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Scholarly Influence Research (SIR)
Abstract
Following previous research findings, this paper argues that the currently predominant method of evaluating scholar performance - publication counts in “quality” journals - is flawed due to the subjectivity inherent in the generation of the list of approved journals and absence of a definition of quality. Truex, Cuellar, and Takeda (2009) improved on this method by substituting a measurement of “influence” using the Hirsch statistics to measure ideational influence. Since the h-family statistics are a measure of productivity and the uptake of a scholar’s ideas expressed in publications, this methodology privileges the uptake of a scholar’s ideas over the venue of publication. Influence is built through other means than by having one’s papers read and cited. The interaction between scholars resulting in co-authored papers is another way to build scholarly influence. This aspect of scholarly influence, which the authors term social influence, can be assessed by Social Network Analysis (SNA) metrics that examine the nature and strength of coauthoring networks among IS Scholars. The paper demonstrates the method of assessing social influence by analysis of the social network of AMCIS scholars and compares the results of this analysis with other co-authorship networks from the ECIS and ICIS communities.
Purpose
The purpose of this paper is to identify the top researchers in information behaviour (IB) based on ideational and social influence indicators.
Design/methodology/approach
The population included papers on IB indexed in the Web of Science from 1980 to 2015. UCINET and Bibexcel were the tools used for measuring the ideational and social influence indicators. The correlations among the study variables were measured by applying SPSS and LISREL.
Findings
There was a significant relationship between IB researchers’ productivity and performance, and between ideational influence and social influence. The structural equation modelling showed that a researcher with top placement in his/her co-authorship network can gain higher ideational influence. In total, it seems that the single and traditional criteria are increasingly replacing new and integrative ones in measuring researchers’ scientific influence in fields including IB studies. Results have shown that based on total scores of the studied indicators, Spink, A., Nicholas, D., Ford, N., Huntington, P., Wilson, T.D., and Jamali, H.R. gained the high scores.
Originality/value
The current study used an integrative method based on influence indicators to identify the influential researchers in IB studies. None of the few studies done using bibliometric methods in the realm of IB has investigated the ideational and social influence indicators altogether.
Provides a nontechnical introduction to the partial least squares (PLS) approach. As a logical base for comparison, the PLS approach for structural path estimation is contrasted to the covariance-based approach. In so doing, a set of considerations are then provided with the goal of helping the reader understand the conditions under which it might be reasonable or even more appropriate to employ this technique. This chapter builds up from various simple 2 latent variable models to a more complex one. The formal PLS model is provided along with a discussion of the properties of its estimates. An empirical example is provided as a basis for highlighting the various analytic considerations when using PLS and the set of tests that one can employ is assessing the validity of a PLS-based model. (PsycINFO Database Record (c) 2012 APA, all rights reserved)
Citation counts and more recently usage statistics provide valuable information about the attention and research impact associated with scholarly publications. The open access publisher Public Library of Science (PLOS) has pioneered the concept of article-level metrics, where these metrics are collected on a per article and not a per journal basis and are complemented by real-time data from the social web or altmetrics: blog posts, social bookmarks, social media and other.
A recent and controversial issue in the academic publishing process is “forced journal selfcitations”—
i.e., the requirement that the author(s) include a minimum number of references to a
journal as a condition of publication. This paper uses stakeholder theory to model the positions
of both authors and journal representatives. We also used two empirical surveys to answer the
questions (1) how ethical are such requests, and (2) how common are they? Our initial, stakeholder
analysis suggests that neither an author nor the IS profession at large is likely to consider such
requests ethical. Our empirical surveys suggest that, although about one-third of the respondents
had encountered such requests, most agreed that such requests are unethical.
Has university scholarship gone astray? Do our academic assessment systems reward scholarship that addresses the questions that matter most to society? Using international business as an example, we highlight the problematic nature of academic ranking systems and question if such assessments are drawing scholarship away from its fundamental purpose. We call for an immediate examination of existing ranking systems, not only as a legitimate scholarly question vis-à-vis performance-a conceptual lens with deep roots in management research-but also because the very health and vibrancy of the field are at stake. Indeed, in light of the data presented here, which suggest that current systems are dysfunctional and potentially cause more harm than good, a temporary moratorium on rankings may be appropriate until more valid and reliable ways to assess scholarly contributions can be developed. The worldwide community of scholars, along with the global network of institutions interacting with and supporting management scholarship (such as the Academy of Management, AACSB, and Thomson Reuters Scientific) are invited to innovate and design more reliable and valid ways to assess scholarly contributions that truly promote the advancement of relevant 21st century knowledge, and likewise recognize those individuals and institutions that best fulfill the university's fundamental purpose.
