The Effects of the Internet of Things and Big Data to Organizations and Their Knowledge Management Practices

Abstract

New technologies are promising us many upsides like enhanced health, convenience, productivity, safety, and more useful data, information and knowledge for people and organizations. The potential downsides are challenges to personal privacy, over-hyped expectations, increasing technological complexity that boggles us. Our point is this change requires scientific discussion from the point of management, leadership and organizations – that is, it is time to discuss the meaning of these challenges seriously also in terms of existing traditions of management science. This review type article discusses the nature and role of the Internet of Things (IoT), Big Data and other key technological waves of ubiquitous revolution vis-á-vis the existing knowledge on management, organizations and knowledge management practices in organizations. Recent changes in the fields of robotics, artificial intelligence and automation technology indicate that all kinds of intelligence and smartness are increasing and organizational cultures are going to change indicating fast changes in the field of modern management and management sciences. Organizational processes form the base for the knowledge-based decision-making. Developing and utilizing smart solutions – like the utilization of Big Data – emphasize the importance of open system thinking. Digitalized services can for instance create new interfaces between service providers and users. Service users create social value while they are participating in co-producing activities. Hence, the IoT and Big Data undoubtedly strengthen the role of participation in service production, service economy, innovativeness in-between organizations (as a joint processes) and leadership models incorporated in service-dominant –logic. Moreover, IoT, Big Data, and especially digitalization bring about the renaissance of knowledge in decision-making.
At organizational level, smart organizations do no rely on knowledge production, but focus on knowledge integration instead. Knowledge integration becomes a key part of management systems. This also means that seminal theories with regard to decision-making and knowledge management do not suffice anymore. At organizational level there is a growing need to develop abilities to act in changing, not easy to forecasted and non-linear situations due to the complexity related to utilization and developing digitalization. Authentic and clinical leadership involves components such as awareness, unbiased processing, action, and relations. IoT and Big Data certainly effect organizations. The connection between IoT, Big Data, management systems as well as knowledge management practices at organizational level has not been analysed thoroughly in the KMO theories or empirically so far. In this scientific article this task and challenge will be performed.

Full text

The Effects of the Internet of Things and Big Data to
Organizations and Their Knowledge Management Practices
Jari Kaivo-oja
1
, Petri Virtanen
2
, Harri Jalonen
3
& Jari Stenvall
2
1
Finland Futures Research Centre, Turku School of Economics, University of Turku,
2
School of Management, University of Tampere,
3
Turku University of Applied Sciences
1 Corresponding author: jari.kaivo-oja@utu.fi, Telephone +358-2 333 9832, Mobile
+358-41 753 0244
Abstract:
New technologies are promising us many upsides like enhanced health, convenience,
productivity, safety, and more useful data, information and knowledge for people and
organizations. The potential downsides are challenges to personal privacy, over-hyped
expectations, increasing technological complexity that boggles us. Our point is this change
requires scientific discussion from the point of management, leadership and organizations –
that is, it is time to discuss the meaning of these challenges seriously also in terms of
existing traditions of management science. This article discusses the nature and role of the
Internet of Things, Big Data and other key technological waves of ubiquitous revolution
vis-á-vis the existing knowledge on management, organizations and knowledge
management practices in organizations. Recent changes in the fields of robotics, artificial
intelligence and automation technology indicate that all kinds of intelligence and smartness
are increasing and organizational cultures are going to change indicating fast changes in the
field of modern management and management sciences.
Organizational processes form the base for the knowledge-based decision-making.
Developing and utilizing smart solutions – like the utilization of Big Data – emphasize the
importance of open system thinking. Digitalized services can for instance create new
interfaces between service providers and users. Service users create social value while they
are participating in co-producing activities. Hence, the IoT and Big Data undoubtedly
strengthen the role of participation in service production, service economy, innovativeness
in-between organizations (as a joint processes) and leadership models incorporated in
service-dominant –logic. Moreover, IoT, Big Data, and especially digitalization bring about
the renaissance of knowledge in decision-making. At organizational level, smart
organizations do no rely on knowledge production, but focus on knowledge integration
instead. Knowledge integration becomes a key part of management systems. This also
means that seminal theories with regard to decision-making and knowledge management do
not suffice anymore. At organizational level there is a growing need to develop abilities to
act in changing, not easy to forecasted and non-linear situations due to the complexity
related to utilization and developing digitalization. Authentic and clinical leadership
involves components such as awareness, unbiased processing, action, and relations.IoT and
Big Data certainly effect organizations. The connection between IoT, Big Data,
management systems as well as knowledge management practices at organizational level
has not been analysed thoroughly in theory or empirically so far. In this article this task will
be performed.
Keywords
The Internet of Things, Internet of Intelligent Things, Big Data, management, leadership,
knowledge management

