Search results

Airplane technology is undergoing several exciting developments, particularly in avionics, material composites, and design tool capabilities, and, though there are many studies conducted on subsets of airplane technology, market, and economic parameters, few exist in forecasting new commercial aircraft model introduction. In fact, existing research indicates the difficulty in quantitatively forecasting commercial airplanes due in part to the complexity and quantity of exogenous factors which feed into commercial airplane introduction decisions. This paper seeks to address this gap.

The analysis is based on a literature review, supplemented by a collection of secondary data. The study then focuses on applying three technology forecasting techniques: multiple regression; linear regression; and the Pearl growth curve.

The results provide a valid model for multiple regression and linear regression on range and composite material percentage for use in commercial airplane forecasting. However, growth curve analysis, comparatively, appears to provide the most intriguing and flexible forecast outlook in alignment with industry dynamics.

Research implications include a caution for forecasters in support of the difficulty of commercial aircraft forecasting due in part to the quantity of exogenous factors, particularly compared with a related industry, military aircraft. Future work could include: utilizing other forecasting techniques that allow for greater numbers of forecast factors, additional future models, additional range aircraft and/or analyzing the impact that competing transportation modes in mid‐range aircraft could have on long‐range aircraft introduction.

The study provides value in extending a previous descriptive paper on airplane parameters. Additionally, it appears to be one of the first quantitative examples supporting previous research indicating the complexity of forecasting airplane new product introduction, but it overcomes some of this complexity by providing a valid model for forecasting with range and composite material percentage as inputs.

Information and communication technology (ICT) is increasingly being used in the home environment, making it a very important and interesting research topic for communication scientists. Future developments will influence the way and the extent to which ICT will be used in the home environment and therefore the way people look for information, communicate, make use of entertainment services and carry out transactions. However, it is still very difficult to make meaningful and accurate forecasts with regard to the possible future use and acceptance of ICT in people’s homes. Important reasons are, for example, that more and more market parties are involved in the development of innovative ICT products and services. This makes developments more complex and the outcomes more uncertain. Furthermore, consumers play an important role in the development of new ICT‐based information, communication, transaction and entertainment services. Since a precise prediction of the possible use of ICT in domestic environments in 2010 is hard to make, other methods of futures research must be used. Combining technological forecasting with scenario thinking is such a research method, whereon, technological forecasting shows the major trends in the specific technology domain, while scenarios cover the possible future worlds. By giving end‐users a central place in these scenarios, the diversity of the use and acceptance of innovative products and services is captured. Thus, the addition of scenarios to the technology trends gives insight into the possibilities (and impossibilities) of new ICT‐technologies and the way they may be used in the home environment.

The purpose of this paper is to propose an approach to achieve better accuracy in technology forecasting (TF) by providing the concepts of the service components and service composition based on the theory of the combining forecasts. Next, it adopts three quantitative analyses as service components to form service composition. This will support the need of more predictable TF, which raises the accuracy of the quantitative analysis and, at the same time, presents the service composition logic in a consistent manner in the form of customized TF.

This paper provides a systematic analysis of the technology forecasts for third‐generation (3G) telecommunication industry. This systematic approach mainly unifies the Bass model, logit model, and least squares analysis forecasting techniques, along with a reasonable assessment of the scope for the normal curve (±1 standard deviation), and attempts to find the maximum possibility frontier of the predictive value.

Through the integration and comparison of these three techniques, not only can the predicted values of the three forecasting methods be determined, but a preferred solution can also be derived through new methods, and in return, to investigate better accuracy and performances. Such an approach can also integrate the advantages of various methods to provide a prediction interval, as well as objective and realistic projections.

This envisaged concept of “service component and service composition” is an integration of backing up in TF instruments in selection and reselection, which in return, provide optimization of service composition and accuracy maximization, as well as better performance prediction. A well‐known limitation of this research is that sudden technology breakthroughs are often unforeseeable in the majority of main‐stream quantitative analyses.

Constructing a new effective approach as results of “service component and service composition” can be compared to the traditional research methods such as Delphi method or other mathematical algorithms. This method generally produces higher quality forecasts than those attained from a single source.

The purpose of this paper is to propose an objective method for technology forecasting (TF). For the construction of the proposed model, the paper aims to consider new approaches to patent mapping and clustering. In addition, the paper aims to introduce a matrix map and K‐medoids clustering based on support vector clustering (KM‐SVC) for vacant TF.

