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.

References

Adamides, E.D., Karacapilidis, N., 2006. Information technology support for the knowledge and social

processes of innovation management. Technovation 26, 50-59.

Adams, P., Fontana, R., Malerba, F., 2013. The magnitude of innovation by demand in a sectoral

system: The role of industrial users in semiconductors. Research Policy 42, 1-14.

Adomavicius, G., Bockstedt, J., Gupta, A., 2012. Modeling Supply-Side Dynamics of IT Components,

Products, and Infrastructure: An Empirical Analysis Using Vector Autoregression. Information

systems research 23, 397-417.

Anderson, M.C., Banker, R.D., Ravindran, S., 2000. Executive compensation in the information

technology industry. MANAGEMENT SCIENCE 46, 530-547.

Anderson, R.E., Black, W.C., Hair, J.F., Tatham, R.L., 1998. Multivariate data analysis. Prentice-Hall

London.

Armstrong, C.P., Sambamurthy, V., 1999. Information technology assimilation in firms: The influence

of senior leadership and IT infrastructures. Information systems research 10, 304-327.

Atuahene-Gima, K., Li, H.Y., 2000. Marketing's influence tactics in new product development: A

study of high technology firms in China. Journal of Product Innovation Management 17, 451-470.

Banker, R.D., Bardhan, I., Asdemir, O., 2006. Understanding the impact of collaboration software on

product design and development. Information systems research 17, 352-373.

Bapna, R., Langer, N., Mehra, A., Gopal, R., Gupta, A., 2013. Human Capital Investments and

Employee Performance: An Analysis of IT Services Industry. MANAGEMENT SCIENCE 59,

641-658.

Barczak, G., Hultink, E.J., Sultan, F., 2008. Antecedents and consequences of information technology

usage in NPD: A comparison of Dutch and US companies. Journal of Product Innovation

Management 25, 620-631.

Barczak, G., Sultan, F., Hultink, E.J., 2007. Determinants of IT usage and new product performance.

Journal of Product Innovation Management 24, 600-613.

Bardhan, I., Krishnan, V., Lin, S., 2013. Business Value of Information Technology: Testing the

Interaction Effect of IT and R&D on Tobin's Q. Information systems research 24, 1147-1161.

Bartl, M., Fuller, J., Muhlbacher, H., Ernst, H., 2012. A Manager's Perspective on Virtual Customer

Integration for New Product Development. Journal of Product Innovation Management 29, 1031-

1046.

Baskerville, R.L., Myers, M.D., 2002. Information Systems as a Reference Discipline. MIS quarterly

26.

Berger, E., Nakata, C., 2013. Implementing Technologies for Financial Service Innovations in Base of

the Pyramid Markets. Journal of Product Innovation Management 30, 1199-1211.

Chen, C., 2006. CiteSpace II: Detecting and visualizing emerging trends and transient patterns in

scientific literature. Journal of the American Society for information Science and Technology 57,

359-377.

Cohen, W.M., Levinthal, D.A., 1990. Absorptive capacity: a new perspective on learning and

innovation. Administrative science quarterly, 128-152.

Danneels, E., 2002. The dynamics of product innovation and firm competences. Strategic

Management Journal 23, 1095-1121.

Davenport, T.H., Prusak, L., 2000. Working knowledge: How organizations manage what they know.

Harvard Business Press.

Davis, F.D., 1989. Perceived usefulness, perceived ease of use, and user acceptance of information

technology. MIS quarterly, 319-340.

de Ven, A.H.V., 2005. Running in packs to develop knowledge-intensive technologies. MIS quarterly

29, 365-377.

Ding, W.W., Levin, S.G., Stephan, P.E., Winkler, A.E., 2010. The Impact of Information Technology

on Academic Scientists' Productivity and Collaboration Patterns. MANAGEMENT SCIENCE 56,

1439-1461.

Durmuşoğlu, S.S., Barczak, G., 2011. The use of information technology tools in new product

development phases: Analysis of effects on new product innovativeness, quality, and market

performance. Industrial Marketing Management 40, 321-330.

Forman, C., van Zeebroeck, N., 2012. From Wires to Partners: How the Internet Has Fostered R&D

Collaborations Within Firms. MANAGEMENT SCIENCE 58, 1549-1568.

Gobble, M.M., 2013. Big Data: The Next Big Thing in Innovation. Research-Technology

Management 56, 64-66.

Grande, E., 2001. The erosion of state capacity and the European innovation policy dilemma - A

comparison of German and EU information technology policies. Research Policy 30, 905-921.

Gray, P.H., Parise, S., Iyer, B., 2011. Innovation Impacts of Using Social Bookmarking Systems. MIS

quarterly 35, 629-643.

Henderson, R.M., Clark, K.B., 1990. Architectural innovation: the reconfiguration of existing product

technologies and the failure of established firms. Administrative science quarterly, 9-30.

Henfridsson, O., Mathiassen, L., Svahn, F., 2009. Reconfiguring Modularity: Closing Capability Gaps

inDigital Innovation. Association for Information Systems.

Hitt, M.A., Hoskisson, R.E., Kim, H., 1997. International diversification: Effects on innovation and

firm performance in product-diversified firms. Academy of management journal 40, 767-798.

Joshi, K.D., Chi, L., Datta, A., Han, S., 2010. Changing the Competitive Landscape: Continuous

Innovation Through IT-Enabled Knowledge Capabilities. Information systems research 21, 472-

495.

