume 35, Issue 4 (12-2024)
Abstract
SMEs (Small and Medium Enterprises) play a vital role in developing countries like Indonesia, contributing 12.85% to the GDP. However, Indonesia ranks low in the Global Index of Digital Entrepreneurship Systems by the Asian Development Bank. A study in Bekasi regency found that nearly 100% of SMEs still rely on conventional systems, facing common issues like low stock accuracy and lack of transparency. While software solutions exist, they often fail to address the real issues SMEs face in the real world. This research aims to create a digital transformation framework tailored to the real issues of SMEs, confirmed by stakeholders. This study used exploratory mixed methods, identifying seven steps for digital transformation: defining customer needs, identifying gaps, setting goals, selecting technology, addressing current problems, planning and financing, and evaluation. These steps cover six dimensions: Customer needs, Processes, Planning and Strategy, Technology, Resources, and Financing. The findings highlight that digital transformation is not just about adopting technology but involves a comprehensive approach grounded in customer needs. This framework offers significant value as a main contribution to academics, practitioners, policymakers, and stakeholders by addressing SMEs’ real-world challenges and ensuring that digital transformation is effective and relevant
