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RESEARCH ARTICLE
Bridging the knowledge gap between
technology and business: An innovation
strategy perspective
Pejman PeykaniID 1, Mehdi Namazi2, Emran MohammadiID
1*
1 School of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran, 2 School of
Progress Engineering, Iran University of Science and Technology, Tehran, Iran
Abstract
Decision-makers (DMs) are not sufficiently exposed to concepts such as efficiency and risk
in innovative activities from the perspective of organizational strategy. The challenges
become even greater when these DMs lack expertise in technology and deal with uncertain
circumstances. In this sense, exchanging expert knowledge between DMs and technical
teams will strengthen the link between technology planning and strategic management. The
purpose of this study is to bridge the knowledge gap between these two groups. It intro-
duces a framework to translate the organization’s strategy into technological decisions at an
acceptable innovation risk level. This framework considers aspects such as knowledge,
type of innovation, and innovation process. This study focuses on determining whether
activities should be accepted or rejected by examining the uncertainty and efficiency of inno-
vation. It also introduces a novel perspective on the hybrid "success-failure" uncertainty of
innovation, and a new measure called "efficiency probability," which DMs and technology
developers can use to intuitively engage in the innovation process. This paper seeks to pro-
pose a practical strategy map for new product development under uncertain conditions. To
achieve this goal, the Fuzzy Front-End (FFE) concept, fuzzy data envelopment analysis
(FDEA) model, and adjustable possibilistic programming (APP) approach are applied. The
results of this study indicate that innovative activities typically have low efficiency and high
uncertainty. Therefore, the decision to implement or abandon them requires reviewing and
balancing the goals and strategic approach of the organization with technological and busi-
ness features.
I. Introduction
The initial phase of the innovation process is the Fuzzy Front-End (FFE). It begins with spot-
ting new opportunities, followed by new business cases and ideas, and closes when companies
decide to execute an innovative project in the succeeding stages of the innovation process [1].
Strictly speaking, FFE embraces all activities accomplished ahead of any formal development
of a product’s details or launch [2].
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OPEN ACCESS
Citation: Peykani P, Namazi M, Mohammadi E
(2022) Bridging the knowledge gap between
technology and business: An innovation strategy
perspective. PLoS ONE 17(4): e0266843. https://
doi.org/10.1371/journal.pone.0266843
Editor: Xingwei Li, Sichuan Agricultural University,
CHINA
Received: November 10, 2021
Accepted: March 29, 2022
Published: April 14, 2022
Copyright: © 2022 Peykani et al. This is an open
access article distributed under the terms of the
Creative Commons Attribution License, which
permits unrestricted use, distribution, and
reproduction in any medium, provided the original
author and source are credited.
Data Availability Statement: All relevant data are
within the paper and its Supporting Information
file.
Funding: The authors received no specific funding
for this work.
Competing interests: No authors have competing
interests The authors have declared that no
competing interests exist.
The front-end is also strategically significant since it assures the coherence between new
products and services to business goals by attaching the innovation process to business strate-
gies [3]. The core of this early phase in the innovation process is decision-making, where
agreement is achieved on many important issues concerning features, position, performance,
technology, resources, and portfolio alignment of new products and services. Therefore, the
abovementioned decisions are packed into proposals and business plans, assisting go/no-go
decisions for the succeeding stage of the innovation process, which is development and com-
mercialization [4]. Van Aken & Nagel [5] claimed that the FFE has a high level of creativity,
ambiguous initiation, various inputs, vague process, and spontaneous participant
involvement.
Hence, the FFE is roughly an iterative and ambiguous process. In contrast, the development
and commercialization level are relatively a straight and clear process. The quality of FFE is
very important for the next phase of the innovation process as product characteristics, con-
straints, and advantages are established in this phase [6], so it has a high potential for cost and
time savings, quality improvements, and product adjustments when the actual product devel-
opment and commercialization begins. Table 1 shows the differences between the FFE phase
and the development and commercialization phase [7]:
Various FFE models are presented in the literature. Koen et al. [2] illustrated the uncertain
and iterative outlook of the FFE in a circular shape, implying that actions in the model can iter-
ate through five steps. However, their model was limited to just one idea selection step and
lacked a decision gate as the quality–control checkpoint [8]. Therefore, Cooper [9] argued the
importance of the go-to development gate as the last step, where an idea could be dropped
before starting a costly development phase. Hence, the go-to development gate was added to
the model of Koen, et al. The go-to development gate involves a review team to determine Go/
Kill/ Hold/ Recycle decisions, approved action plans, a list of deliverables, and some criteria
for product development [9].
The decision-making and awareness at the go-to development gate have a dual nature. On
one hand, the technical managers are aware of weaknesses, capabilities, and limitations of tech-
nology development units as well as possible. On the other hand, the rationale of the organiza-
tion is at the hands of senior executives who explain the strategy and prefer a lighthouse, to
determine the direction of the organization in the turbulent sea of business and technology.
For a technology-driven company, strategy determines the nature of innovative activities.
Although managers know that technology planning and strategic management are inseparable,
a large part of their focus is naturally on management issues. Conversely, technical people
innately focus on technology.
Hence providing tools and processes to facilitate communication between these two groups
can potentially enhance the connection between technology planning and strategic manage-
ment. The review team exploits the company’s approval level to make a Go/ Kill/ Hold/
Table 1. Fuzzy front-end vs development and commercialization phase.
Feature Fuzzy Front-End Development & Commercialization
Type of information Qualitative, informal, and inaccurate. Quantitative, formal, and accurate.
Cost of project kill Small. Considerable.
Width and depth Wide but lean. Narrow but deep.
Output A proposal. A product.
Budgeting Small but varying. Large and planned.
Nature of the system Hard to plan, eureka occasions, tentative, often disordered. Organized, disciplined with clear goals and plans.
Management approaches Informal, tentative, creativity-oriented. Formal, organized.
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Recycle decision for every project, considering various internal and external conditions that
may influence on company’s approval level. Kim & Wilemon [10] provided examples of these
circumstances that are corporate strategy and culture, pressure for development, technology
readiness, available resources, project risk, risk-taking tolerance, and the ambiguity level at the
end of the FFE. Due to the compilation of the above situations, it can be concluded that the
review team aims to maximize the value of innovative ideas and projects, which may generate
acceptable inherent risk.
