Computer-supported Active Transparency for Strategic Open

Innovation

Emmanuel D. Adamides

and Nikos I. Karacapilidis

Industrial Management and Information Systems Lab, MEAD, University of Patras, 26504 Rio Patras, Greece

Keywords: Open Innovation, Computer-supported Argumentation, Knowledge Management.

Abstract: Aiming at facilitating the design and deployment of information systems to support Open Innovation with a

potential of providing sustainable competitive advantage, we rely on the micro-foundations of dynamic

capabilities, namely on the concepts of framing and abduction that are considered as the main elements of

generative sensing. We elaborate the concept of “active transparency” as a step for developing generative

sensing through the implementation of computer-supported argumentation in an open innovation setting. In

particular, we review the relationship between dynamic capabilities and strategic Open Innovation, we

concentrate on active transparency surfacing the important role that argumentation plays in the deployment

of this capability, and we discuss the ICT solutions that enable active transparency and open innovation for

providing competitive advantage.

1 INTRODUCTION

Open Innovation (OI) is an established paradigm of

innovation based on “the use of purposive inflows

and outflows of knowledge to accelerate internal

innovation, and expand the market for external use of

innovation, respectively” (Chesbrough, 2006). The

adoption of OI by an organization implies that the

innovation management process (Tidd and Bessant,

2014) becomes porous, and ideas, concepts, design,

products, services etc. flow in and out of its

boundaries. At the same time, different human and

non-human knowledge sources associated with

internal and external organization actors, such as

managers, users/customers, employees, suppliers,

competitors, researchers, regulators etc., become

interconnected in many different ways, and

information and knowledge items of different forms

flow between them, and are transformed in many

different ways. Clearly, in large complex

organizations, or networks of organisations, this is

accomplished in a complex web of social processes

(Anderson and Hardwick, 2017), in which agents of

different views, interests, cultures and power status

(Mota Pedrosa et al., 2013), usually being situated

geographically and contextually at a distance, are part

of.

https://orcid.org/0000-0001-6796-1349

https://orcid.org/0000-0002-6581-6831

There are four models associated with Open

Innovation (Möslein, 2013). In innovation markets,

organizations and individuals act as seekers of

innovation solutions and solvers of innovation

problems. This model is usually implemented

through intermediaries that facilitate the matching of

problems to solutions. In the model of firm-sponsored

innovation communities, agents of different size and

complexity develop ideas, discuss concepts and

promote innovation. Crowdsourcing is a particular

strategy in the framework of this model, also

associated with innovation contests where a firm gets

ideas for products, services, solutions, or even

business models from different sources (customers,

suppliers, etc.), which are also involved in their

evaluation and selection. When innovation toolkits

are used, users develop solutions in prescribed steps,

sometimes using standard components and modules

in a predefined solution space, interacting with the

company to get feedback. Innovation markets and the

related social product development forums, as well as

the ideas/innovation contests, provide solution spaces

with a high number of degrees of freedom, whereas

innovation and co-design toolkits and innovation

communities, through predefined procedures, restrict

the solution space and processes (Piller and Ihl,

2013).

Adamides, E. and Karacapilidis, N.

Computer-supported Active Transparency for Strategic Open Innovation.

DOI: 10.5220/0007731300170026

In International Conference on Finance, Economics, Management and IT Business (FEMIB 2019), pages 17-26

ISBN: 978-989-758-370-4

In this paper, we focus on the model of innovation

contest for providing strategic advantage, i.e. the use

of OI for obtaining ideas and solutions to problems

set collaboratively by the focal company and its

partners and customers, with a potential of gaining

competitive advantage (Malhotra and Majchrzak,

2016; Tavakoli et al., 2017). It has been argued that

to exploit the strategic potential of such innovation

models, the firm should install mechanisms for

integrating the knowledge provided by external

sources with that held internally. There have been

proposed different mechanisms and model processes

for knowledge integration in strategic organisational

processes (e.g. Nonaka and Takeuchi, 1995; Malhotra

and Majchrzak, 2016) associated with the

development of (dynamic) capabilities (Adamides

and Karacapilidis, 2018). Most of them, however, are

at a macro or meso level of analysis and hence are not

suitable for providing insights and guidelines for the

design of information systems (IT platforms) that

support the operation of such OI models.