TF is an important research and development (R&D) policy issue for both companies and government. Vacant TF is one of the key technological planning methods for improving the competitive power of firms and governments. In general, a forecasting process is facilitated subjectively based on the researcher's knowledge, resulting in unstable TF performance. In this paper, the authors forecast the vacant technology areas in a given technology field by analyzing patent documents and employing the proposed matrix map and KM‐SVC to forecast vacant technology areas in the management of technology (MOT).

The paper examines the vacant technology areas for MOT patent documents from the USA, Europe, and China by comparing these countries in terms of technology trends in MOT and identifying the vacant technology areas by country. The matrix map provides broad vacant technology areas, whereas KM‐SVC provides more specific vacant technology areas. Thus, the paper identifies the vacant technology areas of a given technology field by using the results for both the matrix map and KM‐SVC.

The authors use patent documents as objective data to develop a model for vacant TF. The paper attempts to objectively forecast the vacant technology areas in a given technology field. To verify the performance of the matrix map and KM‐SVC, the authors conduct an experiment using patent documents related to MOT (the given technology field in this paper). The results suggest that the proposed forecasting model can be applied to diverse technology fields, including R&D management, technology marketing, and intellectual property management.

Most TF models are based on qualitative and subjective methods such as Delphi. That is, there are few objective models. In this regard, this paper proposes a quantitative and objective TF model that employs patent documents as objective data and a matrix map and KM‐SVC as quantitative methods.

Technology forecasting (TF) and assessment (TA), all in all, apply to any intentional and deliberate endeavours to forecast and view the potential heading, rate, attributes and impacts of technological change, especially for development, advancement, selection and utilisation of resources, which ultimately helps in the benchmarking. A vast variety of methods are available for TF and TA. Till now, practically, no exertion has been made to choose proper, satisfactory innovation methods or technology. The paper aims to discuss this issue.

In this paper, there is an endeavour to summarise the vast field of TF and TA, through its evolution, functions, applications and techniques. This paper provides the in-depth review of the utilisation of TF and TA methodologies and its improvement, which helps the users in selecting the appropriate method of TF and TA for a specific situation.

This study concludes that the quest for a single strategy for doing forecast and assessment is a misconception. This neglects to perceive that forecast and assessment oblige a suitable blend of strategies and methods drawn from a variety of fields. Researchers and practitioners must be innovative, imperative and specialised in choosing TF and TA methodologies, and cannot be programmed.

The technology seems to be the most significant driver of the present day global developments. Some technologies have far-reaching implications, and the authors need to understand these issues regarding its’ forecasting and its assessment.

The decision of proper worthy procedure amid a circumstance may have an impact on the exactness and reliability of the forecast and assessment. Significant observations regarding learning, action/s, actor/s and expected outcomes are discussed.

This paper aims to forecast the future of data storage technologies, using the case of two major technologies driving the data storage world; hard disk drive (HDD) or conventional longitudinal recording and flash memory.

Four principal tools of forecasting technology are applied to present the coming future of data storage technologies. These tools consist of bibliometric trend analysis, patent trend analysis, technology cycle time (TCT), and growth curve. Numbers of publications each year and cumulative patents are used to analyze the future of these competitive technologies. The median age of the patents is applied to find the technology lifecycle of both technologies. Finally, areal density of HDD is plotted on the growth curve to forecast the saturation point of researching on conventional longitudinal recording.

The results produced from utilizing these tools indicate that these two technologies are continuously under development. It is found that the growth of improving areal density of conventional recording will be nearing zero by year 2013 and the maximum growth rate of development was in year 2006.

The forecasting is based on principal technologies of conventional longitudinal recording of HDD. Also, NOR and NAND are considered as two main technologies dominating flash memory. Other emerging data storage technologies such as holographic data storage, phase‐change memory (PCM) are not included in this paper. Likewise, the growth curve of flash memory is not analyzed as part of this paper.

This paper forecasts the future of data storage technologies. Integration of scientific indicators and growth curves is demonstrating a powerful tool for forecasting technology futures.

The purpose of this paper is to develop an approach that can detect abnormal deviations in the time series models for technology forecasting. The detected modifications provide a basis for understanding the determinants and impact of the corresponding change.

The proposed approach is based on monitoring residual values (the difference between the observation and the forecasted value) continuously using statistical control charts (SCCs). The residuals that are out of the expected limits are considered an alert indicating a remarkable change. To demonstrate the use of the proposed approach, a time series model was fitted to a number of TV-related patent counts. Subsequently, model residuals were used to determine the limits of the SCCs.

