University of Central Florida STARS Electronic Theses and Dissertations, 2004-2019 2013 Exploring The Innovation Environment Within The Systems Engineering Context Of A Defense Organization: A Preliminary Framework Khaled Odeh University of Central Florida Part of the Industrial Engineering Commons Find similar works at: https://stars.edu/etd University of Central Florida Libraries http://library.edu This Doctoral Dissertation (Open Access) is brought to you for free and open access by STARS. It has been accepted for inclusion in Electronic Theses and Dissertations, 2004-2019 by an authorized administrator of STARS. For more information, please contact STARS@ucf. STARS Citation Odeh, Khaled, "Exploring The Innovation Environment Within The Systems Engineering Context Of A Defense Organization: A Preliminary Framework" (2013).
Electronic Theses and Dissertations, 2004-2019.edu/etd/2671 EXPLORING THE INNOVATION ENVIRONMENT WITHIN THE SYSTEMS ENGINEERING CONTEXT OF A DEFENSE ORGANIZATION: A PRELIMINARY FRAMEWORK by KHALED S ODEH B. Embry-Riddle Aeronautical University, 1994 M. Webster University, 2001 A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of Industrial Engineering & Management Systems in the College of Engineering and Computer Science at the University of Central Florida Orlando, Florida Summer Term 2013 Major Professors: Luis Rabelo Ahmad Elshennawy © 2013 Khaled Odeh ii ABSTRACT Innovation may involve the introduction of ideas for designing or producing new products, or introducing improvements to products, processes, services or any other aspect of an organization to the market place. A major element for measuring organizational strength is its perception of innovation and the ability of the organization to build on and sustain such strength.
While there is no shortage of research and study materials on innovation, there is, however, a shortage of thorough and realistic analysis of the intersection of innovation management, and measurement of innovation within the systems engineering context of defense organizations. In addition, while most research studies seem to adopt strictly quantitative innovation factors in determining innovation success and performance, they seem to have overlooked the qualitative side of it. An objective of this research study is to address the need for exploring the innovation environment within the systems engineering context of a defense organization. In addition, the research presents a new model for exploring innovation factors within the examined environment, using both quantitative and qualitative factors.
The research uses a number of data collection instruments that include a survey construct to gather quantitative and qualitative data. The study identified significant factors that could be used to properly determine innovation within the systems engineering context of defense organizations using traditional statistics and data mining modeling. New indicators such as security and organizational leadership are discovered as important to define, monitor, and assess the innovation of the defense industry within the context of systems engineering. iii ACKNOWLEDGMENTS First and foremost, I’d like to thank Almighty God for giving me the chance of being.
I am grateful for the countless bounties he’s bestowed upon me. I pray that this humble effort of mine may become of benefit to my fellow humans. Second; I’d like to offer my deepest gratitude to Professors Luis Rabelo and Ahmad Elshennawy for his enduring patience, everlasting encouragement, and enlightened guidance during the last three years. Professor Rabelo and Professor Elshennawy were an inspirational role model.
I’d also like to thank the elite Doctoral Committee for their guidance, vision, and encouragement. I also would like to take this opportunity to express my indebtedness to all my friends and coworkers for their great assistance with this study. Words can’t express the many things I want to say to my wife Ofelia and two kids Ahmad and Ali. I love you all from the bottom of my heart.
I wish if I can give individual acknowledgment to all those who had contributed somehow to this work, but I can’t, so I am truly grateful to each and every one who has participated by any means to this study. Your contributions are highly appreciated. Khaled S Odeh iv TABLE OF CONTENTS LIST OF FIGURES. viii LIST OF TABLES .x LIST OF ACRONYMS/ ABBREVIATIONS .5 Significance of Study .6 Scope and Focus .7 Background and Contribution of the Researcher .8 Structure of the Dissertation.3 Evolution of Process of Adoption of Innovation .10 Discussion of Research Gaps .1 Potential Research Problem .2 Define Research Questions .3 Data Analysis and Modeling .5 Data Mining Modeling.6 Compare & Contrast with Literature.