1 Introduction
New technologies are promising us many upsides like enhanced health, convenience,
productivity, safety, and more useful data, information and knowledge for people and
organizations. The potential downsides are challenges to personal privacy, over-hyped
expectations, increasing technological complexity that boggles us. Technological
complexity equals also with technology risks – no wonder then that there has been a
growing discussion among the social scientists since over 20 years about the risks of the
modern society [1]. Seemingly, new technologies always involve a fundamental paradox –
i.e. it is simultaneously both a solution and a problem (cf. [2]). The paradox arises, for
example, from the fact that while new technologies expands the information pool from
which to draw decisions, they also simultaneously generate contradictory information that
may make it difficult to achieve consensus.
We believe that the prevailing technological r/evolution changes organizations and
institutions. Consequently, the relative importance of networks and crowds will increase in
relation to hierarchies and markets. If technologies are used wisely, most people will be
better off, but if technologies are used without smartness the results will be messy and even
disastrous. Our point is in this conceptual paper that this change requires scientific
discussion from the point of management, leadership and organizations – that is, it is time
to discuss the meaning of these challenges seriously also in terms of existing traditions of
management science.
This article discusses the nature and role of the Internet of Things (later on referred as IoT),
Big Data and other key technological waves of ubiquitous revolution vis-á-vis the existing
knowledge on management, organizations and knowledge management practices in
organizations. Recent changes in the fields of robotics, artificial intelligence and
automation technology indicate that all kinds of intelligence and smartness are increasing
and organizational cultures are going to change indicating fast changes in the field of
modern management and management sciences. This paper is based on the literature
survey in our previous work [3] and it develops further our main ideas with regard to public
service systems, systemic change factors and the need to re-think current theories of change
management, well-being at work, theories of motivation.
Conceptually speaking, the IoT refers to uniquely identifiable objects (things) and their
virtual representations in an Internet structure.
1
Moreover, the IoT refers to intelligent
devices that have adequate computing capacity. With regard to Big Data then, Boyd and
Crawford [4] have argued that the era of Big Data has begun. Computer scientists,
physicists, economists, mathematicians, political scientists, bio-informaticists, sociologists,
and other scholars are simply clamoring for access to the massive quantities of information
produced by and about people, things, and their interactions. To be more specific, the
concept of Big Data is definitely very vague. For instance, Zalavsky et al. [5] point out that
there is no clear definition for ´Big Data´- whereas it does not mean the only size of the
data per se, it can be described with three characteristics with regard to data, as known as
the three V´s – volume, variety, and velocity of the data. More practically, Mayer-
Schönberger and Cukier [6: p. 7] refer to Big Data as follows: big data refers “…to things
one can do at a large scale that cannot be done at a smaller one, to extract new insights or
create new fors of value, in ways which change markets, organizations, the relationship
between citizens and governments and more.”
This paper is organized as follows. First, we discuss the future of Internet, it´s lates and the
next development phase with special emphasis on the IoT. Secondly, we re-think the key
issues of ubiquitous r/evolution and argue that these phenomena will have an impact on a
multitude scale to e-business as well as to human interaction. Thirdly, we elaborate the
impacts of all mentioned changes to knowledge management and knowledge-based
decision-making. Fourthly, we analyze the connection between IoT, Big Data r/evolution,
1
The basic concept of IoT was initially applied in the Radio-Frequency Identification RFID-tags to mark the
Electric Product Code (Auto ID-Lab). The benefit of IoT concept it that it enables physical objects to be
seamlessly integrated into the information network, where the physical objects can become active
participants in business and management processes. (see .e.g. [7]).