An abstraction to this decision-making orientation is maximizing innovation efficiency
under uncertainty. As explained before, FFE has excessive ambiguity that leads to qualitative,
informal, and rough information for decision-makers. While there are many studies on the
selection of innovative projects, new product development portfolios, and R&D projects [11],
they are mainly focused on rational decision-making and are somehow deficient in empirical
data on the function and performance of political and intuitive decision-making. The findings
reveal that we face an intuitive type of decision-making at the early phase of the innovation
process followed by political approaches while the later phase of the innovation process
demands rational decision-making. That is why Eling et al. [12] marked intuition as an impor-
tant factor influencing the process of decision-making at the go-to development gate.
The review team is responsible for making predictions and commenting on decisions, but
their intuitions may be problematic where the decision-making process is based on the power
or the majority votes. Subsequently, precious intuitions could be vanished or may have to
struggle with the power of the majority [12]. As a result, political and rational decision-making
could hinder intuition. Also, according to Hart et al. [13], the most important criteria for the
review team are technical feasibility, product performance, and customer acceptance. Essen-
tially, the review team is facilitated by a non-voting member and involves senior people who
allocate the resources for the project team.
It is suggested that the review team shall not only involve people with creativity and opti-
mistic personas [2,7], but also critical members [14]. These findings convey the importance of
intuitive decision-making and its vulnerability under the shadow of political and rational
approaches. This is in line with the study of Riel et al. [15] where they focused on the impor-
tance of valuation indices for ideas in pursuance of generating innovation that supports idea
selection and prioritization in basic decisions.
On one hand, the review team contains the appropriate combination of seniority, but it
does not necessarily involve the project team members [14]. Hence, there is a gap between the
development team and the DMs. The fact is that the decisions about innovative activity made
by the review team play an entrepreneurial role within the firm. Therefore, just like any other
entrepreneur, they need to know about the risks and rewards of that activity. The communica-
tion between the development and review team will lead to an understandable and credible
position of each project that considers the innovative aspect of corporate strategy. On the
other hand, the review team has to signal the innovative orientation of corporate strategy
down to the organization, especially to the development team to broadcast coherency between
corporate strategy and innovative activities within the firm.
The decision-makers in the review team are faced with the problem of connecting between
the technical units and the decision-making body so that the organization’s innovation strat-
egy is well implemented. They are seeking a mechanism that can create a convergence between
these two groups so that the inherent uncertainty of innovation and the innovation activities
of the organization could be aligned with the innovation strategy. Besides, clarifying the hid-
den awareness of uncertainty in the technical body of the organization is of great importance.
To address the above decision-making challenges at the go-to development gate, this study
has the following research questions:
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i. How can we harvest the intuitions of both the development and the review team at the go-to
development gate?
ii. How can we include both optimistic and pessimistic intuitions in the decision-making
process?
iii. How can we align the above decisions with corporate strategy?
To answer the above questions, this research focuses on measuring the efficiency of innova-
tion under uncertainty so that the innovative activities can be aligned with the organization’s
innovation strategy.
Researchers have already combined data envelopment analysis techniques with other con-
cepts to provide pragmatic analyzes of the innovation-related topic [16,17]. Combining fuzzy
theory with data envelopment analysis is proved to be an advantageous tool to deal with uncer-
tainty [18,19]. The Fuzzy Front-End of product development always confronts uncertainty, so
we propose an efficient model based on an adjustable fuzzy data envelopment analysis
(AFDEA) for intuitive involvement and decision making within a fuzzy environment. Note
that in the AFDEA model, DMs can easily set different optimistic-pessimistic attitudes merely
by adjusting a parameter. As a final step, a case study from the Iranian industry will illustrate
the applicability of the proposed approach.
The structure of this study is as follows: An introduction to the problem is presented in sec-
tion one. Section 2 explores the research background on the strategic evaluation of innovative
activities under uncertainty. Section 3 provides an introduction to fuzzy measures and fuzzy
data envelopment analysis. The research methodology is presented in Section 4 followed by Sec-
tion 5 which presents empirical results. Summary and conclusions are presented in Section 6.
II. Research background
In 1989, Daniela presented a comprehensive review of the different ways to strategically evalu-
ate innovative projects and found that in a highly competitive world, organizations use meth-
ods with significant strategic benefits [20]. In the same year, Sanchez explored the role of
project evaluation techniques in defining a company’s technology strategy. He classified the
strategies into four groups: planning, economic, market, and technical strategies. Sanchez
asserts that using an economic strategy requires pre-defined selection criteria and applying
selection methods is not flexible and in the case of market strategy, evaluation methods only
help prioritize projects. He also concluded that decisions made by companies that use a techni-
cal strategy are solely based on the technical competence of the various projects that are
intended to be implemented [21].
The first empirical research on the relationship between innovation and strategy in various
articles indicates three main objectives for successful companies [22] in innovation: maximiz-
ing portfolio value, achieving a balance between innovation practices, and alignment with
strategy [23,24]. This, as later developed for the fourth purpose: selecting the right number of
innovative activities [25]. The results highlight the importance of coordinating strategic deci-
sions and innovation. However, it was somewhat unknown to understand how this link may
be realized. Various strategies for managing innovative activities are possible [26] and compa-
nies that openly express areas of strategic focus in their innovation portfolios [27] or the pro-
cess of managing the portfolio of ideas by managing the innovation portfolio [28] have more
fruitful results. To align the innovation portfolio with relevant strategic objectives, specific
tools and methods such as strategic buckets have been proposed [29]. However, experimental
results show that companies that have moved from purely financial instruments to strategic
tools are more successful in innovation [22].
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The above studies focused on different aspects of managing the innovation portfolio, but
they lack the most required insight into the actual implementation of strategic goals in the
decision-making process. This is because ambiguity is at the core of innovation and often leads
to risk and uncertainty. Most researches in this area include a combination of models, meth-
ods, and techniques that support the evaluation of innovative activities, but few show the rela-
tionship between innovation and the company’s strategy of uncertainty. Cheung et al. [30]
pioneered using confusing choices to fill this gap in technology-based companies, but they
focused only on the ambiguity of knowledge. Wang et al. [31] proposed a framework for align-
ing risk management with technological innovation with organizational strategy, but they
ignored the state of innovation which is at the heart of the company’s strategy.
