really is influence. There exists no concrete and static definition of ‘influence’ and what the final monster looks like is hard to tell (Thus, I’d like to keep ‘influence’ represented as a cloud in figure 2). As more measures are introduced the shape of the entity becomes clearer. The current research tries to address a small part of the measure of
influence.
Within bibliometrics one can find techniques such as Impact Factors, Bibliograms, Content Analysis, Data Mining, Infometrics, Webometrics and Citation Analysis. Much of my present research focuses in on Citation Analysis (CA). Within CA there are four commonly used Citation Indices (CI) including: the Institute for Scientific Information’s’ (ISI) Web of Science (WoS), Science Citation Index (SCI), Social Science Citation Index (SSCI), and the Arts and Humanities Citation Index (AHCI). Each of these indices were developed for different audiences and each is bounded by assumptions that make them less than desirable as a way to generalize and compare indices between or across disciplines. As such, a more ecumenical and general bibliographic measure has been developed, the H-family of indices. This research uses and further develops the Hirsch family of indices into the IS discipline.
Sources of data used to compute CA can also vary widely. These include Thomson Scientific, which owns ISI. Thomson Scientific is the first source to create CA data and has been the de-facto CA source in the past 30 years. Elsevier publishes Scopus, which can give some CA data. Google Scholar is a newer kid on the block and an up and coming data source in the eyes of many researchers. While GS has not had the completeness of data that were seen in other sources, GS has been gaining sources at a high rate. New datasets are constantly added and even during the course of the current research I have seen whole journals being added in a matter of a few weeks. As time goes on GS seems to be becoming more and more relevant in scholarly research. The current research takes data from GS.
The current research looks at the ‘measure of influence’ in different levels. The use of different levels is analogous to targeting different sets of populations. For example, political campaigns can be looked at in granular or whole sets of data. The smallest group would be the individual and how they vote. Then neighborhoods, districts, cities, counties, states, and the country as a whole can be analyzed for the success/failure of a political campaign.
For my research the 10 feet view of the world is using individual researcher as the unit of measure. At this low level view, each individual is seen as one unit and the measures are done on the whole catalog of work by one individual. I can go up to the 100 feet view to our second level where I am bundling the individual researchers of different groups. At the 100 feet level I may be targeting a group of individuals at one institution. I can go higher up
and look at the 1,000-foot level to see possibly journal groupings. So I am measuring the whole catalog of work done by one journal. I can go even higher to the 10,000-foot level to categorize by country. The groupings here can continue with other possibilities being conference publications, geographic regions, research methodology, PhD granting institution, PhD mentor group, etc.
When studying CA, and moving to higher levels from the individual researcher, the h-indices are
aggregated from the lower levels. Note that the h-indices are not a zero-sum game. The addition of two researchers does not combine the h-indices to get a sum of the two researchers, rather the library of work of the two researchers is combined and the h-index is recalculated. This adds to the complexity and time consumption of the CA research. 1.6 The Papers of the Multi-Paper Model
This section explains the different papers that will make up the multi-paper model of this dissertation. The current research focuses in on a small aspect of the measure of ‘influence’. There will be four major parts to this research stream. First is the use of bibliometric tools on IS researchers. The second section will look at the social network analysis of IS researchers. The third part will concern with combining both bibliometric tools and SNA to the IS researchers. Finally part four will concentrate on using the bibliometric tools and SNA to areas of IS such as knowledge management and groupware.
1.6.1
Description of each and how each paper fits the RQs and Research program
The research in h-indices and social network analysis will further the research on defining the influence or academic worth of a scholar.
1.6.1.1 Research Program
Within bibliometrics I am aiming particularly at citation analysis (CA). Within CA, I am focused on the h-family of indices; the h-index, g-index, hm-index, and hc-index. I focus these bibliometric measures on different sized target populations from small (individual researchers) to larger ones (journals). The individual researcher level is analyzed using the Hirsch family of indices in Chapter 2. Chapter 2 was initially presented at ICIS 2008 in Paris and was published in JAIS in June of 2009. The focus increases in size as the journal level is analyzed using the Hirsch family of indices in Chapter 3. Chapter 3 was presented at AMCIS 2008 in Toronto, and is currently in the re-write/expansion stage for a journal publication. As a side research while using and studying the h-family of indices I identified a void in the h-family of indices. The introduction of the gc-index is the topic for Chapter 4.
Chapter 4 was developed as a student paper and was presented at SAIS 2009 in Charleston and won ‘runner-up’ for the best student paper award.
The SNA analysis section looked at the co-authorship network of IS researchers in Chapter 5. Chapter 5 has been presented at AMCIS 2010 in Lima, Peru and is the preliminary stages of a re-write and expansion to a journal article. The expansion of the use of the Hirsch indices was targeted for online communities. The h-index was used to identify high-level contributors to a college sports team fan site online community. This paper was submitted to a conference and is waiting the reviewer’s decision and is presented in Chapter 6. Finally the Heinz K. Klein research work that first started me on this path was expanded and is going through a third round of revisions for publication in an EJIS special issue dedicated to Heinz Klein.
1.6.1.2 Research Questions
I acknowledge two important notions. First, that there exists a construct called ‘influence’ and second, that the definition of ‘influence’ is ever changing. First, I acknowledge that ‘influence’ exists; therefore it makes sense to try to define ‘influence’ and have measures for influence. I also know that there are multiple factors that measure into ‘influence’. This brings us to figure F2 (Big Picture). In any social setting one can ‘influence’ one another via social interactions. These social interactions include telephone conversations, face-to-face encounters, letters, word of mouth, etc. For scholarly influence these social interactions include conversations, letters, meetings, and lectures. Conversations are communications that take place in synchronous settings. These include face-to-face meetings and distant communications. Face-to-face meetings can be one-on-one meetings or office visits, conversations that take place in group settings such as meetings and lectures. Visits to conferences can reveal small group or one-on-one meetings or presentations. Distant communication can include conversations that take place via phone or Internet relay chat or Skype. Letters include those sent by traditional post, email, or fax messages. Influence can be presented via working relationships as well. Actions such as awards, or accolades in the work place or at conferences can also be influence. Finally the written works or publications can exert influence. These publications include journal articles, conference proceedings, web sites, and books.
In using the citation analysis tools of bibliometrics I hope to answer the following question. “How can the use of bibliometric tools be used to meaningfully compare and evaluate scholars?” This question can be expanded to other target sets of IS researchers. The target sets can be groupings of research/researchers such as the groupings of research published in one journal (in Chapter 3), a group of researchers from one institution, or a grouping of
researchers by country. On the SNA side I have the research question, “How can social networks and network components be meaningfully compared to evaluate scholars?” This again can be used to evaluate scholars in different connections such as co-authorship and co-citation connections between researchers. Finally the
combination of the two types of analysis leads to the following question, “How do the SNA and H-family metrics provide a clearer picture of the construct ‘scholarly influence’?”