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■ Research Paper

Dynamics of Journal Impact Factors

S. N. Groesser1,2*1 School of Management, Bern University of Applied Sciences, Bern, Switzerland2System Dynamics Group, Institute of Management, University of St. Gallen, St. Gallen, Switzerland

For a journal manager, boosting a journal’s impact factor is an important objective. Theimpact factor is a measure of the frequency with which the average article in a journalis cited in a given period. The dynamics of impact factors are caused by the interplay ofseveral concepts, such as seniority of authors and reviewers, journal policies, onlineavailability of journals and quality of contributions. This paper’s objective is to discussthree strategies to sustainably improve both a journal’s impact factor and its underlyingresources. To this end, a journal’s assets and resources are captured in a structural simula-tion model, which is used for strategy experiments. The paper offers three insights: Itprovides a dynamic hypothesis about the causal structures underlying a journal impactfactor; it highlights the fact that the levels and growth rates of the crucial resources, authorsand reviewers, must be developed in dynamic correspondence. Finally, developing thestock of high-quality reviewers requires time and resources but is more stable than thestock of authors and, hence, has a higher potential for guiding the journal into a regimeof sustainable development. Limitations and future paths are discussed.Copyright © 2012 John Wiley & Sons, Ltd.

Keywords Journal impact factor; scientometrics; citations; causal model; sustainable strategy

Supporting information may be found in the online version of this article.

INTRODUCTION

Journal impact factors (JIF) have become importantindicators in academia, but they are still relativelyseldom discussed. A JIF is a measure of the fre-quency with which the average article in a journalhas been cited by articles in a sample of journalsduring a given period. Impact factors influence

major decisions both in academia and other re-search institutions. For instance, hiring and ten-ure-track decisions are based on publications injournals with high impact factors; also, researchpolicy and grant applications are positivelyinfluenced when the track record of applicantscontain top-tier journal publications.

This paper reveals the structural foundationsof the dynamics of impact factors. Here, I concen-trate on the widely accepted Thomson ReutersJIFW (Garfield, 1955; Garfield, 2006; ThomsonReuters, 2011). In general, the paper is written

*Correspondence to: S. N. Groesser, School of Management, BernUniversity of Applied Sciences, Morgartenstrasse 2c, 3000 Bern 22,Switzerland.E-mail: [email protected]

Copyright © 2012 John Wiley & Sons, Ltd.

Systems Research and Behavioral ScienceSyst. Res. (2012)Published online in Wiley Online Library(wileyonlinelibrary.com) DOI: 10.1002/sres.2142

for an academic audience in the social sciences. Inparticular, it can support current and prospec-tive journal editors in identifying strategicresources and designing policies to developthese resources. However, it is also useful foracademic customers, that is, decision makersin academia who use JIF to substantiate deci-sions, or researchers who apply JIF to allocatetheir resources. Both groups can benefit from adeeper understanding of the mechanisms thatresult in the JIF development and how it canbe influenced.

The paper demonstrates that either a successfulor unsuccessful development of a JIF is not some-thing that happens to a journal; rather that it isstrongly influenced by a journal’s strategy. Editorialboards, the top-management teams of journals,develop, implement and adjust these strategiesand thereby pursue similar objectives as managersin profit-oriented organizations; for instance, topromote a sustainable journal development thatmeets the customers’ demand, to increase a jour-nal’s market share or to gain a competitive advan-tage. Hence, a JIF becomes a performance measureof the work performed by editors and, thus, anelement of their incentive system.

Given the importance of the JIF in academia,especially the Thomson Reuters JIFW, one wouldexpect an intensive debate about its nature,effects and implications for academia as wellas for the wider society. A literature review inthe field of scientometrics,1 however, did notproduce the anticipated result; it shows thatbibliometric research, that is, research using aset of methods to quantitatively analyse scientificliterature, is rather fragmented. The currentdiscussion concentrates on the meaning of theThomson Reuters JIFW (Garfield, 1999, 2006), itsdisadvantages, possible elaborations and alterna-tives approaches (Moed et al., 1999; Bollen et al.,2005; Zitt and Small, 2008; Harzing and van derWal, 2009; Vanclay, 2009; Abramo et al., 2010;Leydesdorff and Opthof, 2010), or the differencesin magnitude of JIF in the fields of science(Althouse et al., 2009). Others have concentratedon the number of citations an individual paperis likely to attract over its lifetime (Mingers and

Burrell, 2006). Just recently, some have addressedthe emerging topics of journal self-citations(Frandsen, 2007; Yu and Wang, 2007; Andradeet al., 2009), an inflation of the JIF (Reedijk andMoed, 2008; Althouse et al., 2009; Vanclay, 2009;Krell, 2010; Neff and Olden, 2010), and theincreasing use of non-citable items to push theJIF (Frandsen, 2008). The ethical responsibilityof journal management has also been addressedand has culminated in the question whethereditors should actively stimulate—or engineer—the impact factors of their journals (Falagas andAlexiou, 2008; Reedijk and Moed, 2008; Vanclay,2009; Krell, 2010; Yu et al., 2010).

To summarize, the current research providesonly piecemeal work about the origins of theimpact factor dynamics. It is surprising that nowork has tried to explain the underlying causesof impact factor dynamics from a structuralperspective. Hence, a significant gap exists inthe understanding of one of the most importantyardsticks in academia. For journal editors, noguidance exists for designing strategies2 to sus-tainably develop their journal in terms ofrising impact factors. In this paper, a sustainabledevelopment is one that meets present needswithout compromising the ability to meet futureneeds (WCED, 1987). Currently, journal managershave to make decisions using incomplete and sub-jective mental models about a complex dynamicsystem, which can result in unintended conse-quences that complicate or even prevent a sustain-able development (Sterman, 1989). Examples ofsuch consequences, for example, ‘growth-and-decline’ or ‘stagnation’ trajectories, seem to bethe journals ‘MIS Quarterly’ or ‘AdministrativeScience Quarterly’.

This paper tries to reduce this gap by devel-oping a formal, systemic understanding of thedynamics of journal impact factors. It developsa dynamic simulation model that accounts forimportant resource accumulations and theirnonlinear, delayed interdependencies (Dierickxand Cool, 1989; Davis et al., 2007; Harrison et al.,

1 Scientometrics is the science of measuring and analysing science.

2 Strategies are comprehensive configurations of decision rules forachieving a desirable long-term objective. A sustainable strategy hereis a configuration of policies, which governs an organization so as toachieve favourable long-term outcomes as well as to provide theorganization with a potent configuration of resources.

RESEARCH PAPER Syst. Res.