A number of patents granted in the year 2012 violated the upper control limit. A further analysis has shown that there is a linkage between the abnormal patent counts and the emergence of LCD TVs.

Change in technology may dramatically affect the accuracy of a forecasting model. The need for a parameter update indicates a significant change (emergence or death of a technology) in the technological environment. This may lead to the revision of managerial actions in R&D plans and investment decisions.

The proposed methodology brings a novel approach for abnormal data detection and provides a basis for understanding the determinants and impact of the corresponding change.

Due to rapid technological evolution driven by display manufacturers, the television (TV) market of flat panel displays has been fast growing with the advancement of digital technologies in broadcasting service. Recently, organic light-emitting diode (OLED) successfully penetrated into the large-size TV market, catching up with light-emitting diode (LED)-liquid-crystal display (LCD). This paper aims to investigate the market penetration of OLED technologies by determining their technology adoption rates based on a diffusion model.

Through the rapid evolution of information and communication technology, as well as a flood of data from diverse sources such as research awards, journals, patents, business press, newspaper and Internet social media, data mining, text mining, tech mining and database tomography have become practical techniques for assisting the forecaster to identify early signs of technological change. The information extracted from a variety of sources can be used in a technology diffusion model, such as Fisher-Pry where emerging technologies supplant older ones. This paper uses a comparison-based prediction method to forecast the adoption and diffusion of next-generation OLED technologies by mining journal and patent databases.

In recent years, there has been a drastic reduction of patents related to LCD technologies, which suggests that next-generation OLED technology is penetrating the TV market. A strong industry adoption for OLED has been found. A high level of maturity is expected by 2026.

For OLED technologies that are closely tied to industrial applications such as electronic display devices, it may be better to use more industry-oriented data mining, such as patents, market data, trade shows, number of companies or startups, etc. The Fisher-Pry model does not address the level of sales for each technology. Therefore, the comparison between the Bass model and the Fisher-Pry model would be useful to investigate the market trends of OLED TVs further. Another step for forecasting could include using industry experts and a Delphi model for forecasting (and further validation).

Fisher-Pry growth curves for journal publications and patents follow the expected sequence. Specially, journal publications and patents growth curves are close for OLED technologies, indicating a strong industry adoption.

Once a policy proposed by the European Commission is approved by European Parliament or Council, its implementation strategy is the responsibility of the member states. Often, there will be several parallel strategies shaped by a series of incentives financed by the government and naturally, the aim is to choose the most cost effective one. For strategy and planning as well as budgeting purposes, forecasts of the adoption rate of these policy implementation strategies will be an indicator as to their effectiveness. A new hybrid approach combining structured analogies and econometric modelling is proposed for producing such forecasts.

With every different policy, there will be different qualitative and quantitative data available for producing such implementation strategy adoption rate forecasts. Hence, the proposed hybrid approach, which combines the strengths and reduces the weaknesses of each of its constituents, can be adjusted to match the quantity and nature of the available data.

This paper reveals a lack of emphasis on such a forecasting application in the existing literature, while stressing its importance to governmental decision makers. What is more, the paper reveals a lack of documentation of this forecasting process in large governmental structures.

If shown to improve the ability to produce such forecasts, the proposed approach could be very beneficial to decision makers when faced with several possible implementation strategies.

The use of expertise is quite common in forecasting policy impact but in an unstructured way. The advanced model proposes structuring the use of analogies in an objective manner. Furthermore, combining with econometric modelling, the incorporation of valuable quantitative information is made possible.

This paper aims to present a forecast for renewable energy production in the USA. Growth curves are used to conduct the forecasts.

The analysis is based upon a literature review, supplemented by collection of secondary data. The study then focuses on applying the Pearl growth curve.

The authors' results show that biomass energy production is growing the fastest followed by geothermal and wind. Additionally, the forecast for solar energy production shows little to no growth over the next two decades.

If the US government hopes to achieve its goals in renewable energy, considerable funding and incentives will have to be put forth to accelerate the growth of renewable energy. Since the biomass technology is already growing nicely it makes sense to put the additional resources behind the other three technologies to close the 10.3 percent gap being forecasted. The government also needs to put more funding into dual renewable plants such as wind or solar combines with pumped hydro, this will ensure environmental and reliability are both maintained. Finally, for renewable energies to be competitive in the long term, considerable research needs to go into driving down the cost so there is not a need for subsidies.

This study provides value in providing a forecast for expected future growth for renewable energy sources.