Many studies on the evaluation of technological innovation based on the company’s strat-
egy used this framework. For example, Herfert & Arbige [32] introduced an iterative process
and showed how innovation is in line with corporate strategy. However, to evaluate innova-
tions regarding corporate strategy they focused only on business and portfolio management
tools. Once again, Rhéaume & Gardoni [33] conducted a comprehensive study on the relation-
ship between a company’s strategy and management of innovative activities like developing
new products. Although, they failed to provide any guidelines or frameworks for translating
innovation strategies into resource allocation.
For incremental ideas, Ansoff et. al [34] tried to fill this gap and used "strategic situation" to
manage technological innovation. They defined the "Incremental" and "Continuous" Strategic
modes. Although their classification seems inherent, it does not provide a clear concept of
whether or not to issue a ruling on innovation. That is why they used this classification to make
decisions about innovative management styles rather than their evaluation. The go-to develop-
ment gate is a bridge between the blurry world of fuzzy front end and clarifies the world of
product development. The multifunctional nature of the project team results in a multifunc-
tional review team. This fact results in a better decision-making capability for the review team.
The review team could also involve suppliers, technology partners, investors, and customers
[14]. However, the seniority of the team members depends on the type and importance of the
idea. For incremental ideas, the review team comprises mid-management while for radical and
important ideas the senior management also is involved [9]. Rhéaume & Gardoni [33] focus
was on explaining the relationship between company strategies and managing innovative
activities. They paid almost no attention to other aspects. Using business management tools,
Herfert & Arbige [32] came up with a good approach to select innovative activities but he did
not mention how the acceptable level of innovation risk is determined by the organization’s
strategy.
Wang et al. [31] provided a framework for managing risk in innovative activities with an
organizational strategy that takes into account knowledge considerations and the type of inno-
vation. However, he did not specify the status of innovative activity in the organization’s strat-
egy. In other words, he argued that the risk management framework can balance the risk of
activities according to the company’s strategy, but he did not specify which strategic approach
each company is taking should take or which activities are likely to be appropriate given the
company’s strategic approach.
Cheung et al. [30], like Rhéaume & Gardoni [33], considered just one indicator and ignored
other indicators. He analyzed the ambiguities of knowledge and provided a way to strategically
select them. Chao & Kavadias [29] only paid attention to the balance between radical and con-
tinuous innovation and did not offer a way to translate strategy into an acceptable level of
ambiguity in the organization.
Table 2 represents a summary of previous research on strategic evaluation of innovative
activities under uncertainty. This table, categorizes studies based on innovation aspects that
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they have considered. These aspects include knowledge, translating strategy to level of risk tol-
erance, risk management, portfolio management guideline, innovation process, innovation
type, and the uncertainty approach. As seen in this table, only Ansoff and his colleagues could
cover all aspects, nevertheless, they did not issue a clear go/no go verdict for a given project.
There is a major gap in all previous studies, as they have examined innovative projects against
corporate strategy, but none could combine the knowledge and intuition of both the review
team and the development team. In other words, they could not bring the management and
technical teams together to reach a better decision.
To address the above gap, this study suggests using expert elicitation, as well as looking at
the corporate strategy through the lens of efficiency and risk. Accordingly, the fuzzy front-end
concept and adjustable fuzzy data envelopment analysis model are applied as a systematic
approach to performance assessment of the innovation strategies for new product
development.
III. Adjustable fuzzy dea approach
Performance evaluation is one of the most essential real-world decision-making problems.
Data envelopment analysis (DEA) is one of the most applicable and popular approaches for
performance appraisal, benchmarking, and ranking the peer decision-making units (DMUs)
in the presence of multiple inputs and outputs [35–37]. One of the main challenges in applying
the traditional DEA approaches to real-life problems is the presence of ambiguity and uncer-
tainty in inputs and/or outputs [38–40]. As a result, using new uncertain DEA models that can
measure the performance of DMUs under an uncertain environment seems to be essential.
Accordingly, an adjustable fuzzy data envelopment analysis (AFDEA) model based on the
general fuzzy measure will be introduced. The AFDEA can be applied for performance
Table 2. Major pieces of research on strategic evaluation of innovative activities under uncertainty.
Researchers Considering
Knowledge
Translating
Strategy to Risk
Tolerance
Possible Risk
Management
Portfolio
Management
Guideline
Considering the
Innovation Process
Considering the
Innovation Type
Uncertainty
Approach
Critique Chao &
Kavadias [29]
✓ ✓ ✓ ✓ Indirect
Only focused on the balance between radical and continuous innovation
Cheung et. al.
[30]
✓ Indirect
Only focused on the ambiguities of the knowledge of innovation
Wang et. al.
[31]
✓ ✓ ✓ ✓ ✓ Balanced Score
Card
Does not consider the status of innovative activities in the organization’s strategy
Herfert et. al.
[32]
✓ ✓ Risk level
Only business and portfolio management tools are offered to evaluate activities against corporate strategy.
Rhéaume et.
al. [33]
✓ ✓ Indirect
No guidelines for translating innovation strategies into resource allocation
Ansoff et. al.
[34]
✓ ✓ ✓ ✓ ✓ ✓ Synergy
Does not issue a clear Yes/No verdict.
