Knowledge Fusion for Cooperative Innovation

from Strategic Alliances Perspective

Jucheng Xiong and Li Li

Shenzhen Graduate School, Harbin Institute of Technology, 518055, Shenzhen, China

Keywords: Knowledge Fusion, Cooperative Innovation, Strategic Alliances, IT-based Knowledge Management.

Abstract: It has been argued that strategic alliances offer opportunities for using purposive inflows and outflows of

knowledge to accelerate cooperative innovation. In this introductory article, we seek to identify the means by

which knowledge fusion helps create new knowledge and technological innovations. By analyzing the

previous researches in terms of fusion and collaboration, we summarize the approaches of knowledge fusion

based on IT application. Meanwhile, we also give effectiveness mechanisms and brief agenda for research in

this important area. This study offers deep theoretical and managerial insights for firms and other institutions

to manage knowledge fusion in strategic alliances.

1 INTRODUCTION

A growing trend in today’s innovation environment is

intensification of co-competition. In order to compete

in a global market, more and more distributed

organizations bound to work in alliances to gather and

share knowledge by using information technology.

Currently enterprises often establish strategic

alliances such as patent pools, industry-university

collaborative innovation alliances, and industrial

technology innovation alliances to cocreate value that

involves the sharing of knowledge and expertise for

developing new or better products (Dyer and Hatch,

2006; Grover and Kohli, 2012). As noted by Grant

(1996), knowledge is the preeminent resource of the

firm and organizational capability involves

integration of distributed knowledge bases. To

maximize the benefits of knowledge integration

emanated in multiple organizations environment, the

issue of knowledge fusion and innovation gained

through collaboration is important (Meijer, 2000;

Rundquist, 2014).

Knowledge fusion is defined as recognition and

combination of knowledge that are located and

extracted from multiple, distributed, heterogeneous

sources to generate new products, services, processes,

capabilities or competencies (Preece et al., 2001;

Heffner and Sharif, 2008). Most contemporary

organizations are pursuing competitive advantage

from the management information systems.

Advanced information technologies (e.g., the

Internet, Word Wide Web, distributed information

systems, data mining and searching, simulation and

modelling) can enhance the ability to recognize,

assimilate, and exploit external knowledge (Alavi and

Leidner, 2001; Dittrich and Duysters, 2007).

However, most research on knowledge fusion are

focusing on IT level (e.g., the ontology, fusion

framework, fusion algorithm, multi-agent systems),

while this is not enough for knowledge fusion, with

many problems remaining to be solved from the

knowledge management perspective.

There is a growing stream of literature

investigating inter-organizations knowledge

management in innovation alliances (Christoffersen,

2013; Vasudeva et al., 2013; Li et al., 2014), in which

collaborators and competitors integrate in the pursuit

of the codevelopment of technological innovations

(Han et al., 2012). Knowledge fusion has been studied

as a conversion procedure in knowledge integration

with a focus on IT tools to support knowledge

availability, sharing, and assimilation. In this paper

we take one step toward addressing the gap between

engineering science and knowledge science in prior

research. We seek answers to the following set of

questions for knowledge fusion management: What

conditions facilitate knowledge fusion in innovation

alliances? What management mechanisms are the

most effective in enabling knowledge fusion?

498

Xiong, J. and Li, L.

Knowledge Fusion for Cooperative Innovation from Strategic Alliances Perspective.

In Proceedings of the 18th International Conference on Enterprise Information Systems (ICEIS 2016) - Volume 1, pages 498-503

ISBN: 978-989-758-187-8

2 CONTEMPORARY RESEARCH

THEMES

The knowledge-based view (KBV) suggest that

superior profitability is likely to be associated with

resource and capability-based advantages which are

likely to drive from superior access to and integration

of specialized knowledge (Grant, 1996). In order to

support the knowledge integration, much of the

research into the management issues concerning the

role of information technologies has been focusing on

the knowledge management system (KMS) (Černe et

al., 2013; Sutanto and Jiang, 2013; Wang et al., 2014).