Copyright © 2012 John Wiley & Sons, Ltd. Syst. Res. (2012)DOI: 10.1002/sres.2142

S. N. Groesser

2007; Schwaninger and Groesser, 2008; Warren,2008). The approach facilitates experimentationwith different journal strategies, which editorsmay implement. The model provides primaryinsights about the JIF’s dynamics and therebyenables journal editors to experiment with differ-ent configurations of policies for designing sus-tainable strategies. Editors’ policies are decisionrules, which they implement to manage the de-velopment of their journal. In this paper, editorshave four such policies at their disposal. First,the stretch objective policy captures the editors’ de-cision rule to set the journal’s JIF objective withrespect to time. Second, the marketing policyexpresses the editors’ attention to marketingspending and effectiveness; for this, each month,a certain fraction of the available resources areinvested into marketing activities. Third, thejunior reviewer training policy states the editors’decision rule to enhance the training of juniorreviewers; again, each month a certain fractionof the available resources are invested in theseactivities. Fourth, the junior reviewer acquisitionpolicy expresses the editors’ attention to gainingnew junior reviewers; also here, a certain fractionof available resources is spent. Important here isthat the editors have to consider policy trade-offsto manage the journal development underthe condition of limited resources. The paperreports on three strategies. The first strategy,the average-growth strategy, corresponds to theintention of editors to achieve a modest andsteady development of the JIF, which results ina development close to the average growth inJIF in a journal category. R&DManagement seemsto be an example for a journal following thisstrategy.3 Editors following the second strategy,the fast-growth strategy, try to achieve a rapidgrowth in impact factor within several years.These editors intend to develop a leading journaland consider almost only marketing which ishighly efficient in boosting the JIF and so have tocut back on reviewer development because ofresource constraints. It seems to be successful atthe outset but comes at the cost of a lower JIF inthe long term and bears high risks of unsustainable

resources of authors and reviewers, which thisstrategy does not develop. The sustainable-growthstrategy emphasizes developing a high-qualityreviewer base by means of reviewer training andacquisition, which puts the journal at a disadvan-tage in the medium-term. However, this improvesits quality significantly and outperforms all testedstrategies. Additional strategies might be alterna-tions between the strategies I have currently tested.For instance, editors could follow a fast-growthstrategy initially to boost the journal’s JIF andthen change to a sustainable-growth strategy. Suchanalysis is subject to future research.

The simulation offers two major insights: First,the levels and growth rates of the crucial resources,authors and reviewers, must be developed indynamic correspondence. Second, developing thestock of high-quality reviewers requires time andresources but is more stable than the stock ofauthors and, hence, has a higher potential forguiding the journal into a regime of positivedevelopment. Given the vast dynamic complexityof the system, journal editors have to be systemsthinkers to achieve sustainable results. They mustwisely withstand medium-term temptations andpressures and implement strategies, which arenot popular at the moment but which becomebeneficial later. The editors’ implementationtask is further complicated because they haveonly limited financial means available and avolatile workforce, consisting of mostly volunteeracademics with heterogeneous competencies.

The paper is structured as follows: The nextsection introduces relevant elements of theacademic journal system. Thereafter, I developthe structural model. The fourth section uses theresults of computer simulations to describe anddiscuss three strategies. The fifth section reflectson the paper’s practical and theoretical contribu-tions as well as its limitations. The last sectionconcludes the paper and suggests paths forfuture research.

JOURNAL IMPACT FACTOR AND EMPIRICALDATA

Academic journals are the outlets of scientificresearch. Most relevant journals are available

3 http://onlinelibrary.wiley.com/journal/10.1111/%28ISSN%291467-9310/homepage/ProductInformation.html.

Syst. Res. RESEARCH PAPER



http://onlinelibrary.wiley.com/journal/10.1111/%28ISSN%291467-9310/homepage/ProductInformation.html

http://onlinelibrary.wiley.com/journal/10.1111/%28ISSN%291467-9310/homepage/ProductInformation.html

only as fee-based subscriptions, not as open-access publications. The online availability ofjournals is mostly provided by library subscrip-tions. Journals are ranked in categories accordingto their overall quality. Numerous journal rankingsexist. Three important examples are: Harzing’sJournal Quality List (http://www.harzing.com/jql.htm), International Scientific Journal Rank-ing (www.scimagojr.com) and the ISI JournalCitation Reports (http://isiknowledge.com/JCR).ISI stands for ‘Institute for Scientific Information’,which publishes several citation indices. Since1992, ISI became a part of Thomson ReutersCorporation. To arrive at such a ranking, severalmetrics are used, one of which is journal impactfactors. One such impact factor is the ThomsonReuters JIFW. For the following, assume thatt stands for the current year. Then, the JIF is calcu-lated by dividing the number of current yearcitations, nt, by the source items, A, published injournal j during the previous 2 years, At-1 and At-2

(Equation (1)). Citeable items are types of docu-ments that constitute the scholarly contribution ofthe journal to the literature and are usually articles,reviews, and proceedings papers; non-citableitems are editorials, news stories, abstracts,letters-to-the-editor or book reviews (McVeighand Mann, 2009).

JIFjt ¼njt

Ajt�1 þ Aj

t�2

(1)

For instance, the JIF for the Academy ofManagement Journal in the year 2009 was6.483. This is calculated by the amount ofcitable items (A2007+2008= 65 + 55 = 120), and theamount of citations of these items in 2009(n2009= 654 + 124 = 778). This signifies that everyarticle in this journal published in the years2007 and 2008 has been cited, on average, about6.5 times by articles published in ISI journals in2009. Other measures for the impact of journalsand articles, although they have been discussedextensively (Biglu, 2008; Zitt and Small, 2008;Abramo et al., 2010; Leydesdorff and Opthof,2010), have not received the same attention asthe two-year JIF of Thomson Reuters. This impact

factor is used in almost every field of science. Acomparison between the different fields showsthat its value can vary from 0.2 to 17.0. These dif-ferences stem from the degree of specializationand formalism in the field of science, differentcitation cultures, the number of journals and severalother factors(Althouse et al., 2009). For this paper, Ihave selected the journal category ‘management’because Systems Research and Behavioral Science isincluded in this category. The category covers jour-nals on management science, organization studies,strategic planning and decision-making methods,leadership studies and total quality management.In the following, I provide empirical data aboutthe category ‘management’ which are used laterto parameterize the simulation model.

Subject Category ‘Management’

In 2010, 4932 articles have been published in140 management journals; the median impactfactor for 2010 year was 1.221 (Thomson Reuters,2011). Figure 1 shows the development of the JIFof five selected management journals from 1997to 2010. One can see that the JIF assumes valuesfrom 0.6 (Journal of Forecasting) to around 6.8 (Acad-emy of Management Review) in the year 2010 andthat significant differences exist between thejournals. Because the paper’s objective is theorybuilding and not theory testing (Schwaningerand Groesser, 2008), I have selected journals thatrepresent extreme cases on both ends of the JIF-spectrum. I do not account for journals thathave entered or left the subject category duringthe 13-year period.