smart organizations with the idea to pinpoint what kind of societal impacts ubiquitous
r/evolution and associated other changes pursue.
2 From Internet Rush to Internet Saturation
In 1991, Weiser [8] (1991) described the vision of the future world under the name of
Ubiquitous Computing. Today, we have smart phones, our cars are computer systems on
wheels, and our homes are turning into smart living environments. Also our production and
delivery systems have developed to smartness and technology solution are really
ubiquitous. SmartFactory and SmartService concepts are already ready for customers and
citizens. In the 1980s electrical engineering was key engineering paradigm. In the 1990s,
the key paradigm was software and mechanical engineering, which emphasized modeling
object, systems and mechatronic objects and systems. After 2000 the key paradigm turned
to useware engineering, which underlines modeling interactions [9].
Today key issues in e-commerce and e-trade are new applications and contexts, where
applications are used. Today´s engineering systems are simple and not too complex, they
are not based on centralized hierarchies, they allow for a really concurrent engineering by
decoupling process, mechanical, electrical, and control design on the basis of semantic
models, they create and apply standards to all levels of automation pyramid in order to
reduce planning effort and allow re-use of components, and introduce technologies and
applications for the human being and organizations [9, p. 137].
As regards to the Internet, it really has introduced the new institutional revolution in the
globe. As a consequence, the importance of networks and crowds has increased in relation
to market institutions. In the future their importance probably increases because of
increasing coverage of Internet broadband and networks and the size of population using
things and “gadgets” related to the Internet increases.
2
Summing up, the key variables in the
Internet /r/evolution are population size (N), the resources feeding supply sub-system (F)
encompassing both the natural resources and food system, and accumulated technological
and scientific knowledge sub-system (K). This N-F-K triangle base is key platform for
Internet r/evolution, human development and economic growth [10, pp. 744-745, pp. 750-
751].
IoT is a part of latest information infrastructure with cloud computing environments,
ubiquitous networks, Linked Data Web and autonomous decentralized systems. These new
technologies provide both computing and communication quantifiable resources that offer
flexible levels of business performance and quality of demand [11, pp. 159-160]. Obviously
IoT will have huge economic and social impacts on business and public sector management
and administration. We´ll discuss these effects later on in this paper.
It appears that the pre-conditions of data and knowledge management will change
fundamentally before 2040. A key aspect of this development process will be IoT. Also
social and economic preconditions for crowdsourcing, Big Data and networking are much
stronger than today by year 2040, when Internet saturation phase will be reached. This
means that the so called Internet rush era is turning to Internet saturation era. Internet and
its architectural principles were designed in the 1970s, in the beginning 5
th
Kondratieff
cycle. Now we are starting 6
th
Kondratieff cycle, where new technologies of the Internet
will be adopted. Recent analyses reveal that the importance of the Internet for human
society is constantly increasing (e.g. [12]). In last four decades the Internet has moved from
being a restricted network of computer science researchers into being the global
infrastructure of service economy and information society.
3
2
Internet penetration index will probably increase in the world in next decades. In 1990 the number of
internet hosts was were small, but today the number of internet hosts is considerable and increasing.
Current forecasts indicate that internet penetration will slow down towards saturation at about 89 % by the
year 2040. It will take 45 years for the Internet penetration process to be globally accomplished. Experts
estimate that average time lag of development phases of Internet is a four years and there months. It is
predicted that in 2015 the penetration index should reach 50 % of the world population. From then on it
should asymptotically approach saturation at 79-89 % by 2035-2040. (see e.g. [10])
3
To take an example: today over one billion people use it to communicate, search and share information, conduct
business and enjoy entertainment. In 2010, online retail sale volume was estimated to be $697.8 billion in 2012 in
the world. Thus, online retail has become an emergent trend throughout the world and the e-commerce industry
continues to grow significantly across the world. Such issues like importance of convenience, personal

3 The Context: IoT as a Consequence of Technology R/evolution
The IoT is a system that rely on autonomous communication of a group of physical objects.
IoT is an emerging global Internet-based information architecture facilitating the exchange
of services and goods. Atzori et al. [13, p. 2793] evaluated that the main domains of Iot will
be: (1) Transportation and logistics domain, (2) healthcare domain, (3) smart environment
(home, office and plant) domain and (4) personal and social domain. In Figure 1 we have
outlined key elements of IoT with key realms of multiverse.
Figure 1. Internet of Things, devices and realms of multiverse (modification from [11], p.
161] Chen & Hu 2013, p. 161).
In Table 1 we have figured out realms of ubiquitous society. This entity is called
multiversity. Table 1 tells to us that leaders, managers, planners – people responsible for
running business – must understand the fundamental nature of three elements of reality:
time, space and matter. [14]
New service designs, architectures and business models are needed in the multiverse, not
only in the universe. What is obvious is that managers must work in order to manage these
critical eight realms of ubiquitous society.
innovativeness, impulsiveness, price consciousness, risk aversion, brand consciousness, variety seeking, attitudes
towards online shopping and online advertising are key issues in the e-commerce market [12].

