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IS INFORMATION TECHNOLOGY AS ENABLER OR TRIGGER
FOR INNOVATION?: A CITATION-BASED MAPPING
ANALYSIS
Abstract
With the development of Information Technologies (IT), product innovation and service innovation
have more and more connection with IT. And the research about IT & Innovation is becoming a hot
research area now. Nambisan (2013) did some fundamental work demonstrating it is the time to study
the relationship between IT and innovation. In this paper, the status of IT & Innovation study is
analysed through citation analysis with help of CiteSpace. Most influential references, most
outstanding authors, most important institute, top-tier journals, and hottest topics in IT & Innovation
study are all identified in this paper. Also, the formation of this research field is described. Finally, we
focus on the basic question in this field, which is the relationship between IT and Innovation. Based on
papers in top journals in this field, models, variables and the role of IT in innovation in previous
research are all discussed.
Keywords: Information Technology, Innovation, Citation Analysis, Enabler, Trigger.
1
1 INTRODUCTION
Innovation is becoming one of the most critical topics for firms to develop, to maintain its strengths or
even to survive in the fast-changing and high-competitive environment (Danneels, 2002; Hitt et al.,
1997). The performance of innovation gets increasing attentions from managers, investors and even
stock holders (Li et al., 2013; Linton, 2012; Yadav et al., 2007). As a result, some indicators about
innovation are also added into the evaluation forms for firms (Liu et al., 2011; Magro and Wilson,
2013). In this case, more and more researchers are joining in the study about innovation (Mansfield,
1991; Wolfe, 1994).
At the same time, with the emergence of large number of Information Technologies (IT), IT-based
innovation is becoming a hot topic. For example, Social Network Services (SNS) can bring innovative
effect on social network productivity combined with certain environment (Magnier-Watanabe et al.,
2010). Some researchers believe that big data may provide great opportunities for future innovation
(Gobble, 2013), and social media may provide new ways to evaluate organizational innovation
capabilities in relation to social media services (Yang et al., 2014). On the other hand, with the
development of research in the field of Information Systems (IS), several researchers believe that the
IS research can make some contribution to uncovering the mechanisms behind the relationship
between IT and innovation (Baskerville and Myers, 2002; Nambisan, 2003). Nambisan (2013)
discussed the relationship between IT and innovation quite deeply (e.g., digital tool or component as
a trigger/enabler). We follow the logic and framework of Nambisan (2013), to map the status of IT &
Innovation research and discuss how to facilitate future research in this multidisiplinary field.
IT innovation is becoming an important research area now. However, the formation of important
topics in this area is at the early stage. Firstly, researchers focus on studying special characteristics of
the innovation in IT firms or industries (Adams et al., 2013; Anderson et al., 2000; Atuahene-Gima
and Li, 2000; Bapna et al., 2013; Berger and Nakata, 2013; Grande, 2001). These research can be
treated as rehearse in this area. After that, some research began to discuss the value of IT in enhancing
or enabling the innovation process both for production innovation (Banker et al., 2006; Durmuşoğlu
and Barczak, 2011; Pavlou and El Sawy, 2006) and service innovation (Henfridsson et al., 2009;
Kauffman and Wang, 2002; Kleis et al., 2012). And now, emerging IT (e.g., mobile internet, cloud
computing, SNS) created new business models which are quite different from the ones in the past.
Some researchers confirmed that IT can act as a trigger for innovation. But more evidence is still
needed for better understanding on this point.
We adopt citation analysis to map the trends of study on IT & Innovation. Quantitative, objective
methods are combined with visualization to answer the following questions: 1) What are the most
influential references, and outstanding institutes in this field? 2) What is the status and trend of
research on IT & Innovation? 3) What is the relationship between IT and innovation, e.g., can IT be a
trigger for innovation?
The second section of this paper is an overview of the literatures on IT & Innovation research. The
third section reports the process and results of the citation data analysis. In the fourth section,
publications on IT & Innovation in six selected top journals are analysed to show current models on
the relationship between IT and Innovation. In the fifth section, we predict the future trends of
research in IT & Innovation, and proposed some possible theories for future research.
2
2 CITATION ANALYSIS ON IT & INNOVATION STUDY
2.1 Literature Overview
We used the term “Innovation” and “Information Technology” for topical searches on Web of Science.