The time series in Figure 1 qualitativelyshow patterns of exponential growth with oscilla-tions (Academy of Management Review, StrategicManagement Journal, and Long Range Planning)or patterns of stagnation with larger or smalleroscillations (Administrative Science Quarterly forthe first and the Journal of Forecasting for the latter).My review of all journals in the category ‘manage-ment’ shows behaviours in the range from ‘stag-nating with oscillations’ to ‘exponential growthwith oscillations’. The journal impact factors ofthefive selected journals showoverall growth ratesfrom 16% (Administrative Science Quarterly) to



S. N. Groesser

http://www.scimagojr.com

http://isiknowledge.com/JCR

480% (Long Range Planning), from 1997 to 2010.Evidently, a journal impact factor is a dynamicphenomenon. However, which mechanisms gen-erate the trajectory of an impact factor? How canjournal editors support the sustainable develop-ment of their journals’ impact factors? Are therelikely counter-intuitive effects of policies, whichmight hinder a positive development? The currentknowledge about journal impact factors does notallow for answering these questions. The dynamicmodel in the next section is a first hypothesis foraddressing them and for informing the designof sustainable journal strategies.

MODEL

In the following, I develop a formal model thataccounts for the relevant structure that allows oneto endogenously generate the dynamics of journalimpact factors. For this, I use the system dynamicsmethodology (Forrester, 1961; Sterman, 2000),which has been often applied in the managementsciences to disentangle a system’s dynamic com-plexity and to conduct policy analysis and design(Repenning, 2002; Black et al., 2004; Sterman etal., 2007; Rudolph et al., 2009). I use this method-ology in the theory-buildingmode (Harrison et al.,2007; Schwaninger and Groesser, 2008).

Model Overview

To understand the dynamics of journal impactfactors, a model with a broad boundary isrequired to capture the structural aspects thatconstitute the system’s dynamic complexity.Hence, the model represents elements of thefollowing agents:

• Physical System: The physical aspects of thesystem that are relevant for the JIF relate tothe material flow of submitted and publishedarticles in the respective stages of a publicationand citation process (i.e. submitted articles,articles in-review, accepted article backlog,published articles and outphased articles).

• Regulatory Institution: The regulatory institu-tion defines the concepts that influence the journalsystem and determines theways inwhich JIFs arecalculated. The agency is clearly represented byThomson ReutersW, which publishes the JIF(Garfield, 1955, 2006; Thomson Reuters, 2011).

• Journal Editors/Editorial Boards: Editors arehuman agents who represent the top manage-ment teams of journals. In this function, theydesign the journal strategy about the JIF ob-jective, acceptance and rejection rates, scopeof the journal, the means to promote thejournal and the timeliness in reviewing sub-mitted manuscripts.

0

1

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3

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1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

Jou

rnal

Imp

act

Fac

tor

YearAcademy of Management Review Strategic Management Journal Administratice Science Quarterly

Long Range Planning Journal of Forecasting

Figure 1 Journal impact factor (1997–2010) of five selected management journals




• Libraries: Libraries are institutions that provideaccess to journals for the interested audience.Their subscriptions make the journal contentavailable online and lead to multiplicationeffects of information in the system.

• Researchers/Authors: Researchers and authorsevaluate journals and decide to write andsubmit their work to these journals, or not. Thebehavioural decisions are governed by thewidely accepted academic incentive system,which includes the JIF. One assumption of thissystem is that the higher the JIF, the morelikely will a researcher attempt to publish inthis journal.

• Reviewers/Referees: Reviewers are researcherswho support editors in reviewing and evalu-ating submitted articles for publication orrejection.

Model Boundary

Compared with the existing research about JIF(see Section 2), the model developed here hasa broad boundary. However, even with thisboundary, I had to exclude several topics. First,publishers’ objectives and policies are notexplicitly modelled; I assume that their interestsare represented by journal editors. Second, themodel does not account for different types ofreview processes (e.g. no substantial review,editorial review, single-blind review); a scientificreview process is assumed. Third, articles underreview are not differentiated in distinct roundsof reviews. Fourth, rejected articles can re-enterthe article-production chain after fundamentalrevisions; this is not treated explicitly in themodel. Fifth, themodel uses the standard two-yearJIF which currently seems to be the standard,which editors, authors, libraries, and universitiesconsider in their decisions (Reedijk and Moed,2008; Binswanger, 2010). Sixth, I assume thatopen-access journals will not be relevant forpublishing high-quality academic work. I havedecided to exclude the mentioned elements be-cause they seem not to be crucial to understandingthe dynamic complexity of the journal impact fac-tor. Seventh, I assume that a journal has a limitedamount of resources available to implement its

strategy. Given the lack of data about journal-internal financial figures, I assume that the avail-able resources are constant over the simulationtime horizon; in addition, I model the limitedresources using abstract resource units.

The final assumption relates to dynamiccomplexity. The model developed here considersone journal, j; other journals are treated asexternal. The current model is a reasonablespecification under the assumption of perfectcompetition, that is, that journal j’s market shareis insignificant with respect to the total market;journal j is one of an infinite population in thesame category. Consequently, j’s strategy andobjectives have no influence on the respectiveaverage values of the journal category. This as-sumption seems to be feasible, given thatchanges in the impact factor of one of thecurrently 140 journals in the category of manage-ment have only a small effect on the grandaverage. However, one might argue that leadingjournals, such as the Academy of ManagementReview, influence the overall journal system withtheir decisions. Such strong nonlinearities mightexist in reality, which could require the assump-tion to be relaxed. Important feedback structuresof the model are explained next. The completemodel is available in the online appendix.

Model Structure

The most tangible elements involve the article-production chain, including their flows ofcitations. The article-production chain is modelledusing a standard aging-chain structure (Sterman,2000, p. 470-474; Ventana Systems, 2012). In theupper half of Figure 2, articles are submitted tojournal j. They accumulate as articles under reviewin j until the editorial board decides about theirfurther treatment. The model does not distinguishbetween immediate rejections (‘desk rejection’) orrejections after a substantial review by referees.Rejected articles exit the system; accepted articlesaccumulate in the backlog of accepted but not yetphysically published articles in j. Most journalshave an online repository, which allows accessingaccepted but not yet published articles. This is animportant way of increasing the availability of



S. N. Groesser

research results; however, citing repository articlesdoes not count for the JIF because these articles arenot yet published. The backlog of accepted articlesis reduced by the publishing rate, which isdetermined by the maximum number of articlesthat can be published per month. After beingpublished, the articles enter the stock relevantarticles in j with a delay of one year and willremain in the stock for 2 years—that is, the timewindow during which citing the articles countfor the Thomson Reuters two-year JIFW. Forinstance, article x is published in 2008; it will berelevant for citation counting in the years 2009and 2010. The outphasing time operationalizesthe two-year duration. After the articles have leftthe relevant time window, they accumulate asclassic articles in memory, which represent thecollective memory of researchers in the field. Inother words, although articles do not belong tothe cohort of citable articles anymore, they arestill influential for the overall perception qualityof journal j. For instance, citations of Sterman’sarticle on ‘Learning in and about ComplexSystems’ (1994) do not count for the JIF ofthe System Dynamics Review anymore. Nonethe-less, the article is still influential—overall it hasbeen cited more than 150 times by authors in dif-ferent fields of science—and contributes to theperceived quality of that journal. After an inter-val of obsolescence, even these ‘classic’-articlesexit the memory of researchers, which might

indicate, for example, either a shift in the gener-ation of researchers or a substitution by a morerecent article.