Kafouros, M.I., 2006. The impact of the Internet on R&D efficiency: theory and evidence.

Technovation 26, 827-835.

Kauffman, R.J., Wang, Y.-M., 2002. The network externalities hypothesis and competitive network

growth. Journal of organizational computing and electronic commerce 12, 59-83.

Kawakami, T., Durmusoglu, S.S., Barczak, G., 2011. Factors Influencing Information Technology

Usage for New Product Development: The Case of Japanese Companies. Journal of Product

Innovation Management 28, 833-847.

Kawakami, T., Kishiya, K., Parry, M.E., 2013. Personal Word of Mouth, Virtual Word of Mouth, and

Innovation Use. Journal of Product Innovation Management 30, 17-30.

Kleis, L., Chwelos, P., Ramirez, R.V., Cockburn, I., 2012. Information Technology and Intangible

Output: The Impact of IT Investment on Innovation Productivity. Information systems research 23,

42-59.

Kohler, T., Matzler, K., Fuller, J., 2009. Avatar-based innovation: Using virtual worlds for real-world

innovation. Technovation 29, 395-407.

Li, Q., Maggitti, P.G., Smith, K.G., Tesluk, P.E., Katila, R., 2013. Top Management Attention to

Innovation: The Role of Search Selection and Intensity in New Product Introductions. Academy of

management journal 56, 893-916.

Linton, J.D., 2012. What's hot and what's not: A summary of topics and papers in technology

innovation management that are getting attention. Technovation 32, 653-655.

Liu, F.-c., Simon, D.F., Sun, Y.-t., Cao, C., 2011. China's innovation policies: Evolution, institutional

structure, and trajectory. Research Policy 40, 917-931.

Magnier-Watanabe, R., Yoshida, M., Watanabe, T., 2010. Social network productivity in the use of

SNS. Journal of Knowledge Management 14, 910-927.

Magro, E., Wilson, J.R., 2013. Complex innovation policy systems: Towards an evaluation mix.

Research Policy 42, 1647-1656.

Mansfield, E., 1991. Academic Research and Industrial-Innovation. Research Policy 20, 1-12.

Moed, H.F., 2006. Citation analysis in research evaluation. Springer.

Nambisan, S., 2003. Information systems as a reference discipline for new product development. MIS

quarterly 27, 1-18.

Nambisan, S., 2013. Information Technology and Product/Service Innovation: A Brief Assessment

and Some Suggestions for Future Research. Journal of the Association for Information Systems 14.

Narin, F., 1976. Evaluative bibliometrics: The use of publication and citation analysis in the evaluation

of scientific activity. Computer Horizons Washington, D. C.

Parry, M.E., Kawakami, T., Kishiya, K., 2012. The Effect of Personal and Virtual Word-of-Mouth on

Technology Acceptance. Journal of Product Innovation Management 29, 952-966.

Pavlou, P.A., El Sawy, O.A., 2006. From IT leveraging competence to competitive advantage in

turbulent environments: The case of new product development. Information systems research 17,

198-227.

Roberts, N., Galluch, P.S., Dinger, M., Grover, V., 2012. Absorptive Capacity and Information

Systems Research: Review, Synthesis, and Directions for Future Research. MIS quarterly 36, 625-

648.

Rogers, E.M., 1995. Diffusion of innovations (4th Edition). Free Press (New York).

ROGERS, E.M., 2003. Diffusion of Innovations (5th Edition).

Sambamurthy, V., Bharadwaj, A., Grover, V., 2003. Shaping agility through digital options:

Reconceptualizing the role of information technology in contemporary firms. MIS quarterly 27,

237-263.

Song, M., Berends, H., van der Bij, H., Weggeman, M., 2007. The effect of IT and co-location on

knowledge dissemination. Journal of Product Innovation Management 24, 52-68.

Synnestvedt, M.B., Chen, C., Holmes, J.H., 2005. CiteSpace II: visualization and knowledge

discovery in bibliographic databases, AMIA Annual Symposium Proceedings. American Medical

Informatics Association, p. 724.

Tambe, P., Hitt, L.M., Brynjolfsson, E., 2012. The Extroverted Firm: How External Information

Practices Affect Innovation and Productivity. MANAGEMENT SCIENCE 58, 843-859.

Tushman, M.L., Anderson, P., 1986. Technological discontinuities and organizational environments.

Administrative science quarterly, 439-465.

Vaccaro, A., Veloso, F., Brusoni, S., 2009. The impact of virtual technologies on knowledge-based

processes: An empirical study. Research Policy 38, 1278-1287.

Venkatesh, V., Bala, H., 2008. Technology acceptance model 3 and a research agenda on interventions.

Decision sciences 39, 273-315.

Venkatesh, V., Morris, M.G., Davis, G.B., Davis, F.D., 2003. User acceptance of information

technology: Toward a unified view. MIS quarterly, 425-478.

Wheeler, B.C., 2002. NEBIC: A dynamic capabilities theory for assessing net-enablement.

Information systems research 13, 125-146.

Wolfe, R.A., 1994. Organizational Innovation - Review, Critique and Suggested Research Directions.

Journal of Management Studies 31, 405-431.

Yadav, M.S., Prabhu, J.C., Chandy, R.K., 2007. Managing the future: CEO attention and innovation

outcomes. Journal of Marketing 71, 84-101.

Yang, M.H., Weng, S.S., Hsiao, P.I., 2014. Measuring blog service innovation in social media services.

Internet Research 24, 110-128.