Timespan was set “all years”, “Science Citation Index Expanded (SCI-EXPANDED) --1900-present”,
“Social Science Citation Index (SSCI)--1996-present” and “Conference Proceedings Citation Index-
Science (CPCI-S) --1990-present” were selected for Database. Finally, 7514 records were found as the
dataset for our citation analysis.
In these years, the number of published papers about Innovation and IT keeps increasing, especially
after 2006. And the increase of the number of citations on the published papers in this area is at a
relatively steady rate.
Figure 1. Published Papers in Each Year
Figure 2. Citations in Each Year
3
2.2 Citation Analysis Approach
In order to map the status of study about IT & Innovation, citation data of journal articles in this field
are adopted in this paper. In fact, the citation data is one of the best current indicators in evaluating the
quality of academic papers (Moed, 2006; Narin, 1976). Influential references, outstanding authors,
important institutions, top-tier journals and hot topics in IT & Innovation study can be pinpointed by
analysing the citation data.
We use CiteSpace as a tool to do this citation analysis. CiteSpace is software used to analyse co-
citation networks. It can give out statistic data and visualization of input dataset (Chen, 2006;
Synnestvedt et al., 2005). During the analysis, we took one year as a slice and top 50 cited terms in
each slice were used for visualization. Some important indicators were provided by CiteSpace.
“Centrality” is “a metric of a node in a network that measures how likely an arbitrary shortest path in
the network will go through the node”; “Burst” is “single or multi-word phrases extracted from the
title, abstract, or other fields of a bibliographic record and the frequency of the term bursts, i.e. sharply
increases, over a period of time”; and “Half-life” is “the number of years that a publication receives
half of its citations since its publication”.
2.3 Citation Analysis Results
2.3.1 Influential References of IT & Innovation Study
From 2004 to 2014, the most influential paper in innovation and information technology is “Diffusion
of innovations (4th ed.)” (Rogers, 1995). This book introduced the diffusion theory of innovation.
After that, “Absorptive capacity: A new perspective on learning and innovation” (Cohen and Levinthal,
1990), “Perceived usefulness, perceived ease of use, and user acceptance of information technology”
(Davis, 1989), “Diffusion of innovations (5th ed.)” (Rogers, 2003) and “User Acceptance of
Information Technology: Toward a Unified View” (Venkatesh et al., 2003) are all influential ones
(Table 1).
The book “Diffusion of innovations” (Rogers, 2003) uncovers how innovation diffuses and studies the
time, way and other natures that the diffusion of innovation bears. It is a classic work about innovation
diffusion theory. It is obvious that 4 papers out of the top 5 ones are about innovation theories. This
indicates that the role of information technologies in the previous research about innovation and IT is
an assistant of innovation.
The hottest paper from 2004 to 2014 is “Multivariate data analysis” (Anderson et al., 1998). Obviously
it is about the math tools. Among top 5 ones, four of them are books and 3 of them are about
mathematics. While the top 5 hottest papers from 2010 to 2014 are “Architectural innovation: the
reconfiguration of existing product technologies and the failure of established firms” (Henderson and
Clark, 1990), “Technology Acceptance Model 3 and a Research Agenda on Interventions” (Venkatesh
and Bala, 2008), “Information technology assimilation in firms: The influence of senior leadership and
IT infrastructures” (Armstrong and Sambamurthy, 1999), “Working knowledge: How organizations
manage what they know” (Davenport and Prusak, 2000) and “Technological discontinuities and
organizational environments” (Tushman and Anderson, 1986) (Table 3). There are 2 papers about IT,
2 ones about management theories and only one about innovation theories.
The paper “Architectural innovation: the reconfiguration of existing product technologies and the
failure of established firms” (Henderson and Clark, 1990) introduced the concept of architectural
innovation. It demonstrated that traditional categorization of innovation (incremental innovation and
critical innovation) is incomplete and potentially misleading. An assumption was made that innovation
of product can be divided into innovation in components and the different ways of the product
architecture. Architectural innovation is defined as the change of products’ architecture without the
change of its components. The subtle challenges of architectural innovation has on established
4
organizations were also analysed. The paper “Technology Acceptance Model 3 and a Research
Agenda on Interventions” (Venkatesh and Bala, 2008) focuses on whether the interventions can play a
role in facilitating greater acceptance and effective utilization of information technology. Based on
previous research about technology acceptance model, this paper studies the determinants of perceived
usefulness and perceived ease of use. It “develops a comprehensive nomological network (integrated
model) of the determinants of individual level (IT) adoption and use” and proposed a research agenda
focused on interventions that can enable the adoption and use of information technology. All these
reveal that IT is becoming a more and more important topic for research about innovation and IT.