While being in the stock of relevant articles ofj, the references to these articles are counted ascitations of relevant articles in j. The citationscan stem from two sources: citations from articlespublished in journals i (not j), or citations fromarticles published in journal j in the respectiveperiod. The citations to an article remain in thecitations stocks for 2 years. For example, thecitations of article x in j, which has been publishedin 2008, accumulate in the citations stock for 2009and 2010. In the year 2011, the citations of articlex exit the respective stock. I have modelled thecitations to articles by using a traditional co-flowformulation (Sterman, 2000, p. 497-503; VentanaSystems, 2012). In the following, I introducethe decisions of editors, libraries, reviewers andauthors as well as their underlying structures.Figure 3 builds on a reduced form of Figure 2and details the agents’ measures.

The first loop concerns in principle a stock-management structure (Sterman, 2000, p. 675-680;Ventana Systems, 2012) that governs the journal’srejection rate (B1, Backlog Control). The balancingloop intends to equilibrate the article acceptancerate j and the publishing rate j, which determinethe behaviour of backlog of accepted articles. Therejection rate is also embedded in a second impor-tant loop, which leads to quality improvements. A

relevant articlesin j

articlesoutphasing

outphasingtime

-

publishingrate j

+

citations ofrelevant articles

in j outflow ofcitations

inflow ofcitiations

backlog ofaccepted

articles in j

+

articleacceptance rate j

rejection rate

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articles inreview in jarticle

submission rate

+

maximum number ofarticles per month

+

rejectedarticles

++

citations of articlesin j from articles in j

citations of articles inj from articles in not j

+

current impactfactor of j

classic articlesin j in memory

outflowclassics

+

obsolescencetime

-

-

+

+

+

+

Figure 2 Article production chain (physical structure) and citation dynamics represented in a simplified system structurediagram. Note: A rectangle represents a stock or an accumulation (like water in a bathtub); flows, depicted as pipes with

valves, fill or drain the stock (Sterman (2000) provides further details about the notation)




higher rate of rejection results, ceteris paribus, in ahigher average quality of an accepted article; theassociation between both variables has been mod-elled as an s-shaped relationship (Sterman, 2000,p. 552-562; Rogers, 2003). In turn, articles withhigher quality then increase the attractiveness ofjournal j. Given the academic incentive system inthe USA, the UK and increasingly also in Contin-ental Europe, researchers are prompted to writefor journals with higher quality. Hence, the higherjournal j’s attractiveness, the more researcherswant to publish in and write for j, which leadsto more submissions. The author-transition ratefollows the formulation of the adoption rateof a generic-diffusion model (Sterman, 2000,p. 300-302). R1 is based on the rejection rateand describes a quality-driven growth process,which fuels the stock of researchers and submis-sions (R1, Quality-driven Growth). In addition,higher attractiveness of j also results in a higherjunior reviewer transition rate for journal j. Thereviewer-transition rate uses also the formula-tion of the adoption rate of a generic-diffusionmodel. I assume that the quality of the newreviewers is homogeneous and lower relative to

the quality of j’s senior reviewers. I furtherassume that it is more feasible and easier torecruit juniors than seniors because the latterare already situated in the academic system, itsdisciplines and its journals, which reducebenefits and motivation for seniors to leavetheir current position—they are economicallyand emotionally locked in. At first, newreviewers lower the fraction of senior reviewersand thus lead to a dilution of the averagereviewer quality (B2, Quality Dilution)—anaspect which has been extensively studied inproject management (e.g. Abdel-Hamid, 1988;Lyneis and Ford, 2007). Over time, however,some junior reviewers gain experience, growmature and become senior reviewers; this Ihave modelled by a standard aging-chainstructure (Sterman, 2000, p. 470-474). The seniorreviewers then contribute to increase the averagequality of articles, which leads to higher journalattractiveness and over time to higher rates ofsubmissions (R2, Quality Boost).

The next paragraph explains two reinforcingmechanisms having to do with the number ofcitations of relevant articles in j. The first is

relevantarticles in j

outdatedarticles

publishingrate jbacklog of

acceptedarticles in j

articleacceptance

rate jarticles inreview in j

articlesubmissions

rejectedarticles

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articles in j

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inflow ofcitiations

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journals i

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journal j

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reviewers ofjournal j

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Delay R1 Quality-drivenGrowth

R4 Access-drivenGrowth

QualityDilution

B2

R2 QualityBoost R3

Growth fromBase

backlog coverageobjective

-


+

+

+

Figure 3 Physical structure and measures of authors, reviewers and libraries about the journal impact factor



S. N. Groesser

Growth from Base (R3). The researchers who arecommitted to journal j write for this journal.The publication of their accepted papers createsan inflow of citations of relevant articles inj, which increases j’s JIF, which in turn alsoimproves journal j’s impact factor relative to thecurrent average impact factor of all other journalsi. The metric for relative impact factor of j comparesthe current performance of journal j to theaverage performance of all sampled journals i ina specific category, for example, Management. Ahigher relative-impact factor increases attractive-ness of journal j, which leads to even moreresearchers writing for j. I have modelled j’sattractiveness using the additive formulationconcept (Richardson and Pugh, 1981, p. 148-152;Sterman, 2000, p. 527-528) because I assume thatthe effect of each input of j’s attractiveness isseparable. The relative-impact factor of j is alsoused by libraries for their subscription manage-ment. Journals with higher impacts are consid-ered more relevant in the respective field,and hence, they are included in the libraries’subscriptions. As a result, j’s articles are moreeasily available online and are cited more often.I have modelled the association between j’srelative-impact factor and its online availabilityas a s-shaped relationship. Here, the concept‘online availability’ accounts for the subscrip-tions of libraries; it does not account for the pre-print online depository of accepted articles.The mechanism described just previously I callGrowth from Base (R3) because the citations torelevant articles come from researchers who areaware of journal j and more closely related to itsfield. The online availability then leads to evenmore citations to the relevant articles in j becauseresearchers who previously have not been awareof the journal can now easily find, directly access,and cite j’s articles (R4, Access-driven Growth).Until now, I have introduced six mechanisms,

which describe important measures of authors,reviewers and libraries (Figure 3). In the follow-ing, I introduce four measures of editors tooperationalize their journal strategy (Figure 4).The first is about the impact-factor objective ofjournal j relative to journals i. It shows thatthe objective of j is contingent on the develop-ment of all journals i. The stretch-objective policy

captures the editors’ decision rule to set theobjective of the journal’s JIF objective withrespect to time. The editors use the lever ‘stretchobjective of j’s editors’ which influences the JIF-objective for j. A shortfall in the relative-impactfactor of journal j motivates editors to takeremedial actions.

A second measure is to increase the awarenessof relevant research communities about journal jby marketing efforts, for example, reaching outto authors at conferences. These efforts likelyincrease the number of researchers who areaware of and author for journal j. The moreresearchers and authors who are aware of j, themore citations of j’s articles come about, whichin turn increases j’s impact factor and lessensthe short fall addressed by the editors. The mech-anism B3 describes the effect marketing has onincreasing awareness (B3, Awareness Push). Themarketing policy expresses the editors’ attentionto marketing spending and effectiveness. Eachmonth a certain fraction of the availableresources, which is defined by the policy lever‘investment fraction for marketing’, is invested intomarketing activities.