To facilitate the design and deployment of

information systems to support OI with a potential of

providing competitive advantage, in this paper, we

rely on the micro-foundations of dynamic

capabilities, namely the concepts of framing and

abduction (Dong et al., 2016), which are considered

the main elements of generative sensing. We

elaborate the concept of “active transparency”

(Adamides and Karacapilidis, 2018) as a step for

implementing generative sensing through the

implementation of computer-supported argumenta-

tion in an open innovation setting. We provide design

specifications for such a system and an example of its

potential use. Following in Section 2, we review the

relationship between dynamic capabilities and OI for

competitive advantage. Then, we concentrate on

active transparency surfacing the important role that

argumentation plays in the deployment of this

capability. In Section 4, we discuss the ICT solutions

that enable active transparency and open innovation

for providing competitive advantage. Finally, in

Section 5, we draw the conclusions.

2 DYNAMIC CAPABILITIES AND

STRATEGIC OPEN

INNOVATION

It has been argued that organizations that aim at

strategic OI need to develop a set of capabilities for

absorbing and assimilating knowledge from different

sources in an efficient and effective manner (Hosseini

et al., 2017). These capabilities are associated to the

organisation’s absorptive capacity and the

development of an infrastructure for cooperative

learning. In general, capabilities are constituted by

assets/resources, such as ICT artefacts, and

routines/processes for deploying these assets (Amit

and Schoemaker, 1993). OI-based strategic

capabilities are linked to the notion of dynamic

capabilities (Teece et al., 2016), i.e. to the ability to

select or change operational/ordinary capabilities and

switch strategies between breadth (diversity) and

depth (intensity) in the effective use of internal and

external knowledge sources about products, services,

business models, etc. In more practical terms, they are

linked to an organization’s ability to innovate through

the appropriation of the right knowledge by sensing

the environment, seizing opportunities and

transforming its innovation process(es) and value

offerings. Sensing is associated with exploration,

whereas seizing with both exploitation of the

internalized environmental signals, ideas, concepts,

technologies etc., as well as with the exploration of

the external environment for gaining economic value

from the innovative products and/or services

developed through transforming activities. External

knowledge integration and learning are products of

the execution of these activities, which in the inbound

OI approach exhibit a certain degree of openness

(Tavakoli et al., 2017), while their effectiveness

depends on the organization’s level of absorptive

capacity (ACAP) (Cohen and Levinthal, 1990), as

well as on its degree of “active transparency”, which

may be defined as a form of generative sensing (Cui

et al., 2015; Dong et al., 2016).

Active transparency allows an organisation to

control proactively and effectively its interface with

the external environment, as far as knowledge inflows

and outflows are concerned. Active transparency

refers to an active organisational interface that filters

and distils internal and/or external knowledge before

it is integrated. In this line, it supports the collective

development of hypotheses about problems and their

innovative solutions – in general, hypotheses about

the possible use and effects of incoming and outgoing

knowledge items – as well as the testing for their

validity. In effect, active transparency is a capability

that is constituted by the capabilities of generative

sensing and argumentation. Generative sensing, in

turn, is a component of dynamic capabilities founded

on the micro-capabilities of framing problems/issues

and selecting/inferring their solutions using an

abductive logic (abduction) (Dong et al., 2016).

Argumentation refers to the use of formal schemata to

collectively – in an OI fashion – set propositions and

FEMIB 2019 - International Conference on Finance, Economics, Management and IT Business

collectively decide on the validity of propositions.

Formal argumentation schemata control the

proposition-setting and decision-making processes by

regulating the relative power (positional and rhetoric)

of participants and their arguments (dominant

argumentation logic/repertoire).

