Patent indicators for monitoring convergence - examples from NFF and ICT

The blurring of boundaries between hitherto distinct scientific disciplines, technologies or markets is a common and powerful phenomenon. Concentrating on monitoring convergence through patent indicators, we discuss convergence with examples from the area of Nutraceuticals and Functional Foods (NFF)/Cosmeceuticals as well as information technologies, consumer electronics, and telecommunications (ICT). We analyze 7455 scientific and patent references on phytosterols with the aid of SciFinder Scholar and 3836 documents employing STN AnaVist. Furthermore, we test an IPC co-classification based approach on 859,469 ICT-related and 341,846 NFF-related patents. Our results show clear indications for convergence and a proof of principle for our monitoring concept. Academics may better evaluate environmental parameters, like cases of convergence, influencing companies' actions. Implications for practitioners are based on a more reliable assessment prior to the forming of strategic alliances or mergers and acquisitions. This could help avoid costly adventures such as the mergers and acquisitions seen in ICT.     

The interaction effect of strategic orientations on new product performance in the high-tech industry: A nonlinear model

Innovation and market orientation are two strategic orientations or business philosophies that can guide a company in its business activities. Although the interaction effect of these two strategic orientations is conceptually recognized as a critical factor for new product's success, empirical results are mixed. This paper examines this issue in terms of innovation orientation, two types of market orientations (responsive and proactive), and new product performance. Based on a sample of 107 high-tech firms, the results of this study show that the interaction between innovation orientation and two types of market orientation yields different patterns of nonlinear responses for new product performance. Specifically, new product performance, when derived from the interaction between innovation orientation and responsive market orientation, is in the form of an inverted U, i.e., the interaction effect is contributory to firm performance until an optimal level is reached, and then the effect becomes detrimental thereafter. However, new product performance derived from innovation orientation and proactive market orientation is in the form of a U, i.e., the interaction effect is detrimental to firm performance until a threshold level is reached and then the effect becomes contributory thereafter. The different patterns of new product performance indicate that the costs and organizational learning effects of these two strategic orientation interactions are dissimilar. The paper concludes with a discussion of theoretical and practical implications of these findings.     

Suspending disbelief: Influencing engagement in scenarios of forest futures

Scenario analysis is an approach to long-term planning that informs decision-making in contexts of highly uncertain future conditions. Scenario-based studies are rapidly growing in popularity, yet many aspects of the method are not fully understood. Participants' willingness to suspend disbelief in possible futures is an integral component of scenario-based studies, essential for considering alternative future scenarios, yet little is known about the factors that affect it. Participation in a scenario project does not necessarily imply willingness or ability to suspend disbelief; participants may not suspend disbelief simply upon request. However, the scenario literature says little about the aspects of scenario-based studies that influence suspension of disbelief.This article explores the factors that influenced the suspension of disbelief in one scenario exercise about the future of Canada's forests. Participants differed in their abilities to suspend disbelief in the scenarios in part because of their past experiences and expertise in particular areas, and their assessments of probable and desired future outcomes. Elements of the project design and implementation influenced participants' abilities to engage the scenarios. The results have implications for those interested in using scenarios for helping people and communities envision possible - and sustainable - futures.     

Managing TFT-LCDs under uncertainty: When crystal cycles meet business cycles

Understanding the correlation between the crystal cycle and the business cycle is important, because it can help managers to anticipate change, reduce environmental uncertainty, and formulate operational objectives. To this end, we focused on China and the U.S. in our analysis. We found that the economic indicators that were the most relevant in the characterization of China's huge and burgeoning TFT-LCD market are gross domestic production and industrial production. We complemented this finding by conducting similar analyses in the U.S. market using a more comprehensive list of economic indicators.     

