IT Assessment

Proceedings of the American Society for Engineering Management 2017 International Annual Conference E-H. Ng, B. Nepal, and E. Schott eds.

Copyright, American Society for Engineering Management, 2017

FORECASTING TECHNOLOGY OBSOLESCENCE: ASSESSING THE

EXISTING LITERATURE, A SYSTEMATIC REVIEW

Albert J. Parvin Jr. Southwest Research Institute

Mario G. Beruvides, Ph.D., P.E.

Texas Tech University

[email protected] ____________________________________________________________________________________________ Abstract The purpose of this paper is: to provide a systematic review of the existing literature on the topic area of forecasting technology obsolescence and to provide guidelines and recommendations for future research. All business and government organizations that can be affected by technological change inevitably engage in forecasting for the primary reason of making informed decisions. Technology forecasting is the systematic attempt to anticipate and understand the potential direction, rate, characteristics, and effects of technological change, based on assumptions about the external world. Forecasting gives probabilistic information on what is believed will happen based on current assumptions, providing decision makers with insights on what the future might look like to make informed decisions. An organization's withdrawal from a technology segment area is often difficult (typically due to sunk cost bias and general lack of information) and often occurs too late; resulting in financial losses and missed opportunities. The ability to predict when a technology sector has reached maturity has high financial implication (e.g. when to exit from a technology sector and when resources should be transitioned from one technology sector to another.) A systematic review of technology forecasting and applicability to predict the maturity and obsolescence of a technology segment in its life cycle is explored. Guidelines for future research into forecasting the maturity and obsolescence of a technology sector in its life cycle are presented. Keywords Technology Forecasting Obsolescence, Systematic Literature Review Introduction William Shakespeare wrote, “If you can look into the seeds of time, and say which grain will grow and which will not, speak then unto me.” No one can prophesize the future with certainty, but by understanding the likely and potential outcomes of the future, an organization can improve their chances of maximizing gain and reducing losses (Morlidge, 2010). This paper ventures to start the examination process to analyze if technology forecasting improvement can be attained by looking both at the emerging and declining stages of the technology life cycle of technology sectors. To accomplish the above goal, a systematic literature review of this topic area was explored, and recommendations for future research into forecasting the maturity and obsolescence of a technology in its life cycle are presented herein. Systematic Review The analyses conducted followed the systematic review process for management research which enables researcher(s) to document literature reviews in stepwise and organized manner, reducing errors and bias. A systematic literature review can lead to identification, appraisal, and summarization of a research topic in question (Tranfield, 2003). A systematic literature review attempts to identify, appraise and synthesize all the empirical evidence that meets pre- specified eligibility criteria to answer a given research question or provide insight into a topic area (Higgins JPT, 2011; Tranfield, 2003). An adapted Tranfield’s systematic review steps, Exhibit 1, was followed in this paper.

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Exhibit 1. Systematic Review Steps (adapted from Tranfield, 2003)

Step 0: Identification of the need for a review (Significance) The premise of this topic area has direct implications in adding and pushing the boundaries of the paradigms of technology forecasting in management, economics, and policy. In this endeavor, it is important to begin looking at these paradigms as they relate to this paper’s topic to build a foundation of knowledge. Following the teachings of the philosopher C.I. Lewis, Edwards Deming’s theory of profound knowledge is based on the premise that management is prediction (Pyzdek & Keller, 2012). According to Deming, knowledge is acquired as one makes a rational prediction. The theory of knowledge helps us to understand that management in any form is prediction (Deminig, 2000). Prediction does not imply certainty, but implies understanding and reducing uncertainties (Deminig, 2000). Thus if uncertainties are reduced, value has been created for management. Without information, management is no more than guesswork. Decision making is driven by information. Technology forecasting gives managers improved visibility of likely outcomes and potential risks and opportunities, thus improving their decisions which impact the economics of an organization (Martino, 1983). Forecasting impacts decision making; for example, a forecast of rain may cause you to take an umbrella. Technology forecasting endeavors to anticipate and recognize the potential direction, rate, characteristics, and effects of technology or technological change; enabling better planning and decision making (Firat, Woon, & Madnick, 2008). A good forecast can help maximize gain and minimize loss from future conditions (Firat et al., 2008). The technology life cycle (TLC) s-curve was introduced by Everett Roger’s diffusion of innovation theory in 1962, and technology forecasting traditionally has been focused on determining when a technology enters the emerging stage of the TLC and not the declining/obsolesces stage. An organization's withdrawal from a technology sector area is often difficult (typically due to sunk cost bias and general lack of information) and often occurs too late; resulting in financial losses and missed opportunities. The ability to forecast when a technology sector has reached maturity has high financial implication (e.g. when to exit from a technology sector and when resources should be transitioned from one technology sector to another.) According to economist Milton Friedman, the main purpose of a business is to maximize profits for its owners or shareholders while maintaining corporate social responsibility (Boundless, 2016). To maximize profit (which also includes minimizing loss), an organization must continually strive for better anticipation, better situational awareness, and greater responsiveness, all which can be enhanced by forecasting (Morlidge, 2010). Policy guides decision makers (e.g. management) actions by setting directions and boundaries. Policies become the rules that a business follows, directly impacting economics. Alan Greenspan, the former Chairman of the Federal Reserve, said, “Implicit in any monetary policy action or inaction is an expectation of how the future will unfold, that is, a forecast” (Hendry & Ericsson, 2001, p. 125). This implies that there is a causal relationship between policy and forecasting. Policies guide future decisions, thus impacting forecasting, and decision-makers use technology forecasting information to formulate policy. Step 1: Preparation for Review A scoping exercise was conducted to determine the objective and focus literature review and identify existing relevant work and pattern in this topic area (Tranfield, 2003). Another feature of the scoping exercise is to determine if the

