Mixing Ethnography and Information Technology Data Mining to Visualize Innovation Networks in Global Networked Organizations

2014 ◽  
pp. 203-234 ◽  
Author(s):  
Julia C. Gluesing ◽  
Kenneth R. Riopelle ◽  
James A. Danowski
Keyword(s):  
Data Mining ◽  
Information Technology ◽  
Innovation Networks ◽  
Networked Organizations

scholarly journals Reference model for building innovation networks in information technology

2021 ◽  
Vol 28 (3) ◽  
Author(s):  
Adauto Lucas da Silva ◽  
Fábio Müller Guerrini
Keyword(s):  
Information Technology ◽  
Research Methodology ◽  
Formation Process ◽  
Reference Model ◽  
Knowledge Development ◽  
Innovation Networks ◽  
Organizational Processes ◽  
Exploratory Case Study ◽  
Modeling Methodology

Abstract: The change in organizational processes by means of associating the new resources made available by IT (Information Technology) with the intrinsic characteristics of business-related areas embodies one of the innovation concepts. This work proposes a reference model for the formation of innovation networks focused on implementing systems that represent innovations by associating IT with the business area. The research methodology is based on an exploratory case study, composed of several analysis units, and on the organizational modeling methodology EKD (Enterprise Knowledge Development) to formulate the reference model. This research proposition contributes to systematize the formation process of collaborative alliances among the innovation agents in order to implement Information Technology-based systems.

THE RESEARCH TREND OF INFORMATION TECHNOLOGY AND DECISION MAKING IN 2009

2010 ◽  
Vol 09 (01) ◽  
pp. 1-8 ◽  
Author(s):  
YONG SHI
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Information Technology ◽  
Decision Support ◽  
Risk Analysis ◽  
Multiple Criteria Decision Making ◽  
Advisory Board ◽  
Research Trend ◽  
Research Topics

The research topics of the 39 papers published in the International Journal of Information Technology and Decision Making (IT&DM) in 2009 can be classified into three major directions: decision support, multiple criteria decision making, and data mining and risk analysis. The Editor-in-Chief, on behalf of the editorial board and advisory board, highlights the key ideas of these contributions. The seven papers in first issue of 2010 IT&DM are also introduced.

Promoting knowledge sharing performance in a knowledge management system: do knowledge workers’ behavior patterns matter?

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Navid Nezafati ◽  
Shokouh Razaghi ◽  
Hossein Moradi ◽  
Sajjad Shokouhyar ◽  
Sepideh Jafari
Keyword(s):  
Data Mining ◽  
Information Technology ◽  
Knowledge Workers ◽  
Reward System ◽  
Knowledge Management System ◽  
Behavior Patterns ◽  
Content Type ◽  
Organizational Variables ◽  
Top Managers ◽  
The Impact

Purpose This paper aims to identify the impact of demographical and organizational variables such as age, gender, experiences use of knowledge management system (KMS), education and job level on knowledge sharing (KS) performance of knowledge workers in knowledge activities of a KMS. Specifically, it seeks to explore that is there any relationship between the KS behavior patterns of high KS performance knowledge workers with their performance. Furthermore, this study using its conceptual attitude model aims to show that whether knowledge workers’ behavior patterns in sharing information and knowledge throughout a KMS have any specific effect or not. Design/methodology/approach This paper proposed a framework to mine knowledge workers’ raw data using data mining techniques such as clustering and association rules mining. Also, this research uses a case-based approach to a knowledge-intensive company in Iran that works in the field of information technology with 730 numbers of workers. Findings Findings suggest that demographical and organizational variables such as age, education and experience use of KMS have positive effects on knowledge worker’s KS behavior in KMSs. In fact, people who have lower age, higher education degrees and more experience use of KMS, have more participation in KS in KMS. Also, results depict that the experienced use of KMS has the most impact on the intention of KS in this KMS. Findings emphasize on the importance of the influence of the behavioral, organizational environments and psychological factors such as reward system, top management support, openness and trust, on KS performance of knowledge workers in the KMS. In fact, according to data, the KMS reward system caused to increasing participation of the users in KS, also in each knowledge activity that top managers participate in, the scores were higher. Practical implications This research helps top managers in designing policies and strategies to improve the participation of knowledge workers in KS and helps human resource managers to improve their membership policies. Also, assist Information Technology (IT) managers to enhance KMSs’ design to leverage with organization strategies in the field of improving KS and encourage people to participate in KMS. Originality/value This research has two key values. First, this paper applies a data mining framework to mining and analyzing data and this paper uses actual data of a KMS in a specialist company in Iran, with about 27,740 real data points. Second, this paper investigates the impact of demographical and organizational attributes on KS behavior, which little is empirically known about the impact of demographical variables on KS intention.

Prediksi Tingkat Kelulusan Tepat Waktu Mahasiswa Menggunakan Algoritma Naïve Bayes pada Universitas XYZ

2020 ◽  
Vol 12 (2) ◽  
pp. 104-107
Author(s):  
Nurhayati . ◽  
Nuraeny Septianti ◽  
Nani Retnowati ◽  
Arief Wibowo
Keyword(s):  
Data Mining ◽  
Information Technology ◽  
Data Processing ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Bayes Method ◽  
Processing Data ◽  
Student Graduation ◽  
Phase Data ◽  
Bayes Algorithm

Data processing is imperative for the development of information technology. Almost any field of work has information about data. The data is made use of the analysis of the job. Nowadays, information data is imperatively processed to help workers in making decisions. This study discusses student prediction graduation rates by using the naïve Bayes method. That aims at providing information to college if they can use it properly to utilize the data of students who graduated by processing data mining. Based on the data mining process, steps founded that used producing information, namely predicting student graduation on time. The method of this study is Naïve Bayes with classification techniques. At this study, researchers used a six-phase data mining process of industry crossing standards in data mining known as CRISP-DM. The results of research concluded that the application of the Naive Bayes algorithm uses 4 (four) parameters namely ips, ipk, the number of credits, and graduation by getting an accuracy value of 80.95%.