Contemporary environment with open information

systems (Li et al., 2014) make the combinative

capabilities become more and more important. This

ability of the firm to generate new combinations of

existing knowledge is improved with the knowledge

fusion theory developed.

The academic results and practical applications of

KRAFT (Knowledge Reuse and Fusion/

Transformation) project are considered the most

representative study in knowledge fusion research.

KRAFT is conceived to investigate how existing

proposals for distributed information systems

architectures can support fusion of knowledge in the

form of constraints expressed against an object data

model (Gray et al., 1997). The literature on

knowledge fusion in the field of computer science has

explored the role of KMS in knowledge storage,

sharing, reuse, revealing, generation, entry,

integration, transportation, search and indexing

(Preece et al., 2001; Smimov et al., 2013). The

primary emphasis of this literature is on the

architectures and fusion algorithms (Jiang et al., 2012;

Zhou et al., 2013).

At the same time, research in knowledge fusion

among multiple organizations has raised several

questions that must be addressed. Heffner et al. (2008)

articulate the knowledge fusion for technological

innovation in organizations as a critical theme for

future research. They propose that we need to

integrate a number of heretofore disparate research

streams, thereby providing a management framework

for examining the knowledge fusion activities of

organizations connect current researching on

knowledge management. A management attitude

towards knowledge fusion and innovation is

discussed by Meijer (2000), who points out that

problem solving comes down to creative processes

which very much depend on thought processes that

primarily take place inside the brains of individuals,

under the influence of the group or the environment

in which they do their creative work. By emphasizing

how IT-based knowledge fusion is occurred in

innovation alliances, fusion mechanisms research can

help decision making and problem solving. Figure 1

illustrates the knowledge fusion management

framework in strategic alliances.

3 KNOWLEDGE FUSION IN

INNOVATION ALLIANCES

The capacity of the information technology to

capture, store, and analyze information offers many

opportunities for cocreation of business value

(Grover et al., 2012), especially in alliances that trust

and formal contracts can offer opportunities for

knowledge sharing and leveraging. Traditionally,

innovation has been created and marketed under

closed settings, in which companies internally

manage all of the processes involved in the

innovation life cycle. Despite the nascent stage of

development, many contemporary business

enterprises have jumped on the bandwagon of the

emerging industrial trend, participating in open

Figure 1: Knowledge fusion management in strategic alliance.

Knowledge Fusion for Cooperative Innovation from Strategic Alliances Perspective

499

Figure 2: Two knowledge fusion approaches in different environment.

innovation alliances in pursuit of leveraging

purposive knowledge inflows and outflows (Han et

al., 2012).Knowledge acquisition and conversion are

crucial to the knowledge fusion, since it makes

possible a much greater degree of innovation ability.

Figure 2 illustrates the two knowledge fusion

approaches through which firms can acquire and

convert knowledge in different ways.

Firms are usually to exploit external knowledge

sourcing by capturing or engaging in alliances. From

transaction cost economics (TCE) perspective,

capturing takes place when organizational boundaries

exist and knowledge is valuable, which influenced by

the risk of opportunism, information asymmetries,

and asset specificity. In contrast, the resource based

view (RBV) can extend understanding of firm

boundaries because it explicitly recognizes

knowledge as a critical resource (Carayannopoulos

and Auster, 2010). Joint and interactive learning

represents a coupled form of knowledge fusion

(Rosell et al., 2014), where acquisition and

conversion take place through cooperative efforts

between organizations that maintain their separate

identities while sharing inputs and control. It seems

that innovation alliances offer an interesting context

within which knowledge fusion can be studied.

Knowledge fusion can also be facilitated by the

prosperity of collaborators as well as rivals in multi-

organizational environment.

Figure 3: Innovation alliance features effect on knowledge

fusion.

