A survey of Quality Engineering-Management journals by bibliometric indicators

A survey of Quality Engineering-Management journals by bibliometric indicators 1 FIORENZO FRANCESCHINI, DOMENICO MAISANO POLITECNICO di TORINO Dipartimento di Sistemi di Produzione ed Economia dell Azienda (DISPEA) Corso Duca degli Abruzzi 24, 1129 - Torino, ITALY, Tel. +39 11 547225, Fax. +39 11 547299, e-mail: fiorenzo.francescini@polito.it Abstract Tis article analyzes some of te most popular scientific journals in te Quality field from te point of view of tree bibliometric indicators: te Hirsc () index for journals, te total number of citations and te -spectrum. In particular, -spectrum is a novel tool based on, making it possible to (i) identify a reference profile of te typical autors of a journal, (ii) compare different journals and (iii) provide a roug indication of teir bibliometric positioning in te scientific community. Results of tis analysis can be elpful for guiding potential autors and members of te scientific community in te Quality Engineering/Management area. A large amount of empirical data are presented and discussed. Keywords: Hirsc index, Hirsc spectrum, citations, journal autors, Quality Engineering/Management journal, bibliometrics. 1. Introduction In te world of scientific researc tere is a large number of journals, wic represent te natural destination of te output of researcers. Tese journals cover many different scientific disciplines and can be differentiated by subject/topic of interest, reputation and popularity witin te scientific community. Even if representing a relatively limited portion of te Engineering field, Quality (i.e. Quality Engineering/Management) is not an exception: in tis area te are several scientific journals, constantly growing in number. Considering te perspective of a researcer of tis area, many questions may be raised: Wat criteria can be used to evaluate and compare different journals in te Quality field?, How is it possible to find roug information on te autor population of eac Quality journal?, Wat is te bibliometric positioning of different Quality journals and teir influence on te scientific community?. Answering te previous questions is not a trivial task. Tere are many ways to monitor, compare and study ow scientific journals cange over te years, like considering teir circulation, te reputation/prestige of te editorial board or te presence of articles submitted by eminent autors. However, tese evaluations are often subjective and not very reliable. A more objective tool for tis purpose can be represented by bibliometric indicators, wic are based on citation statistics. Altoug indicators can sow some weak

2 points, most of te time tey seem to be te main way for evaluating, comparing and ranking scientific journals [1-3]. Te goal of tis paper is to analyse some of te major Quality journals from te point of view of tree bibliometric indicators. Analysis results can be elpful for guiding potential autors and members of te scientific (academic) community in te Quality Engineering/Management area. Indicators are respectively te Hirsc () index for journals, te total number of citations (C) and te -spectrum. index for journals and C are relatively diffused wereas -spectrum is introduced for te first time in order to complement te oters [4-5]. Differently from oter very diffused indicators like ISI Impact Factor (ISI-IF), Cited Half-life and Immediacy Index wic are evaluated only for te journals indexed by Tomson Scientific te indicators we propose can be applied to every kind of journal [, 7]. Particular attention is given to - spectrum. Tis indicator can be used for several practical purposes: to define te profile of te typical autors of a specific journal. Tis profile may represent a reference for oter (potential) autors; to elp a journal s editorial board to periodically monitor te effect of te paper selection policy, from te viewpoint of te population of te journal autors. In tis sense, -spectrum may become an indicator of editorial strategy. to provide a roug indication on te bibliometric positioning of a journal on te scientific community. All te tree examined indicators are based on citation statistics and, as well as ISI-IF, tey sould not be used for comparing journals of different disciplines (e.g. Medicine, Pysics, Engineering, Matematics etc.), owing to te different citation rates [8]. Te remaining of tis paper is organised into tree sections. Section 2 provides a description of te bibliometric indicators tat are used in te analysis. Section 3 focuses on te analysis metodology. Section 4 presents some analysis results and reflections about tem. Finally, conclusions are given, summarising te original contribution of tis paper. 2. Bibliometric indicators 2.1 -index Te -index is a relatively recent bibliometric indicator for evaluating te scientific productivity and diffusion of one autor in terms of publications and citations respectively. is defined as te number suc tat, for one autor s publications, publications received at least citations wile te oter publications received no more tan citations [4, 9]. Fig. 1 illustrates te calculation of for a fictitious autor. In general, te larger, te larger te diffusion and prestige of one autor in te scientific community.