Other measures relate to the development ofreviewers. The third measure is about the effortsto develop the capabilities of junior reviewers.By means of advanced training workshops inreviewing, the competencies of junior reviewerscan be improved, accelerating their progressionto the rank of senior reviewers. This wouldincrease the average quality of reviews and also,indirectly, the quality of articles. Consequently,the journal would become more attractive, receivemore citations and, hence, would rise in itsrelative JIF. The mechanism ‘Quality from ReviewerTraining’ (B4) helps to reduce the gap in relativeimpact factor of j. The junior-reviewer training policystates the editors’ decision rule to enhance thetraining of junior reviewers. The policy lever‘investment fraction for junior-reviewer training’defines the proportion of resources invested injunior reviewer training each month.

Regarding reviewers, a second possibility existsfor influencing the current situation. Editors,perhaps supported by journal j’s publisher, caninitiate efforts to recruit new reviewers for journalj. The junior-reviewer acquisition policy expresses




the editors’ attention to gain new junior reviewers.Also here, a certain fraction of available resourcesis spent on a monthly basis; the editors define thefraction by the policy lever ‘investment fraction foradditional junior reviewers’. This measure wouldincrease the number of new reviewers. As a resultin the short-term, the average review quality andhence also the quality of papers would be reduced.If this measure would be used extensively, theattractiveness of journal j would decrease, itsimpact factor would be lower and the gap inrelative journal impact would actually increase.Conceivably, this result might then lead to evenfurther efforts to acquire additional reviewers,which would only worsen the current situation.R5 describes a feedback mechanism that couldcause counter-intuitive behaviours in the system.

Figures 2–4 have introduced the basic mechan-isms aswell as editors’ policies and their respectivelevers. As has been indicated, the interrelatednetwork of feedback loops has the potential forgenerating counter-intuitive and policy-resistant

behaviours. The simulation experiments in thenext section support designing a strategy to avoidor overcome such tendencies.

MODEL ANALYSIS

The previous section has introduced the essentialmodel structure, which then I have parameterized,calibrated, and validated. Some of the validationefforts are documented in the appendix. To thisend, I have used relevant data about journals inthe ISI journal category ‘management’ (ThomsonReuters, 2011). The model is simulated for240 months and is able to represent the generalpatterns of these journals (Figure 1) and abstractsfrom the short-term oscillations; here, I must alsonote that I do not intend to represent any onejournal in particular. Rather, I use the model tocarry out three strategy experiments, which I nameaverage-growth strategy, fast-growth strategy andsustainable-growth strategy. Here, a strategy is

relevantarticles in j

outdatedarticles

publishingrate jbacklog of

acceptedarticles in j

articleacceptance

rate jarticles inreview in j

articlesubmissions

rejectedarticles

outdating time

-

rejection rate

-+

citations ofrelevant

articles in j

outflow ofcitations

inflow ofcitiations

+

-

backlogcoverage

+

+

current impactfactor of other

journals i

impact factorobjective of journal j

gap in relativeimpact factor

+

relative impactfactor of j

-+

-

onlineavailability +

attractivenessof journal j

+

researcherswriting for

journal j

researchertransition rate

avg quality ofarticle

+

+

marketingefforts

+

+

+

+

researchersnot writingfor journal j

juniorreviewers of

journal j

junior reviewertransition rate

notreviewers of

journal j

+

fraction of seniorreviewers-

+

+

+

+senior

reviewers ofjournal j

maturation rate

+

Delay

Delay

reviewertraining efforts

+

revieweracquisition efforts

+

+

Delay

+

Delay

+

+

++

backlog coverageobjective

-


+

+

+

+

Figure 4 System structure diagram about editors’ measures



S. N. Groesser

operationalized by a specific configuration ofpolicies. The editors can change the four policiesby means of their respective policy levers, whichhave been introduced in Section 3 (Model Struc-ture); the values of the policy levers are shownin Table 1.I operationalize the resource allocation for

marketing, junior-reviewer training, and junior-reviewer acquisition using a proportional splitstructure (Ventana Systems, 2012), which allocatesthe available resources according to the relativevalue of the policy levers, that is, the investmentfractions. This standard formulation ensures thatthe same amount of resource is used in eachsimulation run and that the sum of allocations is1. In other words, allocating more to one measureconsequently limits the amounts allocated toothers. However, because I do not have empiricaldata about the financial resources required toimplement the different measures, I have usedabstract units of resources. Also, because of thelack of data, I do not argue with the parametervalues of the policy levers but rather with thequalitative logic represented by these values. Theparameter changes are introduced at t=12(months). The simulation results are provided inFigures 5–11.

Average-Growth Strategy

The average-growth strategy supplies the base case.It represents editors who intend to match thegrowth in j’s JIF to the growth of the averageJIF in the category ‘management’. In addition,the editors do not favour one specific measurebut rather are relatively indifferent toward theavailable measures; that is, the development ajournal’s resources (e.g. authors and reviewers)is not particularly emphasized. The result is thatj’s impact factor develops exponentially with alow rate of growth (Figure 5), just as does the ex-ogenous variable ‘current impact factor of otherjournals i’. The current impact factor of j growsby 160% until t= 240, which can be consideredas a low-to-medium growth for a managementjournal (Figure 1).Because j’s impact factor develops as average,

no management activities are required. The online

Table1Param

eter

settings

forthethreestrategy

experiments

Policyleve

r/param

eter

Stretch

objectiveof

j’sed

itors*

Inve

stmen

tfraction

for

marke

ting

*

Inve

stmen

tfraction

forjunior

review

ertraining

*

Inve

stmen

tfraction

forad

ditiona

ljunior

review

ers*

Strateg

yexperim

ent

Goa

lof

strategy

I:Ave

rage

-growth

strategy

Stan

dardstrategy

;growth

ofJIF-grow

thof

othe

rjourna

ls.D

evelop

men

tof

resource

isno

tpa

rticularly

emph

asized

.

t=0:

0t=

240:

00.39

0.22

0.39

II:F

ast-grow

thstrategy

Improv

etheJIFfastin

thene

xtyears;becomea

lead

ingjourna

lin

thefield.

Journa

lman

agers

emph

asizemarketin

g;othe

rresourcesare

considered

notimpo

rtan

t.

t=0:

0t=

12:0

.5t=

240:

1.5

0.9

0.1

0

III:Su

staina

ble-grow

thstrategy

Edito

rspa

yattentionto

thejourna

l’sim

portan

tresourcesfirst.The

n,they

focu

son

improv

ing

theJIFin

thelong

-term.

t=0:

0t=

12:0

.2t=

240:

1.5

0.33

0.27

0.4

*The

policyleve

rsaredescribed

inSection3(m

odel

structure).