Current
Research
✓ ✓ ✓ ✓ ✓ ✓ Adjustable
Fuzzy-DEA
The dynamic status of activities is not considered
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evaluation of peer homogeneous DMUs under fuzzy data and linguistic variables. Notably, in
the AFDEA model, the attitude of the decision-maker could be determined by the optimistic-
pessimistic parameter. The indices, parameters, and decision variables that will be employed
in the AFDEA model are as follows:
Now, assume that the fuzzy inputs and outputs have a triangular fuzzy (TRF) distribution
as ~PðP1; P2; P3Þ and ~QðQ1;Q2;Q3Þ where P1<P2<P3 and Q1<Q2<Q3. Finally, according to
Peykani et al. [41], the AFDEA model will be introduced as Model (1):
Max FTRF ð1Þ
S:t: XJ
j¼1
d � o
1 � o
� �
Q1
jg þ 1 � d
1 � o
� �
Q2
jg
� �
bj � FTRF � Dx
XJ
j¼1
d
o
� �
Q2
jg þ o � d
o
� �
Q3
jg
� �
bj � FTRF � D 1 � xð Þ
XI
i¼1
1 � d
1 � o
� �
P2
ig þ d � o
1 � o
� �
P3
ig
� �
ai � 1þ Dx
XI
i¼1
o � d
o
� �
P1
ig þ d
o
� �
P2
ig
� �
ai � 1þ D 1 � xð Þ
Indices: i the index of inputs i = 1,. . .,I j the index of outputs j = 1,. . .,J k the index of DMUs k = 1,. . .,K g the index of DMU under evaluation Parameters: Pig the ith input of DMU under evaluation ~P~
ig the ith fuzzy input of DMU under evaluation
Qjg the jth output of DMU under evaluation ~Q~
jg the jth fuzzy output of DMU under evaluation
Pik the ith input of kth DMU ~P~
ik the ith fuzzy input of kth DMU
Qjk the jth output of kth DMU ~Q~
jk the jth fuzzy output of kth DMU
ω the optimistic-pessimistic parameter of general fuzzy measure δ a confidence level for satisfying the uncertain constraint Δ a large number Decision Variables: αi the weight for the ith input βj the weight for the kth output F a continuous variable for converting the objective function to constraint ξ binary variable for linearization of incompatible constraints
https://doi.org/10.1371/journal.pone.0266843.t003
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XJ
j¼1
1 � d
1 � o
� �
Q2
jk þ d � o
1 � o
� �
Q3
jk
� �
bj � XI
i¼1
d � o
1 � o
� �
P1
ik þ 1 � d
1 � o
� �
P2
ik
� �
ai � Dx; 8k
XJ
j¼1
o � d
o
� �
Q1
jk þ d
o
� �
Q2
jk
� �
bj � XI
i¼1
d
o
� �
P2
ik þ o � d
o
� �
P3
ik
� �
ai � Dð1 � xÞ; 8k
d > o � Dx
d � oþ Dð1 � xÞ
x 2 f0; 1g
ai; bj � 0; 8i; j
Also, assume that the fuzzy inputs and outputs have a trapezoidal fuzzy (TLF) distribution
as ~PðP1; P2; P3; P4Þ and ~QðQ1;Q2;Q3;Q4Þ where P1<P2<P3<P4 and Q1<Q2<Q3<Q4. Finally,
the AFDEA model will be presented as Model (2):
Max FTLF ð2Þ
S:t: XJ
j¼1
d � o
1 � o
� �
Q1
jg þ 1 � d
1 � o
� �
Q2
jg
� �
bj � FTLF � Dx
XJ
j¼1
d
o
� �
Q3
jg þ o � d
o
� �
Q4
jg
� �
bj � FTLF � Dð1 � xÞ
XI
i¼1
1 � d
1 � o
� �
P3
ig þ d � o
1 � o
� �
P4
ig
� �
ai � 1þ Dx
XI
i¼1
o � d
o
� �
P1
ig þ d
o
� �
P2
ig
� �
ai � 1þ Dð1 � xÞ
XJ
j¼1
1 � d
1 � o
� �
Q3
jk þ d � o
1 � o
� �
Q4
jk
� �
bj � XI
i¼1
d � o
1 � o
� �
P1
ik þ 1 � d
1 � o
� �
P2
ik
� �
ai � Dx; 8k
XJ
j¼1
o � d
o
� �
Q1
jk þ d
o
� �
Q2
jk
� �
bj � XI
i¼1
d
o
� �
P3
ik þ o � d
o
� �
P4
ik
� �
ai � Dð1 � xÞ; 8k
d > o � Dx
d � oþ Dð1 � xÞ
x 2 f0; 1g
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ai; bj � 0;8i; j
It should be noted that in the above models, Δ = 0, Δ = 1, and Δ = 0.5 show the pessimistic,
optimistic, and compromise attitudes of the DM, respectively. In other words, the adjustable
fuzzy DEA model is able to cover all previous fuzzy chance-constrained DEA models that pre-
sented based on necessity (pessimistic attitude), possibility (optimistic attitude), and credibility
(compromise attitude) measures in fuzzy DEA field. Since the discriminatory power of the
AFDEA model is more than the traditional DEA model, the AFDEA model can be used to
fully rank the DMUs under a fuzzy environment. In this way, we can use AFDEA to assess sys-
tem performance in the fuzzy front-end from a variety of optimistic as well as pessimistic
perspectives.
IV. Methodology
To model the process of an innovative project required human resources in terms of man-
hour, fixed costs such as equipment, software licenses, and laboratory costs, and current costs
are considered as project inputs and the outputs are considered as created value and probabil-
ity of project success. Due to the nature of research and development, the assumption is that,
the output of a project is a value that results from the outcomes of innovative activities.
Items such as production-ready prototypes as well as deposition of knowledge and technol-
ogy in organization boost the created-value and items such as discredit and lost opportunity
are considered as counter-value. For example, if the project succeeds, the output may be a
combination of a patent and a product to be successfully marketed or, in the event of project
failure, the value of knowledge applicable for other activities and projects, taking into account
the counter-value of failure discredit, deduced from the organization. The model is shown in
Fig 1 and presumes all inputs and outputs as fuzzy variables.
Decision-making at the go-to development gate maximizes the innovation efficiency under
uncertainty, while the information is mostly vague, verbal, approximate, and unofficial. To
overcome this issue, we choose fuzzy data development analysis as a part of the proposed deci-
sion support framework.
When a decision-maker attempts to decide whether to include a project within a portfolio
of projects or not, he/she certainly thinks about the risks and uncertainties that may affect the
expected outcome of that project. Knowledge is known as a major contribution to research on
projects and their developments. There is a major difference between innovative projects and
other types of projects. According to research by Shuang and his colleagues [42], knowledge is
Fig 1. Product development process model.
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the major perspective of product development project selection. Other perspectives include
economics, technology, operation, strategy, customer, partner, and resources. Knowledge con-
tribution of innovative projects can be categorized into three criteria [43–45]:
• Individual learning.
• Organizational level learning.
• Organizational culture change.
These categories contribute to the whole organization and contrary to other project per-
spectives, they remain as a positive contribution even if the project fails. This influence is
much higher in the case of the project’s successes. this means that innovative projects still
bring value to organizations even if they fail and we should consider this “failure value” in the
project selection scenario.
