[AISWorld] H-Index for MIS - 2017

[Michael Cuellar]

It is important that we continue to develop improved measures and methodologies for assessing scholarly impact. And therefore, I think it’s great that University of Arizona sought to create an automated method of computing the h-index. It partially automates the work of disambiguating the names of scholars. Until we get to such time as the use of ORCID (orcid.org) or equivalent is common in publication, this is a needed contribution. I hope that they publish this and/or include it in an R package. Also, they use Google Scholar as the database which as they indicate has more coverage than other database. While somewhat dirty, the greater coverage makes it a good data source for this type of analysis. 

That said, we need to be careful in publishing numbers such as this. The use of single number evaluations is highly problematic. This is especially the case with the h-index. Simply using the one number can be highly misleading if used for evaluating scholars. The h-index itself, while a good omnibus measure of ideational influence, is biased in several ways. First it is insensitive to citations beyond the h number. Once you get beyond h, it doesn’t matter if a paper has 10 or 2000 citations. Second, it is biased in favor of age of the paper. Since the h-index does not decline over time, the longer a paper has been out, the more time it has to pick up citations and thereby increase the value of the index (Truex III et al. 2011). Third, it ignores the contribution of co-authors. A solo-authored paper counts as much as a paper with 500 authors to each author. Finally, it is biased in favor of publication sources that produce more papers. The more papers a source produces, the more opportunity the source has to pick up citations.

For these reasons, when we developed the Scholarly Capital Model (Cuellar et al. 2016), we proposed to use three h-indices: the standard h, the g-index (Egghe 2006) which corrects for highly cited papers and the hc-index (Sidiropoulos et al. 2006) which corrects for recency. We believed that this provides a more rounded perspective on ideational influence than simply using the h-index. To consider co-authors, you might want to consider using the hi, norm index (Harzing 2016) which takes co-authorship count into consideration. For publication sources with large production such as journals, using the hm-index might be warranted (Molinari and Molinari 2008).

And again, the h-family of indices captures only the idea of ideational influence, the uptake of a scholar’s ideas by the field. It doesn’t consider spreading influence by other means such as connectedness or venue representation. To get a real idea of what a scholar brings to the table, you need to consider these other ideas using social network analysis tools such as we did in developing the SCM.

Also, the idea of using such lists as those of the senior scholars while useful as a demonstration probably needs to be upgraded by an automated method of identifying the field. We proposed using the methodology describe in Mingers and Leydesdorff (2014). That would probably be an upgrade to the methodology describe in this paper.

I hope they continue to advanced their efforts by developing more measures. It would be great to see an SCM calculator created using the Mingers and Leydesdorff methodology and the addition of additional statistics such as eigenvector centrality and hi, norm. 

Cuellar, M. J., Takeda, H., Vidgen, R., and Truex III, D. P. 2016. "Ideational Influence, Connectedness, and Venue Representation: Making an Assessment of Scholarly Capital," Journal of the Association for Information Systems (17:1), pp. 1-28.

Egghe, L. 2006. "Theory and Practice of the G-Index," Scientometrics (69:1), pp. 131-152.

Harzing, A.-W. 2016. "Publish or Perish." Harzing, p. This software extracts data from Google Scholar and produces a list of citing articles with a number of citation indices.

Mingers, J., and Leydesdorff, L. 2014. "Identifying Research Fields within Business and Management: A Journal Cross-Citation Analysis," Journal of the Operational Research Society (66), pp. 1370-1384.

Molinari, J.-F., and Molinari, A. 2008. "A New Methodology for Ranking Scientific Institutions," Scientometrics (75:1), pp. 163-174.

Sidiropoulos, A., Katsaros, D., and Manolopoulos, Y. 2006. "Generalized H-Index for Disclosing Latent Facts in Citation Networks," arXiv:cs.DL/o606066 (1).

Truex III, D. P., Cuellar, M. J., Takeda, H., and Vidgen, R. 2011. "The Scholarly Influence of Heinz Klein: Ideational and Social Measures of His Impact on Is Research and Is Scholars," European Journal of Information Systems (20:4).

> On Feb 21, 2017, at 2:02 PM, sandeep suntwal <sandeepsuntwal at email.arizona.edu> wrote:
> 
> Dear All,
> 
> Please find attached the H-Index list for MIS for 2017. Please feel free to
> reach out to me as per the instructions in the document in case of any
> comments, queries or suggestions.
> 
> 
> Thank You and Regards,
> Sandeep Suntwal
> AI Lab - Eller College of Management
> University of Arizona
> <h-index_mis_2017.pdf>_______________________________________________
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