Table 1. Realms in the ubiquitous society and in the multiverse [14, p. 17].
The application are of the IoT are numerous, basically meaning smart things and smart
systems such as smart homes, smart cities, smart industrial automation and smart services.
IoT systems provide better productivity, efficiency and better quality to numerous service
providers and industries. Iot is based on social, cultural and economic trust and associated
trust management skills, which broadly speaking mean developed security services and
antifragility operations. Critical issues of IoT security field are [15, p. 1505]: trusted
platforms, low-complexity, encryption, access control, secure data, provenance, data
confidentiality, authentication, identity management, and privacy enhancing technologies
(PETs).
Security of IoT requires data confidentiality, privacy and trust. These security issues are
managed by distributed intelligence, distributed systems, smart computing and
communication identification systems. [15, p. 1505, p. 1508]. Finally, in Figure 2 we have
figured out the functioning pattern of markets networks and crowds. IoT can be found
between these key systems of global economy. Probably there is a lot of potential for
smartness between these key systems. Data, information and knowledge about
communication and interaction of these systems will be vital issue for the future of
management.

Figure 2. The functioning pattern of markets networks and crowds.
Especially IoIt, Internet of Intelligent Things, as some experts emphasize smart Machine-
to-Machine communication, provides much potential for crowdsourcing of markets and
networks. IoIT provides also much potential for smart networking (between markets and
networks and between various networks). We expect that one obvious consequence of IoIT
will be the broader scope of deliberate democracy. Finally, the legal framework of
IoT/IoIT is very vague, or it does not exist. Such issues like standardization, service design
architecture, service design models, data privacy and data security create management and
governance problems, which are not totally solved inside current service architectures. IoT
has also become subject to power politics because of risks of cyber war, cyber terror and
cyber criminality [16, p. 341, p. 347].
In Fig. 3 we present a global reference scenario for IoT-aided robotics and AI applications.
We can see that IoT will be central for the collection of BigData. BigData will be collected
from the (1) environment, (2) from human beings and (3) from robots and AI applications.

Figure 3. A global reference scenario for IoT aided robotics and AI application (a
modification of [17, p. 34].
Fig. 3 describes key elements of future management system. Robots and AI application can
assist and help managers and leaders in many ways.
4. The Second Coming of Knowledge Based Decision-making?
Seminal contributions by Simon [18] and Choo [19] and many others have showed that
organizations use information and knowledge both for improving the quality of decisions
and for legitimizing decisions including also those decisions made by poor knowledge.
Feldman and March [20] have written one of the most persuasive articles explaining why
organizations fail to use information in effective way in decision-making. According to
them, organizations’ knowledge behavior is rather perverse. By this they mean that
although organizations “systematically gather information more information than they use,
yet [they] continue to ask for more”. The oversupply of information happens due to several
reasons. The main reason is that organizations incentives for information are biased in
sense that they tend to underestimate the costs of information gathering relative to its
benefits. Typically, decisions about information are made in a different part of organization
than where the actual information gathering is carried out. This division of using and
gathering information enable decision-makers to launch information gathering process
which may has value for them, albeit from the organizational perspective create more costs
than benefits. This kind of behavior is rational for individual decision-maker as it creates an
illusion of managing uncertainty. It is rational because “an intelligent decision maker
knows that a decision made in the face of uncertainty will almost always be different from
the choice that would have been made if the future had been precisely and accurately
predicted” [20, p. 175].
Rationality of information oversupply relates also to strategic value of information. This
manifests itself, for example, in cases where information is not, in the first place, used for
doing sound decisions, but for persuading someone to do something. In organizational life,
information is seldom neutral. Instead most information is subject to misrepresentation
[Ibid, p. 176]. Worth noting is that information not only unveils some aspect of the issue at
the stake, but also hides other aspects of the same issue. Feldman and March [20 p. 176]
concluded that “it is better from the decision maker’s point of view to have information that
is not needed [in decision making] than not to have information might be needed”.