citations for eac rank publication 1 3 2 2 3 18 4 12 5 9 8 7 8 8 9 1 5 11 4 12 3 13 2 14 2 15 1 1 1 Fig. 1 Example of calculation of te -index for a (fictitious) autor. Publications are sorted in descending order wit respect to te citation number. In tis case =7 since seven publications received at least seven citations eac. It can be noticed tat corresponds to te size of a particular subset containing te most cited publications (-core) [4]. A peculiarity of is tat it cannot decrease wit time. In fact, it aggregates te number of papers and te corresponding number of citations, and bot tese variables do not decrease over time. For example, in case of career interruption or retirement, te -index of one autor remains constant or may increase (if already publised papers accumulate new citations). Te negative consequence of tis fact is tat is not perfectly suitable to compare scolars wit different seniority, being in favour of tose wit long careers [3]. Ever since its introduction, received muc attention and also some criticism; in any case it as te unquestionable merit of being simple, syntetic and robust [1-24]. Anoter tangible sign of te popularity of is te appearance of many proposals for new variants and improvements [8, 25-3]. Braun et al. [28] proposed using te -index for evaluating and comparing scientific journals as well. In detail, te -index of a journal is te number suc tat, for te group of articles publised by te journal in a precise time period (e.g. one year), articles received at least citations wile te oters received no more tan citations. Tus, te way of calculation is te same as tat one sown in Fig. 1, wit te only exception tat te articles are related to a journal in a specific publication period. 2.2 Total number of citations C is te total number of citations so far received by te journal issue(s) publised in a specific period (e.g. in one year). Tis information is immediately available from te most diffused searc engines (i.e. Google Scolar, Web of Science and Scopus) and does not require any calculation [7, 23, 37]. 2.3 -spectrum -spectrum is defined as te distribution representing te values associated to te autors (and co-autors) of a specific journal, considering a specific publication period [5]. Te term spectrum is originated from te fact tat tis distribution provides an image of te journal autor population in a precise time period. Advantages of tis new indicator are discussed later on. -core 3

4 3. Metodology We selected twelve different journals from te most popular and representative in te Quality Engineering/Management discipline [7, 38]. Tese journals belong to different publisers and only few of tem (see Tab. 1) are indexed by Tomson Scientific. Also, Tab. 1 reports te journal acronyms used ereafter in te text. Journal name Acronym Publiser Indexed by Tomson Scientific IIE Transactions (on Quality and Reliability Engineering) IIETR Taylor & Francis Yes International Journal of Quality and Reliability Management IJQRM Emerald No Journal of Quality in Maintenance Engineering JQME Emerald No Journal of Quality Tecnology JQT ASQ Yes Managing Service Quality MSQ Emerald No Quality Engineering QE ASQ No Quality Management Journal QMJ ASQ No Quality Progress QP ASQ No Quality and Quantity QQ Springer Yes Quality and Reliability Engineering International QREI Wiley Yes Tecnometrics TM ASQ Yes Total Quality Management & Business Excellence TQM Taylor & Francis No Tab. 1 List of te twelve Quality journals selected for te analysis. Journals are sorted in alpabetical order wit respect to te journal acronym. For eac journal we calculate, C, and te -spectrum relative to different years. Citation statistics are collected using Google Scolar (GS) as searc engine. It was decided to use tis database (i) because of its grater coverage and (ii) since it can be easily accessed troug te Publis or Peris (PoP ) freeware software, specially designed for citation analysis wit GS [23]. Neverteless, te analysis can be repeated using oter databases, like Web of Science or Scopus. Indicators are calculated taking into account te citations accumulated up to te moment of te analysis (in our case, June 29). It is wortwile remarking tat QP is not a refereed arcival journal like te oters, and it is generally addressed to practitioners rater tan academics. Despite tis significant distinction, QP as been included in te list of journals because it sometimes contains ideas or insigts of interest for te academic world. Furtermore, we point out tat tat IIETR is composed of four focus issues: Design and Manufacturing, Operations Engineering and Analysis, Quality and Reliability Engineering, and Sceduling and Logistics. For omogeneity wit te oter journals, only te contributions related to Quality and Reliability Engineering are taken into account in te analysis. As a consequence, te number of examined articles and te corresponding autors associated to IIETR are significantly smaller tan tose associated to te oter journals. 4. Empirical data analysis 4.1 and C viewpoint Fig. 2 and Fig. 3 represent te values of and C for te twelve Quality journals in Tab. 1 in twenty consecutive years (from 1989 to 28). For example, in te year 2 JQT s is 14, meaning tat te 14 most cited articles publised in JQT ave received at least 14 citations eac.