The

policych

ange

sareintrod

uced

att=

12(m

onths).




availability of j decreases slightly and stays belowunity (Figure 6). This is because libraries do notsubscribe to journals with a JIF that is onlyaverage—they are biased towards journals inthe upper 50% of the list. The fraction ofsenior reviewers oscillates around the value of0.5 (Figure 7); the oscillation is mainly caused

by changes in junior reviewers who join and leavej faster than seniors when the perceived attractive-ness of j changes. In addition, the ratio of authorsand reviewers increases constantly to a valueof up to 5 [dmnl] and above (Figure 10). J’s attrac-tiveness alternates because of complex interactionsamong the perceived quality of articles in j, its

4

3

2

1

0

33 3 3 3 3 3 3 3 3 3 3 3 3 3

22

22

22

22 2

22

2 2 2 2

11

1 1 1 11

11

11

11

1 1

0 24 48 72 96 120 144 168 192 216 240Time (Month)

Dm

nl

current relative impact factor of j : sustainable-growth strategy (III) 1 1 1 1 1 1 1 1current relative impact factor of j : fast-growth strategy (II) 2 2 2 2 2 2 2 2 2current relative impact factor of j : average-growth strategy (I) 3 3 3 3 3 3 3 3 3

current relative impact factor of j

Figure 5 Time series of the impact factor of journal j

online availability of j2

1.5

1

0.5

0

33

3 3 3 3 3 3 3 3 3 3 3 3

2 22

2

22

2 2 2 22

2

22 2

11

11 1

11

1

11

11 1 1 1

0 24 48 72 96 120 144 168 192 216 240Time (Month)

Dm

nl

online availability of j : sustainable-growth strategy (III) 1 1 1 1 1 1 1 1 1online availability of j : fast-growth strategy (II) 2 2 2 2 2 2 2 2 2 2online availability of j : average-growth strategy (I) 3 3 3 3 3 3 3 3 3 3

Figure 6 Online availability of journal j



S. N. Groesser

online availability, the gap in impact factor ofj relative to i and other factors (Figure 4). In thisaverage-growth case, the attractiveness of j causesthe number of authors writing for j roughly totriple within 20years (Figure 8). Also, the averagequality of published articles increases over timebecause of improvements in the reviewer stock,

more submissions, and higher rejection rates.The oscillations in quality stem from fluctuationsin the stocks of senior and junior reviewers. Thecomputed time series, for both the resources andresource-related properties, indicate the base caseand are used to evaluate the outcome of the twoother strategies.

fraction of senior reviewers1

0.75

0.5

0.25

0

3

3

3 3

33

33 3 3 3 3 3

3

2 2 22 2

2

2

2

2

22 2 2 2 2

11

1 11

11

11

11

11

1 1

0 24 48 72 96 120 144 168 192 216 240

Time (Month)

Dm

nl

fraction of senior reviewers : sustainable-growth strategy (III) 1 1 1 1 1 1 1 1fraction of senior reviewers : fast-growth strategy (II) 2 2 2 2 2 2 2 2 2 2fraction of senior reviewers : average-growth strategy (I) 3 3 3 3 3 3 3 3 3 3

Figure 7 Fraction of senior reviewers for all three strategies

researcher writing for j800

600

400

200

0

33

3 3

3

3

33

3

3

3

33 3

22 2 2 2

22

22 2 2 2 2 2 2

1 1 11

1

11

1

1 11

11 1

1

0 24 48 72 96 120 144 168 192 216 240Time (Month)

pers

on

researcher writing for j : sustainable-growth strategy (III) 1 1 1 1 1 1 1 1 1researcher writing for j : fast-growth strategy (II) 2 2 2 2 2 2 2 2 2 2researcher writing for j : average-growth strategy (I) 3 3 3 3 3 3 3 3 3 3

Figure 8 Researchers writing for journal j




Fast-Growth Strategy

Editors following the fast-growth strategy intendto develop a leading journal in the field ofmanagement with respect to the JIF in a shortamount of time. The journal managers’ mentalmodels suggest that marketing (Awareness Push,

B3; Figure 4) is the primary means to achieve ahigh JIF in a short time span. Such reasoninggoes on to maintain that with increasedawareness of j, there is no need to spend scarcefinancial and time resources to develop reviewers’competences and reviewer capacity—the assump-tion is that reviewers are attracted to the journal by

2

1.5

1

0.5

0

3 3 33 3 3 3

33

3 3 3 33

2 2 2 2 2 2 2 2 2 2 2

2 2 2 2

1 1 11

11

11

1

1

1

1

11

1

0 24 48 72 96 120 144 168 192 216 240Time (Month)

qual

ity u

nit/a

rtic

le

avg quality of articles published : sustainable-growth strategy (III) 1 1 1 1 1 1 1 1avg quality of articles published : fast-growth strategy (II) 2 2 2 2 2 2 2 2 2avg quality of articles published : average-growth strategy (I) 3 3 3 3 3 3 3 3 3

avg quality of articles published

Figure 9 Average quality of articles published

6

4.5

3

1.5

0

33

33

3

33

33

3

33 3 3

2 2 2 2 2 22

2

2

22 2 2 2 2

1 11 1

1 1 11

1 1 11

1 1

1

0 24 48 72 96 120 144 168 192 216 240Time (Month)

Dm

nl

ratio authors to reviewers : sustainable-growth strategy (III) 1 1 1 1 1 1 1 1 1ratio authors to reviewers : fast-growth strategy (II) 2 2 2 2 2 2 2 2 2 2ratio authors to reviewers : average-growth strategy (I) 3 3 3 3 3 3 3 3 3 3

ratio authors to reviewers

Figure 10 Ratio of authors and reviewers



S. N. Groesser

the increased impact factor alone. In addition, theeditors set the following aggressive objective: j’sJIF has to outperform the average journal by 50%,starting at t=12, and should expand this advan-tage to 150% at t=240 (Table 1).Based on intensive marketing efforts, j becomes

more widely recognized, with the consequencethat the impact factor increases rapidly duringthe months immediately following the change instrategy. In month t=122, however, growth stallsand the JIF is even reduced thereafterwith a devas-tating event around t=170: the editors and thepublisher of j decide to discontinue the journalbecause the backlog of articles is below a criticalthreshold. Consequently, j is not included in the listof ISI-ranked journals anymore. The significantinitial growth in JIF cannot be sustained in thelong-term because the emphasis on marketingleads to additional authors who submit to j butwhose submissions cannot be managed appropri-ately because reviewing capacity does not growaccordingly (see ratio of authors and reviewers,Figure 10). In fact, the reduced emphasis on devel-oping junior reviewers does not account for theretirement of senior reviewers, with the resultthat the stock of senior reviewers dries up.Consequently, the average quality of j’s articlescannot be sustained, given that the capacity of

senior reviewers is stretched to its maximum. Ittakes significant time until the loss in qualitybecomes obvious (Figure 9) because of the backlogof accepted articles and their publication. This lossin quality is amplified by R1, which acts now as avicious cycle: authors stop writing, and juniorreviewers cease reviewing for journal j. Thesubmission rate then stalls, which moves theeditors to reduce the rejection rate, which in turnlowers the average quality of the articles evenmore, with a consequent even lower attractive-ness of j; R1 destroys both j’s quality and its im-pact factor.