Eventually, knowledge based decision-making can be seen as a widely repeated truism – a
statement of obvious truth without any spesific meaning. This is because it is quite difficult
to imagine what else than knowledge, could provide sound basis for organisation’s
decisions. Although beliefs, intuitions, and sometimes pure guesses may play important in
everyday decision-making, organisations’ strategic and operative choices cannot in the first
place be based on them. An organization that openly admits that its’ decisions are mainly
pulled out of the hat does not attract trust within or outside of its borders.
Knowledge and information have probably played a critical role in organisational decision-
making for as long as man has trusted on organisations, however, it was not early than the
beginning of 1990 when the theory knowledge-based organization were developed.
However, Grant [21] (1996) and Spender [22] (1996) laid down the cornerstone, which
became known as the knowledge-based view of the firm. As an example of the increasing
interest in knowledge as organisational resources provides the rapid growth of academic
papers which used knowledge management (KM) in their theoretical lenses. In five years
period just before (1990–1995) Grant’s and Spender’s articles, the number of papers which
touched upon the knowledge management issues in peer-reviewed journals found in four
data basis (Academic Search Elite, ProQuest, Elsevier Science Direct and Emerald Insight)
was 87 articles, where as in five years period right after Grant’s and Spender’s papers
(1996–2001) the number had grown to 2435.
Despite of increasing academic, as well as, practical efforts, the consensus related to
knowledge in decision-making is nowhere in sight. From this paper´s view, a main divide
is, whether knowledge is seen as a static asset owned by organization or as a social
construction emerged from interaction. Static view on knowledge implies the managebiality
of knowledge, where as social view emphasizes that knowledge cannot be managed, only
enabled. Worth noting is that different approaches have different practical implications
related to the role of information technology. Static view on knowledge has contributed
“IT-track KM”, while social view on knowledge has brought “People-track KM”. “IT-track
KM” treats knowledge as object that can be identified and handled in information systems.
“People-track KM” deems the role of IT as useful but not critical because it emphasizes
assessing, changing and improving human individual skills and/or behaviour. Related to
differences in the role of IT, the two views on knowledge have also contributed two
different knowledge management strategic. According to Hansen et al. [23] (1999)
organisations rely on (consciously or unconsciously) either codification or personalisation
knowledge management strategies. Codification strategy rests on explicit knowledge, i.e.
knowledge that can be easily captured, organised and communicated [24], whereas
personalisation strategy deals with tacit knowledge, i.e. knowledge that cannot be extracted
from individuals [25]. Hansen et al. (1999) [23] concluded that organisations that try to
exploit both strategies risk the failure of both. As an approximate division, they suggest an
80–20 split: 80 % of the organisation’s knowledge practices follows one strategy, 20 % the
other.
From this paper´s perspective, the most interesting question is not, however, the division of
KM strategies. Instead, the identified two views on KM and the role of IT in them begs to
question what possibilities come along with the emergence of Big Data. Does Big Data lay
down a basis for more smart, intelligence and even wise decision-making? Does Big Data
bring knowledge based decision-making into higher level?
In order to reflect the question, we need to analyse the functions of knowledge and
information in decision-making. One possible useful approach to analysing decision-
making is defining it as a moment which divides time into two eras, before and after
decision. Broadly adapting Andersen [26], it can be argued that knowledge shapes the
distinction fixed/open contingency concerning social operations (Figure 4).

Figure 4. Decision as a dividing system [25].
It is important to recognize that while decisions fulfill expectations they simultaneously
produce insecurity in the sense that “it becomes obvious that a different decision could have
been reached” [25]. To manage uncertainty related decision-making organizations’ need
information and knowledge to convince internal and external stakeholders that choices are
made rationally. Although, conflicting interests and problems of gathering the all relevant
information means that rationality in decision-making is only bounded [18], [19] Choo
(1996), for example, has suggested that by information and knowledge, however, it is
possible to create an impression of rational and reasoned behavior, which, in turn,
contributes to internal trust and to preserve external legitimacy [19, pp. 329-330]. This
means that sound knowledge before decision also helps the implementation of decisions. It
is also good to understand that the problem of bounded rationality is key motivation for
organizational foresight activities. Brunsson [26] (1985), for example, has argued that
successful management has more to do with the ability to motivate people and to create
organizational culture than making rational decisions. According to Brunsson [26, p. 4]
“organization´s main problem is not choosing, but it is taking organized action.”
Seemingly, what matters is not knowledge as “universal truth” but as “serviceable truth”
[6].
The above discussion shows that information is gathered and knowledge used both for
improving the quality of decisions and for attaining potential decision consequences.
Occasionally organization’s knowledge behavior is based on rationalistic ideal, whereas
sometimes it is highly symbolic. Adopting the conventional view of Big Data [6], it is
suggested that the true value of Big Data in decision making lies on its’ ability to
simultaneously promote (bounded) rational behavior (i.e. provide the best possible
information) and to limit symbolic use of information (i.e. oversupply of information that
have no value in improving decision’s quality). More generally, it can be hypothesized that
Big Data predicts the renaissance of knowledge management. Perhaps, the division of KM
strategies into codification and personalization strategies should also be reconsidered.
5. Big Data Revolution and Smart Organizations
Next we discuss the role of Big Data with regard to organizations and start with an
example. Kuper and Szymanski [28, pp. 5-6] speak about modern football as ´a numbers
game´ and ground their argument because of the use of data. According to them, Opta
Consulting Company was established in London in 1996 to collect match data for the
English Premier League. The management consultancy´s main aim was to build a brand by
creating soccer rankings. Soon The Premier League´s main sponsor paid for the so-called
Opta-index and thereafter clubs and media – thus the football enthusiasts as well – got the
data gathered buy OPTA for free. For instance, clubs started to learn fact they had never
contemplated before: how many kilometres each player ran per match, how many tackles
and passes he made, from which part of the pitch the goals were scored, and the like. The
numbers revolution has been going on in football, as far as Kuper and Szymanski [28] are
concerned, since twenty years now. This development has resulted to the fact, and almost
unseen by fans, that the majority of the (big) clubs (at least) have arrived at statistical

