**4.1. Definition of improved PPCI**

I tried to include broad feature values of papers which might affect their citedness from patents as widely as possible to grasp characteristics of patent-paper citations comprehensively. Six feature values (document type, international co-authorship, impact factor (hereafter IF), paper-paper citations, institutional sectors and disciplines) shown in **Table 4** were selected from [13]. In **Table 4**, the variable "Review" and "Int-Coauthored" represents the feature value "document type" and "international co-authorship," respectively, and the variables "University" to "Other" and "AGS" to "SSS" represent "institutional sectors" and "disci-

I executed logistic regression analyses of which independent variables were six feature values of papers mentioned above and dependent variables were distinct from whether papers were cited from (all or high-feature-valued) patents (1) or not (0). To ignore the shape of distributions of patent-paper citations, I discarded information on the number of citations but used

IFs were obtained from the Journal Citation Reports produced by Clarivate Analytics. Since IFs changed every year, years of IFs were defined as publication years of papers. This was because I intended to use them as the journals' quality indicators independent of the target papers. IFs in a year Y were calculated using papers published in years Y-1 and Y-2; therefore, they did not contain the target papers in the calculation. As it was well known, values of IFs differed largely by discipline; therefore, they were normalized by the following process: (1) IFs were attributed to each paper in the WoS (but IFs could not be given to some papers exceptionally); (2) mean values of IFs attributed to papers by ESI discipline were calculated for each year; (3) IF attributed to each paper was normalized by mean IF of its ESI discipline. The threshold values of feature values of patents were decided according to the criteria: number of papers cited in high-feature-valued patents should be almost the same. As the number of papers cited from the top 1% patent-patent forward citation patents was predetermined, it was used as the reference value of number of papers cited from high-feature-valued patents. Threshold values were set to 15 for patent family size, 0.85 for patent generality index. Therefore, patents of which patent-patent forward citations were within top 1% or patent family sizes or patent generality indexes were equal to or more than the abovementioned

Document types "Article" and discipline "Clinical Medicine (CLM)" were set to reference,

The results of the logistic analyses were shown in **Table 4**. Since patent-paper citations from high-feature-valued patents ((b), (c), (d)) were subsets of the whole patent-paper citations,

As for document type, reviews showed positive relationships to probabilities of being cited from both patent ((a)) and all three types of high-featured-valued patents ((b)-(d)). The result on patent ((a)) reinforced the result by Hicks et al. [11]. This result showed that indicators

International co-authorship showed no statistically significant relationship to any kinds of paper citedness. While Japan's co-authorships with any country were combined into the same

thresholds were defined as high-feature-valued patents in this study.

since they were classified exclusively.

they showed somewhat similar tendencies.

should be weighted by document type as far as possible.

plines," respectively.

160 Scientometrics

distinction of cited or not.

In the previous study, we proposed an impact indicator of patent-paper citations, named patent-paper citation index (PPCI) [9]. PPCI is based on rates of the papers cited from patents in the targets' publications. We proposed a method to overview targets' research activities from both scientific and technological impacts compared to the world average by using normalized citation impact (NCI) [13] in combination. Differences in both document types and disciplines were ignored in the previous study [9]. However, the analysis in Section 3.3 revealed their effects on papers' tendencies to be cited from patents. Therefore, I propose an improved version of PPCI in this section.

NCI, which was the basis of PPCI, is the ratio of the number of paper-paper citations which the target paper got to the expected value of that of the same cohort papers in the world. NCI is calculated for paper by paper, so when it is applied to an aggregate, such as institutions or countries, the average per their publications' NCI is applied. On the other hand, PPCI is based on the rate of papers cited in patents in targets' publications. Indeed, it is preferable to apply the same definition as NCI to secure symmetry; we applied the abovementioned definition to avoid influence of limited highly cited papers, since the rate of papers cited from patents was relatively smaller than that from papers.