As already mentioned, this model assumes that a research and development project can be
valuable even if it fails, as the accumulated knowledge is the last thing that the failed project
brings to the organization. Generally, the value of a project in case of failure is much less than
its value if the project succeeds. Hence, for the calculation of created value, the following three
parameters are specified:
• Projections of project success and fail
• Prediction of created value If successful
• The estimated value created in case of failure of the project
Therefore, the created value is calculated as Eq (2):
V ¼ ðPs � VsÞ þ ðPf � Vf Þ ð3Þ
In this formula, V is the total created value, Ps is the possibility of success and Pf is the possi-
bility of failure, Vf and, is the value created in the event of success Vs is the value created in the
event of failure.
To collect data for analysis, we consider research and development as a widely accepted and
well-established innovation process within various industries. This makes the results of data
analysis easier to digest by practitioners as well as researchers.
Different factors add certain kinds of uncertainty into R&D project making, so each project
is a unique experience. Hence gathering probabilistic information about R&D projects is a
challenge. To generate stochastic input for our decision model, we invite experts with special
knowledge to express their ideas about the likelihood of values or events in question.
V. Empirical results
The data was compiled from an R&D department at a large IT company that has a long history
of developing technologies for data communication. According to Namazi [46], this R&D unit
has been a role model within the country, winning international innovation awards. Conse-
quently, this unit can be regarded as a representative at the national and international levels. It
can be seen from the history of this R&D unit that it comes out every 4 years with an updated
platform and a range of main and by-products. At the time of data collection, the R&D center
was planning for the next 5 years, in which 33 projects were proposed for implementation.
Additionally, the unit employed 40 knowledge-based employees, including 10 senior research-
ers and engineers who, in addition to technical knowledge, had a thorough understanding of
the product market.
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Hence, 33 projects were selected from a research and development department, then 10
senior experts with research and development, marketing, and product management history
were selected to elaborate on their opinions about those projects.
Experts were asked to give their opinions based on their experience and understanding of
the level of complexity, technology, required knowledge, future risks, and market needs
regarding each project. They also were asked to discuss the possibility of combined technical
and marketing success or failure of projects, the expected value in the event of success or fail-
ure of the project in addition to estimates of required resources such as man-hours, fixed costs,
and research and development costs.
After data collection, we will feed the data into the FDEA model and then map the analysis
results into the proposed strategy map. The above-mentioned process is summarized in Fig 2.
We insist that we have asked out an expert about the possibility of success and failure in the
realm of fuzzy theory. As they were fully aware of fuzzy theory concepts, they simply gave their
opinions about possibilities on a fuzzy natural language expression as illustrated in Table 3.
Experts were asked to retrieve their responses in a fuzzy way to collect the fuzzy data.
According to Table 3 triangular verbal fuzzy variables were used to reflect the probability of
project success. In addition, regarding the value of success and failure value, the mean of opin-
ions was considered as a fuzzy norm (triangular fuzzy midpoint), and minimum and maxi-
mum values were given as upper bound and lower bound of a fuzzy number, respectively. The
Project Planning Unit was also asked to provide triangular fuzzy numbers for the project’s
required resources. The results are shown in Tables 4 and 5. It is imperative to note that
according to Eq (3), the outputs will eventually be merged to produce a single output to calcu-
late performance.
Now, using adjustable fuzzy data envelopment analysis, we calculate the efficiency of proj-
ects in two ways:
1. Pessimistic: By selecting the required measure and setting the parameter to a 100% confi-
dence level where the model constraints are maximally met.
2. Optimistic: Selecting a measure of probability and set the confidence level parameter to 0%
which means that the model constraints are kept at a minimum level.
Fig 2. Research process.
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Table 3. Verbal fuzzy variables.
Linguistic Variables Possibility of Success
Low Mid High
Extremely possible 70 90 100
Possible 60 80 90
Almost Possible 30 50 70
Hardly Possible 10 20 40
Not Possible 0 10 30
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The model output is shown in Table 6. It is natural to calculate the lowest performance in
the skeptical state and the highest performance in the optimistic one for the projects. The full
results of calculations are presented in Appendixes A to C in S1 Appendix.
It may be argued here that credit rating can be used to make decisions. Credit rating, the
average of the two measures, seems not to be appropriate for our decision-making because it
ignores the gap between the possibility and the requirement of much awareness of the two per-
spectives. On the contrary, the validity view assumes the importance of the two possibilities for
the same decision-maker if this assumption is not necessarily true. As a result, the credit per-
spective may lead us astray in decision-making. For this reason, we combined performance-
based performance diagrams with the requirement of validity. As shown in Fig 3, the blue lines
are the average efficient rank of 17, which creates four regions or quadrants:
Table 4. Fuzzy inputs.
Projects Man-Hour Fixed Costs Current Costs
Min Mid Max Min Mid Max Min Mid Max
P1 32 40 48 240 300 360 80 100 120
P2 24 30 36 160 200 240 120 150 180
P3 0.16 0.2 0.24 1.6 2 2.4 0.8 1 1.2
P4 8 10 12 20 25 30 1.6 2 2.4
P5 4 5 6 0.8 1 1.2 1.6 2 2.4
P6 8 10 12 4 5 6 8 10 12
P7 16 20 24 3.2 4 4.8 8 10 12
P8 2.4 3 3.6 40 50 60 16 20 24
P9 12 15 18 1.6 2 2.4 1.6 2 2.4
P10 1.6 2 2.4 1.6 2 2.4 4 5 6
P11 4 5 6 1.6 2 2.4 0.8 1 1.2
P12 8 10 12 20 25 30 1.6 2 2.4
P13 8 10 12 4 5 6 5.6 7 8.4
P14 8 10 12 3.2 4 4.8 0.8 1 1.2
P15 4 5 6 0.8 1 1.2 0.8 1 1.2
P16 8 10 12 1.6 2 2.4 3.2 4 4.8
P17 3.2 4 4.8 0.8 1 1.2 0.8 1 1.2
P18 2.4 3 3.6 0.4 0.5 0.6 0.8 1 1.2
P19 4 5 6 8 10 12 4 5 6
P20 3.2 4 4.8 4 5 6 0.16 0.2 0.24
P21 1.6 2 2.4 40 50 60 16 20 24
P22 0.8 1 1.2 1.6 2 2.4 0.4 0.5 0.6
P23 0.8 1 1.2 0.8 1 1.2 4 5 6
P24 1.6 2 2.4 0.8 1 1.2 0.8 1 1.2
P25 0.4 0.5 0.6 0.16 0.2 0.24 0.8 1 1.2
P26 0.4 0.5 0.6 0.4 0.5 0.6 0.4 0.5 0.6
P27 1.6 2 2.4 40 50 60 16 20 24
P28 0.4 0.5 0.6 0.16 0.2 0.24 0.8 1 1.2
P29 0.16 0.2 0.24 0.08 0.1 0.12 0.16 0.2 0.24
P30 0.8 1 1.2 0.8 1 1.2 0.4 0.5 0.6
P31 0.16 0.2 0.24 1.6 2 2.4 0.8 1 1.2
P32 0.4 0.5 0.6 0.16 0.2 0.24 0.8 1 1.2
P33 0.4 0.5 0.6 0.16 0.2 0.24 0.8 1 1.2
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• Comfort Zone (Bottom-Left): These projects perform fairly well in both pessimistic and opti-
mistic approaches.