Absorptive capacity also contributes to an

organization’s capability/readiness of recognizing the

value of new external information, but also to

assimilating it, and applying it to commercial ends

(Cohen and Levinthal, 1990). Recent research

directly associates absorptive capacity to the dynamic

capabilities framework and stresses its importance as

a degrees-of-freedom provider towards innovation

and change (Zahra and George, 2002; Teece et al.,

2016). Absorptive capacity is a function of the

richness/diversity of the pre-existing knowledge

structure, both personalized (tacit) and

impersonalized (codified). Hence, although many

consider ACAP as a dynamic capability (e.g.

Lichtenthaler and Lichtenthaler, 2009), it is better to

consider it as an intangible, accumulating and

depleting strategic asset used in organisational

processes/routines. Obviously, both the active

transparency and ACAP of an organisation depend on

the corresponding qualities of its individual members.

The very processes of knowledge creation and

integration are largely associated with interaction and

socialisation (Nonaka and Takeuchi, 1995). So, in OI

settings (innovation contests and crowdsourcing), a

knowledge management strategy that aims at the

efficient and effective creation of strategically-useful

knowledge from different intra- and inter-

organizational sources, as well as at augmenting

learning capacity, should be primarily targeted on the

use of ICT for the development and use of social

capital, rather than on the installation of technology

systems for the storage, transformation and

distribution of codified knowledge (Lichtenthaler and

Lichtenthaler, 2009; Adamides and Karacapilidis,

2018). Towards this objective, and on the basis of the

above discussion, methods and tools for supporting

active transparency, as well as for supporting intra-

organisational collaboration are required. Hence, the

objective of OI contests for gaining competitive

advantage suggests a personalization rather than a

codification knowledge management meta-strategy

(Scheepers et al., 2004), in which information and

communication technology has an important role to

play. There is a wide range of technologies that can

be used for augmenting learning processes and

building ACAP and active transparency, the most

important of which are discussed in Section 4. Before

that, however, we further elaborate on active transpa-

rency and the role of argumentation within it.

3 THE CONCEPT OF ACTIVE

TRANSPARENCY FOR

STRATEGIC INNOVATION

As it was indicated before, active transparency is an

organizational capability directly related to

generative sensing, which is a particular type of

sensing capability, focused on generating and testing

hypotheses about new technologies, new products

and novel strategies (Dong et al., 2016). As such,

active transparency controls the inflow and outflow

of organizational knowledge (Karacapilidis et al.,

2003), actively contributing into knowledge

integration in both ways. Active transparency

presupposes the capabilities of problem framing and

abduction, which in OI settings are accomplished by

a set of internal and external organizational actors in

an argumentative fashion. In this way, it directly

addresses the very definition of innovation as a

“process where knowledgeable and creative people

and organizations frame problems and select,

integrate, and augment information to create

understanding and answers” (Teece, 2001). As

activities in all the phases of the innovation process

constitute problem resolution tasks (Leonard and

Sensiper, 2003), propositions and evaluation of

propositions need to take place all along the

innovation process. Obviously, innovation for

sustainable competitive advantage means a move

towards strategy innovation rather than incremental

change and should be based on the refinement and

integration of knowledge, and not on a number of

discrete unfounded ideas. In fact, propositions for

novel technologies, products or strategic initiatives to

innovative business models are

arguments/propositions with supporting evidence,

which however have to be evaluated and accepted in

a collective manner (Wright, 2012).