Planning the review  Step 0 - Identification of the need for a review  Step 1 – Preparation/Background  Step 2 - Development of a review protocol Identification, Selection and Quality Assessment  Step 3 - Publication identification  Step 4 - Selection of publications  Step 5 - Quality assessment Data Extraction and Synthesis  Step 6 - Data extraction and monitoring progress  Step 7 - Data synthesis: Realist review Reporting and Dissemination  Step 8 - The report and recommendations  Step 9 - Getting evidence into practice

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work to be conducted is original and will add to the knowledge of the field of study. It also serves to narrow the scope of the review and provide the relevant background information for the researchers. An operational definition gives communicable meaning to spoken or written words, when it is applied in a specific context, forming a "common language" between individuals and groups (Deming, 2000). These concepts are foundational to be able to transfer knowledge between individuals. For the purpose of this paper analysis, the following critical terms will be defined. Terms: • Forecasting: A projection based on assumptions about future states of the world; a decision-making tool used to

help manage the uncertainty of the future (Morlidge, 2010, p. 38). • Prediction: A definitive and specific statement about a specific event in the future (e.g. what will happen)

(Morlidge, 2010, p. 38). • Technology: The application of scientific knowledge through the use of tools, techniques, and procedures to

accomplish practical purposes (Martino, 1983, p. 1). • Technology Forecasting: A systematic attempt to anticipate and understand the potential direction, rate,

characteristics, and effects of technological change, based on assumptions about the external environment and future actions (Firat et al., 2008, p. 1).

• Technology Life Cycle: The cumulative product development of a technology or technology performance over time, having four distinct stages: research and development, ascent, maturity, and decline (Boundless, 2017; Gao, 2013).

• Technology Obsolescence: the stage at which a technology is no longer adequate or can be easily replaced by a superior technology.

• Policy: The set of basic principles and associated guidelines, formulated and enforced by the governing body of an organization, to direct and limit its actions in the pursuit of long-term goals (Clark, 2016, p. 1).

• Technology Policy: A set of government actions that affect the generation, acquisition, adaptation, diffusion and use of technological knowledge in a way that the government deems useful for the society rather than individuals (Chang, 2002, p. 1).

• Technology Foresight: The process involved in systematically attempting to look into the longer-term future of science, technology, the economy and society with the aim of identifying the areas of strategic research and the emerging of generic technologies likely to yield the greatest economic and social benefits (Martin, 1995, p. 140).