insights that are incrementally changing the whole nature of the game. [28, pp. 147-148, pp.
154-155].
Football clubs are organizations per se. The developments taken in using big data in the
area of football indicate that you definitely need data to get ahead. If you study figures, you
will see more and win more, that is. The point from football is that the beneficaries of big
data are twofold: the spectators and fans on the other hand, and the clubs on the other.
This section of our paper discusses the role of big data revolution vis-á-vis organizational
intelligence. Att he ouset, we argue that Big Data private and public organizations many
ways. First, it can mean new business possibilities (for private business/companies) and
better legitimacy and accountability(public policies and public services) at various levels.
Secondly, it affects services – they can be better since the knowledge base makes it possible
to access services easier or the knowledge-base can provide better focus to (co-)product
services appropriately. Thirdly, it causes – if and when organizations base their actions on
business intelligence - better production logic. In pratice this happens as transformation
from mass-production to customized service-dominant –logic. Finally, Big Data affects
organizations brand (in the case of private business/companies) and trustworthiness (in the
case of public policies and public services).
As follows, we will argue that there are a number of factors affecting how the possibilities
of Big Data are enhanced at organizational level. According to our view, there are number
of possible drivers and possible dysfunctions that either enhance or hinder the possibilities
offered by Big Data. These factors relate to the operating environment, agency,
accountability, organizational coping mechanisms leadership model, information flows,
innovation philosophy, production logic, and change philosophy.
As a whole, today´s organizations and their operating environments are complex entities
and research-wise constantly ´on the move´. This has brought about the need to manage
organizations as complex systems and to understand the logic of organizational learning
organizational-wise. Given the salient nature of current economic constraints, tightening
competition in business sector, problems with public sector spending and productivity, and
ever growing customer demandes, the need to analyse organizational intelligence seems all
the greater. In a word, organizations, dependent on the sector they operate, need to function
smarter than they used to be.
Basically, the overall structure of an organization consists of leadership, strategy and
foresight, people, partnerships and resources, as well as organizational processes [see e.g.
29]. This means that the modus operandi of any intelligent organisation can be defined by
using these organizational features and adding the intelligent modes of action based on
these elements. The conceptual idea with regard to intelligent organization needs to be
clarified here. Namely, the intelligence of organization refer, to put it bluntly, to two
dimensions those being knowledge management and customer-cenrted thinking throughout
the organization [see e.g. 30]. This approah is somewhat different that has been put forward
previously in desribing the nature of business success criteria. For intance, Peters and
Waterman, Jr. [31, pp. 8-11] argued in their Magnum Opus that the success criteria for a
successful organisation consist of various elements. These include, strategy, skills, shared
values, structure, systems, style and staff (see also [32]). Currently, based on the evolvoing
understanding with regard to organizations and to incorporate modern systems theory and
open systems view in particular, the logic of intelligence has evolved as well and resulted in
a new view to understand the role of knowledge flows in-between organizations and the
role of customer needs as a foundation of service-dominant –logic. Given this, according to
modern systems theory, organizations are viewed as open systems obtaining inputs from
their environment, processing these inputs and producing outputs [33, pp. 39-40]; [34].
An intelligent organization is, by nature, and in essence a distributed knowledge system or
sense-making community to put the idea forward by the terminology by Tsoukas [35]
(2005) and Choo [36] (1998). This view holds that the resources the give organization
deploys are neither given, nor discovered, but created in the process of making sense of the
knowledge (e.g. [35, 38]. This comes very close to what Nonaka and Takeuchi [39] (1995)
have described as a process during which tacit knowledge is converted into explicit
knowledge within the structures of a given organisation. As knowledge becomes an asset in
terms of organizational competitiveness, mechamisms of learning, unlearning and