Truly innovative propositions are not based on

existing technologies, products and strategies and are

easily accepted in the initial format proposed. They

are the result of argumentative

discussions/negotiations between external and

internal organizational actors. Argumentation

contributes to the extraction/elicitation/filtering of

knowledge from diverse sources (to support

arguments) and to their integration (conflict

resolution and agreement). Nevertheless, this process

of convergence of perspectives and agreements does

not take place in a political vacuum. Politics in the

Computer-supported Active Transparency for Strategic Open Innovation

change/innovation process is important and “creating

affective change and adaptation within the

organisation depends upon effective use of politics”

(Eisenhardt and Zbaracki, 1992). Inevitably, this

“political” perspective leads to one of the central

issues of active transparency and its implementation,

and consequently of open innovation, that of the

relative distribution of power among all the agents

participating in the contest/crowdsourcing, and the

regulation of its influences on the outcome of the

knowledge integration process through asymmetric

forms of argumentation.

In general, the purpose of an argument is to show

that a non-trivial assertion (a proposition whose

validity is not obvious without further details and

cannot proved or verified by evidence) may claim

validity (von Werder, 1999). Argumentation is a

context-based sense-making process, which varies

according to (socially) constructed rules and (social)

groups. According to Bloor (1980), characteristic

forms of argument will emerge in a social setting,

standing out by their frequency (e.g. seeking

argument justification with reference to a specific

report, or with reference to what the industry leaders

do, etc.). Inevitably, this gives each social

(organisational) structure its dominant argumentation

repertoire of explicit legitimation, which solidifies

and increasingly constrains social and organisational

behaviour, and is used for characterising and

evaluating actions, events and other organisational

phenomena “which are often organised around

specific metaphors and figures of speech” (Potter and

Wetherell, 1987). As a result, institutionalised

justifications exist as objective, widely available

rules, and, directly or indirectly, tell organisation

members how to argue (Sillince, 1999). Clearly, the

institutionalization of an argumentation form is not a

positional- and rhetorical-power-neutral process,

neither a static one. In innovation propositions,

organisation members with high positional power

need not justify their arguments extensively, while

those with rhetorical power (which is related to the

positional power) may bias the organisation

discourse, both in short and long term, towards

specific forms that have more affinity with the

institutionalised argumentation forms, undermining

other forms which may include more substantive

arguments. This is one of the drawbacks of “closed”

innovation and at the same time a sign for caution for

open innovation. Argumentation for postulating

(innovative) propositions should encourage external

actors to contribute giving them appropriate power to

support their arguments by using a variety of

justification/claim logics. ICT can contribute to this

by sealing off these processes from their actual

social/organisational context in a controlled manner

(Kallinikos, 2011).

Many argumentation models (formalisms) have

been proposed in the literature, especially in

connection to computer-supported argumentation

systems (Bentahar et al., 2010). Gürkan et al. (2010)

integrated three such formalisms (IBIS, the Toulmin

framework, and the concept of argument schemes of

Walton) in an inclusive model, which consists of the

problem/issue in hand, the ideas/proposals/positions

for its solution, and pro and contra arguments related

to proposals. Pro and contra arguments are justified

by claims consisting of grounds and warrants. Pairs

of grounds and warrants define four main argument

schemes (which are related to the argumentation

repertoires mentioned above), namely, arguments

based on expert opinion (accept claim because

someone is an expert), popular opinion (something is

generally accepted as true because it is generally

accepted as true), analogy (A works because it

resembles B that have been proven to work in the

past) and causal associations (A works because B

works, and there is a positive correlation between the

two). Clearly, all four schemes can be employed in an

OI-based strategy.

The quality of propositions and the

knowledge/insights produced is a function of the

argumentation rationality

, i.e. the thoroughness of

the proposition preparation as revealed by the

arguments put forward to support it (von Werder,

1999). In relation to the above argumentation models,

abuse of positional power means that the proponent

does not justify claims and/or pro/contra arguments,

or does not justify the selection of a specific

argumentation scheme, or does not justify the issue of

specific rhetoric arguments, or even does not justify

the truth of warrants. Similarly, the abuse of

rhetorical power implies that the proponent knows

how others react to rewards and practices rhetoric

argumentation accordingly, giving little emphasis on

the validity and truth of arguments and statements

(“populist” behaviour). Such behaviours result in

effectively weak arguments and shaky propositions

distorted by power relations. So the result of the

knowledge integration effort and innovation will not

necessarily match the organisation’s strategic needs.