Many studies have examined the role of

knowledge management in alliances (Mesquita et

al.,2008; Shin and Lee, 2013). However, we limit our

review to the studies that focus on knowledge fusion

between two alliance partners and its impacts on the

cocreating innovation capabilities. Fusions refers

generally to the blending of different things into

something new, something more than the mere sum

of the parts, which in the process of combination

release or generate tremendous energy . Based on an

analysis of KRAFT project, the core of knowledge

fusion is the knowledge conversion which depends on

an iterative exploration cycle and information

application by capturing both explicit and tacit

knowledge. The first step of knowledge fusion is

knowledge acquisition which established on the basis

of the knowledge sharing. In innovation alliances, it’s

facilitated to acquire external knowledge sourcing

through cooperation between organizations that

maintain their separate identities while sharing

complementary capability and assets. Figure 3

illustrates the innovation alliance features effect on

knowledge fusion.

4 EFFECTIVENESS

MECHANISMS FOR

KNOWLEDGE FUSION

We believe that IT-based knowledge fusion from

distributed databases and knowledge bases represents

one of the most important streams in creativity and

innovation that will gain greater importance as firms

expand collaborative relationships in innovation

alliances. In order to strengthen and promote

knowledge fusion we offer some brief effectiveness

mechanisms to solve the problems and challenges in

practice (Table 1).

1. To expand the knowledge source network. Our

framing drew largely from the strategy alliance

perspective with the assumption that firms will form

a cooperative bond and be willing and able to share

knowledge through thoughtful use of IT. However,

there are several other aspects that need to be

emphasized in order to set a comprehensive research

agenda.

ICEIS 2016 - 18th International Conference on Enterprise Information Systems

500

Table 1: Effectiveness mechanisms for knowledge fusion.

Prerequisites Enablers Results

Knowledge Source Network Knowledge sharing and

leveraging

 Position

 Size

 Density

Knowledge spread

Knowledge Fusion Process Knowledge acquisition,

conversion, creation,

and application

 Relationship

 Complementary

 Synergic

Dynamic and continuous set

of processes and practices

IT-based Knowledge Fusion

Support System

IT infrastructures and

elegant thinking

 Brain

 Machine intelligence

System quality, information

quality, and usefulness

Management Initiatives The role and impact of

 Organization

 Control

 Feedback

Virtuous circle of knowledge

fusion and innovation

For instance, although innovation alliances offer

opportunities for knowledge sharing and leveraging

beyond the firm boundary, they also carry the risk of

knowledge leakage to partner firms. Furthermore, of

the two main types of knowledge, explicit and tacit,

the latter is especially important due to its limited

transferability because the tacit knowledge is

acquired by and stored within individuals in highly

specialized form. In order to solve these problems, it

is necessary to expand the knowledge source

network, which should not only focus on the

knowledge bases but also build some efficient

communication channels (e.g., expert systems,

discussion forums, knowledge directories, and public

innovation platform). For this proposes it is

reasonable to reduce potential barriers in knowledge

sharing between firms, explore the tacit knowledge

transfer ways and means, increase intelligibility of

knowledge representation for the users, and promote

the spread of open knowledge sources.

2. To focus on the knowledge fusion process.

While conceptually the idea of knowledge fusion is

intuitive and simple, the process through which

innovators can successfully implement it is likely to

pose several challenges. How locate data and

knowledge relevant to their current needs. The ability

of knowledge acquisition which involves searching

and retrieving from a wide array of knowledge is the

prime condition. This process decides the quantity

and quality of the available knowledge resources for

knowledge conversion and creation. Regarding

interdependencies, the ultimate goal of the knowledge

fusion is to use the new knowledge in practice. One

of the important implications of the framework is that

knowledge fusion consists of a dynamic and

continuous set of processes and practices embedded

in individuals, as well as in groups and IT structures.

So the process of knowledge fusion is not discrete and

independent. Another implication of this framework

is that knowledge fusion processes of acquisition,

conversion, creation, and application are essential to

effective innovation. We contend that the application

of IT can create an infrastructure and environment

that contribute to knowledge fusion by actualizing,

supporting, augmenting, and reinforcing the fusion

processes.