• Good Wills Zone (Bottom-Right): These projects only work well in an optimistic approach.
• Caution Zone (Top-Left): These projects only work well in a pessimistic sense.
• Hazard Zone (Top-Right): These projects perform relatively low in both optimistic and pes-
simistic approaches.
In addition, projects under the red dotted line will perform better in pessimistic conditions,
while projects over the red line will perform better under optimistic conditions. Projects 1 and
2 are in the "hazard zone" and Project 9 is in the "good faith zone" which means no matter how
Table 5. Fuzzy outputs.
Projects Possibility of Success Success Value Possibility of Fail Fail Value
Min Mid Max Min Mid Max Min Mid Max Min Mid Max
P1 30 50 70 80 97 100 30 50 70 16 33 45
P2 60 80 90 50 79 100 10 20 40 5 24 40
P3 70 90 100 40 59 80 0 10 30 1 13 24
P4 70 90 100 60 73 80 0 10 30 1 14 24
P5 60 80 90 40 60 90 10 20 40 2 11 27
P6 30 50 70 30 56 100 30 50 70 2 9 25
P7 70 90 100 60 87 100 0 10 30 6 15 40
P8 60 80 90 30 53 70 10 20 40 2 10 21
P9 70 90 100 30 70 100 0 10 30 2 8 30
P10 60 80 90 20 37 50 10 20 40 1 5 10
P11 30 50 70 30 40 60 30 50 70 1 4 9
P12 70 90 100 30 57 80 0 10 30 1 7 24
P13 60 80 90 30 64 80 10 20 40 2 7 12
P14 60 80 90 20 53 80 10 20 40 1 7 20
P15 60 80 90 20 54 80 10 20 40 1 9 20
P16 30 50 70 20 50 80 30 50 70 1 5 8
P17 70 90 100 40 83 100 0 10 30 1 9 20
P18 60 80 90 30 41 60 10 20 40 1 4 9
P19 10 20 40 30 43 70 60 80 90 1 2 7
P20 70 90 100 30 43 70 0 10 30 2 5 14
P21 60 80 90 10 34 40 10 20 40 1 2 4
P22 30 50 70 30 51 80 30 50 70 1 2 4
P23 60 80 90 10 30 50 10 20 40 1 1 3
P24 70 90 100 20 37 50 0 10 30 1 3 5
P25 70 90 100 30 36 40 0 10 30 1 2 4
P26 70 90 100 20 33 50 0 10 30 1 1 3
P27 60 80 90 10 27 50 10 20 40 1 2 3
P28 70 90 100 10 23 30 0 10 30 1 1 2
P29 70 90 100 10 20 30 0 10 30 1 1 2
P30 70 90 100 10 30 50 0 10 30 1 2 5
P31 60 80 90 10 33 60 10 20 40 1 2 6
P32 70 90 100 10 23 40 0 10 30 1 1 2
P33 70 90 100 10 28 50 0 10 30 1 1 3
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hard or easy it is, the performance of Projects 1 and 2 is not good, and Project 9 works well
only in optimistic conditions. Individual Projects 5 and 19 are located at the border of "com-
fort zone", "caution zone" and "comfort zone", respectively. We can hope Project 5 has a mod-
erate efficiency in an optimistic approach. The status of Project 19 is only slightly better
because it will have average performance in both cases. Projects 3 and 17 are good choices,
while optimism is needed for Project 7. Projects 8, 27, and 21 need optimism to be added to
the portfolio. Project 4 can also be a good option if optimism is concerned, while Project 12 is
clearly at the center of the chart and no definitive judgment can be made. Project 31 can be a
good option as it is efficient and performs well in both approaches. Project 13 is in the “hazard
zone” near the center and Project 6 is also in that area, so we don’t have much hope for the
decision-maker to put them in the portfolio unless there is a specific reason. Likewise, deci-
sions about other projects can be easily made.
Table 6. Projects efficiencies in optimistic and pessimistic fuzzy approaches.
Projects Necessity (Confidence Level = 100%) Possibility (Confidence Level = 0%)
P1 0.001852 0.051176
P2 0.002667 0.050013
P3 0.014028 4.671429
P4 0.014917 1.314921
P5 0.012024 1.967143
P6 0.004107 0.375000
P7 0.012181 0.553846
P8 0.012024 0.255000
P9 0.008450 2.244118
P10 0.012024 0.763636
P11 0.006012 1.844182
P12 0.010898 1.314921
P13 0.008562 0.462120
P14 0.009478 2.666667
P15 0.012024 3.294118
P16 0.004525 0.660000
P17 0.014028 4.364706
P18 0.012024 2.468571
P19 0.002004 0.294000
P20 0.014028 5.300000
P21 0.012024 0.201429
P22 0.006012 4.560000
P23 0.012024 1.440000
P24 0.014028 2.120588
P25 0.014028 2.942857
P26 0.014028 4.362857
P27 0.012024 0.247500
P28 0.014028 2.185714
P29 0.014028 6.557143
P30 0.014028 3.993878
P31 0.012024 3.021429
P32 0.014028 2.900000
P33 0.014028 3.635714
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VI. Summary and conclusions
Technology managers are always struggling to select projects and ideas for their team and they
are faced with a high level of uncertainty while evaluating projects. This paper presents a
method to evaluate uncertain innovative activities using expert elicitation on resources and
outcomes of projects. The hierarchical structure that governs large corporations makes the
senior executives’ perception of technology influenced by intra-organizational political behav-
ior [47], and this affects the technology decision-making process. When technological innova-
tion is at the forefront of a company’s growth, the company’s survival depends heavily on the
quality of decisions about technology.