Improved PPCI was defined as Eq. (1):

$$p\_{\psi d} = \frac{\left(n\_{\psi^i}^{'}/n\_{\psi}\right)}{\left(N\_{\psi^i}^{'}/N\_{\psi}\right)}\tag{1}$$

All three sectors were located on the left half of the plane, which meant average scientific impacts of them were below world average during three periods. Two sectors, public institute and corporation, were located on the second quadrant; therefore, their average technological impacts were above the world average. In particular, corporation showed a remarkably high PPCI values and seemed to have been specializing in technological impact only period by period. University, which published most of the Japanese papers, was located on the third quadrant, which meant both scientific and technological impacts were below world average.

Exploring Characteristics of Patent-Paper Citations and Development of New Indicators

http://dx.doi.org/10.5772/intechopen.77130

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**5. Development of high-feature-valued patent-paper citation index** 

patents might reveal hidden structure of the targets' research performance.

I showed that tendencies of paper citations from high-feature-valued patents differed from whole patents in some cases. It is suggested that indicators based on high-featured-valued

I tried to develop another indicator symmetrical to the PPCI to use them in combination. Here, we introduced the indicators based on paper citations from high-feature-valued patents, named high-feature-valued patent-paper citation index (HFPPCI). HFPPCI is a generic name of set of indicators, since there were many kinds of patent feature values. Of the many kinds of patent feature values, I will show the analysis of three patent feature values (patent

However, their PPCI had been increasing period by period.

**Figure 4.** Chronological change of NCI and PPCI of three Japanese sectors.

**(analysis 3)**

**5.1. Definition**

where.

*nijd*: number of target *j*'s papers with document type *d* published in discipline *i*;

*nijd* ′ : number of target *j*'s papers cited in patents with document type *d* published in discipline *i*;

*Nid*: number of total papers with document type *d* published in discipline *i*; and

*Nid* ′ : number of total papers cited in patents with document type *d* published in discipline *i*.

Target j's field weighted PPCI was calculated as follow:

$$\begin{array}{c} \text{Tran } P\_i \text{ yraus } \cdots \text{ eng} \\\\ P\_j = \frac{\sum\_i \sum\_j p\_{ijl} \times n\_{ijl}}{\sum\_i \sum\_i n\_{ijl}} = \frac{\sum\_i \sum\_l (N\_{il} \times n'\_{ijl} / N\_{il})}{\sum\_i \sum\_l n\_{ijl}} \end{array} \tag{2}$$

To increase visibility, we normalized PPCI by Eq. (3):

$$\text{Normalized } P\_{\rangle} = \frac{(P\_{\rangle} - 1)}{(P\_{\rangle} + 1)}\tag{3}$$

Hereafter, improved Normalized PPCI (Eq. (3)) is merely called as PPCI.

While the whole counting method was used to count Japanese sectors' publications in the previous study [9], the fractional counting method by number of addresses which appeared in each paper was used. The whole counting method always attributed one count to each target appeared in a paper, so they are easy to understand intuitionally; however, it often causes overrating to multiauthored papers.

#### **4.2. Chronological changes of NCI and PPCI of Japanese sectors**

Next, I tried to apply PPCI to three Japanese sectors (university, public institute, corporation) to show how PPCI could describe the scientific and technological impact of aggregate of meso (sector) level. This was mainly aimed to figure out on which level of aggregates PPCI could be used. The chronological change of both NCI and PPCI was shown in **Figure 4**.

**Figure 4.** Chronological change of NCI and PPCI of three Japanese sectors.

All three sectors were located on the left half of the plane, which meant average scientific impacts of them were below world average during three periods. Two sectors, public institute and corporation, were located on the second quadrant; therefore, their average technological impacts were above the world average. In particular, corporation showed a remarkably high PPCI values and seemed to have been specializing in technological impact only period by period. University, which published most of the Japanese papers, was located on the third quadrant, which meant both scientific and technological impacts were below world average. However, their PPCI had been increasing period by period.