Executive Overview Scholarly impact is one of the strongest currencies in the Academy and has traditionally been equated with number of citations— be it for individuals, articles, departments, universities, journals, or entire fields. Adopting an alternative definition and measure, we use number of pages as indexed by Google to assess scholarly impact on stakeholders outside the Academy. Based on a sample including 384 of the 550 most highly cited management scholars in the past three decades, results show that scholarly impact is a multidimensional construct and that the impact of scholarly research on internal stakeholders (i.e., other members of the Academy) cannot be equated with impact on external stakeholders (i.e., those outside the Academy). We illustrate these results with tables showing important changes in the rank ordering of individuals based on whether we operationalize impact considering internal stakeholders (i.e., number of citations) or external stakeholders (i.e., number of non-.edu Web pages). Also, we provide tables listing the most influential scholars inside the Academy who also have an important impact outside the Academy. We discuss implications for empirical research, theory development, and practice regarding the meaning and measurement of scholarly impact. Each August, we come to talk to each other [at the Academy of Management's annual meetings]; during the rest of the year we read each other's papers in our journals and write our own papers so that we may, in turn, have an audience the following August: an incestuous, closed loop. Donald C. Hambrick, former Academy of Manage-ment president (1994, p. 13)
The article critically examines how work is shaped by performance measures. Its specific focus is upon the use of journal lists, rather than the detail of their construction, in conditioning the research activity of academics. It is argued that an effect of the ‘one size fits all’ logic of journal lists is to endorse and cultivate a research monoculture in which particular criteria, favoured by a given list, assume the status of a universal benchmark of performance (‘research quality’). The article demonstrates, with reference to the Association of Business Schools (ABS) ‘Journal Guide’, how use of a journal list can come to dominate and define the focus and trajectory of a field of research, with detrimental consequences for the development of scholarship.
Most governmental research assessment exercises do not use citation data for the Social Sciences and Humanities as Web of Science or Scopus coverage in these disciplines is considered to be insufficient. We therefore assess to what extent Google Scholar can be used as an alternative source of citation data. In order to provide a credible alternative, Google Scholar needs to be stable over time, display comprehensive coverage, and provide non-biased comparisons across disciplines. This article assesses these conditions through a longitudinal study of 20 Nobel Prize winners in Chemistry, Economics, Medicine and Physics. Our results indicate that Google Scholar displays considerable stability over time. However, coverage for disciplines that have traditionally been poorly represented in Google Scholar (Chemistry and Physics) is increasing rapidly. Google Scholar’s coverage is also comprehensive; all of the 800 most cited publications by our Nobelists can be located in Google Scholar, although in four cases there are some problems with the results. Finally, we argue that Google Scholar might provide a less biased comparison across disciplines than the Web of Science. The use of Google Scholar might therefore redress the traditionally disadvantaged position of the Social Sciences in citation analysis.
Harzing (Scientometrics, 2013) showed that between April 2011 and January 2012, Google Scholar has very significantly expanded its coverage in Chemistry and Physics, with a more modest expansion for Medicine and a natural increase in citations only for Economics. However, we do not yet know whether this expansion of coverage was temporary or permanent, nor whether a further expansion of coverage has occurred. It is these questions we set out to respond in this research note. We use a sample of 20 Nobelists in Chemistry, Economics, Medicine and Physics and track their h-index, g-index and total citations in Google Scholar on a monthly basis. Our data suggest that—after a period of significant expansion for Chemistry and Physics—Google Scholar coverage is now increasing at a stable rate. Google Scholar also appears to provide comprehensive coverage for the four disciplines we studied. The increased stability and coverage might make Google Scholar much more suitable for research evaluation and bibliometric research purposes than it has been in the past.
This paper considers the use of the h-index as a measure of a journal’s research quality and contribution. We study a sample of 455 journals in business and management all of which are included in the ISI Web of Science (WoS) and the Association of Business School’s peer review journal ranking list. The h-index is compared with both the traditional impact factors, and with the peer review judgements. We also consider two sources of citation data – the WoS itself and Google Scholar. The conclusions are that the h-index is preferable to the impact factor for a variety of reasons, especially the selective coverage of the impact factor and the fact that it disadvantages journals that publish many papers. Google Scholar is also preferred to WoS as a data source. However, the paper notes that it is not sufficient to use any single metric to properly evaluate research achievements.