However, as stated before, decision-making in the early stages of innovation should not be
based on political or individual power, but on the intuition and consensus of decision-makers.
This paper showed that these innovative activities mainly have low efficiency and high uncer-
tainty, and the decision to implement or abandon them requires reviewing and balancing the
goals and strategic approach of the organization with technological and business features.
According to the above introduction, the following practical recommendations are given for
the attention of respected managers:
Fig 3. Project performance strategy map with two feasibility measures.
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• The strategic position of innovative activities can be determined by focusing on efficiency
and uncertainty at the same time.
• The decision-making team should consider multi-purposes as much as possible and take
advantage of the participation of different experts and stakeholders.
• Use both optimistic and pessimistic views to balance the decisions.
• The use of the intuition of those who are going to develop the product/technology would
greatly improve the decision-making process. These people are usually buried under the
organizational hierarchy. Developing a strategic discourse at all levels of the organization
will enhance the quality of this partnership.
• Providing a clear definition of the acceptable risk would ensure that all decision-makers
have a common understanding of this definition.
This research has practically introduced technical knowledge and market awareness into
decision-making by surveying experts and gaining their opinions about the value and proba-
bility of success of projects. Moreover, it could provide a solution for risk management of
innovative activities in the form of a portfolio of projects with differentiated risks and effi-
ciency. Hence, as seen in Table 2, the results of this study are only comparable to the research
by Ansoff et. al. [34] as it has covered all aspects previously focused on by researchers. In con-
trast to previous work, this study provides a solution for strategic project selection as well as
considering the technology development team’s intuition from both optimistic and pessimistic
perspectives. The research focuses only on the fuzzy front-end of the innovation process,
which is a limitation. Similarly, this research did not examine the relationship between projects
and their dynamic status.
For future research directions, the Z-number theory can be applied to deal with data ambi-
guity and increase the reliability of expert opinions. Additionally, other fuzzy concepts such as
fuzzy type-2 and random fuzzy variables can be utilized to present the new version of the fuzzy
DEA approach. Furthermore, the scenario-based robust optimization approach can be applied
for considering different possible scenarios for new product development.
Supporting information
S1 Appendix.
(DOCX)
S1 File.
(DOCX)
Acknowledgments
The authors would like to thank the anonymous reviewers and the editor-in-chief for their
constructive comments and suggestions.
Author Contributions
Conceptualization: Pejman Peykani, Mehdi Namazi.
Data curation: Mehdi Namazi.
Formal analysis: Pejman Peykani, Emran Mohammadi.
Investigation: Pejman Peykani, Mehdi Namazi.
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Methodology: Pejman Peykani.
Software: Pejman Peykani.
Supervision: Emran Mohammadi.
Validation: Mehdi Namazi, Emran Mohammadi.
Writing – original draft: Pejman Peykani, Mehdi Namazi.
Writing – review & editing: Emran Mohammadi.
References 1. Khurana A., & Rosenthal S. R. (1997). Integrating the fuzzy front end of new product development.
IEEE Engineering Management Review, 25(4), 35–49.
2. Koen P., Ajamian G., Burkart R., Clamen A., Davidson J., D’Amore R., et al. (2001). Providing clarity
and a common language to the “fuzzy front end”. Research-Technology Management, 44(2), 46–55.
3. Wheelwright S. C., & Clark K. B. (1992). Revolutionizing Product Development: Quantum Leaps in
Speed, Efficiency, and Quality. Simon and Schuster.
4. Khurana A., & Rosenthal S. R. (1998). Towards holistic “front ends” in new product development. Jour-
nal of Product Innovation Management, 15(1), 57–74.
5. Van Aken J. E., & Nagel A. P. (2004). Organizing and managing the fuzzy front end of new product
development. Eindhoven Centre for Innovation Studies, 2004 12.
6. Hauser J., Tellis G. J., & Griffin A. (2006). Research on innovation: a review and agenda for marketing
science. Marketing Science, 25(6), 687–717.
7. Koen P., Ajamian M., Boyce S., Clamen A., Fisher E., Fountoulakis S., et al. (2002). Fuzzy front end:
effective methods, tools, and techniques. The PDMA Tool Book, 1, 5–35.
8. Cooper R. G., & Edgett S. J. (2008). Ideation for product innovation. What are the best methods?
PDMA Visions Magazine, 1(1), 12–17.
9. Cooper R. G. (2008). Perspective: The stage-gate® idea-to-launch process—update, what’s new, and
NexGen systems. Journal of Product Innovation Management, 25(3), 213–232.
10. Kim J., & Wilemon D. (2002). Focusing the fuzzy front–end in new product development. R&D Manage-
ment, 32(4), 269–279.
11. Kolbe L. M., Bossink B., & De Man A. P. (2019). Contingent use of rational, intuitive and political deci-
sion-making in R&D. Management Decision. 58(6), 997–1020.
12. Eling K., Griffin A., & Langerak F. (2014). Using intuition in fuzzy front-end decision-making: A concep-
tual framework. Journal of Product Innovation Management, 31(5), 956–972.
13. Hart S., Jan Hultink E., Tzokas N., & Commandeur H. R. (2003). Industrial companies’ evaluation crite-
ria in new product development gates. Journal of Product Innovation Management, 20(1), 22–36.
14. De Gast D. A. (2012). The DNA of the fuzzy front-end evaluation of an innovation program. Master The-
sis, Delft.
15. Riel A., Neumann M., & Tichkiewitch S. (2013). Structuring the early fuzzy front-end to manage ideation
for new product development. CIRP Annals, 62(1), 107–110.
16. Namazi M., & Mohammadi E. (2017). A genetic-DEA approach to reveal property rights priorities. Inter-
national Journal of Data Envelopment Analysis, 5(1), 1123–1138.
17. Namazi M., & Mohammadi E. (2018). Natural resource dependence and economic growth: a TOPSIS/
DEA analysis of innovation efficiency. Resources Policy, 59, 544–552.
18. Emrouznejad A., & Tavana M. (2014). Performance Measurement with Fuzzy Data Envelopment Analy-
sis. Springer, Berlin, Heidelberg.
19. Peykani P., Mohammadi E., Pishvaee M. S., Rostamy-Malkhalifeh M., & Jabbarzadeh A. (2018). A
novel fuzzy data envelopment analysis based on robust possibilistic programming: possibility, necessity
and credibility-based approaches. RAIRO-Operations Research, 52(4–5), 1445–1463.