Another important conclusion can be made is that research on innovation and IT is becoming merged
from two different directions. The two clusters of cited references from 2004 to 2014 in figure 3
clearly show two different research groups exist. One group cited “Absorptive capacity: A new
perspective on learning and innovation” (Cohen and Levinthal, 1990) most. The other group cited
“Diffusion of innovations” (Rogers, 1995) and “Perceived usefulness, perceived ease of use, and user
acceptance of information technology” (Davis, 1989) most.
The first group is the “Innovation study group” which is trying to uncover the mechanisms of how
innovation appears and how to promote the probability of innovation. The paper “Absorptive capacity:
A new perspective on learning and innovation” (Cohen and Levinthal, 1990) proposed the definition
of a firm’s absorptive capacity. It refers that the basis to recognize a firm’s absorptive capacity is its
prior related knowledge and diversity of background. The factors influencing absorptive capacity in an
organization are individual members, and the role of diversity of expertise within an organization. In
fact, absorptive capacity is one of the most important topics to research mechanisms about innovation.
The second group is the “IS study group” which topics about IT connected to innovation.
In this case, the Innovation study group study deep in theories about innovation, and the IS study
group focus on IT mostly. In the period from 2010 to 2014, the two clusters merged together (Figure 3,
the right part). This fusion of the two groups demonstrates the emergence or the growth of the IT
innovation study. In-depth innovation theories and various research on information technologies are
combined together to focus on the relationship between innovation and IT. (The two clusters of
authors in 2004-2014 and their fusion in 2010-2014 are other evidences (Figure 4).)
Table 1. Top 5 References in IT & Innovation Study from 2004 to 2014 by Frequency
Freq Author Year Title Source
465 Rogers EM. 1995 Diffusion of innovations (4th ed.). New York:
Free Press.
DIFFUSION
INNOVATION
367 COHEN
WM
1990 Absorptive capacity: A new perspective on
learning and innovation
ADMIN SCI
QUART
339 DAVIS FD 1989 Perceived usefulness, perceived ease of use,
and user acceptance of information technology
MIS QUART
319 Rogers E.
M.
2003 Diffusion of innovations (5th ed.). New York:
Free Press.
DIFFUSION
INNOVATION
233 Venkatesh V 2003 User Acceptance of Information Technology:
Toward a Unified View
MIS QUART
231 FORNELL
C
1981 Evaluating Structural Models with
Unobservable Variables and Measurement
Error
J MARKETING
RES
Table 2. Top 5 References in IT & Innovation Study from 2010 to 2014 by Frequency
Freq Author Year Title Source
5
215 Rogers E.
M.
2003 Diffusion of innovations (5th edition) DIFFUSION
INNOVATION
198 Rogers EM. 1995 Diffusion of innovations (4th edition) DIFFUSION
INNOVATION
182 COHEN
WM
1990 Absorptive capacity: A new perspective on learning
and innovation
ADMIN SCI
QUART
180 DAVIS FD 1989 Perceived usefulness, perceived ease of use, and user
acceptance of information technology
MIS QUART
146 FORNELL
C
1981 Evaluating Structural Models with Unobservable
Variables and Measurement Error
J MARKETING
RES
144 Venkatesh V 2003 User Acceptance of Information Technology: Toward
a Unified View
MIS QUART
Table 3. Top 5 References in IT & Innovation Study from 2010 to 2014 by Burst
Burst Author Year Title Source
4.81 HENDERSON
RM
1990 Architectural innovation: the reconfiguration of
existing product technologies and the failure of
established firms
ADMIN SCI
QUART
3.4 Venkatesh V 2008 Technology Acceptance Model 3 and a Research
Agenda on Interventions ([Ranked among the 50
papers to receive Emerald's Citations of
Excellence award for 2012])
DECISION SCI
2.84 Armstrong CP 1999 Information technology assimilation in firms:
The influence of senior leadership and IT
infrastructures
INFORM SYST
RES
2.81 Davenport TH 1998 Working knowledge: How organizations manage
what they know
WORKING
KNOWLEDGE
OR
2.32 TUSHMAN ML 1986 Technological discontinuities and organizational
environments
ADMIN SCI
QUART
Figure 3. Most Cited References in IT & Innovation Study 2004-2014(left)and 2010-2014(right)
6
Figure 4. Most Cited Authors in IT & Innovation Study 2004-2014(left) and 2010-2014(right)
2.3.2 Important Institute for IT & Innovation Study
The most productive institutions from 2004 to 2014 in this field are “Georgia Inst Technol”,
“Michigan State Univ” and “Univ Wisconsin”. And the ones for 2010-2014 are “Georgia Inst
Technol” and “Seoul Natl Univ”. The fastest growing-up institutions are “Univ Manchester”, “Wuhan
Univ Technol” and “Tokyo Inst Technol” for 2004-2014 and “HEC Montreal”, “Seoul Natl Univ” and
“Harvard Univ” for 2010-2014.