5 25 2 15 1 for twelve Quality journals in twenty consecutive years IEETR IJQRM JQME JQT MSQ QE 5 1989 199 1991 1992 1993 1994 1995 199 1997 1998 1999 2 21 22 23 24 25 2 27 28 year 25 2 15 QMJ QP QQ QREI TM TQM 1 5 1989 199 1991 1992 1993 1994 1995 199 1997 1998 1999 2 21 22 23 24 25 2 27 28 year Fig. 2 values for te twelve Quality journals (see Tab. 1), in twenty consecutive years (from 1989 to 28). Values are calculated taking into account te citations accumulated up to te moment of te analysis (June 29). For te purpose of readability, journal profiles are first sorted in alpabetical order wit respect to te journal acronyms and ten divided in two groups of six eac. C 24 C for twelve Quality journals in twenty consecutive years 2 1 12 IIETR IJQRM JQME JQT MSQ QE 8 4 year C 24 2 1 12 QMJ QP QQ QREI TM TQM 8 4 year 1989 199 1991 1992 1993 1994 1995 199 1997 1998 1999 2 21 22 23 24 25 2 27 28 1989 199 1991 1992 1993 1994 1995 199 1997 1998 1999 2 21 22 23 24 25 2 27 28 (253) () (241) Fig. 3 C values for te twelve Quality journals (see Tab. 1), in twenty consecutive years (from 1989 to 28). Values are calculated taking into account te citations accumulated up to te moment of te analysis (June 29). For te purpose of readability, journal profiles are first sorted in alpabetical order wit respect to te journal acronyms and ten divided in two groups of six eac. Te profile of TM as many peaks precisely tose related to 1992, 2 and 24 falling beyond te upper limit of te vertical axis scale. Te corresponding numeric values are reported in brackets. In tese years, C values are inflated by a small number of big it articles wit a uge number of received citations. In general, and C ave quite similar patterns. Teir empirical correlation is represented in Fig. 4, taking into account tree of te twelve examined journals. Considering te scientific production of one scolar,