The online availability of j is relatively high inthe first years; libraries subscribe to j because ofthe marketing efforts. However, this does nottranslate into sustainable resources, for example,a stock of highly capable reviewers. Althoughjunior reviewers are attracted to j at the begin-ning, with the result that the fraction of seniorreviewers stays at a lower level than in the basecase, the under-supported measure for reviewerdevelopment does not use this advantage todevelop senior reviewers. With lower impactfactors, junior reviewers quickly cease reviewingfor j, leaving only senior reviewers to managethe submissions, with the result that the fractionof senior reviewers approaches the value of [1]

1

0.75

0.5

0.25

0

3 33 3

3

3 3 3 3 3 3 3 3 3

22 2 2 2

22

2

2 22

2 2 2 2

1

11

1

11 1 1 1 1 1 1 1 1 1

0 24 48 72 96 120 144 168 192 216 240Time (Month)

Yea

r

backlog coverage : sustainable-growth strategy (III) 1 1 1 1 1 1 1backlog coverage : fast-growth strategy (II) 2 2 2 2 2 2 2 2 2backlog coverage : average-growth strategy (I) 3 3 3 3 3 3 3 3

backlog coverage

Figure 11 Backlog coverage and average publication time as performance measure for journal j




(Figure 7).4 With an overtaxed reviewer stock,the journal quality stalls, leading more reviewersto leave. At the end of this development, thefraction of senior reviewers is almost at unity—indicating an unsustainable condition. Inaddition, the diminishing JIF leads to lossesamong authors. Around t= 170, the journalceased to exist (Figure 5); accordingly, the averagequality of newly published articles in j drops to [0](Figure 9).5

Sustainable-Growth Strategy

Suppose that other journal editors emphasizeinvesting more in a journal’s resources and lessin marketing efforts (Table 1). The sustainable-growth strategy is based on the assumption thatimproving reviewer quality will merit rewardsin due time. Also, the editors initially set the JIFobjective for j lower than the JIF objective of thefast-growth strategy: an increase by 20% at t= 12,which ought to grow to 150% at t= 240. Thestrategy could be paraphrased as developing ajournal’s important resources first, then focusingon improving the JIF in the long-term.

Based on this strategy, libraries initially includej less often in their subscriptions compared withthe other cases; thus, its online availability islower. This situation changes around t= 173(Figure 6): investments in reviewer quality, whichlag significantly in their effects because of publi-cation delays and article backlog, results in a highaverage level of quality of j’s articles (Figure 9).With this, the articles are more often cited, whichhelps to improve j’s impact factor. Initially, thefraction of senior reviewers is higher (Figure 7);the ratio of authors and reviewers (Figure 10) islower than in the case of the fast-growth strategy.

However, the continuous investments in increas-ing as well as enhancing the reviewer stockresult in a competent reviewer base, yieldinghigh-quality articles which, in turn, attract fur-ther authors to j. The virtuous cycle R1 (Figure 4)helps to grow the journal’s impact factor overtime. This results approximately in a threefoldincrease in author base and a sixfold increase inaverage quality within 20 years.

DISCUSSION

Evaluation of Strategies

Three strategies have been described. Whenevaluating them, one has to specify the relevanttime horizon. Here, I distinguish two, ‘shorterthan 10 years’ and ‘longer than 10 years’. Giventhe time constants in the publication system,for example, for review and publication, thisdifferentiation seems reasonable. The fast-growthstrategy is superior in developing the JIF in thefirst time horizon, and the sustainable-growthstrategy in the longer one. However, evaluatinga strategy based only on the JIF might be toonarrow. It is also important to account for theresources that underlie the outcome variable(Wernerfelt, 1984, 2010; Barney, 1996). Foracademic journals, some of the importantresources are the fraction of senior reviewers,the stock of authors, the online availability ofthe journal and the average quality of thearticles. When the two strategies are evaluatedwith regard to these resources, then, interest-ingly, the sustainable-growth strategy is clearlysuperior across both time horizons. It maintainsa significantly higher average article quality, alarger stock of authors and a more balancedfraction of senior reviewers. Although the fast-growth strategy has the advantages gained bypushing the JIF, it achieves them at the expenseof both available and potential resources.Moreover, a relevant performance measure fora journal is the backlog coverage. Figure 11shows the development of the backlog coveragefor the three strategies. The sustainable-growthstrategy is able to develop a beneficial backlogcoverage of about 0.8 (years), which is slightly

4 The exit rate of senior reviewers is considerably lower than the onefor junior reviewers. This means that, although the stock of seniorreviewers is also declining and reaches a value of [0] in the long-term,the fraction of seniors increases in the mid-term.5 The variable ‘relevant articles in j’represents the articles in j, that, ifcited, count to the journal j’s impact factor. In case of the ThomsonReuters two-year JIFW, articles are in the stage of being relevant articlesfor 2 years. The discrete change in the average quality of these relevantarticles is caused by the editors’ decision to discontinue journal j. Incase the journal is discontinued, it will be excluded from the ThomsonReuters JIF.



S. N. Groesser

higher than for the average-growth strategy. Thefast-growth strategy cannot sustain the initialbacklog coverage level. In fact, this strategyreduces backlog of articles steadily until thejournal has to cease (Figure 11).To summarize, strategy evaluation depends on

the time horizon and the focus of the beholder: Ifa medium-term horizon with a concentration onthe JIF is assumed, then the fast-growth strategyseems to be the one to choose, at the expense ofa degraded journal resource base. However, ifthe beholder assumes a more comprehensive aswell as long-term perspective, the sustainable-growth strategy is preferable. This qualitativeinsight about the strategic orientation of a journalis relatively independent from the specific param-eter values chosen for the policy levers. Univariateand multivariate sensitivity analyses demonstratethe robustness of the described behaviour patternsfor j’s JIF while varying systematically the param-eter values of the policy levers (the documentationof some sensitivity analyses is provided in theonline appendix due to space restrictions). Thethree ranges that could be identified under ex-treme parameter variations correspond to theresulting behaviour patterns of the three strategiesthe paper has demonstrated.

Theoretical Discussion

On a more abstract level, the editors of a journalmust account for the fact that journal quality isinfluenced by authors and reviewers. From amanagement perspective, both of these resourcesmust be managed in dynamic balance with eachother. A disequilibrium between them results ineither lower quality or longer review times and,hence, in lower impacts. The fundamentalmanagement task is therefore dynamically tocontrol the adequate relation of authors toreviewers, especially their respective rates ofgrowth. That is a challenging task because thestock of authors is more dynamic compared withthe stock of reviewers. The first can be increasedfaster but also can suffer a higher attrition rate;the second imposes longer delays and follows amore nonlinear behaviour. The control of such aheterogeneous dynamic system becomes an

overwhelming task for humans (Dörner, 1980;Sterman, 1989; Kluge, 2008). Because of this,journal managers, in principle, face the samechallenges of bounded rationality, misperceptionof delays and feedback as industrial mangers do(Ford, 1990; Paich and Sterman, 1993; Stermanet al., 2007). This paper has helped to carve outand make visible the different dynamics of thosestocks that are relevant for the evolution of a JIF;future research needs to test these dynamicrelationships empirically.