In open innovation, once a proposition is framed

collectively in an argumentative fashion, then its

validity needs to be tested through abduction.

Abduction is a microfoundation of generative sensing

and active transparency. It is a form of logical

reasoning in which hypotheses/propositions, which

are intuitive “guesses” (and not necessarily logically

FEMIB 2019 - International Conference on Finance, Economics, Management and IT Business

sound) are introduced and then tests are performed to

validate them (Dong et al., 2016). The proposition is

a hypothetical mechanism (the product of abduction),

which, if it existed, would generate (would be

responsible for) the observed phenomenon/problem,

or a phenomenon different from what was normally

expected (Papachristos and Adamides, 2016). The

proposition may be the result of formal argumentation

and thus logically sound as far as the collective

process is concerned. However, most likely, it will be

unfounded regarding its content, since most

participants have limited, or no, knowledge of the

specifics of the issue/problem and the context around

the issue.

In explanatory abduction, the environment is

scanned for truly surprising ideas and facts. Here the

participation, collaboration and argumentation of

external and internal agents follow initially a dialogic

conversation model where the diversity of ideas and

propositions is the principal objective (Karacapilidis

et al., 1997; Sennett, 2012). This is followed by

dialectic conversation to arrive at a single, or a small

number of propositions. For instance, as a very simple

example, consider a number of executives involved in

a discussion about the causes and possible

(innovative) solutions to the pollution problem of an

industrial district. The argumentation of executives

may result in a consensus that the main sources of

pollution are the industrial waste of a paints-

producing company. This forms the (unfounded)

proposition to be validated by collecting data to

construct the underlying mechanisms that when put in

place – in reality or simulated – will (re)produce the

phenomenon (pollution in our case), and hence

support this claim and reject any alternative claims –

for instance, a claim that a food processing company

also situated in the district is the main pollutant. Once

the alternative claims have been discarded, the

hypothesis becomes a sort of conclusion to be used in

(the next phase of) innovative abduction.

In innovative abduction, inferences are made

about the strategic options/innovations and/or the

initiatives that need to be accomplished for their

implementation. Here, the premise is that the paints

company is the main pollutant and the hypothesis to

test is the (possible) use of specific chemical waste

treatment technologies that will convert waste to

energy source for the food processing company. The

hypothesis will be validated by collecting data,

consulting specialists, even performing simulation

experiments. It is possible that this hypothesis will not

be valid, so an alternative hypothesis, e.g. mixing

with other chemical wastes and treatment to heat the

nearby village, need to be tested. Once this proves to

be possible, hypotheses about the implementation of

the technological innovation will be set and tested. In

this way, eventually, the industrial district will arrive

collectively at an innovative solution (innovation)

that provides competitive advantage to the

participating companies through cost reduction and

the construction of a green image.

4 THE PROPOSED TOOLSET

Open Innovation can be facilitated and significantly

augmented through a diversity of software tools and

associated technologies. In this section, we identify

the main categories of these tools and comment on

their capacity to support and enhance the explanatory

and innovative abduction processes. Based on their

main purpose, they can be classified into two broad

categories: (i) tools that mainly serve the collection,

integration and consolidation of underlying

information, knowledge, opinions and values, thus

supporting the collective development of hypotheses

about problems and their innovative solutions, and

(ii) tools that aid the analysis and validity testing of

the components of the overall argumentation process

and the assessment of stakeholders’ attributes in

terms of credibility and expertise. It is the former tool

category that enables an organization to conduct a

formal argumentation process towards framing the

problem and postulating the related propositions,

while the latter assists in performing

experimentations with alternative mechanisms and

approaches to reproduce or strengthen a proposition.