Hirsc empirically sowed tat C is approximately proportional to 2 [4]. Analysing te patterns in Fig. 4, tis beaviour seems to apply to te for journals as well. 2 18 1 14 12 1 8 4 2 JQT 2 4 8 1 12 C 2 18 1 14 12 1 8 4 2 VS C 2 4 8 1 12 C QE 2 18 1 14 12 1 8 4 2 2 4 8 1 12 C Fig. 4 Relationsip between and C considering data related to tree Quality journals (i.e. JQT, QE and QP), over twenty consecutive years (from 1989 to 28). Hirsc empirically sowed tat, for one researcer, C is approximately proportional to 2 [4]. Tis beaviour seems to apply also to te for journals and can be extended to te remaining journals. Te C profile of TM looks rater nervous, wit many peaks tat often fall beyond te upper limit of te vertical axis scale. Te reason is tat in several years, suc as 1991, 1992, 2 and 24 C values are inflated by a small number of big it articles wit a uge number of received citations. For instance, in 2, TM publised 3 articles tat received so far more tan 15 citations eac. On te oter and, te TM s profile is rater smoot. Tis is an empirical demonstration tat, being insensitive to accidental excess of lowly and igly cited articles, is a robust indicator [19]. Furtermore, in 1999 and 2 we can observe a peak in te and C profiles of TQM. Again, tis is due to te presence of a relatively large number of igly cited publications. Profiles relative to te oter journal are fairly more regular, wit moderate fluctuations. Profiles of TQM, QMJ, MSQ and JQME are not complete since tese journals appeared for te first time after 1989. and C can be used to compare different journals. It must be pointed out tat citation accumulation of one article requires a certain amount of time to become stable. According to some autors, about five years for journals in te management/engineering field [, 39, 4]. Tis pysiological beaviour is well represented in Fig. 2 and Fig. 3 and applies to most of te journals: in te last years (e.g. from 24 to 28), and C values tend to decrease and are significantly smaller tan in te previous years. Tus, and C are not suitable to evaluate te most recently publised journals and, muc less, to compare tem wit oter older publications. Besides, being sensitive to te number of articles per issue, if calculated on a yearly basis, and C tend to favour journals wit many articles/issues per year. Apart from te last five years, most of te journal values are included between 5 and 15. Similarly, most of te journal C values are included between 1 and 1. Fig. 5-a sows te journal and C mean values (respectively and C ) and te corresponding standard deviations (respectively s and s C ), in te years 1989-23. It can be interesting to see ow tese typical values compare to tose of oter adjacent scientific fields. Fig. 5-b reports te values related to tree major journals in te Manufacturing area. QP

7 21 (a) Quality journals 14 C 21 (b) Manufacturing journals 14C 18 C 12 18 C 12 15 1 15 1 12 8 12 8 9 9 4 4 3 2 3 2 IIETR IJQRM JQME JQT MSQ Journal IIETR IJQRM JQME JQT MSQ QE QMJ QP QQ QREI TM TQM 11.5 13.7 7.9 14.7 9.8 7. 4.7 11.9 7.3 9.1 17.3 13. s 1. 3.7 2. 2.8 4.8 2. 1. 2.4 1.7 2.4 2.2. C 445.2 22. 195. 838. 38.2 25.5 84. 3.1 214.4 274. 1985.4 717.9 sc 14.2 574.9 25.2 287.8 297.3 11. 114.5 72.5 21. 53. 147. 119.8 QE QMJ QP QQ QREI TM TQM CAMT IJAMT POM Journal CAMT IJAMT POM 17.9 13. 13.4 s 5.5 5.7 5.4 C 132.7 8.7 818.1 sc 78.5 85.8 33.2 Fig. 5 and C mean values - respectively and C - and corresponding standard deviations - respectively s and s C - (a) for twelve Quality Journals and (b) for tree additional Manufacturing journals, in te years 1989-23. Manufacturing journal acronyms are: CIRP Annals - Manufacturing Tecnology (CAMT), International Journal of Advanced Manufacturing Tecnology (IJAMT), Production and Operations Management (POM). 4.2 -spectrum viewpoint Te -spectrum analysis can be divided in two distinct activities: construction and comparison of te -spectra related to te twelve journals in te same reference year (i.e. 28), so as to investigate ow te -spectrum canges from journal to journal; construction and comparison of te -spectra related to te same journal(s) in five consecutive years (precisely, from 24 to 28), so as to investigate ow a journal s -spectrum tends to cange over time. Analysis in te year 28 For eac journal, we identify te autors of papers publised in 28. Ten, te -indexes of te individual autors are calculated. Finally, te distribution of te autors -indexes is constructed. Te output of tis analysis is illustrated in Fig., sowing te -spectra related to te journals in Tab. 1. At a first glance, all tese distributions are rigt-skewed and ave a caracteristic profile, wic is approximately decreasing. Analysing te distributions in more detail, some interesting aspects emerge. Fig. 7 sows te -index average value ( ), te corresponding standard deviation (s) and te number of autors (N) associated to eac journal. Journals are sorted in descending order wit respect to. It can be seen tat, despite teir similar sape, distributions are appreciably different in terms of values of and s.

