Diffusion of selected concepts in information systems and management: 1973‐2004
| Published date | 01 June 2006 |
| DOI | https://doi.org/10.1108/02635570610666430 |
| Date | 01 June 2006 |
| Pages | 663-679 |
| Author | Keith Harman,Alex Koohang |
| Subject Matter | Economics,Information & knowledge management,Management science & operations |
Diffusion of selected concepts in
information systems and
management: 1973-2004
Keith Harman
Prescott Valley, Arizona, USA, and
Alex Koohang
University of Wisconsin – Milwaukee, Milwaukee, Wisconsin, USA
Abstract
Purpose – The purpose of the study is to explore the extent to which the diffusion of concepts related
to information systems and management approximates the rate and the cumulative frequency
distribution patterns assumed to reflect the diffusion of innovations.
Design/methodology/approach – The diffusion of those concepts was measured via citation
analysis of 4,014 publications (journal articles, books, and dissertations) for the period 1973-2004.
Findings – Two key findings emerged from the study. First, the cumulative frequency distribution
approximates the S-curve of adoption. Second, the rate of adoption is exponential and corroborates an
epidemiological model of the rate of adoption recently reported in the literature.
Research limitations/implications – Further research is needed to identify and examine topics or
concepts that have run their course and subsequently offer an excellent opportunity to perform
ex-post-facto studies on the life cycle of innovative concepts or topics. From these studies will be
baseline data and easily identifiable “actors” in the diffusion process (authors, editors, reviewers, and
dissertation committees) that will provide the impetus for continued, progressively complex research
models.
Practical implications – The practical implications of a deeper understanding of the diffusion of
innovations are immense. It will enhance understanding of how to better promote research and
development and technology transfer. It will enhance understanding of how better to market the fruits
of those endeavors.
Originality/value – This paper’s findings bring to the scholarly community in the digital era the
importance of understanding how new concepts and theories are brought to light and evaluated.
Keywords Information systems,Innovation
Paper type Research paper
Introduction
Four decades after the publication of the now classic treatise Diffusion of Innovations
by Rogers (1962), scholarly interest in the diffusion of innovations remains at a
relatively high level. Rogers’ (2003) book is now in its fifth edition. It reflects enduring
interest in the topic and scholars’ general acceptance of the “S-curve” that depicts the
diffusion of innovations and scholars’ general acceptance of Rogers’ typology of
“adopter categories,” e.g. “innovators” vs “laggards” (Dubin, 1983 and Torraco, 19 97 as
cited in Lundblad, 2003). A simple search of ProQuest Database using the search string
“diffusion of innovations” yields nearly 1,400 titles with over 1,000 titles accounted for
by scholarly publications.
The current issue and full text archive of this journal is available at
www.emeraldinsight.com/0263-5577.htm
Diffusion of
selected concepts
663
Industrial Management & Data
Systems
Vol. 106 No. 5, 2006
pp. 663-679
qEmerald Group Publishing Limited
0263-5577
DOI 10.1108/02635570610666430
Lundblad (2003) presented an extensive critique of Rogers’ theory and concluded
that its four basic components or constructs remain virtually unchanged after four
decades. Those components included:
(1) the innovation itself,
(2) the communication of the innovation,
(3) the time span or duration of time passed between the introduction of an
innovation and its widespread acceptance, and
(4) the social system into which the innovation is introduced (Lundblad, 2003,
p. 63).
Wejnert (2002) offered additional insights by providing an overview of various models
of innovation spawned by Rogers’ theory and tracing the intellectual heritage of
Rogers’ theory to Tarde’s (1903) seminal book The Laws of Imitation. Hivner et al.
(2003) offered an interesting example of the application of Rogers’ theory by presenting
an epidemiological model of the diffusion of innovations. Maienhofer and Finholt
(2002) offered another interesting example of the application of Rogers’ theory by
presenting a computer simulation of diffusion innovation.
Diffusion theory is rooted in studies of mass media communications and
advertising (Rogers and Shoemaker, 1971). Rogers believed that the mass media
genre of diffusion research would remain robust even as scholars in an
increasingly wide array of other fields apply Rogers’ theory (McGrath and Zell,
2001).
Given the tradition of research on the adoption of innovations, it is surprising
that there is a relative dearth of studies on scholarly communications via scholarly
publications. Scholars interested in the diffusion of innovations among the
academic community have focused upon publication processes and outlets that
mediate or control the flow of scholarly discourse (Elton, 2003; Kamhawi and
Weaver, 2003; Orlans, 1998; White et al., 2004). Journal articles, books, and
doctoral dissertations offer a set of data to understand how new or innovative
theories or concepts are diffused among scholars (Borgman, 1990; Findlay and
Sparks, 2002; Hildreth and Kimble, 2004). It is reasonable to suggest that this also
applies to the diffusion of theories and concepts germane to field such as
information systems and management.
The slope of the S-curve model infers that at some point the rate of adoption
will increase. Scholars have confirmed this concept. Ravichandran (2003) studied
the adoption of TQM and organizational factors that impact it and reported that
rates of adoption increase over time. Hsu and Mesak (2001) followed up on an
earlier study by Olshavsky (1980) who reported that the rate of adoption of
innovations was generally increasing but with substantial differences depending
upon the innovation. Hsu and Mesak (2001) confirmed the findings reported by
Olshavsky (1980) who confirmed findings reported by Mansfield (1961). Bayus
(1992) offered additional confirmation.
Those studies have been primarily focused upon the rate of adoption of products as
measured by purchases of tangible products. There is a dearth in the literature
regarding the rate of adoption of ideas or concepts as measured by scholarly
publications about them. The present study addresses that gap in the literature.
IMDS
106,5
664
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