Collaboration Support. The emergence of the Web

2.0 era led to the introduction of a plethora of tools,

which feature novel collaboration paradigms and

enable users’ engagement at a massive scale. These

tools cover a broad spectrum of needs ranging from

knowledge exchanging, sharing and tagging, to social

networking, group authoring, mind mapping and

discussing. For instance, Facebook

(http://www.facebook.com) and LinkedIn

(http://www.linkedin.com) are representative

examples of social networking tools that facilitate the

formation of online communities among people with

similar interests; tools such as MindMeister

(http://www.mindmeister.com) and Mindomo

(http://www.mindomo.com) aim to collectively

organize, visualize and structure concepts via maps to

aid brainstorming and problem solving; Debatepedia

(http://wiki.idebate.org) and Cohere

(http://cohere.open.ac.uk) are typical tools aiming to

Computer-supported Active Transparency for Strategic Open Innovation

support online discussions over the Web; phpBB

(http://www.phpbb.com) and bbPress (http://www.

bbpress.org) are Web 2.0 applications enabling the

exchange of opinions, focusing especially on the

provision of an environment in which citizens can

express their thoughts without paying much attention

to the structure of the discussion. At the same time,

there are tools enabling a more structured, and

therefore more focused and effective consultation

(Karacapilidis et al., 2009; Karacapilidis et al., 2004).

The abovementioned tools enable the massive and

unconstrained collaboration of users engaged in

discussions like the one sketched in the example

given at the end of the previous section; however, the

amount of information produced and exchanged (as

well as the number of events generated) within these

tools often exceeds by far the mental abilities of users

to: (i) keep pace with the evolution of the

collaboration in which they engage, and (ii) keep

track of the outcome of past sessions. Current Web

2.0 collaboration tools exhibit two important

shortcomings making them prone to the problems of

information overload and cognitive complexity. First,

Web 2.0 collaboration tools lack reasoning services,

with which they could actively and meaningfully

support a more productive collaboration. Second,

these tools are “information islands”, thus providing

only limited support for interoperation, integration

and synergy with third party tools. While some

provide specialized APIs with which integration can

be achieved, these are primarily aimed at developers

and not end users.

Argumentation Support. As far as argumentation is

concerned, various tools focusing on the sharing and

exchange of arguments, diverse knowledge

representation issues and visualization of

argumentation have been developed. Tools such as

Araucaria (http://araucaria.computing.dundee.ac.uk)

and Compendium (http://compendium.open.ac.uk)

allow users to create issues, take positions on these

issues, and make pro and contra arguments. They can

capture the key issues and ideas and create shared

understanding in a knowledge team; in some cases,

they can be used to gather a semantic group memory.

In the example described in Section 3, such tools may

facilitate the collection, structuring and visualization

of alternative causes and solutions to the pollution

problem (together with the propositions speaking in

favour or against them). However, these

argumentation support tools have the same problems

with the Web 2.0 collaboration tools discussed above;

they too are standalone applications, lacking support

for interoperability and integration with other tools

(e.g. with data mining services foraging the Web to

discover interesting patterns or trends). They also

cope poorly with voluminous and complex data as

they provide only primitive reasoning services. This

makes these tools also prone to the problem of

information overload. Argumentation support

services recently developed in the context of the

Dicode project (Karacapilidis, 2014) address most of

these issues through innovative virtual workspaces

offering alternative visualization schemas that help

stakeholders control the impact of voluminous and

complex data, while also accommodating the

outcomes of external web services, thus augmenting

individual and collective sense-making.

In any case, argumentation support tools reveal

additional shortcomings that prevent them from

reaching a wider audience. In particular, their

emphasis on providing fixed and prescribed ways of

interaction within collaboration spaces make them

difficult to use as they constrain the expressiveness of

users, which in turn results in making these systems

being used only in niche communities. Adopting the

terminology used in the most common theoretical

framework of situational awareness shaped by

Endsley (1995), this category of tools only partially

cover the needs of the three stages of situational

awareness, namely perception (i.e. perceive the

status, attributes, and dynamics of relevant elements

in the setting under consideration), comprehension

(i.e. perform a synthesis of disjointed elements of the

previous stage through the processes of pattern

recognition, interpretation, and evaluation), and

projection (i.e. extrapolate information from previous

stages to find out how it will affect future instances of

the operational setting).

