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1 Innovation: The Five Disciplines for Creating What Customers Want by Curtis R.
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Carlson and William W. Wilmot (Crown Publishing Group, 2006). 2 The New Age of Innovation: Driving Cocreated Value through Global Networks by C. K. Prahalad and M. S. Krishnan (McGraw-Hill, 2008). 3 Innovation to the Core: A Blueprint for Transforming the Way Your Company Innovates by Peter Skarzynski and Rowan Gibson (Harvard Business School Press, 2008).
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THE ROLE OF CREATIVITY, INVENTION, AND INNOVATION IN SOLID WASTE MINIMIZATION
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4 The Art of Innovation: Lessons in Creativity from IDEO, America s Leading 5
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Design Firm by Tom Kelley with Jonathan Littman (Currency/ Doubleday, 2001). The Ten Faces of Innovation: IDEO s Strategies for Defeating the Devil s Advocate and Driving Creativity throughout Your Organization by Tom Kelley with Jonathan Littman (Currency/Doubleday, 2005). Innovation and Entrepreneurship by Peter F. Drucker (Butterworth Heineman, 2007). Making Innovation Work: How to Manage It, Measure It, and Pro t from It by Tony Davila, Marc J. Epstein, and Robert Shelton (Wharton School Publishing, 2005). Harvard Business Review on Innovation by Clayton M. Christensen, Michael Overdorf, Ian MacMillan, and Rita McGrath (Harvard Business School Press, 2001). The Myths of Innovation by Scott Berkun (O Reilly, 2007). Ten Rules for Strategic Innovators: From Idea to Execution by Vijay Govindarajan and Chris Trimble (Harvard Business Press, 2005). Innovation Nation: How America Is Losing Its Innovation Edge, Why It Matters, and What We Can Do to Get It Back by John J. Kao (Free Press, 2007). The Game-Changer: How You Can Drive Revenue and Pro t Growth with Innovation by A. G. La ey and Ram Charan (Crown Business, 2008). Diffusion of Innovations, 5th ed., by Everett M. Rogers (Simon & Schuster, 2003).
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SOLID WASTE GENERATION MODELING: CHARACTERIZATION, PREDICTION, AND EVALUATION
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INTRODUCTION
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12.1 Model Overview
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Concepts for the integrated environmental model developed for this research were created to accomplish the research goal of better predicting and evaluating the solid waste generation quantities of businesses and government agencies. The underlying hypotheses for the model concepts were also developed to guide the research process. This chapter discusses the intended functions of the proposed model and the theoretical structure of the model concepts hypotheses. This research involved examining the feasibility of the model concept to determine if such a model with the speci ed functions could be established. The model concepts were important to the research process because their successful development would create new knowledge in the eld and provide insights into individual company waste generation.
12.2 Functions of the Proposed Model
This research investigated the development of a model to better understand solid waste generation and recycling rates of individual U.S. companies and government agencies. To begin the model development process, the proposed functions of the model were established to guide the research. Research was conducted to develop the model with two primary functions:
1 Prediction of the solid waste generation quantities and waste stream compositions
of individual companies and government agencies. 2 Standardized solid waste generation evaluation of individual companies and government agencies with statistical performance parameters. The performance parameters work as a report card to monitor and control solid waste generation of individual companies. The performance indices offer a uniform comparison basis and provide insights into waste generation of U.S. industries.
INTRODUCTION
U.S. businesses and government agencies Waste generators
Characterize businesses according to waste composition
Identify significant variables that influence waste generation
Develop model to quantify and evaluate solid waste generation
Hypothesis 1 Similar businesses generate similar solid waste compositions
Hypothesis 2 Significant variables can be determined
Hypothesis 3 Solid waste generation performance parameters can be established
Model concepts and hypotheses.
12.3 Theoretical Structure of the Model Concepts and Hypotheses
A theoretical structure for the proposed model was established based on the intended model functions and theoretical concepts. To acheive the goal, related hypotheses were developed to build the evaluation model. Three concepts were interrelated to develop the proposed model and hypotheses required to accomplish the research goal. These concepts are listed below and can be seen in Fig. 12.1.
1 Group businesses that generate similar waste stream compositions. 2 Identify signi cant variables that aid in the prediction of the solid waste rates from
businesses and government agencies that generate similar compositions. 3 Develop and validate a model to evaluate solid waste generation.
RESEARCH METHODOLOGY
This chapter discusses the methodology that was used to create the waste estimation model discussed in the previous chapter. The methodology applied the scienti c method to investigate the proposed model concepts and theories discussed in the previous chapter. The methodology was designed to incorporate the positive aspects of previous studies and to overcome many of the shortcomings to lead to new knowledge. An overview of the research methodology follows:
1 Determine types of data required for the model concept and hypotheses. 2 Collect data. 3 Characterize businesses according to waste stream compositions.
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a. Conduct multivariate cluster analysis on mean material composition percentages using SIC codes as initial grouping basis (ANOVA and F-tests). b. Perform mean and variance hypotheses tests to validate results (F-tests and Bartlett tests). c. Analyze and interpret the results. Identify dominant variables that in uence waste quantities. a. Conduct multivariable stepwise regression analysis. b. F-tests, R2 analyses, t-tests to determine signi cant variables. c. Validate regression model assumptions. d. Analyze and interpret the results (including correlation analysis). Develop evaluation parameters for solid waste generation. Integrate the model and develop the program for the model. Validate the model. a. Evaluate and analyze additional data collected from other methods versus the model. b. Conduct case studies using the regression models and performance parameters. Discuss summary of ndings and conclusions.
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