8 4 3 3 TM 28 - Autors' relative frequency VS -index =7.2 s=.5 N=97 4 3 3 JQT 28 - Autors' relative frequency VS -index QE 28 - Autors' relative frequency VS -index 4 =.7 =5.7 s=.2 3 s=7. N=7 3 N=7 2 2 2 4 3 3 IIETR 28 - Autors' relative frequency VS -index =5.1 s=4.1 N=17 4 3 3 QQ 28 - Autors' relative frequency VS -index QREI 28 - Autors' relative frequency VS -index 4 =5.1 =4.5 s=5.9 3 s=5.2 N=84 3 N=1 2 2 2 4 3 3 2 4 3 3 2 QMJ 28 - Autors' relative frequency VS -index MSQ 28 - Autors' relative frequency VS -index TQM 28 - Autors' relative frequency VS -index 4 4 =4.4 =4.4 =3.8 s=5.1 3 s=5. 3 s=5.1 N=34 3 N=9 3 N=17 2 2 2 JQME 28 - Autors' relative frequency VS -index IJQRM 28 - Autors' relative frequency VS -index QP 28 - Autors' relative frequency VS -index 4 4 =3. =2.7 =2.3 s=3. 3 s=3.9 3 s=3.2 N=48 3 N=119 3 N=7 2 Fig. -spectra (autors relative frequency VS -index) for twelve Quality journals (see Tab. 1), in te year 28. For eac journal, te autors -index average value ( ), te corresponding standard deviation (s) and te number of autors (N) are reported. Spectra are sorted in descending order wit respect to values. 8 for twelve different Journals in te year 28 7 5 4 3 2 1 Journal TM JQT QE IIETR QQ QREI QMJ MSQ TQM JQME IJQRM QP 7.2.7 5.7 5.1 5.1 4.5 4.4 4.4 3.8 3. 2.7 2.3 s.5.2 7. 4.1 5.9 5.2 5.1 5. 5.1 3. 3.9 3.2 N 97 7 7 17 84 1 34 9 17 48 119 7 TM JQT QE IIETR QQ QREI QMJ MSQ TQM JQME IJQRM QP Journal Fig. 7 Syntetic results of te analysis of twelve Quality journals, in te year 28. Te table reports te, s and N values relative to eac journal. In te bar-cart, journals are sorted in descending order wit respect to. Furtermore, it is interesting to notice tat considering te same journal and s ave generally similar values. Teir empirical correlation is nearly linear wit a rater ig coefficient of determination

(R 2 =.85, see Fig. 8). On te oter and, tere is no empiric correlation between and N or s and N (R 2 ). 9 s 8 7 svs s 5 4 3 2 R 2 =.85 1 2 3 4 5 7 8 Fig. 8 Relationsip between s and related to te -spectra in Fig.. On te basis of tis result, it seems quite appropriate using as a syntetic indicator to perform quick evaluations and comparisons among different -spectra. Analysis over five consecutive years Te second part of our study is aimed at finding ow -spectra canges over time. To tis purpose, te construction of te journal -spectrum is extended to five consecutive years (from 24 to 28). For simplicity, Fig. 9 reports te resulting spectra concerning only tree of te previous twelve journals (JQT, QE and QP).