Prediction vs. Forecast. A common misconception is that prediction and forecasting are synonymous, they are not. A prediction is a definitive and specific statement about a specific event in the future (e.g. what will happen), whereas, a forecast is a probabilistic statement about an event (e.g. what we think will happen) (Morlidge, 2010). Why is this distinction important? We might believe we want to know the future but we only want to know the future to do something about it, that is, to change it to increase the probability of success (Morlidge, 2010). In other words, a perfect prediction is perfectly useless (Morlidge, 2010). To further expand on the conceptual analysis of the difference between predictions and forecasts, let us examine each in terms of actions. A forecast results in action(s) to be taken, whereas, a prediction by definition results in no action. If action is taken on a prediction, it is by definition no longer a prediction for the event is no longer definitive (Morlidge, 2010). The concept of forecasting has high value to business. Forecasting provides decision makers with insights that can be used to proactively manage the gap between what the future might look like and what we want it to look like (Morlidge, 2010). Policy vs. Strategy. Strategy and policy are two different concepts, yet they are often used interchangeably, even though they have different meanings (Surbhi, 2015). Policies and strategies have different purposes. A policy provides the rules for decisions and actions taken by an organization, whereas, a strategy is the plan of action governed by the policy (Surbhi, 2015). A policy is what is, or what is not done, while a strategy is a methodology used to achieve a target as guided by a policy (Bayley, 2012). To summarize, a policy guides decision makers (e.g. management) actions by setting a direction and boundaries, and a strategy is the plan of action. Step 3: Review Protocol (Scope) While conducting the analysis for this research, a review protocol was developed to filter articles based on relevance. Exhibit 2 includes the review protocol used to identify and select the studies for the systematic review of this paper’s topic area.

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Exhibit 2. Systematic Literature Review Protocol.

Systematic Literature Review Protocol Purpose To understand work being done in Technology Forecasting related to Technology

Obsolescence and Diffusion. 1. Search databases using keywords and their synonyms.

2. Narrow down search for a more detailed review of abstract and titles. 1. Exclude material which is not focused on Technology Forecasting related to Technology

Obsolescence and Diffusion. 2. Exclude duplicates.

1. ProQuest (www.proquest.com) 2. Emerald Insight (http://www.emeraldinsight.com) 3. Scopus (www.scopus.com)

Step 3 and 4: Publication Identification and Publication Selection An initial 200,119 events were identified using the keyword searches in each selected databases with no filter exclusions. The high number of publications events can largely be attributed to the “search in” filter criteria set to include all available data, resulting in literature hits that were poorly related to the central search topic. To acquire topic relevant publication, the database search filters were adjusted to search the abstracts of peer review and scholar journals, resulting in 730 events. The review protocol’s exclusion criteria, which includes the exclusion of duplicate entries, resulted in 123 specific events based on the search results from the keywords for inclusion in the final literature review (Appendix A). This data set had no exclusion criteria for publication date. Exhibit 3 summarizes the results of Steps 3 and 4.

Exhibit 3. Search Summary Methodology and Results.

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Step 5: Publication Quality In this step, each publication selected in the literature review is evaluated to qualitatively describe the current state of the chosen research area (i.e. Technology Forecasting Obsolescence…) (Tranfield, 2003). An adapted Gattoufi, Oral, Kumar, and Reisman (2004) framework was used to categorize the selected publication works. The framework classified the works based on their nature (application versus theory) and the research strategy type described in Exhibit 4. A combined total of 66 percent of the publication examined were categorized a Ripple or Embedding research, as illustrated in Exhibit 5. The topic field had slow to very little growth from the first relevant published article in 1967 to the mid-2000, Exhibit 6. In the mid-2000s, the knowledge growth accelerated, this author speculates that this is due to the rapid adoption and evolution of technology boom in the mid-2000s. A breakdown of publication by category is shown in Exhibit 7. The dominance of publication in the Ripple category is an indication that researchers are still expanding the knowledge of the field using existing established theory of the topic area and will be discussed more during Steps 8 and 9.

Exhibit 4. Publication Classification Categories. (Adapted from Gattoufi, Oral, Kumar, & Reisman, 2004).

1. Ripple: an extension of previous theoretical or applied type of research in a given discipline or subdiscipline.

2. Embedding: the development of a more generalized formulation or a more global theory by embedding several known models or theories.

3. Transfer of Technology: the use of what is known in one discipline to model a problem domain falling in some other, perhaps disparate, discipline.

4. Bridging: the bridging of known models or of known theories resulting in the growth of the contributing and/or some initially unrelated field of knowledge.

5. Creative Application: the direct (not by analogous) application of a known methodology to a problem or research question that was not previously so addressed.

6. Structuring: the process of organization and documentation of the organizational phenomena not previously structured axiomatically or in the form of models. (These models may represent one or more disciplines)

7. Statistical Modelling: models that arise from analyses performed on empirically obtained data. These models arise from statistical manipulations such as regression or cluster analysis rather than from logical derivations based on various assumptions.

Exhibit 5. Total Publication Events by Classification Category.

Bridging 5%

Creative Application

8%

Embedding 15%

Ripple 51%

Statistical Modeling

5%

Structuring 9%

Transfer of Technology

7%

Total Events

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Exhibit 6. Total Relevant Publication Events per Year.