Organizational Data Mining (ODM)

2011 ◽  
pp. 1-8
Author(s):  
Hamid R. Nemati ◽  
Christopher D. Barko
Keyword(s):  
Artificial Intelligence ◽  
Data Mining ◽  
Decision Making ◽  
Information Technology ◽  
Competitive Advantage ◽  
Current Status ◽  
Decision Making Process ◽  
Actionable Knowledge ◽  
The Core ◽  
Mining Tools

An increasing number of organizations are struggling to overcome “information paralysis” — there is so much data available that it is difficult to understand what is and is not relevant. In addition, managerial intuition and instinct are more prevalent than hard facts in driving organizational decisions. Organizational Data Mining (ODM) is defined as leveraging data mining tools and technologies to enhance the decision-making process by transforming data into valuable and actionable knowledge to gain a competitive advantage (Nemati & Barko, 2001). The fundamentals of ODM can be categorized into three fields: Artificial Intelligence (AI), Information Technology (IT), and Organizational Theory (OT), with OT being the core differentiator between ODM and data mining. We take a brief look at the current status of ODM research and how a sample of organizations is benefiting. Next we examine the evolution of ODM and conclude our chapter by contemplating its challenging yet opportunistic future.

Translating Advances in Data Mining in Business Operations

2008 ◽  
pp. 2734-2748
Author(s):  
Henry Dillon ◽  
Beverley Hope
Keyword(s):  
Data Mining ◽  
Information Technology ◽  
Data Analysis ◽  
Knowledge Discovery ◽  
Knowledge Discovery In Databases ◽  
Business Context ◽  
Business Operations ◽  
Analysis Tools ◽  
It Managers ◽  
Tools And Techniques

Knowledge discovery in databases (KDD) is a field of research that studies the development and use of various data analysis tools and techniques. KDD research has produced an array of models, theories, functions and methodologies for producing knowledge from data. However, despite these advances, nearly two thirds of information technology (IT) managers say that data mining products are too difficult to use in a business context. This chapter discusses how advances in data mining translate into the business context. It highlights the art of business implementation rather than the science of KDD.

Data Warehouse and Business Intelligence Systems in the Context of E-HRM

2009 ◽  
pp. 223-229
Author(s):  
Martin Burgard ◽  
Franca Piazza
Keyword(s):  
Data Mining ◽  
Information Technology ◽  
Resource Management ◽  
Human Resource Management ◽  
Data Warehouse ◽  
Human Resource ◽  
Business Intelligence ◽  
Multidimensional Data ◽  
Business Intelligence Systems ◽  
Analytical Processing

The increased use of information technology leads to the generation of huge amounts of data which have to be stored and analyzed by appropriate systems. Data warehouse systems allow the storage of these data in a special multidimensional data base. Based on a data warehouse, business intelligence systems provide different analysis methods such as online analytical processing (OLAP) and data mining to analyze these data. Although these systems are already widely used and the usage is still growing, their application in the area of electronic human resource management (e-HRM) is rather scarce. Therefore, the objective of this article is to depict the components and functionality of these systems and to illustrate the application possibilities and benefits of these systems by selected application examples in the context of e-HRM.

Defining Knowledge Constituents and Contents

2016 ◽  
Vol 5 (1) ◽  
pp. 1-7
Author(s):  
Sead Spuzic ◽  
Ramadas Narayanan ◽  
Megat Aiman Alif ◽  
Nor Aishah M.N.
Keyword(s):  
Artificial Intelligence ◽  
Data Mining ◽  
Information Technology ◽  
Knowledge Extraction ◽  
Information Environment ◽  
Electronic Records ◽  
Software Complexity ◽  
New Information ◽  
Key Terms

While it appears that a consensus is crystalising with regard to the hierarchy of concepts such as “knowledge”, “definition” and “information”, there is an increasing urgency for improving definitions of these terms. Strategies such as “knowledge extraction” or “data mining” rely on the increasing availability of digital (electronic) records addressing almost any aspect of socio-economic realm. Information processors are invaluable in the capacity of turning large amount of data into information. However, a new problem emerged on the surface in this new information environment: numerous concepts and terms are blurred by ambiguous definitions (including the concept of 'definition' itself). This triggered a need for mitigating hindrances such as homonymy and synonymy, leading further to demands on the decoding software complexity of which equals the artificial intelligence applications. Information technology presumably copes with this diversity by providing the information decoding 'tools'. This opens a never-ending opportunity for further permutations of tasks and service abilities. The solution, however, is to address the causes rather than indulge in multiplying the superficial remedies. Clearly, the multiplicity of definitions for the same concepts, false synonyms and so forth show that there is a need for introducing definitions of sufficient dimensionality. In this article, a number of examples of important concepts are presented first to point at the ambiguities associated with them, and then to propose their disambiguation. The minimum intent is to demonstrate how these key terms can be defined to avoid ambiguities such as pleonasm, homonymy, synonymy and circularity.

Sign in / Sign up

Export Citation Format

Share Document