1 4 3 3 2 4 3 3 2 4 3 3 2 4 3 3 2 JQT 24 - Autors' relative frequency VS -index QE 24 - Autors' relative frequency VS -index QP 24 - Autors' relative frequency VS -index =7.92 4 =4.13 4 =2.4 =2.35 s s=.33 3 s s=4.54 3 s s=3.12 N=2 3 N=14 3 N=8 4 3 3 2 JQT 2 - Autors' relative frequency VS -index QE 2 - Autors' relative frequency VS -index QP 2 - Autors' relative frequency VS -index 4 4 =5.92 =3.71 =2.4 s s=5.39 =5.4 3 s s=4.11 3 s s=2.1 N=1 3 N=82 3 N=83 JQT 27 - Autors' relative frequency VS -index QE 27 - Autors' relative frequency VS -index QP 27 - Autors' relative frequency VS -index =.31 4 =5.1 =5.2 4 =2.5 =2.49 s s=.19 =.2 3 s s=7.12 = 3 s s=.52 N=71 3 N=1 3 N=77 JQT 28 - Autors' relative frequency VS -index QE 28 - Autors' relative frequency VS -index QP 28 - Autors' relative frequency VS -index =. =.7 4 =5.7 =5. 4 =2.3 =2.25 s s=.23 3 s s=7. 3 s s=3.18 =3.2 N=7 3 N=7 3 N=7 2 2 2 2 2 JQT 25 - Autors' relative frequency VS -index QE 25 - Autors' relative frequency VS -index QP 25 - Autors' relative frequency VS -index 4 4 =. =.7 =3.3 =3.2 =2.2 =2.17 s s=.4 3 s s=3.1 3 s s=2.51 N=1 3 N=1 3 N=82 2 2 2 2 2 Fig. 9 -spectra associated to tree Quality journals (JQT, QE and QP), in five consecutive years (from 24 to 28). For eac spectrum,, s and N are reported. For eac of tese journals, te -spectrum seems relatively robust and stable over te five examined years. Tis beaviour can be extended to te nine remaining journals, as it emerges analysing te profiles in Fig. 1. Possible variations in one journal profile are due to (i) cange of te journal editorial board, (ii) variation of te article selection policy, (iii) appearance of a competing journal etc Considering te sape of -spectrum profiles, moderate fluctuations can be observed over te years (see Fig. 1). Two possible reasons of te profiles relative stability are: autors of a particular journal tend to be attracted to it over te years; te editorial board policy tends to be consistent over time.

11 1 9 8 7 5 4 3 for ten Quality journals in five consecutive years IJQRM JQT QE JQME MSQ IIETR 2 2 24 25 2 27 28 24 25 2 27 28 IIETR.1.8.8 5.8 5.1 QMJ 3.7 5.8 4.3 3.4 4.4 s 3.4. 5.9 4.1 4.1 s 4.5 8..5 4.7 5.1 N 22 25 19 13 17 N 31 28 35 25 34 5. 4. 4.2 4. 2.7 2.4 2.2 2. 2.5 2.3 IJQRM s 4.4 4.5 4.7 4.4 3.9 QP s 3.1 2.5 2..5 3.2 N 117 11 111 11 119 N 8 82 83 77 7 4. 4. 5. 2.7 3. 5.1 5. 3.4 4.1 5.1 JQME s 3.9 3.8 5.4 2.9 3. QQ s 4.4 5.8 3.3 4.9 5.9 N 5 57 57 5 48 N 77 8 84 87 84 7.9.7 5.9.3.7 5.5 4. 4.8 5.1 4.5 JQT s.3. 5.4.2.2 QREI s 5.3 4.8 4.8 5.3 5.2 N 2 1 1 71 7 N 12 135 141 15 1. 5. 5.1 3.7 4.4 9.2 9.5 7.3 8.5 7.2 MSQ s 5.8 5.2 4.9 3.5 5. TM s 7.9 8.1 5..9. N 8 75 82 82 9 N 54 3 9 8 97 4.1 3.3 3.7 5.2 5.7 4.8 4. 4.5 3.7 3.8 QE s 4.5 3. 4.1 7.1 7. TQM s 5.4 4.3 5.3 4.8 5.1 N 14 1 82 1 7 N 128 153 155 157 17 1 9 8 7 5 4 3 QMJ QQ TM QP QREI TQM Fig. 1 Graps sowing te time evolution for te twelve Quality journals (see Tab. 1), in five consecutive years (from 24 to 28). For te purpose of readability, journal profiles are first sorted in alpabetical order wit respect to te journal acronyms and ten divided in two groups of six eac. Tables report te corresponding s and N values. Since, tere can be small variations from one year to te next, we noticed tat te caracteristic sape of one journal s -spectrum becomes more and more consolidated by increasing te reference time period. Tis aspect is sown in Fig. 11, reporting te -spectra for tree of te twelve Quality journals, in tree different time periods (one year, tree years and five years, respectively). Numerical data related to te - spectra of all te examined journals are reported on Tab. 2.