Exhibit 7. Total Relevant Publication Events by Classification Category per Year.

Step 6 and 7: Data Extraction and Data Synthesis The data-extraction process requires a clear identification of steps to be taken to sort through the data (Tranfield, 2003). Typically, double extraction processes are employed, where two independent researchers analyze the data and compare their finding to reconcile any differences if needed (Tranfield, 2003). For this study, a simple word count analysis for frequently used words in the publication abstracts and titles was conducted and the following data was extracted. This eliminates any unintended bias that might be imposed by the researcher if using a manual extraction method. An online word sorter, Wordcounter (www.wordcounter.com) was used to sort for word usage frequency in the selected publication’s titles and abstracts. Wordcounter outputs the ranking of the most frequently used words in any given body of text, grouping roots words and eliminating common usage words (e.g. a, the, is, he, she, etc.…). A total of 25,431 words were sorted, and the top 10 used words and their frequency are shown in Exhibit 8.

0

2

4

6

8

10

12

14

1967 1974 1981 1988 1995 2002 2009 2016

N um

be r o

f P ub

lic at

io n

Ev en

ts

Years

Publication Events Per Year

Running Total Per Year

0

1

2

3

4

5

6

7

8

9

10

1967 1974 1981 1988 1995 2002 2009 2016

N um

be r

of P

ub lic

at io

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en ts

Years

Publication Events by Catagory per year

Bridging Creative Application Embedding Ripple

Statistical Modeling Structuring Transfer of Technology

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Exhibit 8. Publications Word Frequency Analysis. Rank Word Frequency 1 Product 160 2 New 133 3 Market 119 4 Develop 111 5 Data 103 6 Method 100 7 Cycle 99 8 Patent 95 9 Analysis 88 10 System 86

Steps 8 and 9: Conclusion and Recommendations Quantitative Assessment: The publication data from this systematic literature review indicates there is growing interest in forecasting effectiveness in determining technology obsolescence and diffusion, based on the increase of publications in this topic area over the last 12 years, as seen in Exhibit 6. The primary focus of the published literature can be classified as “Ripple” per the categorization definition used; an extension of previous theoretical or applied type of research in a given discipline or subdiscipline, indicating that the expansion of existing theories and method still has room to grow (Gattoufi, Oral, Kumar, & Reisman, 2004). The categories of Creative Application and Transfer Technology are common with the expansion of a research area paradigm, in which the topic boundaries are explored, thus for this subject area, the recent increase of these publication categories indicate the boundaries of this topic area are still in flux. Bridging often results in a change in basic assumptions and/or growth of a research area, typically occurring once previous theory expansion have been exhausted in an area, yet the topic problem area is still present. Bridging and Embedding are at total 20% of the publication studied in the paper, indicating that current theory expansion has not been exhausted. Structuring and Statistical modeling are the foundations of a will established body of knowledge, 14% of the publication fell into these categorizations, thus there is room for growth in this topics area body of knowledge. Based on the results of the word frequency data extraction and abstract summary review, product development and product market were primary themes in the literature review. Patent analysis for forecasting technology lifecycle was another reoccurring research area. Business relation topic words such a policy, manage(ment), and strategy did not fall into the top 10 word frequency. Although if combined into a single category of business management (policy, manage(ment), and strategy were ranked 29, 16, and 37 respectively) would be ranked in the top 5, thus is a major theme of this topic area. The majority of the publication focused on technology forecasting of emerging technology and the diffusion of technology. Much of the direct forecasting of technology obsolescence was directed to specific technology components (e.g. computer components). Only six publications examined directly focused on forecasting the technology obsolescence. Although technology forecasting has been around since the 1960’s, the topic area of forecasting technology obsolescence and diffusion is relatively young, showing a significant upward growth trend for the last 12 years. The following areas of future research are recommended;

• The qualification of methods for accurately forecasting the technology lifecycle. • The examination of policy on the technology lifecycle. • The examination of the causal relationship between an emerging technology and declining stages of the

technology it is replacing. Conclusion This research analysis provided a general overview of forecast method, systematically reviewed the current literature as it relates to this topic, assessing the current effectiveness of the existing research to forecast technology maturity and provided recommendations for future research.