Figure 5. Full Shot of Institute on IT & Innovation Study 2004-2014(left) and 2010-2014(right)
Table 4. Top 6 Institute in IT & Innovation Study from 2004 to 2014 by Frequency
Freq Burst Centrality Author Year Half-life
33 3.93 0.02 Georgia Inst Technol 2004 0
33 0.05 Michigan State Univ 2004 0
31 0 Univ Wisconsin 2004 0
29 0.02 Harvard Univ 2006 0
29 0.02 Penn State Univ 2005 0
29 3.56 0 Seoul Natl Univ 2007 0
7
Table 5. Top 5 Institute in IT & Innovation Study from 2004 to 2014 by Burst
Freq Burst Centrality Author Year Half-life
28 4.45 0 Univ Manchester 2004 0
21 4.34 0 Wuhan Univ Technol 2007 0
14 4.12 0 Tokyo Inst Technol 2004 0
14 4.09 0 Univ Massachusetts 2008 0
33 3.93 0.02 Georgia Inst Technol 2004 0
Table 6. Top 6 Institute in IT & Innovation Study from 2010 to 2014 by Frequency
Freq Burst Centrality Author Year Half-life
22 0.01 Georgia Inst Technol 2011 0
20 2.15 0 Seoul Natl Univ 2011 0
16 0 Univ Twente 2010 0
16 0 Univ Toronto 2010 0
16 0 Univ Washington 2011 0
16 0 Penn State Univ 2010 0
Table 7. Top 5 Institute in IT & Innovation Study from 2010 to 2014 by Burst
Freq Burst Centrality Author Year Half-life
8 2.18 0 HEC Montreal 2012 0
20 2.15 0 Seoul Natl Univ 2011 0
15 2.14 0 Harvard Univ 2012 0
8 2.07 0 Simon Fraser Univ 2010 0
11 1.97 0 Natl Chung Cheng Univ 2010 0
2.3.3 Top-Tier Journal of IT & Innovation Study
Table 8. Top 10 Journals in IT & Innovation Study from 2004 to 2014 by Frequency
Freq Centrality Journal Year Half-life
1649 0.11 MANAGE SCI 1989 15
1382 0.29 MIS QUART 2002 2
1242 0.19 RES POLICY 2003 1
1179 0.03 ORGAN SCI 2000 4
1116 0.04 STRATEGIC MANAGE J 1991 13
1114 0.03 ADMIN SCI QUART 1993 11
1054 0.08 ACAD MANAGE REV 2002 2
1012 0.17 HARVARD BUS REV 1990 14
990 0.1 ACAD MANAGE J 2001 3
935 0.07 DIFFUSION INNOVATION 1995 9
8
Table 9. Top 10 Journals in IT & Innovation Study from 2004 to 2014 by Burst
Freq Burst Centrality Journal Year Half-life
121 13.76 0.01 STRATEG MANAGE J 2007 7
58 11.49 0 INFORMATION TECHNOLO 2001 3
481 8.59 0.06 SLOAN MANAGE REV 1994 10
156 7.03 0 MANAGE DECIS 2007 6
206 6.85 0.06 INT J PROD ECON 2009 4
39 6.63 0 J COMPUT-MEDIAT COMM 2007 7
198 5.7 0.01 J BUS VENTURING 2003 7
116 5.22 0 INT J PROD RES 2005 8
183 4.82 0.03 J INFORM TECHNOL 2001 3
175 4.57 0.16 INT J MED INFORM 2001 7
2.3.4 Hot Topics in IT & Innovation Study
Except for “innovation” and “information technology”, the most influential topics in this field from
2004 to 2014 are “technology”, “performance”, “model”, “information”, “management”, “adoption”,
“systems” and “knowledge”. The hottest topics from 2004 to 2014 are “e-learning”, “firm”,
“economics” and “entrepreneurship”. While the hottest ones in period from 2010 to 2014 are
“entrepreneurship”, “e-commerce” and “integration”. Based on the summary, research in this field
focus on technologies and performance in organizations, models are an important topic for research of
innovation combined with IT. Also, adoption of IT is studied a lot. The hot topics in the two different
periods show that research on e-commerce and entrepreneurship keeps growing up these years in this
field and integration is a very new topic on studying interaction between innovation and IT.
