PurposeIn this paper, an analysis is presented of the research funding towards nanotechnology at the National Nanotechnology Initiative (NNI) and its relationship to the research output in Nanoscope, an application area of nanotechnology.Design/methodology/approachThe paper analyzes the data collected from 1997 till 2006 and derives a definitive time lag between the allocation of research funds and issued patents and published journals. This assessment is achieved by identifying growth trends in patents, funds and publications and doing a curve‐fit analysis using the Fisher‐Pry model. Linear regression analysis is used to show the correlation between the funding and research outputs. Alongside, non‐linear programming objective function optimization technique is used to derive the time lag in years for each of the research outputs from the year of funds granted.FindingsThis paper demonstrated that there is a strong correlation between research funding and different research outputs. The time lag between funding and patents issued is evident from the patent trend analysis and Bibliometric analysis. In the case of Nanoscope, the patent time lag was found to be approximately five to six years, for journal article it was approximately two to three years and conference presentations happened right after the funding. The research outputs showed similar trends and were found to be interdependent as evident from our mathematical analysis.Research limitations/implicationsWhile this study has shown that lag times exist within the chosen example of Nanoscope, and furthermore can be calculated to a precise degree, further data points in terms of additional emerging technologies would support the hypothesis in a more general term. A future study can look at developing technology roadmaps of the future based on the funding happening today.Originality/valueThe work takes bibliometric analysis to a further intelligence and establishes key linkages between these indicators.
Purpose
This paper aims to extend the known boundary conditions of the negative binomial distribution (NBD) model, and to test the applicability of conditional trend analysis (CTA) – a key method to identify whether changes in overall sales are accounted for by previous non-buyers, light buyers or heavy buyers – in industrial purchasing situations.
Design/methodology/approach
The study tested the NBD model and CTA in an industrial marketing context using a 12-month data set of purchases from an Australian supplier of a range of industrial plastic resins.
Findings
The purchase data displayed a good NBD fit; the study therefore extends the known boundary conditions of the model. The application of CTA provided second-period purchasing frequency estimates showing no significant difference from actual data, indicating the applicability of this method to industrial purchasing.
Research limitations/implications
Data relate to just one supplier. Further research across several industries is required to confirm the generalizability and robustness of NBD and CTA to industrial markets.
Practical implications
Marketing decisions can be improved through appropriate analysis of customer purchasing data. However, without access to equivalent competitor data, industrial marketers are constrained in benchmarking the purchasing patterns of their own customers. The results indicate that use of the NBD model enables valid benchmarking for industrial products, while CTA would enable appropriate analysis of purchases by different classes of customer.
Originality/value
This paper extends the known boundary conditions of the NBD model and provides the first published results, indicating the appropriateness of CTA to predict purchasing frequencies of different industrial customer classes.
Interactive narratives are inextricable from the way that we understand our encounters with digital technology. This is based upon the way that these encounters are processually formed into a narrative of episodic events, arranged and re-arranged by various levels of agency. After describing past research conducted at the iCinema Research Centre at the University of New South Wales, this paper sets out a framework within which to build a relational theory of interactive narrative formation, outlining future research in the area.
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