3. To develop IT-based knowledge fusion support

system. The knowledge fusion support systems

heavily rely upon advanced IT infrastructures. Our

analysis of the literature suggests that IT can lead to a

great depth and breadth of knowledge fusion in

organizations. Usually, the knowledge fusion system

architecture includes the construction of meta-

knowledge, calculation of fusion knowledge metric,

knowledge fusion algorithm, and post processing for

fusion knowledge, all of these function modules are

depend on the IT tools and capabilities. As with most

information systems, the success of knowledge fusion

support system partially depends upon the extent of

use, which itself may be tied to system quality,

information quality, and usefulness. At the current

stage the knowledge fusion patterns and algorithms

are hot research topic in some specific area, but they

are not enough to support the common knowledge

fusion systems. Some future research is needed such

as agent architectures, prototypes for knowledge

sharing, virtual reality-based ontology, algorithms

and cooperation models. Thus, building IT-based

knowledge fusion support system needs

comprehensive consideration of knowledge

management and information systems.

4. To find the relationship between IT and

knowledge fusion management initiatives. It is

important to note that managing knowledge fusion in

innovation alliances is an important issue and that the

main challenge is primarily related to the role and

impact of IT. We have discussed the potential role of

IT relates to more extensive network and

Knowledge Fusion for Cooperative Innovation from Strategic Alliances Perspective

501

communication channels, faster access to knowledge,

just in time learning, and more rapid application of

new knowledge. Meanwhile, we should clear that the

actual knowledge fusion for problem solving only

happens in the minds of humans. It is the manager’s

task to provide the technical and environment in

which the innovators are inspired to be creative and

feel free to communicate. Managers should realize

that IT tools are used to support the human’s creative

work but the IT-based systems themselves are

incapable of keeping pace with dynamic needs of

knowledge fusion. So the most important

consideration is to coordinate machine intelligence

and human creativity when individuals or teams

engage in a cooperative research and development

project. This could create a virtuous circle of

knowledge fusion and innovation.

5 CONCLUSIONS

In this paper, we have presented a discussion of

knowledge fusion in innovation alliance based on a

review, interpretation, and synthesis of a broad range

of relevant literature. We also have highlighted IT-

based knowledge fusion that is of increasing

importance for firms that seek to be cooperative and

innovative. With respect to innovation, innovators

can be involved in multiple knowledge fusion process

chains. In order to solve problems and make

decisions, knowledge fusion can take place in human

brains and intelligent machines with the help of IT.

The patterns and algorithms are the core modules in

the knowledge fusion model. Furthermore, we have

given effectiveness mechanisms from four layers:

knowledge source network, the process of knowledge

fusion, IT-based knowledge fusion support system,

and management initiatives.

Through this special issue, our goal is to seek

effective ways to manage the IT-based knowledge

fusion for innovation. As we summarize above, an

outline of the knowledge fusion system have been

described from the co-competitive perspective. The

analysis also yields some conclusions that are

potentially important for firm managers and alliance

practitioners. They need to regard the choice of

knowledge disclosure level and reduce the transaction

costs in the process of knowledge acquisition. As the

information technology entered a big data era,

dynamics of competition and cooperation among

firms continue to evolve, and IT-based

infrastructures, devices, and software tools create

opportunities for knowledge fusion. The ongoing

work includes available knowledge resources,

advanced man-machine interactive, efficient

knowledge fusion patterns and algorithms, consistent

update knowledge database, and effective

new knowledge evaluation.

ACKNOWLEDGEMENTS

Research works in this paper are financially

supported by Soft Science Research Project of

Guangdong (Grant No. 2013B070206002), Research

Planning Foundation in Humanities and Social

Sciences of the Ministry of Education of China (Grant

No. 13YJAZH044) and National Natural Science

Foundation of China (Grant No. 61173052, and No.

71103050).

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