Human life is dependent upon the natural environment, which, most would agree, is rapidly degrading. Business enterprises are a dominant form of social organization and contribute to the worsening, and enhancement, of the natural environment. Scholars ...
A discipline such as business and management (B&M) is very broad and has many
fields within it, ranging from fairly scientific ones such as management
science or economics to softer ones such as information systems. There are at
least two reasons why it is important to identify these sub-fields accurately.
Firstly, for the purpose of normalizing citation data as it is well known that
citation rates vary significantly between different disciplines. Secondly,
because journal rankings and lists tend to split their classifications into
different subjects, for example the the Association of Business Schools (ABS)
list, which is a standard in the UK, has 22 different fields. Unfortunately, at
the moment these are created in an ad hoc manner with no underlying rigour. The
purpose of this paper is to identify possible sub-fields in B&M rigorously
based on actual citation patterns. We have examined 450 journals in B&M which
are included in the ISI Web of Science (WoS) and analysed the cross-citation
rates between them enabling us to generate sets of coherent and consistent
sub-fields that minimise the extent to which journals appear in several
categories. Implications and limitations of the analysis are discussed
We review the status of European publishing in high-impact Information System (IS) journals finding that the European publication record is disappointing. We consider popular explanations to this state of affairs and find them neither credible nor useful for improving the European record. We propose several constructive reasons for this including (1) the lack of appreciation of the article genre, (2) weak publishing cultures, (3) inadequate Ph.D. preparation for article publishing, (4) weak reviewing practices, (5) poorer command of research methods, (6) poorer understanding of the reviewing protocols, and (7) institutional shaping of research funding in Europe. We formulate several recommendations to affect these causes at the individual, institutional, journal, and European community level.
The statistical tests used in the analysis of structural equation models with unobservable variables and measurement error are examined. A drawback of the commonly applied chi square test, in addition to the known problems related to sample size and power, is that it may indicate an increasing correspondence between the hypothesized model and the observed data as both the measurement properties and the relationship between constructs decline. Further, and contrary to common assertion, the risk of making a Type II error can be substantial even when the sample size is large. Moreover, the present testing methods are unable to assess a model's explanatory power. To overcome these problems, the authors develop and apply a testing system based on measures of shared variance within the structural model, measurement model, and overall model.
Scholars of the theory of the firm have begun to emphasize the sources and conditions of what has been described as “the organizational advantage,” rather than focus on the causes and consequences of market failure. Typically, researchers see such organizational advantage as accruing from the particular capabilities organizations have for creating and sharing knowledge. In this article we seek to contribute to this body of work by developing the following arguments: (1) social capital facilitates the creation of new intellectual capital; (2) organizations, as institutional settings, are conducive to the development of high levels of social capital; and (3) it is because of their more dense social capital that firms, within certain limits, have an advantage over markets in creating and sharing intellectual capital. We present a model that incorporates this overall argument in the form of a series of hypothesized relationships between different dimensions of social capital and the main mechanisms and processes necessary for the creation of intellectual capital.
Publication of articles in so called "reputed" journals and achieving high citation counts for the publications are becoming increasingly important in establishing the scientific achievements of individual scholars and institutions. Given that a journal's reputation is based predominantly on the extent to which its articles are cited, some editors, reviewers, and journal publishers tend to overtly request references to their articles or journals. This is justifiably found unethical by many of us. However, we shall argue that such explicit requests for referencing is only the tip of the iceberg. There is a widespread, covert understanding among potential authors that unless they cite work of editors and/or have references to the journal where one is submitting a manuscript, the probability of getting the paper accepted for publication may suffer. This consideration is a much more powerful influence than the overt requests some may have experienced as authors. Overt, as well as covert, activities aimed at bumping up individual and journal citations is a dysfunctional result of an increasingly competitive scholarly environment, where the value of success is high, and failure is a very unpleasant option. In the short run, making the community aware of dysfunctional behaviors with respect to citations might help counteract the most blatant exertion of power. However, addressing the more covert use of power requires a more in depth look at ourselves and the way we conduct and assess scholarship. In the long run, the IS field may even need to seriously assess the extent to which our research efforts serve standards primarily internal to our field (similar to rites of other tribes) rather than delivering value to society.