Social Media Monitoring. Social Media Monitoring

and Analytics is an evolving marketing research field

that refers to the tracking or crawling of various social

media content as a way to determine the volume and

sentiment of online conversation about a brand or

topic (Bekkers et al., 2013). Their added value lies on

the fact that such investigations can be performed at

real time and in a highly scalable way. Well-known

tools of this category include Hootsuite

(https://hootsuite.com), Trackur (http://www.trackur.

com), and Sysomos (https://sysomos.com). These

tools can support the required “attention mediation”

suggested by Klein and Convertino (2015), by

providing a structured way to represent the “big

picture”. Disclosing the analytics and reports implies

the provision of feedback to the involved population

on how their input has been taken into account. In the

example discussed in Section 3, a social media

FEMIB 2019 - International Conference on Finance, Economics, Management and IT Business

monitoring tool may provide valuable feedback from

the citizens affected by the pollution problem about

the solutions being shaped.

Opinion Mining. Opinion mining tools employ

natural language processing, machine learning, text

analysis and computational linguistics to extract

relevant information from the vast amounts of human

textual communication over the Internet or from

offline sources (Dhokrat et al., 2015). In fact, the

propagation of opinionated textual data has caused

the development of Web Opinion Mining (Taylor et

al., 2013) as a new concept in Web Intelligence,

which deals with the issue of extracting, analyzing

and aggregating opinions from large quantities of

textual data. The analysis of the sentiment of citizens'

opinions, known as Sentiment Analysis, is significant

for both the private and the public sector, because it

allows determining how people feel about a product

or service, or about a public issue under

consideration. We can distinguish between two types

of tools in this category; those that provide a

framework for data mining algorithms (e.g.

Rapidminer (https://rapidminer.com), KNIME

(https://www.knime.org) and WEKA (http://

www.cs.waikato.ac.nz/ml/weka)), and online

platforms that can visualize opinion mining analytics

on predefined Web 2.0 Sources (e.g. sentiment viz

(https://www.csc2.ncsu.edu/faculty/healey/tweet_viz

/tweet_app) and Socialmention

(http://www.socialmention.com)). Opinion miming

methods can be used in combination with the

abovementioned engagement and collaboration tools

as well as social media monitoring tools. In the

example discussed at the end of Section 3, an opinion

mining tool may systematically identify and extract

affective states and subjective information about an

alternative cause of (or solution to) the pollution

problem, and reveal meaningful insights that may

advance the related discussion.

Reputation Management. Reputation Management

refers to the need to seek references for an individual

or organization participating in social networks and

communities regarding their intellection or influence

(He et al., 2012). This need is partially addressed by

existing online reputation management services,

which monitor one’s influence based on his/her

activities in the social web, such as Klout

(http://www.klout.com) and Naymz (http://www.

naymz.com); or in the research domain measure one’s

scientific performance based on citation analysis,

such as Google Scholar (http://scholar.google.com)

and Research Gate (http://www.researchgate.net).

Another stream of reputation management systems is

using customer feedback to gain insight on suppliers

and brands, or get early warning signals to reputation

problems (e.g. eBay RMS). Current reputation

assessment algorithms can assign a reputation score

to individuals and enable the identification of experts.

In any case, the identification of promising ideas and

proposals from large corpuses demands contributors

to be assessed against their expertise on specific

topics related to the problem under investigation. By

collecting data concerning the knowledge, credibility

and expertise of individuals, reputation scores are

calculated for each individual with respect to different

thematic areas using a synthetic algorithm; based on

these reputation scores, content generated by the most

knowledgeable experts over the web can be shown

first in users’ searches, and this enables the

identification of and the focus on the highest quality

content that has been already generated in various

electronic sources by experts (‘passive expert-

sourcing’; such an approach has been developed in

the European project EU-Community

(Androutsopoulou et al., 2016)). In the example

sketched in Section 3, a reputation management tool

may identify and assess the rhetorical and political

power of stakeholders involved in the pollution

problem under consideration.