Implications for Practice

Based on discussions with several journal editors,it seems to me that the practice of journal man-agement is underdeveloped. For instance, itseems that the strategic direction of journals isoften not discussed by the editorial board. Thisis particularly noteworthy because the initialdirection taken by a journal’s strategy has thepotential to significantly alter the developmenttrajectory of a journal’s important resources. Thismight even lead to the phenomenon of pathdependency, which would strongly reduce futurestrategic options of journal development.

Based on the paper’s findings, several areas ofjournal management would seem to requireeditors’ attention. First, the editors of a specificjournal need to discuss their important resourcesand resource-related properties and reach aconsensus about them. Second, the developmentof a resource monitoring and controlling systemis helpful in accounting systematically for thestocks of reviewers and authors. For both stocks,especially their growth rates, the quality andfrequency of both reviewing tasks and submis-sions would have to be monitored, and anintegrated development process would have tobe designed for strategically developing bothresources. Third, explicit discussions aboutimportant policies are necessary, for example,the rejection rate, the importance of marketing,and the approach to reviewer developmentbecause they operationalize a journal’s strategy.

Beyond that, the following topics would alsomerit scrutiny: the projected time horizon andthe journal’s strategic objective. The simulation




model developed in this paper could inform sucha discussion. In view of the three implicationsfor practice outlined above, the managementof a journal is a full-time profession rather thana voluntary part-time endeavour. Currently, onlya few management journals have full-time staffdedicated to journal management; my hypothesisis that a journal’s overall quality and impact factorstrongly depends on the full-time employmentof its editors.

Limitations

This paper has several limitations. The first is itsdefinition of sustainability. I base the idea ofa sustainable-growth strategy on the notion ofsustainable development, which the paper triesto operationalize from a resource-based perspec-tive. This operationalization might be open todiscussion. Second, the general trends of expo-nential growth, equilibrium, or stagnation couldbe generated with a much simpler, even a one-stock model (Sterman, 2000). However, a simplermodel would be unable to provide the sameinsights about the system structure and itsdynamics as the policy model developed here.Only the concreteness of the model shows usthat the dynamic balance between authors andreviewers is crucially important for developingthe JIF. Third, there exist still further optionsfor influencing a journal’s impact factor. Forinstance, the timing in publishing influentialpapers: The earlier a paper of highest quality ispublished in the running year, the more citationsit will receive (Falagas and Alexiou, 2008);the later in the year the same paper is published,the shorter the time available for developing itsimpact and collecting citations. Also, another tim-ing-related measure is the end-of-year editorialsummaries of publications for the year thenending, which lead to additional citations. Also,not considered here are new types of manuscript,which might count as citable items (see Reedijkand Moed, 2008). Finally, the possibility of chan-ging the number of issues and articles, which ajournal can publish in a given year, is also nottaken up here. The paper in its current versionhas developed a model, which is versatile

enough to include these measures, which I havejust mentioned. Future research would have tointegrate the measures as well as the likely unin-tended consequences in case they are applied.For instance, in case editors extensively motivatecurrent authors to cite articles of their ownjournal more often, this results in a higher JIFand also a higher self-citation rate. Because ofthis, the current scientific discussions aboutself-citation rates might gain momentum, whichin turn might result in a tendency to degradejournals with a high self-citation rate. Based onthe experience gained by extensive experimenta-tions with the simulation model, I assumethat the fundamental author and reviewer dy-namics and their dynamic correspondencesremain intact even when additional measuresare applied. To summarize, although severallimitations apply, the paper delivers insightsinto dynamics that pertain to both theory andpractice.

CONCLUSIONS

The currency of today’s academic market ispublications in top-ranked journals. Rankings ofjournals and their impact factors are importantsignals of academic quality, which influenceresource allocation in academia. Because journalscompete in the academic arena for recognition,journal impact factors have become an elementin the structural fabric of the academia. Despiteextensive research about journal impact factors,their shortcomings and the possible ways ofreducing these shortcomings, no integrated struc-tural understanding of the system underlying thedynamics of impact factors is available. This paperprovides a first dynamic hypothesis designed toclose this gap. This hypothesis is operationalizedas a system dynamics simulation model with abroad model boundary that integrates multiplegroups of agents, for example, editors, libraries,authors and reviewers. The model is used to ex-periment with, analyse, and design differentgrowth strategies for journals. Besides formulatinga base case, the paper reports on two otherstrategies. The fast-growth strategy has been definedto quickly increase the JIF. This strategy is



S. N. Groesser

successful only when the journal’s objective fo-cuses exclusively on the JIF and only over themedium-term. Following this strategy, the JIFenjoys a boost during the first 10 years. This strongand fast increase is achieved at the expense,however, of underlying resources, which are deci-mated. Alternatively, the sustainable-growth strategyis successful if the journal management assumesa comprehensive definition of their objective—that is, not only boosting the JIF but also devel-oping and sustaining the journal’s significantresources. Given this broad understanding ofgoals and means, the sustainable-growth strategyis superior over the medium-term and also thelong-term and finally outperforms the fast-growth strategy.Comparing the simulation experiments results

in two major insights: first, the levels and growthrates of the crucial resources, authors andreviewers, must be developed in dynamic corres-pondence. Second, developing the stock of high-quality reviewers requires time and resources,but the stock growth is more stable than the stockof authors and hence has a higher potential forguiding the journal into a regime of positivedevelopment. The dynamic hypothesis devel-oped in the paper is a further contribution. Itdemonstrates a first step towards formalizingthe underlying mechanisms of journal impact fac-tors and providing insights into their dynamics.With this step, a significant gap in the literaturehas begun to close.The paper opens up also several avenues for

future research. One would base the modelanalysis on a single specific and insightful casestudy, for which the historical behaviour wouldbe numerically available. With those factors inhand, an empirical test of the dynamic hypothesisdeveloped in this paper would become feasible.Another avenue would be to use different impactalgorithms, for example, the five-year journalimpact factor or the Eigenfactor (Harzing andvan der Wal, 2009) or the EigenfactorTM (Zitt andSmall, 2008; Zitt, 2010) and to compare theiroutcomeswhen using the different strategies. Finalavenues might be to account for other disciplinesthan management or to integrate additionalconcepts into the formal model, for example, self-citation or scientific research networks.

ACKNOWLEDGEMENTS

I wish to thank two anonymous reviewers ofSystems Research and Behavioral Science, MeikeTilebein, University of Stuttgart, and the partici-pants in the 2011 European System DynamicsWorkshop, Frankfurt, Germany, as well as theparticipants in the 2011 System Dynamics WinterConference at Austin, USA, for their helpfulcomments. Thanks also go to the editors ofthe Special Issue. In addition tomy interview part-ners, Katrin Wegmüller-Wyder (Bern UniversityLibraries) and Katherine McNeill (MIT Libraries).In addition, I acknowledge the collegial encour-agement of the System Dynamics Group at theMassachusetts Institute of Technology, Boston,USA, and the Swiss National Science Foundationfor its support of this work (PBSGP1_133613).

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