12 4 3 3 2 -spectrum of JQT in one year (28) 4 3 3 2 4 3 3 2 -spectrum of QE in one year (28) -spectrum of QP in one year (28) 4 4 =.7 =5.7 =2.3 s=.2 3 s=7. 3 s=3.2 N=7 3 N=7 3 N=7 2 2 2 2 -spectrum of JQT aggregating 3 years (2-28) -spectrum of QE aggregating 3 years (2-28) -spectrum of QP aggregating 3 years (2-28) 4 4 =.3 =4.8 =2.3 s=. 3 s=.4 3 s=4.4 N=199 3 N=213 3 N=227 2 -spectrum of JQT aggregating 5 years (24-28) -spectrum of QE aggregating 5 years (24-28) -spectrum of QP aggregating 5 years (24-28) 4 4 =.7 =4.2 =2.3 s=.1 3 s=5.3 3 s=3.8 N=322 3 N=417 3 N=395 2 Fig. 11 -spectra for tree Quality journals (JQT, QE and QP), calculated considering tree different reference time periods (respectively, one year, tree years and five years). For eac journal,, s and N values are reported. It can be seen tat te larger te time period, te more consolidated te journal s -spectrum. 1 year 3 years 5 years 1 year 3 years 5 years 5.1 5.9.2 4.4 4.1 4.3 IIETR s 4.1 4.8 4.8 QMJ s 5. 5.5 5.9 N 17 42 84 N 34 94 153 2.7 3. 4.1 2.3 2.3 2.3 IJQRM s 3.9 4.4 4.5 QP s 3.2 4.4 3.8 N 119 34 573 N 7 227 395 3. 3. 3.9 5.1 4.2 4.5 JQME s 3. 4.2 4.1 QQ s 5.9 4.8 5. N 48 17 277 N 84 255 412.7.3.7 4.5 4.8 4.9 JQT s.2..1 QREI s 5.2 5.2 5.1 N 7 199 322 N 1 457 712 4.4 4.4 4.8 7.2 7.7 8.2 MSQ s 5. 4.5 5. TM s..4.9 N 9 233 388 N 97 25 313 5.7 4.8 4.2 3.8 4. 4.1 QE s 7..4 5.3 TQM s 5.1 5.1 5. N 7 213 417 N 17 482 73 Tab. 2 Numerical data related to te -spectra of te twelve examined Quality journals. Journals are sorted in alpabetical order wit respect to te journal acronym. Data are evaluated considering tree different reference time periods (respectively, one year, tree years and five years). Tis table reports te values of, s, and N associated to te resulting -spectra. 4.2.3 Furter reflections on te -spectrum -spectrum may ave many different practical utilizations, suc as: providing a snapsot of te autor population of a specific journal, representing a reference for oter (potential) autors. For example, assuming tat a (potential) autor wit =3 compares imself wit te