competence building become incalculably valuable features (e.g. [40]). This means that the
traditional views of well-being at work and motivation theories with regard to work (e.g.
[41] [42]) have to be re-thought and complemented with knowledge generated with regard
to organisational learning and individual competencies.
Research literature indicates that performance measurement ought to be multi-dimension
(e.g. [43]). Research literature also suggests that performance measurement does not
necessarily mean that organizational decision-making is approproate or evidence-based
(e.g. [44, pp. 6-12]: Consequently, organizations may end up in casual benchmarking,
doing what seems to have worked in the past, and to follow deeply held yet uneximined
ideologies. Looking from the public sector, public policy evaluation and public sector
accountability point of view, the causal relation between implementation of public policies
and programmes and their effects are far from self-evident (e.g. [45]; [48]).
We argue that there are two kind of societal effects of the deployment of Big Data when we
look at the matter from the organizations´ point of view. First, there are the effects related
to the objectives organizations try to achieve, i.e. services, products and manufactured
goods which are their key mandates in the market. These effects can be pinpointed both to
private and public sector, but from a bit different angle – namely, these effects include new
business possibilities (private business/companies) and better legitimacy (public policies
and public services), as well as better services for customers and service users. Secondly,
there are certain effects which concern organizations themselves. Big Data enables
organizations to construct their strategies on knowledge which consequently mean that they
possess better foresight know-how to understand the profound changes in their operating
environments. It also pave way for better production logic which incorporates the shift from
mass-production to customized service-dominant –logic (e.g. [47]), which eventually
means better brand and trustworthiness for the organizations as a whole. Therefore, it is
noteworthy to say, that in organizational terms information – e.g. Big Data in particular –
and technology are arguably one of the most important systemic changes factors, which
affect organizations and organizational life. In Table 2, we have put forward nine
organizational dimensions (left column) through which we we try to make sense of the
possible drivers and possible dysfunctions at organizational level with regard to Big Data.
Table 2. Organizational dimensions as possible drivers and dysfunctions
enhancing/limiting the use of Big Data.
Dimension
Possible drivers
enhancing Big
Data utilization
Possible
dysfunctions
limiting Big
Data utilization
Interpretation
of operating
environment
Open system
Closed system
Agengy
Network
Organizations as
information flows
Hierarchy
Single
organizations
Accountability
Horizontal + vertical
Vertical
Organizational
coping
mechanism
Foresight-based
resilience
Retrospective
analysis –based
rigidity
Leadership
Business intelligence
Conventional
management

and leadership
Information
flows
Intra-organizational
Inter-
organizational
Innovation
philosophy
Open
Closed
Production
logic
Service-dominant –
logic, “customers
first”
Taylorian
production ideal
“productivity
first”
Change
philosophy
Immanent, emergent,
cyclical
Phase-based,
linear
Based on Table 2, we argue that there definitely are certain organizational drivers which
enhance Big Data utilization in society. As organizations operate in open system as
networks, the role of information becomes truly valuable commodity. Knowledge, based on
information intra-organizational information flows, and incorporated to organizational life
through the mechanisms of foresight and planning, is the cornerstone of business
intelligence. This calls for new understanding on the organizations´ accountability function
(e.g. [48, 49]) – putting the emphasis on measuring and analysing accountability both
vertically (reporting about the outputs and outcomes of an organization from bottom-up)
and horizontally (reporting to customers, citizens, media, and the like). And it is important
to see, that not only accountability aspects are at stake here.
This new understanding ´requirement´ concern also innovation and change philosophy
organization possesses. Innovation paradigm opens up because of the availability of
information – tomorrows strategies and innovations are orchestrated ´together´ instead of
organizational siloes. We have argued earlier [3] that traditional change management
models have to a certain extent come to impasse. Traditional top-down change management
models do not function anymore because – to use the expression of Kets de Vries [50, p. 1]
– organisations are like automobiles. They do not run themselves, except downhill. They
need people to make them work. In fact, this calls for psycho-dynamic-systemic way of
looking at people in organisations and a new focus on elusive micro-processes that take
place in organisations. This is precisely why change management ought to be conceived
two-dimensionally – it concerns individuals working within an organisation as well as the
organisation which is about to change (e.g. [51, 52, 35]). As a whole, Big data also
strengthens the transformation from mass-production logic towards more customized and
personalized production-logic. In order to keep ´fit´ in the tightening competion, more
focus should be put on both products and services organizations are delivering.
We have indicated possible dysfunctions of the Big Data utilization in the third column in
Table 2. Hierarchical thinking, vertical accountability philosophy, the non-existence of
modern foresight procedures, conventional management and leadership mechanisms and
skills, inter-organizational information understanding, closed, single-organization –based
innovation thinking, and phase-based & linear change philosophy in organizations are
examples of dysfunctions which can be detected when and if the possibilities of Big Data
are not put into practice.
Finally, we might add that the use of Big Data and the growing know-how about its limits
strengthen organizational resilience. According to McManus et al. [53] (2008), for instance,
the task of building more resilient organizations is complicated by an inability to translate
the concept of resilience into tangible working constructs for organizations. In fact,
resilience is often considered to be a crisis or emergency management issue and the link
between creating resilient day-to-day operations and having a resilient crisis response and
recovery is typically not well understood by organizations. We would like to add that
resilience can be defined as an organization’s capacity to anticipate disruptions, adapt to
disruptive events, and create lasting value in a turbulent environment (e.g. [3]).
Organizational resilience is thus the ability of an organization to overcome an internal or
external shock and to return to a stable state [54]. Needless to say, resilience is the key