Figure 6. Topics in IT & Innovation Study from 2004 to 2014
Table 10. Top 10 Topics in IT & Innovation Study from 2004 to 2014 by Frequency
Freq Burst Centrality Author Year Half-life
1723 0.24 innovation 2004 0
707 0.03 technology 2004 0
9
667 0.28 information-technology 2004 0
554 0.1 performance 2004 0
426 0.12 model 2004 0
424 3.47 0.21 information 2004 0
418 0 management 2004 0
402 0.16 adoption 2004 0
360 0.04 systems 2004 0
350 0.08 knowledge 2004 0
Table 11. Top 10 Topics in IT & Innovation Study from 2004 to 2014 by Burst
Freq Burst Centrality Author Year Half-life
39 5.74 0 e-learning 2009 0
181 5.56 0.2 firm 2004 0
51 5.51 0.03 economics 2004 0
57 5.18 0 entrepreneurship 2010 0
50 4.72 0 technological innovation 2009 0
47 4.14 0 open innovation 2013 0
76 3.83 0.03 biotechnology 2004 0
39 3.69 0 perceived ease 2005 0
133 3.67 0.04 information-systems 2004 0
27 3.59 0 coordination 2007 0
Table 12. Top 10 Topics in IT & Innovation Study from 2010 to 2014 by Frequency
Freq Centrality Author Year Half-life
886 0.3 innovation 2010 0
402 0.56 information-technology 2010 0
378 0.05 technology 2010 0
335 0.18 performance 2010 0
225 0 management 2010 0
224 0.05 model 2010 0
223 0.13 adoption 2010 0
218 0.07 information 2010 0
208 0.13 knowledge 2010 0
190 0.22 perspective 2010 0
Table 13. Top 3 Topics in IT & Innovation Study from 2010 to 2014 by Burst
Freq Burst Centrality Author Year Half-
life
35 4.07 0 entrepreneurship 2010 0
10
38 3.06 0 e-commerce 2010 0
57 2.22 0 integration 2012 0
2.4 Relationship between IT and Innovation
Using the item “Innovation” and “Information technology”, we search papers in the top journals of
both IS research and innovation research. The three top journals in IS research are MIS Quarterly,
Information Systems Research and Management Science. The three top journals in innovation research
are Research Policy, Journal of Product Innovation Management and Technovation. 499 journal
articles appear in the dataset according to the research result on Web of Science. Then we read all the
titles and most abstracts of all the papers and get a list of papers specifically focusing on the
relationship between information technology and innovation. This list only contains 24 papers (Table
14). The short length of the selected papers list means that Information Technology and Innovation are
both hot topics in social research. According to the content of the eliminated papers, we can conclude
that the innovation of IT is frequently studied and that IT firms are often selected for some innovation
research.
The current research on relationship between IT and Innovation is mostly on the firm level. Most
research treat innovation as a firm’s performance and study the effect of IT on the performance of
firms. Some papers focus on the New Production Development (NPD). Most studies believe IT related
variables can enable the NPD process or innovation. The IT related Independent Variables (IV) vary is
different papers. IT spending, IT basis and IT usages are mostly adopted in the 24 papers. Dependent
Variables (CV) also differ from each other in different research. That is because there is not a unified
measurement of Innovation. And the current research works much on IT’s effect on firms’ absorptive
capabilities.