This Stud. Examines the Leading IS Researchers and the Universities That Supp. Them. We Reviewed Publ. from Nine Leading IS Journals during Calendar Years 2003 to 2007. during That Time, 3,404 Researchers Contributed Toward 2 155 Publ. Articles. Our Anal. Shows That Most of the Leading Researchers Were Affil. with In-rfstitutions in N. Amer.. Our Stud. Also Includes An Anal. of the Publication Productivity of Both IS Fac. and Doctoral Students. This Res. Contributes to the Scientometric Lit. by Providing A Means for Assessing IS Publication Productivity.
Journal self-citation and its effect on impact factors is a much more controversial and hotly debated topic than most academics realize. In this paper I present empirical and editorial support that practices and policies of editors, publishers authors and reviewers intended to raise a journal's impact factors by any means other than publishing the highest quality original work of authors may in fact do more harm than good to the journal and to the academy as a whole. Finally, I echo the call of those that have studied and written on the issue to abandon the practice for the sake of scientific integrity.
Forced journal self-citation, as defined in this paper, has serious implications for the IS field. We introduce a statistical perspective on how common the practice is, discuss whether it is appropriate or not, and evaluate its ethicality. We find that journal self-citations do influence journal impact factors, a measure of journal quality and a tool for many schools in their promotion and tenure process. We suggest that forced self-citations are not considered appropriate by community standards nor are they ethical in terms of the greatest good. We therefore propose that impact factors be disseminated both with and without self-citations to make the practice of forced self-citation more transparent to the IS community. An example of the proposal is shown.
Systems as diverse as genetic networks or the World Wide Web are best described as networks with complex topology. A common property of many large networks is that the vertex connectivities follow a scale-free power-law distribution. This feature was found to be a consequence of two generic mech-anisms: (i) networks expand continuously by the addition of new vertices, and (ii) new vertices attach preferentially to sites that are already well connected. A model based on these two ingredients reproduces the observed stationary scale-free distributions, which indicates that the development of large networks is governed by robust self-organizing phenomena that go beyond the particulars of the individual systems.
QUESTION: How do theories which are generally considered interesting differ from theories which are generally considered non-interesting ? ANSWER: Interesting theories are those which deny certain assumptions of their audience, while noninteresting theories are those which arm certain assumptions of their audience. This answer was arrived at through the examination of a number of famous social, and especially sociological, theories. That examination also generated a systematic index of the variety of propositional forms which interesting and non-interesting theories may take. The fertility of this approach suggested a new field be established called the Sociology of the Interesting, which is intended to supplement the Sociology of Knowledge. This new field will be phenomenologically oriented in so far as it will focus on the movement of the audience's mind from one accepted theory to another. It will be sociologically oriented in so far as it will focus on the dissimilar base-line theories of the various sociological categories which compose the audience. In addition to its value in interpreting the social impact of theories, the Sociology of the Interesting can contribute to our understanding of both the common sense and scientific perspectives on reality.
The process of theory construction in organizational studies is portrayed as imagination disciplined by evolutionary processes analogous to artificial selection. The quality of theory produced is predicted to vary as a function of the accuracy and detail present in the problem statement that triggers theory building, the number of and independence among the conjectures that attempt to solve the problem, and the number and diversity of selection criteria used to test the conjectures. It is argued that interest is a substitute for validation during theory construction, middle range theories are a necessity if the process is to be kept manageable, and representations such as metaphors are inevitable, given the complexity of the subject matter.
Scitation is the online home of leading journals and conference proceedings from AIP Publishing and AIP Member Societies
The practice of editorial "self-referencing" - journal editors requiring that authors include in their papers a specific number of citations from the journal in which they seek to publish - has recently garnered significant interest within the IS academic community. A recent survey of AISWorld suggests that the vast majority of respondents find this practice inappropriate and unethical. Rather than dismissing this behavior as patently unethical, this paper seeks to better understand the possible motivations for this editorial behavior. The notion of the ethical dilemma is introduced, as well as a framework to assist in analyzing them. Ultimately, the analysis suggests that while editors may feel they have worthwhile reasons for requiring self-references, the potential long term risks to the journal, the academy, and the body of knowledge outweigh those reasons.
The IS field is a fragmented field with many different research strategies and topics. To complicate this matter, there are many different publication venues and geographic locations. This study will try to look at the co-authorship social network (SN), using three different venues. One of the venues is the top journal in our field, the other a regional conference, and the third is a top French IS journal. The study will take a social network analysis (SNA) approach to see if there are differences in these venues and to take a preliminary look at the IS field. The results indicate that even though we research under the umbrella of IS, differing venues seem to have differing cliques of researchers. The divide between North American and France is also seen in how different the publication strategies seem to be between the two geographic areas.