feature of smart organization. The main point is the resilience does not occur by accident or
by chance. It is the effect of smart actions and smart leadership. The capacity of resilience
must be developed by smart organizational decisions.
6. Conclusions
Bearing in mind their importance already today, IoT and Big Data most definitely are key
factors affecting societal development in the future. Private and public organizations have
begun to gain critical insights from the Big Data and ubiquitous technology through various
management systems. Basically, the issue at stake here is the fact that it is not just the
question how to manage and control the technological possibilities. The development also
concern leadership functions. Namely, taking seriously Internet of Things and ubiquitous
technology may lead towards the revolution of digitalization which effects on management
processes in organizations. The deployment of on-going key processes call for leadership.
Both the utilization and the development of technologies are the key challenges in the
revolution.
To conclude, the key aspects of digital revolution in management process are to be
considered as smart solutions in the future. Organizational processes form the base for the
knowledge-based decision-making. Developing and utilizing smart solutions – like the
utilization of Big Data – emphasize the importance of open system thinking. Digitalized
services can for instance create new interfaces between service providers and users.
Service users create social value while they are participating in co-producing activities.
Hence, the IoT (or in some contexts IoIT) and Big Data undoubtedly strengthen the role of
participation in service production, service economy, innovativeness in-between
organizations (as a joint processes) and leadership models incorporated in service-dominant
–logic.
IoT, Big Data, and especially digitalization bring about the renaissance of knowledge in
decision-making. At organizational level, smart organizations do no rely on knowledge
production, but focus on knowledge integration instead. Knowledge integration becomes a
key part of management systems. This also means that seminal theories with regard to
decision-making and knowledge management do not suffice anymore. What is needed a
new understanding of organizations functioning in the framework of open systems. Open
systems are interlinked with each other by boundaries constituted and manifested by
knowledge. Managing these boundaries require that knowledge is exchanged, traded and
made understandable in organizations (e.g. [55]). Moreover, the true value of Big Data in
decision-making and in organizational terms lies on its’ ability to simultaneously promote
(bounded) rational behavior (i.e. provide the best possible information) and to limit
symbolic use of information (i.e. oversupply of information that have no value in improving
decision’s quality). This also affects organizations´ ability for resilience. We think that
resilience should be seen as an organization’s capacity to anticipate disruptions, adapt to
disruptive events, and create lasting value in a turbulent environment. “Built to last” and
“built to be changed in modular way” are broader management issues, which should be
planned carefully to develop visionary organizations.
To conclude, looking from management point of view, there is a growing need to develop
abilities to act in changing, not easy to forecasted and non-linear situations due to the
complexity related to utilization and developing digitalization. Authentic and clinical
leadership involves components such as awareness, unbiased processing, action, and
relations ([56], [57], [58], [50]). Authentic leaders are deeply aware of how they think and
behave and are perceived by others as being aware of their own and others’ values/moral
perspectives, knowledge, and strengths, and aware of the context in which they operate
[59].

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