Quantitative mapping of scientific research—The case of electrical conducting polymer nanocomposite

This study aims to understand knowledge structure both quantitatively and visually by integrating keyword analysis and social network analysis of scientific papers. The methodology proposed in this study is capable of creating a three-dimensional “Research focus parallelship network” and a “Keyword Co-occurrence Network”, together with a two-dimensional knowledge map. The network and knowledge map can be depicted differently by choosing different information for the network actor, i.e. country, institute, paper and keyword, to reflect knowledge structures from macro, to meso, to micro-levels. A total of 223 highly cited papers published by 142 institutes and 26 countries are analyzed in this study. China and the US are the two countries located at the core of knowledge structure and China is ranked no. 1. This quantitative exploration provides a way to unveil important or emerging components in scientific development and also to visualize knowledge; thus an objective evaluation of scientific research is possible for quantitative technology management.     

Different approaches of pattern management and strategic intelligence

The world around us contains endless amounts of information. That information is mostly loose in our minds. Very often, it does not at first contact fit in with our conventional understanding, experience or any context we are used to. Hence, we may say that we are overwhelmed with constantly changing raw data, and strategic actors especially tend to be short of more rapid, up-to-date, valid and in-depth understanding of the transforming business landscape and social environment. Strategic intelligence is an emerging field of business consulting, which aims to undertake the task of revealing large, complex or complicated issues of transformation in a more understandable form. Pattern management, however, can be seen as one field or one approach of strategic intelligence. It is an approach that may, on one hand, be based more on empiric data and formal structures than other forms of strategic intelligence, but, on the other hand, it is a very heuristic approach to integrate quantitative data, reasoning and narratives. The main attempts of this article are, first, to show, what are in general the most commonly used ways of managing, finding, drawing, reasoning or anticipating patterns from our environment, and second, to locate how the concept of pattern can be understood in different ways. From the gathered knowledge, this article presents three main categories of reasoning patterns: empirical calculation (EC) is common especially in enterprise consulting. Theory proving with observations (TPO) is common especially in natural sciences, and real combining (RC) is common especially in qualitative research and in narrative.     

Bibliometric analysis of distributed generation

This paper presents an application of term frequency (TF) as a means of identifying useful trends from text documents. Of particular interest is the relationship between publication patterns, as characterized by TF, and the underlying technological developments. To demonstrate the usefulness of our approach, a case study on distributed generation (DG) was conducted. Important sub-domains of DG research were identified and the associated TF values were extracted using relevant keywords. The evolution of these values through time helps to highlight key trends in the development of DG-related technologies.     

Decreasing energy use in buildings by 50% by 2050 — A backcasting study using stakeholder groups

This paper describes a backcasting study focusing on fulfilment of a national target to decrease energy consumption in residential and commercial buildings by 50% by 2050 compared with the consumption in 1995, and identifying possible measures for achieving it. A method based on a combination of backcasting methodology and focus group methodology was used. Two different scenarios were developed. They were used in discussions with stakeholders in the building sector, to explore and identify measures and actors important for target fulfillment. The main outcomes were ideas for strategies and measures needed to achieve the target. The current potential for target fulfilment was also analysed and discussed. The discussions in the different stakeholder groups were mainly concerned with changes in attitude and behaviour and the need for radical changes in social structures. For example, enhanced communication between actors in the building chain, and the need for relevant feedback in order to illustrate the link between the effort in decreasing energy use and actual outcome. The findings suggest that there is sufficient technical potential to achieve the target by 2050 but that this potential will not be realised to a sufficient extent. Achieving the target would be facilitated by policy that is oriented more towards identifying actors with direct influence to promote change. An analysis of incentives for these actors to act, and how different actors can cooperate for energy-efficient solutions should be integrated into the process of suggesting and implementing policy measures.     