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A metaphor of classical social theory concerning the “intersection” of persons within groups and of groups within the individual is translated into a set of techniques to aid in empirical analysis of the interpenetration of networks of interpersonal ties and networks of intergroup ties. These techniques are useful in the study of director interlocks, clique structures, organizations within community and national power structures, and other collectivities which share members. The “membership network analysis” suggested in this paper is compared to and contrasted with sociometric approaches and is applied to the study by Davis et al. (1941) of the social participation of eighteen women.
This study examines the relation between author productivity and the network structure of collaboration among 1,215 authors who published consumer behavior articles in selected journals from 1977 to 1996. We find that the distribution of publication productivity is skewed, following an empirical regularity that has also been observed in other disciplines. After finding a strong relation between author productivity and collaboration, we next explore the structure of co-authorships. Network analysis reveals a "macronetwork" that includes virtually all of the most frequently published authors and many "micro" networks that surround them. Further analysis shows strong relations between publication productivity and the social-structural position of authors in co-author networks. Implications of these findings for understanding the institutional, intellectual, and social structure of knowledge production in consumer behavior are discussed.
This article examine the progress of MIS as a scholarly field of study since 1980. In this examination, MIS is identified as emerging from a supporting base of three foundational fields: computer science, management science, and organization science. Hypotheses related to this emergence are tested by an analysis of data on 271 MIS articles published during th e period 1980-84 in six academic journals and one conference proceedings. Each article is described by its work point (the field of study represented by the publication in which the article appears) and its reference point (as represented by the distribution of the article's publication citations). Results of the analysis support the proposition that MIS is emerging as a distinct field of study, with its own cumulative tradition.
Calls for new directions in MIS research bring with them a call for renewed methodological rigor. This article offers an operating paradigm for renewal along dimensions previously unstressed. The basic contention is that confirmatory empirical findings will be strengthened when instrument validation precedes both internal and statistical conclusion validity and that, in many situations, MIS researchers need to validate their research instruments. This contention is supported by a survey of instrumentation as reported in sample IS journals over the last several years. A demonstration exercise of instrument validation follows as an illustration of some of the basic principles of validation. The validated instrument was designed to gather data on the impact of computer security administration on the incidence of computer abuse in the U.S.A.
This paper presents a social network analysis (SNA) of the European Conference on Information Systems (ECIS) community based on patterns of co-authorship. ECIS contributions are separated into research papers and panels to create social networks that are then analyzed using a range of global network level and individual ego (co-author, panellist) measures. The research community is found to have few properties of the 'small world' and to represent an agglomeration of co-authorships. The panels network has the properties of a 'small world' and displays a stronger sense of social cohesion. An analysis of individual actors (egos) provides insight into who is central to the ECIS community. Based on the SNA, a range of possible interventions are proposed that could aid the future development of the ECIS community. The paper concludes by considering the usefulness of SNA as a method to support IS research.
This study is the second of two studies which assess the intellectual development of MIS. The present study documents the current intellectual structure of MIS research based on an author co-citation analysis. Five invisible colleges, or informal clusters of research activity, were identified (foundations; psychological approaches to MIS design and use; MIS management; organizational approaches to MIS design and use; and curriculum). When contrasted with the earlier study, these results suggest that MIS has made significant progress toward a cumulative research tradition.
This study investigates the appropriateness of using publication of an article in a top (specifically, top five) management journal as a proxy for its quality. Social Science Citation Index citation counts were collected over 7-year event windows for articles published in 34 management journals in 1993 and 1996. Overall, the authors found that articles published in the five journals most often considered to be the top ones in management tend to be cited more often than ones published in the other journals. Far more important, however, across three different criteria for placing articles into top versus non-top categories, there were substantial classification errors from using journal ranking as a proxy for quality. This finding suggests that both administrators and the management discipline will be well served by efforts to evaluate each article on its own merits rather than abdicate this responsibility by using journal ranking as a proxy for quality.
The statistical tests used in the analysis of structural equation models with unobservable variables and measurement error are examined. A drawback of the commonly applied chi square test, in addition to the known problems related to sample size and power, is that it may indicate an increasing correspondence between the hypothesized model and the observed data as both the measurement properties and the relationship between constructs decline. Further, and contrary to common assertion, the risk of making a Type II error can be substantial even when the sample size is large. Moreover, the present testing methods are unable to assess a model's explanatory power. To overcome these problems, the authors develop and apply a testing system based on measures of shared variance within the structural model, measurement model, and overall model.