Table 14. Summary of Previous Research about the Relationship between IT and Innovation
No. Paper IV DV Level Role
1 (Bardhan et al., 2013) IT spending, R&D spending,
IT* R&D Tobin'Q
Firm Enabler
2 (Kawakami et al.,
2013)
Personal WOM, Virtual
WOM,
Intensity of Use, Varity
of Use
Indivi
dual
Enabler
3 (Parry et al., 2012) Personal WOM, Virtual
WOM, Purchase Intent(PI)
Indivi
dual
Enabler
4 (Bartl et al., 2012) Some discussion on IT & Innovation Firm Enabler
5
(Forman and van
Zeebroeck, 2012)
basic Internet, local
characteristics that may
influence inventive output,
factors that can affect the
volume of collaborations in
a firm pair
Collaborative Patent
Firm Enabler
6
(Adomavicius et al.,
2012) information technology
components, products,
infrastructure
Information
Ttechnology
Components, Products,
Infrastructure
Firm Enabler
7
(Roberts et al., 2012) IT Capabilities (Outside-In
Capabilities, Spanning
Capabilities, Inside-Out
Capabilities)
Absorptive Capacity
(Identify valuable
external knowledge,
Assimilate or transform
Firm Enabler
11
valuable external
knowledge, Apply
assimilated external
knowledge)
8
(Tambe et al., 2012) external focus variable,
workplace decentralization,
IT usage (email percentage),
R&D expense per
employee
product development
outcomes (FIRST,
SPEED, and
PLMGMT)
Firm Enabler
9 (Kleis et al., 2012) IT expense, R&D expense,
Sales, IT*R&D
Patents, Patents
citation
Firm Not clear
10 (Kawakami et al.,
2011) Case study
Firm Enabler
11
(Gray et al., 2011) The number of times an
individual accesses social
bookmarks, The number of
people an individual
connects to by accessing
their social bookmarks
one's level of personal
innovativeness
Indivi
dual
Enabler
12 (Joshi et al., 2010) IT-Enabled Absorptive
Capacity (IT-ACAP)
Commercialized
innovation
Firm Enabler
13
(Ding et al., 2010)
Access to IT
productivity of
scientists,
collaboration
Indivi
dual
Enabler
14 (Vaccaro et al., 2009) Case study Firm Enabler
15 (Kohler et al., 2009) Case study Firm Mixed
16
(Barczak et al., 2008) antecedents to IT Usage (IT
Infrastructure, IT
embededness, NPD Process
Formalization, Colocation
of Team Members,
Outsourcing of NPD
Projects, Length of Time on
the Job)
New Product
Performance (Speed to
market, Market
performance)
Firm Enabler
17
(Barczak et al., 2007) Antecedents (Project risk,
Existence of champion,
Autonomy, Innovative
climate,
IT infrastructure, IT
embeddedness )
New Product
Performance (Speed to
market, Market
performance)
Firm Enabler
18
(Song et al., 2007) Computer-Mediated
Communication
Technologies, Co-location of
R&D Staff, interaction of the
both
Level of
Knowledge
Dissemination
Firm Enabler
19
(Kafouros, 2006)
two features of the Internet
(search and communication)
three critical
dimensions of R&D
efficiency (cost, time
and quality),
absorptive capacity
Firm Enabler
12
20 (Adamides and
Karacapilidis, 2006) Theoretical research
Firm Enabler
21 (de Ven, 2005)
Theoretical research Firm Must
tool
22
(Sambamurthy et al.,
2003) IT competence (Investment
scale, IT capabilities)
Agility (Customer
agility, Partnering
agility, Operitional
agility)
Firm Enabler
23 (Nambisan, 2003) Review / Trigger
24 (Wheeler, 2002) Theoretical research Firm Enabler
3 CONCLUSION
This paper focuses on a young research field -- IT-based innovation research. According to the number
of publications and citations in each year, it is predictable that IT-based innovation will become a hot
research topic in the near future. The formation of this field is shown through visualization of cited
references and outstanding authors. The study of IT Innovation is coming from two different
directions: the information systems research group and the innovation research group. Finally, a basic
question in this field is discussed according to current research. The question is what the relationship
between IT and innovation is. Current research mainly treats IT as an enabler for innovation. However,
some researchers propose that IT can act as a trigger for innovation. Some new studies begin to focus
on uncovering whether the trigger effect exists, and if it does, how it actually works.
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