Patent and publishing activity sequence over a technology's life cycle

The use of multiple indicators in analyzing technological developments and exploiting the increasing availability of information has been recommended widely in order to decrease systematic biases between single measures. One of the few frameworks that take multiple sources into account is the Technology Life Cycle indicators that provide a measure for the totality of sources available for analysis and take their timeliness into account, although the linear model that the framework represents is often questioned. The aim of this paper is to provide bibliometric studies with insight into the timely relevance of using different databases. To assess the reporting sequence between different databases, this paper measures the reporting activity of three case technologies in different databases and analyzes the yearly reporting volumes of a number of items that mention the technology in the databases as suggested by the TLC indicators. The results of this paper suggest that, when science is the source of new ideas and the driver for technological development and innovations, communication can happen in the order suggested by the TLC indicators. However, this model does not seem to be a general model for detecting and forecasting a technological life cycle. In addition, the results of the paper point to the possibility of studying non-linear models of innovation and contexts where this type of dynamics might take place.     

Detecting emerging research fronts in regenerative medicine by the citation network analysis of scientific publications

In this paper, we detect emerging research fronts in a huge number of academic papers related to regenerative medicine, a field of radically innovative research. We divide citation networks into clusters using the topological clustering method, track the positions of papers in each cluster, and visualize citation networks with characteristic terms for each cluster. Analyzing the clustering results with the average published year and parent-child relationship of each cluster could be helpful in detecting recent trends. In addition, tracking topological measures, within-cluster degree z and participation coefficient P, enables us to determine whether there are emerging knowledge clusters. Our results show the success of our method in detecting emerging research fronts in regenerative medicine, and these results are confirmed as reasonable by experts. Finally, we predict the future core papers, with the potential of many citations, via the betweenness centralities in the citation network of the research into adult and somatic stem cells.     

Contagion effects of national innovative capacity: Comparing structural equivalence and cohesion models

The effective promotion of national innovation performance is a crucial component of national innovation policy. This study examines network contagion effects of national innovative capacity via the international diffusion of embodied and disembodied technology by two different social network models: the cohesion model, based on diffusion by direct communication, and the structural equivalence model, based on diffusion by network position similarity. This investigation then utilizes data of 42 countries during 1997 to 2002 to empirically examine their network relationship. The analytical results demonstrate that international technology diffusion influences national innovation performance through contagion effects, but that the international similarity of national innovative capacity performance is more accurately predicted by network position than by interactions with others; and this study result provides a new perspective for science and technology policy makers.     

How to motivate people to put their money where their mouth is: What makes employees participate in electronic prediction markets?

Although prediction markets are widely acknowledged to be as effective as other institutions predicting future events, little is known regarding their application in organizational settings. For organizations, prediction markets offer a new technical possibility to make use of the beliefs, information and knowledge of their employees for organizational decisions and forecasts. Based on survey data of 147 users in German-speaking Europe, we show that expected rewards increase user satisfaction. While contribution effort and general reciprocity in isolation have no impact on satisfaction, perceived general reciprocity in conjunction with high rewards is likely to increase user satisfaction. Our findings extend our knowledge on user behavior and emphasize the importance of incentives in prediction markets.     

Technology clustering based on evolutionary patterns: The case of information and communications technologies

Technology trend analysis anticipates the direction and rate of technology changes, and thus supports strategic decision-making for innovation. As technological convergence and diversification are regarded as emerging trends, it is important to compare the growth patterns of various technologies in a particular industry to help understand the industry characteristics and analyse the technology innovation process. However, despite the potential value of this approach, conventional approaches have focused on individual technologies and paid little attention to synthesising and comparing multiple technologies. We therefore propose a new approach for clustering technologies based on their growth patterns. After technologies with similar patterns are identified, the underlying factors that lead to the patterns can be analysed. For that purpose, we analysed patent data using a Hidden Markov model, followed by clustering analysis, and tested the validity of the proposed approach by applying it to the ICT industry. Our approach provides insights into the basic nature of technologies in an industry, and facilitates the analysis and forecasting of their evolution.