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Appendix A: Identified Relevant Publications. Ang, B. W. and T. T. Ng (1992). "The use of growth curves in energy studies." Energy 17(1): 25-36. (1989). "Entry, Exit And Diffusion With Learning By Doing." The American Economic Review 79(4): 690. (1989). "Helping Congress Look Ahead." The Futurist 23(3): 23. Adams, F. G. (1987). "Revolution in the world of econometric business forecasting." Business Horizons 30(1): 46- 51. Adamuthe, A. C., et al. (2014). "Technology Forecasting: The Case of Cloud Computing and Sub- technologies." International Journal of Computer Applications 106(2). Altuntas, S., et al. (2015). "Forecasting technology success based on patent data." Technological Forecasting and Social Change 96: 202-214. Amara, R. (1988). "What We Have Learned About Forecasting and Planning." Futures 20(4): 385. Andersen, P. D., et al. (2007). "Managing long-term environmental aspects of wind turbines: A prospective case study." International Journal of Technology, Policy and Management 7(4): 339-354. Asha, B. (2008). "Stochastic Model for the Electronic Access of an Article." SRELS Journal of Information Management 45(4): 391-398. Bardhan[AP1] , A. K. and U. Chanda (2008). "A model for first and substitution adoption of successive generations of a product." International Journal of Modelling and Simulation 28(4): 487-494. Berg, S. V. (1973). "Determinants of technological change in the service industries." Technological Forecasting and Social Change 5(4): 407-426. Berkhout, F. (1996). "Life Cycle Assessment and Innovation in Large Firms." Business Strategy and the Environment 5(3): 145. Briciu, C. V., et al. (2016). Methods for cost estimation in software project management. IOP Conference Series: Materials Science and Engineering. Caldwell, B., et al. (2005). "Forecasting multiple generations of technolo evolution: challenges and possible solutions." International Journal of Technology Intelligence and Planning 1(2): 131-149. Cavaller, V. (2009). "Scientometrics and patent bibliometrics in RUL analysis: A new approach to valuation of intangible assets." VINE 39(1): 80-91. Cetron, M. J. and O. Davies (2005). "Trends Now Shaping the Future." The Futurist 39(3): 37-50. Chanda, U. and A. K. Bardhan (2008). "Modelling innovation and imitation sales of products with multiple technological generations." Journal of High Technology Management Research 18(2): 173. Chanda[AP8] , U. and R. Aggarwal (2014). "Optimal inventory policies for successive generations of a high technology product." Journal of High Technology Management Research 25(2): 148-162. Chang, P. C. and Y. K. Lin (2010). "New challenges and opportunities in flexible and robust supply chain forecasting systems." International Journal of Production Economics 128(2): 453-456. Chang, S. H. and C. Y. Fan (2015). Telematics technology development forecasting: The patent analysis and technology life cycle perspective. Lecture Notes in Electrical Engineering. 349: 149-158. Chang, S.-B., et al. (2009). "Exploring technology diffusion and classification of business methods: Using the patent citation network." Technological Forecasting and Social Change 76(1): 107. Chen, Y. H., et al. (2011). "Technology forecasting and patent strategy of hydrogen energy and fuel cell technologies." International Journal of Hydrogen Energy 36(12): 6957-6969. Cheng, A. C. and C. Y. Chen (2008). "The technology forecasting of new materials: The example of nanosized ceramic powders." Romanian Journal of Economic Forecasting 9(4): 88-110. Cheng, Y., et al. (2017). "Forecasting of potential impacts of disruptive technology in promising technological areas: Elaborating the SIRS epidemic model in RFID technology." Technological Forecasting and Social Change 117: 170-183. Chien, C.-F., et al. (2010). "Manufacturing intelligence for semiconductor demand forecast based on technology diffusion and product life cycle." International Journal of Production Economics 128(2): 496-509. Cho, Y. and T. Daim (2016). "OLED TV technology forecasting using technology mining and the Fisher-Pry diffusion model." Foresight 18(2): 117-137. Christodoulos, C., et al. (2010). "Forecasting with limited data: Combining ARIMA and diffusion models." Technological Forecasting and Social Change 77(4): 558-565. Christodoulos, C., et al. (2015). "On the Efficiency of Grey Modeling in Early-Stage Technological Diffusion Forecasting." International Journal of Technology Diffusion 6(2): 1-11. Committee on Forecasting Future Disruptive, T., et al. (2010). Persistent forecasting of disruptive technologies - report 2.