KNOWLEDGE DISCOVERY AND ANALYSIS .2 Preliminary Innovation Model - Quantitative and Qualitative Factors .3 Potential Quantitative and Qualitative Innovation Factors .5 Validity, Reliability, and Consistency of the Construct .7 Principal Component Analysis (PCA) to Visualize Linear Separability .8 Factorial Analysis and Logistic Regression .1 Forward with Factorial Analysis .9 Data Mining Modeling.1 The Selection of a Neural Network Architecture .2 Elimination of Input Variables (i.2 Classification/Regression Trees .10 Summary of Data Analysis and Findings .1 Survey Construct Data Analysis and Findings. CONCLUSIONS AND RECOMMENDATIONS .4 Contributions to the Body of Knowledge .1 Agent: The Innovator Assessor .2 Agent: Project Review Board (PRB) .3 Agent: The Chief Technological Officer (CTO). 137 APPENDIX A: SURVEY QUESTIONS. 139 APPENDIX B: IRB HUMAN SUBJECTS PERMISSION LETTER.
145 APPENDIX C: SURVEY QUESTIONS CODED TO BE USED IN THE DATA ANALYSIS. 147 vi APPENDIX D: COLLECTED SURVEY DATA. 152 LIST OF REFERENCES. 153 vii LIST OF FIGURES Figure 2-1: First generation technology push models (1950s to mid-1960s).
Source: Rothwell (1991, Ref. 17 Figure 2-2: Second generation demand pull models. Source: Rothwell (1991, Ref. 17 Figure 2-3: The coupling or interactive model of innovation.
Source: Rothwell (1993, Ref. 18 Figure 2-4: An integrated (fourth generation) innovation model. Source: Rothwell (1993, Ref. 19 Figure 2-5: An example of systems integration and networking model.
Source: Trott (1998), cited in Mahdi (2002, Ref. 21 Figure 2-6: Funnel Model Nine Stages Measuring Innovation, Morris (2008). 40 Figure 2-8: Literature Review for Innovation Measurement Models. 56 Figure 3-1: Methodology Phase and Flow.
59 Figure 4-1: Proposed Innovation Measurement Model (Preliminary). 83 Figure 4-2: Visualization of the entire survey results using Weka (http://www.nz/ml/weka/). 92 Figure 4-3: Total Variance Explained. 93 Figure 4-4: First Principal Component vs.
Second Principal Component displaying a good level of linear separability. 100 Figure 4-5: Most Important Variables Using Logistic Regression. 105 Figure 4-6: A Multilayer Neural Network. 108 Figure 4-7: Selection of the Neural Network Architecture with two (2) Hidden Units.
110 Figure 4-8: Importance of Input Variables (e., factors) via Sensitivity Analysis. 112 Figure 4-9: Elimination of Input Variables (i. 113 Figure 4-10: Finalized Neural Network Developed with the Most Important Factors. The Neural Network uses 13 Factors as Inputs, 2 Hidden Neurons in one Hidden Layer, and One Output Neuron in the Output Layer representing Innovation.
115 Figure 4-11: CART Classification Tree for Diagnostic Biomarker for Bacterial Infection (adapted and modified from http://1.com/case-study-new-biomarker-discovered-for- critically-ill-childeren-diagnostic/). 118 Figure 4-12: Example of Classification/Regression Tree Developed for Innovation using the Training Data Set. This tree has 5 nodes. 119 viii Figure 4-13: Classification/Regression Tree using CART to model the Innovation Environment Factors of this research.
As an example: IF ProfitGrowth is greater than 3.5 and IF Security is greater than 2.5 Then is BLUE (Innovative Yes). 120 Figure 4-14: Most Importance of Variables Using Classification/Regression Tree using CART and the Training Data Set of 200. 121 Figure 4-15: Analysis of Significant Factors of all Three Methods. 124 ix LIST OF TABLES Table 2-1: Evolution of Innovation Metrics by Generation; Source: Center of Accelerating Innovation, George Washington University (2006).
16 Table 2-2: Systems Engineering Definitions Evolution (Adapted from Teper (2010)). 39 Table 4-1: Eliminated Factors. 85 Table 4-2: Respondents’ Innovation Determination Classifications (Yes and No). 89 Table 4-3: Respondents’ Innovation Determination Classifications (1 to 3 Scale).