Dynamic Simulation. Dynamic simulation

environments (Agent-based, Discrete Event and

System Dynamics) are used to model and simulate

complex realities in various domains. In its

conventional use, simulation allows for testing

alternative solutions, as well as predicting and

assessing the impact of prospective choices, reducing

the associated uncertainty. In an

abductive/retroductive mode, simulation modelling is

used for representing and simulating underlying

mechanisms/ hypotheses that are suspected to be

responsible for phenomena observed (Papachristos

and Adamides, 2016), or for testing the effectiveness

of postulated unfounded solutions/innovations. In the

example mentioned in Section 3, a system dynamics

simulation model could be used to represent product,

by-product and waste flows in an industrial district,

and it could be used to test the hypothesis that the

paint company is the main pollutant, as well as the

hypothesis that waste can be treated and converted to

energy source for the food processing company

effectively. Well known examples of visual

simulation environments include ExtendSim

(https://www.extendsim.com/), Vensim

Computer-supported Active Transparency for Strategic Open Innovation

(http://www.vensim.com) and Anylogic

(http://www.anylogic.com).

Decision Making Support. Data warehouses, on-

line analytical processing, and data mining have been

broadly recognized as technologies playing a

prominent role in the development of current and

future Decision Support Systems (Karacapilidis,

2006), in that they may aid users make better, faster

and informed decisions. However, one critical point

that is still missing is a holistic perspective on the

issue of decision making. This originates out of the

growing need to develop applications by following a

more human-centric (and not problem-centric) view,

in order to appropriately address the requirements of

public sector stakeholders. Such requirements stem

from the fact that decision making has also to be

considered as a social process that principally

involves human interaction (Smoliar, 2003). The

structuring and management of this interaction

requires the appropriate technological support and

has to be explicitly embedded in the solutions offered

for this purpose. The above requirements, together

with the ones imposed by the way open innovation

stakeholders work and collaborate today, delineate a

set of challenges for further decision support

technology development. Such challenges can be

addressed by adopting a knowledge-based decision-

making view, while also enabling the meaningful

accommodation of the results of social knowledge

mining processes (revealing the needs, perceptions,

opinions of the general public). Knowledge

management activities, such as open innovation

related knowledge elicitation, representation and

distribution influence the creation of the decision

models to be adopted, thus enhancing the decision

making process, while evaluation of contributions in

the decision making process act as a reputation

mechanism and provide incentives for engagement.

5 CONCLUSIONS

In this paper, we elaborated the concept of “active

transparency” as a step for developing generative

sensing through the implementation and deployment

of computer-supported argumentation in a strategic

Open Innovation setting. Aiming at developing an OI

platform with a potential of providing sustainable

competitive advantage, we reviewed the relationship

between dynamic capabilities and strategic Open

Innovation, we focused on active transparency

stressing the important role that argumentation plays

in the deployment of this capability, and we discussed

the ICT solutions that enable active transparency and

open innovation for providing competitive advantage.

In any case, we argue that the seamless

interoperability and integration of these ICT solutions

is a hard issue. An ideal OI platform should be able to

loosely combine existing standalone tools and web

services to provide an all-inclusive infrastructure for

the effective and efficient support of diverse OI

stakeholders. Such a solution will not only provide a

working environment for hosting and indexing of OI-

related services, and the required retrieval and

meaningful analysis of large-scale data sets; it will

also leverage existing technologies and social

networking solutions to provide stakeholders with a

simple and scalable solution for targeted

collaboration, resource discovery and exploitation, in

a way that facilitates and boosts OI activities

(Adamides and Karacapilidis, 2018).

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