The state of research on technological uncertainties, social uncertainties and emerging markets: A multidisciplinary literature review

The objective of this research work is to study the progress of research on technological uncertainties, social uncertainties and emerging markets and outline and identify the key disciplines, journals, articles and authors. For this the author studied the existing literature from the various fields in which technological uncertainties, social uncertainties and emerging markets research work have been published using ISI Web of Knowledge database. The paper finds that there is increasing research work on technological uncertainties, social uncertainties and emerging markets and the bibliographical search resulted in ninety-one documents written by one-hundred-sixty-one authors in eighty-four journals in seventy-two disciplines. The five major disciplines and their underlying journals are business and economics, agriculture, psychology, public administration, and environmental sciences and ecology accounting for majority of publications. In journals the most prolific, measured by number of articles published are Harvard Business Review, Social Science and Medicine, World Development, and Higher Education; and most influential, measured by the global citation received, are Harvard Business Review, Social Science and Medicine and Sociological Review. The top 10% of the journals are responsible for 23% of all publications but 85% of all global citations received. This highlights that despite the high, diverse and increasing number of journals; only few are dominating and shaping the research arena of technological uncertainties, social uncertainties and emerging markets. Further, in the ten most cited articles, no author appears more than once.     

Forecasting of electricity costs based on an enhanced gray-based learning model: A case study of renewable energy in Taiwan

This work presents a novel gray-based cost efficiency (GCE) model that integrates the gray forecasting model into a two-factor cost efficiency curve model for renewable energy (RE) technologies and identifies the optimal forecasting model for power generation cost of RE technologies. The analytical framework of proposed GCE model improves short-term prediction of power generation cost, and can be applied during the early developmental stages for RE technologies. Empirical analysis is based on wind power data for Taiwan. Time lag of knowledge stock was simulated to represent the actual relationship between R&D expenditures and cost reductions in power generation by knowledge stock. Analytical results demonstrate the GCE model is a useful tool to quantify the influences of cost reductions in power generation. The implications of analytical results are that institutional policy instruments play an important role in RE technologies achieving cost reductions and market adoption. The proposed GCE model can be applied to all high-technology cases, and particularly to RE technologies. The study concludes by outlining the limitations of the proposed GCE model and directions for further research.     

A grey decision and prediction model for investment in the core competitiveness of product development

Because enterprises do not disclose their internal specific cost information to the public and, moreover, every firm has its own product character and financial constitutions, it is difficult to offer fixed guidelines for investment decisions. Thus, an enterprise may be uncertain when required to choose the most promising set of possible investments.The goal of this research is to use a grey relation analysis and analytic hierarchy process (AHP) to probe the core product development and competitiveness of an enterprise from limited data and, furthermore, by constructing the grey model, GM(1,1), to validate the feasibility of this assessment of the core competitiveness and investment strategy. In this study, a precision mechanical manufacturer is taken as an example, and the forecast estimate from this method is compared to those of a linear regression and the actual values to demonstrate the feasibility of applying this methodology to investment decision making, hence, demonstrating the value of this research.     

A review of experience curve analyses for energy demand technologies

Transitioning towards a sustainable energy system requires the large-scale introduction of novel energy demand and supply technologies. Such novel technologies are often expensive at the point of their market introduction but eventually become cheaper due to technological learning. In order to quantify potentials for price and cost decline, the experience curve approach has been extensively applied to renewable and non-renewable energy supply technologies. However, its application to energy demand technologies is far less frequent. Here, we provide the first comprehensive review of experience curve analyses for energy demand technologies. We find a widespread trend towards declining prices and costs at an average learning rate of 18 ± 9%. This finding is consistent with the results for energy supply technologies and for manufacturing in general. Learning rates for individual energy demand technologies are symmetrically distributed around the arithmetic mean of the data sample. Absolute variation of learning rates within individual technology clusters of 7 ± 4%-points and between technology clusters of 7 ± 5%-points both contribute to the overall variability of learning rates. Our results show that technological learning is as important for energy demand technologies as it is for energy supply technologies. Applying the experience curve approach to forecast technology costs involves, however, unresolved uncertainties, as we demonstrate in a case study for the micro-cogeneration technology.