20. Danila N. (1989). Strategic evaluation and selection of R&D projects. R&D Management, 19(1), 47–62.
21. Sanchez A. M. (1989). R&D project selection strategy: An empirical study in Spain. R&D Management,
19(1), 63–68.
22. Cooper R. G., Edgett S. J., & Kleinschmidt E. J. (1998). Best practices for managing R&D portfolios.
Research-Technology Management, 41(4), 20–33.
PLOS ONE Bridging the Knowledge Gap between Technology and Business
PLOS ONE | https://doi.org/10.1371/journal.pone.0266843 April 14, 2022 17 / 18
23. Cooper R. G., Edgett S. J., & Kleinschmidt E. J. (1997). Portfolio management in new product develop-
ment: lessons from the leaders—I. Research-Technology Management, 40(5), 16–28.
24. Cooper R. G., Edgett S. J., & Kleinschmidt E. J. (1997). Portfolio management in new product develop-
ment: lessons from the leaders—II. Research-Technology Management, 40(6), 43–52.
25. Cooper R. G., Edgett S. J., & Kleinschmidt E. J. (2002). Portfolio management: fundamental to new
product success. The PDMA Tool Book for New Product Development, 1, 331–364.
26. Nobeoka K., & Cusumano M. A. (1997). Multiproject strategy and sales growth: the benefits of rapid
design transfer in new product development. Strategic Management Journal, 18(3), 169–186.
27. Bart C., & Pujari A. (2007). The performance impact of content and process in product innovation char-
ters. Journal of Product Innovation Management, 24(1), 3–19.
28. Heising W. (2012). The integration of ideation and project portfolio management—a key factor for sus-
tainable success. International Journal of Project Management, 30(5), 582–595.
29. Chao R. O., & Kavadias S. (2008). A theoretical framework for managing the new product development
portfolio: When and how to use strategic buckets. Management Science, 54(5), 907–921.
30. Cheung M. T., Greenfield P. F., & Liao Z. (2009). Selecting R&D projects for technology-based innova-
tion: Knowledge management in the face of Embarrass De Choix. Journal of General Management, 35
(2), 61–80.
31. Wang J., Lin W., & Huang Y. H. (2010). A performance-oriented risk management framework for inno-
vative R&D projects. Technovation, 30(11–12), 601–611.
32. Herfert K. F., & Arbige M. V. (2008). Aligning an R&D portfolio with corporate strategy. Research-Tech-
nology Management, 51(5), 39–46.
33. Rhéaume L., & Gardoni M. (2016). Strategy-making for innovation management and the development
of corporate universities. International Journal on Interactive Design and Manufacturing, 10(1), 73–84.
34. Ansoff H. I., Kipley D., Lewis A. O., Helm-Stevens R., & Ansoff R. (2019). Optimizing the strategic port-
folio. Implanting Strategic Management, 197–253. Palgrave Macmillan, Cham.
35. Peykani P., Mohammadi E., & Emrouznejad A. (2021). An adjustable fuzzy chance-constrained network
DEA approach with application to ranking investment firms. Expert Systems with Applications, 166, 113938.
36. Peykani P., Farzipoor Saen R., Seyed Esmaeili F. S., & Gheidar-Kheljani J. (2021). Window data envel-
opment analysis approach: a review and bibliometric analysis. Expert Systems, 38(7), e12721.
37. Peykani P., Hosseinzadeh Lotfi F., Sadjadi S. J., Ebrahimnejad A., & Mohammadi E. (2021). Fuzzy
chance-constrained data envelopment analysis: a structured literature review, current trends, and future
directions. Fuzzy Optimization and Decision Making, 1–65.
38. Peykani P., Mohammadi E., Farzipoor Saen R., Sadjadi S. J., & Rostamy-Malkhalifeh M. (2020). Data
envelopment analysis and robust optimization: a review. Expert Systems, 37(4), e12534.
39. Peykani P., Mohammadi E., Jabbarzadeh A., Rostamy-Malkhalifeh M., & Pishvaee M. S. (2020). A
novel two-phase robust portfolio selection and optimization approach under uncertainty: A case study of
Tehran stock exchange. Plos One, 15(10b), e0239810. https://doi.org/10.1371/journal.pone.0239810
PMID: 33045010
40. Peykani P., Gheidar-Kheljani J., Rahmani D., Karimi Gavareshki M. H., & Jabbarzadeh A. (2022).
Uncertain super-efficiency data envelopment analysis. Advances in Econometrics, Operational
Research, Data Science and Actuarial Studies, 311–320. Springer, Cham.
41. Peykani P., Mohammadi E., Emrouznejad A., Pishvaee M. S., & Rostamy-Malkhalifeh M. (2019). Fuzzy
data envelopment analysis: an adjustable approach. Expert Systems with Applications, 136, 439–452.
https://doi.org/10.1016/j.eswa.2019.06.039
42. Geng S., Chuah K. B., Law K. M., Cheung C. K., Chau Y. C., & Rui C. (2018). Knowledge contribution
as a factor in project selection. Project Management Journal, 49(1), 25–41.
43. Nonaka I., & Takeuchi H. (1995). The knowledge-creating company: How Japanese companies create
the dynamics of innovation. Oxford University Press.
44. Argote L., & Ingram P. (2000). Knowledge transfer: a basis for competitive advantage in firms. Organi-
zational Behavior and Human Decision Processes, 82(1), 150–169.
45. Watkins K. E., & Marsick V. J. (1993). Sculpting the learning organization: Lessons in the art and sci-
ence of systemic change. Jossey-Bass Inc., San Francisco, CA: Jossey-Bass.
46. Namazi M. (2017). Technological capability building in the satellite communications industry in Iran.
The Development of Science and Technology in Iran: Policies and Learning Frameworks, 203–224.
New York: Palgrave Macmillan.
47. Malik O. F., Shahzad A., Raziq M. M., Khan M. M., Yusaf S., & Khan A. (2019). Perceptions of organiza-
tional politics, knowledge hiding, and employee creativity: The moderating role of professional commit-
ment. Personality and Individual Differences, 142, 232–237.
PLOS ONE Bridging the Knowledge Gap between Technology and Business
PLOS ONE | https://doi.org/10.1371/journal.pone.0266843 April 14, 2022 18 / 18
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