13 QP autors in 28, e will fall on te 8 t percentile of te corresponding -spectrum, or anoter autor wit =1 will fall on te 55 t percentile. elping a journal s editorial board to periodically monitor te practical effect of te article selection policy from te point of view of te autor population. In tis sense, -spectrum may be interpreted as a signal of editorial strategy. For example, if decreases significantly from one year to te next, it probably means tat among autors te portion of young researcers or professionals/managers (generally, wit small values) tends to increase wit respect to te portion of senior academics (generally, wit ig values). providing a roug indication of one journal s bibliometric positioning on te scientific community. -spectra can be reliable tools for evaluating a journal at te very moment of te publication, despite te fact tat tey are based on te publications/citations accumulated before te publication itself. Tere are empirical proofs of te fact tat te citations received by a new article are generally consistent wit te citations received by previous articles of te same autor, tat is to say te autor s reputation [39]. Being te number of autors per journal quite large (typically more tan -7 autors per year), it is reasonable to assume tat te autors reputation will be generally respected. 4.3 Remarks on te combined use of different bibliometric indicators Evaluating and comparing scientific journals by bibliometric indicators is a very delicate task. To make tis activity as muc complete as possible, it is convenient to use a combination of different indicators and to construct a bibliometric map. Eac indicator can be used to define an axis of tis map. Te map allows te bibliometric positioning and comparison of journals, and can be subdivided in journal reputation regions, according to wic journals are classified (see Fig. 12). Alternatively, te different bibliometric indicators can be syntesised into a single global ranking by a proper aggregation tecnique [1, 41]. A more detailed description of te bibliometric map and te tecniques for aggregating indicators will be analysed in detail in future works. Finally, it is wortwile underlining te difference between -spectrum, wic is related to te reputation of one journal s autors, and ISI-IF, C, for journals and oter traditional bibliometric indicators, wic are related to te citations effectively accumulated by one journal s articles. Generally speaking, te academic reputation of a journal's autor group is not te equivalent of te reputation of te journal, as well as not te equivalent of te influence of te journal. For tis reason, tese different indicator typologies represent two complementary ways to evaluate/compare scientific journals. For example, a combined use of tese indicators can be performed for identifying te following situations: 1. Journals wit medium-ig autors reputation (in terms of values) but few received citations. Tis can be te case of relatively recent journals wic are still struggling to become popular in te scientific community.

14 2. Journals containing articles wit a ig number of citations, submitted by autors wit low -indexes. Tis can be te case of journals open beyond te academic world, for instance to professionals and industrial managers (like QP, as mentioned before). Alternatively, tey can be journals wit a relatively large group of young autors, consisting of brilliant young researcers wit relatively low citation indexes. 1 9 8 7 5 4 3 2 1 region 3 region 1 -C map region 4 region 2 5 1 15 2 25 Fig. 12 Example of a simplified map for comparing journals on te basis of different bibliometric indicators. Te map associates values (vertical axis) wit C values (orizontal axis) and makes it possible to identify four regions: (1) journals wit low autors reputation (in terms of values) and few received citations; (2) journals containing articles wit a ig number of citations, submitted by autors wit low -indexes; (3) journals wit medium-ig autors reputation but few received citations and (4) journals containing articles wit a ig number of citations, submitted by autors wit ig -indexes. C 5. Conclusions Tis paper analyzed twelve of te major journals in te Quality Engineering/Management field by tree bibliometric indicators: for journals, citation number and -spectrum. Differently from oter diffused indicators like ISI-IF, tese indicators can be applied to every kind of journal not necessarily tose indexed by Tomson Scientific or oter organizations. Citation statistics are collected using te GS freeware searc engine. One novelty of tis paper is te introduction of te -spectrum, a new tool based on te -index. It is interesting to observe tat te -spectrum as a peculiar sape and it is rater robust over te years. Furtermore, it can be calculated at te very moment of te journal publication, unlike ISI-IF (wic is calculated one-two years after te publication), and C. Differently from and C, -spectrum does not tend to favour journals wit many articles/issues per year. Te bibliometric analysis we proposed can be elpful for different reasons: (i) it provides a reference for te (potential) autors of te major scientific journal on Quality sector; (ii) it makes it possible to perform roug comparisons between different journals and estimate teir bibliometric positioning; (iii) it supports a journal s editorial staff to periodically monitor te effect of te paper selecting policy. Several ideas for furter researc activities may originate from tis work. It would be interesting to extend te analysis to a wider set of journals and to oter disciplines suc as manufacturing, industrial engineering and mecanical engineering and define a guideline for ranking journals by using several

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