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Cunningham, S. W. and J. H. Kwakkel (2014). "Tipping points in science: A catastrophe model of scientific change." Journal of Engineering and Technology Management - JET-M 32: 185-205. Daim, T. and P. Suntharasaj (2009). "Technology diffusion: Forecasting with bibliometric analysis and Bass model." Foresight 11(3): 45-55. Daim, T. U., et al. (2006). "Forecasting emerging technologies: Use of bibliometrics and patent analysis." Technological Forecasting and Social Change 73(8): 981-1012. Daim, T. U., et al. (2008). "Forecasting the future of data storage: case of hard disk drive and flash memory." Foresight 10(5): 34-49. Daim, T. U., et al. (2011). "Technology forecasting for residential energy management devices." Foresight 13(6): 70-87. Dardan, S. L., et al. (2007). "The diffusion of customer-related IT among large and mid-sized companies." Information Resources Management Journal 20(4): 12-24. De Jouvenel, H. (2000). "A Brief Methodological Guide to Scenario Building." Technological Forecasting and Social Change 65(1): 37-48. Decanio, S. J. and A. J. Laitner (1997). "Modeling Technological Change in Energy Demand Forecasting: A Generalized Approach." Technological Forecasting and Social Change 55(3): 249-263. Decker, R. and K. Gnibba-Yukawa (2010). "Sales forecasting in high-technology markets: A utility-based approach." Journal of Product Innovation Management 27(1): 115-129. Dell'era, C. and R. Verganti (2011). "Diffusion processes of product meanings in design-intensive industries: Determinants and dynamics." Journal of Product Innovation Management 28(6): 881-895. Devezas, T. C. (2005). "Evolutionary theory of technological change: State-of-the-art and new approaches." Technological Forecasting and Social Change 72(9): 1137-1152. Devezas, T. C. (2005). "Evolutionary theory of technological change: State-of-the-art and new approaches." Technological Forecasting and Social Change 72(9): 1137-1152. Dobrov, G. (1979). "A Strategy for Organized Technology." Technological Forecasting and Social Change 13(3): 257-271. Domicio Da Silva Souza, I., et al. (2012). "A patent survey case: how could technological forecasting help cosmetic chemists with product innovation?" Journal of cosmetic science 63(6): 365-383. Ernst, H. (1997). "The Use of Patent Data for Technological Forecasting: The Diffusion of CNC-Technology in the Machine Tool Industry." Small Business Economics 9(4): 361-381. Etheridge, W. S. (1979). "DEMAND FOR METALS." Wear: 269-289. Featherston, C. R. and E. O'Sullivan (2017). "Enabling technologies, lifecycle transitions, and industrial systems in technology foresight: Insights from advanced materials FM." Technological Forecasting and Social Change 115: 261-273,275-277. Fouquet, R. and P. J. G. Pearson (2006). "Seven centuries of energy services: The price and use of light in the United Kingdom (1300-2000)." Energy Journal 27(1): 139-177. Fujimaki, R., et al. (2016). "From prediction to decision making - Predictive optimization technology." NEC Technical Journal 11(1): 62-65. Fulford, D. S., et al. (2016). "Machine learning as a reliable technology for evaluating time/rate performance of unconventional wells." SPE Economics and Management 8(1): 23-39. Gottinger, H. W. (1987). "ECONOMETRIC ESTIMATION OF A TECHNOLOGY DIFFUSION MODEL - PART 2." International Journal of Technology Management 2(1): 101-118. Gottinger, H. W. (1989). "A Strategic Management Decision Support Tool for Technology Management." International Journal of Technology Management 4(2): 141. Henselewski, M., et al. (2006). Evaluation of knowledge management technologies for the support of technology forecasting. Proceedings of the Annual Hawaii International Conference on System Sciences. Horta-Bernús, R. and M. Rosas-Casals (2015). "Obsolescence in Urban Energy Infrastructures: The Influence of Scaling Laws on Consumption Forecasting." Journal of Urban Technology 22(2): 3-17. Hsin-Ning, S., et al. (2010). "Foresight on Taiwan nanotechnology industry in 2020." Foresight : the Journal of Futures Studies, Strategic Thinking and Policy 12(5): 58-79. Hsu, C.-Y. (2014). "Integrated data envelopment analysis and neural network model for forecasting performance of wafer fabrication operations." Journal of Intelligent Manufacturing 25(5): 945-960. Hsu, L.-C. and C.-H. Wang (2007). "Forecasting the output of integrated circuit industry using a grey model improved by the Bayesian analysis." Technological Forecasting and Social Change 74(6): 843-853. Huang, C.-Y. and G.-H. Tzeng (2008). "Multiple generation product life cycle predictions using a novel two-stage fuzzy piecewise regression analysis method." Technological Forecasting and Social Change 75(1): 12.

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