89 Table 4-4: Respondents’ Innovation Determination Classifications (1 to 4 Scale). 90 Table 4-5: Respondents’ Innovation Determination Classifications (1 to 5 Scale). 91 Table 4-6: Total Variance Explained. 93 Table 4-7: Rotated Factor Matrix.
94 Table 4-8: Reliability Statistics. 95 Table 4-9: Modeling Methodologies. 96 Table 4-10: Basic Features of each Modeling Methodology. 96 Table 4-11: PCA Using MATLAB.
99 Table 4-12: Forward LR Variables in the Equation. 102 Table 4-13: Logistic Regression Performance with FA (SSE is Summatory of the Squared Error). 105 Table 4-14: Neural Network Performance. 116 Table 4-15: Classification/Regression Tree Performance.
121 Table 4-16: Performance for Factors for all Three Methods (Logistic Regression, Neural Networks, and Classification/Regression Trees). An ensemble is a voting strategy of the different methods. 125 Table 5-1: Recipe Innovation Project Data. 135 x LIST OF ACRONYMS/ ABBREVIATIONS BSC Balanced Scorecard BCG Boston Consulting Group CART Classification and Regression Tree CEO Chief Executive Officer CSIIC Canadian Science and Innovation Indicators Consortium FDI Foreign Direct Investment GDP Gross Domestic Product GPS Global Positioning System INCOSE International Council on Systems Engineering KM Knowledge management LR Logistic Regression NN Neural Networks NPD New Product Development OECD Organization for Economic Co-operation and Development PCA Principal Component Analysis R&D Research and Development R2I Return on Innovation Investment ROI2 Return on Innovation Investment RT Regression Trees SPSS Statistical Package for Social Sciences S&T Science and Technology xi SMEs Small and Medium-sized Enterprises TCI Team Climate Inventory UK United Kingdom USA United States of America xii CHAPTER 1.1 Background Innovation capability is the most important determinant of firm performance (Mone et al.
Innovations are described with different emphasis. Narvekar and Jain (2006) describe innovation from the dimension of new technologies and their development through marketing- based new technology and inventions. Betz (2001) thinks of innovation as new things/artifacts to increase business success and sustainability. Sullivan (1990) has a more commercial viewpoint emphasizing new ways of doing activities through commercialization of technologies.
Finally, Freeman (1982) has a more radical viewpoint considering innovation as formed by different components of novelty that increase profit by the use of knowledge to generate new products or services, new processes, new structures and new markets. Onkham et al., (2013) explains very well that Innovations are categorized within five categories: product, process, market development, new sources, and new organizational structures. In addition, a sixth one can be added that is business model innovation that is the most radical one. Product innovation generates new products or improves the product’s quality and/or performance.
Process innovation is a new task or activity to manufacture products or services in order to decrease cost and/or increase productivity. New market innovation focuses on new customers. This can involve new strategies or new marketing attractiveness. 1 Sources of development suppliers innovation focuses in increasing the number of suppliers in way to reduced cost and/or get strategic advantages and higher levels of quality.
Organizational structure innovation focuses on the development of management and its structure which relate to process innovation. Business model innovation is the change of the entire business model (i., how it makes money) and reason for existence of the organization. The literature presents enough evidence to show that competitive success is dependent upon an organization’s management of the innovation process and proposes factors that relate to successful management of the innovation process (cf. inter alia Balachandra and Friar, 1997; Cooper, 1979 a,b; De Brentani, 1991; Di Benedetto, 1996; Ernst, 2002; Globe et al.
This process should be assessed to ensure effectiveness. To assess innovation capability, organizations should have a meaningful way of measuring innovation and how it is managed within the organization. A diversity of approaches, prescriptions and practices have been implemented and searched that can be confusing and contradictory. Farther more one of the engines of economy growth in the United States of America (USA) is the defense industry and at the heart of the defense industry is systems engineering.
This study is concerned with identifying the significant and critical factors that could determine if a project is a successful innovation project within the context of systems engineering in defense organizations as related to the engineered systems/products built by 2 them. The defense industry has a history of creating revolutionary innovations.