The Effects of the Internet of Things andBig Data to Organizations and Their

Knowledge Management Practices

Jari Kaivo-oja1, Petri Virtanen2, Harri Jalonen3 & JariStenvall2

1 Finland Futures Research Centre, Turku School of Economics, University of Turku, 2 School of Management, University of Tampere, 3 Turku University of Applied Sciences1 Corresponding author: jari.kaivo-oja@utu.fi, Telephone+358-2 333 9832, Mobile +358-41 753 0244Abstract:New technologies are promising us many upsides like enhancedhealth, convenience, productivity, safety, and more usefuldata, information and knowledge for people and organizations.The potential downsides are challenges to personal privacy,over-hyped expectations, increasing technological complexitythat boggles us. Our point is this change requires scientificdiscussion from the point of management, leadership andorganizations – that is, it is time to discuss the meaning ofthese challenges seriously also in terms of existingtraditions of management science. This article discusses thenature and role of the Internet of Things, Big Data and otherkey technological waves of ubiquitous revolution vis-á-visthe existing knowledge on management, organizations andknowledge management practices in organizations. Recentchanges in the fields of robotics, artificial intelligenceand automation technology indicate that all kinds ofintelligence and smartness are increasing and organizationalcultures are going to change indicating fast changes in thefield of modern management and management sciences.Organizational processes form the base for the knowledge-based decision-making. Developing and utilizing smartsolutions – like the utilization of Big Data – emphasize theimportance of open system thinking. Digitalized services canfor instance create new interfaces between service providersand users. Service users create social value while they areparticipating in co-producing activities. Hence, the IoT andBig Data undoubtedly strengthen the role of participation inservice production, service economy, innovativeness in-between organizations (as a joint processes) and leadershipmodels incorporated in service-dominant –logic. Moreover,IoT, Big Data, and especially digitalization bring about therenaissance of knowledge in decision-making. Atorganizational level, smart organizations do no rely onknowledge production, but focus on knowledge integrationinstead. Knowledge integration becomes a key part of

management systems. This also means that seminal theorieswith regard to decision-making and knowledge management donot suffice anymore. At organizational level there is agrowing need to develop abilities to act in changing, noteasy to forecasted and non-linear situations due to thecomplexity related to utilization and developingdigitalization. Authentic and clinical leadership involvescomponents such as awareness, unbiased processing, action,and relations.IoT and Big Data certainly effectorganizations. The connection between IoT, Big Data,management systems as well as knowledge management practicesat organizational level has not been analysed thoroughly intheory or empirically so far. In this article this task willbe performed.KeywordsThe Internet of Things, Internet of Intelligent Things, BigData, management, leadership, knowledge management

1 IntroductionNew technologies are promising us many upsides like enhancedhealth, convenience, productivity, safety, and more usefuldata, information and knowledge for people and organizations.The potential downsides are challenges to personal privacy,over-hyped expectations, increasing technological complexitythat boggles us. Technological complexity equals also withtechnology risks – no wonder then that there has been agrowing discussion among the social scientists since over 20years 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 newtechnologies expands the information pool from which to drawdecisions, they also simultaneously generate contradictoryinformation that may make it difficult to achieve consensus. We believe that the prevailing technological r/evolutionchanges organizations and institutions. Consequently, therelative importance of networks and crowds will increase inrelation to hierarchies and markets. If technologies are usedwisely, most people will be better off, but if technologiesare used without smartness the results will be messy and even

disastrous. Our point is in this conceptual paper that thischange requires scientific discussion from the point ofmanagement, leadership and organizations – that is, it istime to discuss the meaning of these challenges seriouslyalso in terms of existing traditions of management science.This article discusses the nature and role of the Internet ofThings (later on referred as IoT), Big Data and other keytechnological waves of ubiquitous revolution vis-á-vis theexisting knowledge on management, organizations and knowledgemanagement practices in organizations. Recent changes in thefields of robotics, artificial intelligence and automationtechnology indicate that all kinds of intelligence andsmartness are increasing and organizational cultures aregoing to change indicating fast changes in the field ofmodern management and management sciences. This paper isbased on the literature survey in our previous work [3] andit develops further our main ideas with regard to publicservice systems, systemic change factors and the need to re-think current theories of change management, well-being atwork, theories of motivation.Conceptually speaking, the IoT refers to uniquelyidentifiable objects (things) and their virtualrepresentations in an Internet structure.1 Moreover, the IoTrefers to intelligent devices that have adequate computingcapacity. With regard to Big Data then, Boyd and Crawford [4]have argued that the era of Big Data has begun. Computerscientists, physicists, economists, mathematicians, politicalscientists, bio-informaticists, sociologists, and otherscholars are simply clamoring for access to the massivequantities of information produced by and about people,things, and their interactions. To be more specific, theconcept of Big Data is definitely very vague. For instance,Zalavsky et al. [5] point out that there is no clear definitionfor ´Big Data´- whereas it does not mean the only size of thedata per se, it can be described with three characteristicswith 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 asfollows: big data refers “…to things one can do at a largescale that cannot be done at a smaller one, to extract newinsights or create new fors of value, in ways which changemarkets, organizations, the relationship between citizens andgovernments and more.”

1 The basic concept of IoT was initially applied in the Radio-FrequencyIdentification RFID-tags to mark the Electric Product Code (Auto ID-Lab).The benefit of IoT concept it that it enables physical objects to beseamlessly integrated into the information network, where the physicalobjects can become active participants in business and management processes.(see .e.g. [7]).

This paper is organized as follows. First, we discuss thefuture of Internet, it´s lates and the next development phasewith special emphasis on the IoT. Secondly, we re-think thekey issues of ubiquitous r/evolution and argue that thesephenomena will have an impact on a multitude scale to e-business as well as to human interaction. Thirdly, weelaborate the impacts of all mentioned changes to knowledgemanagement and knowledge-based decision-making. Fourthly, weanalyze the connection between IoT, Big Data r/evolution,smart organizations with the idea to pinpoint what kind ofsocietal impacts ubiquitous r/evolution and associated otherchanges pursue.

2 From Internet Rush to Internet SaturationIn 1991, Weiser [8] (1991) described the vision of the futureworld under the name of Ubiquitous Computing. Today, we havesmart phones, our cars are computer systems on wheels, andour homes are turning into smart living environments. Alsoour production and delivery systems have developed tosmartness and technology solution are really ubiquitous.SmartFactory and SmartService concepts are already ready forcustomers and citizens. In the 1980s electrical engineeringwas key engineering paradigm. In the 1990s, the key paradigmwas software and mechanical engineering, which emphasizedmodeling object, systems and mechatronic objects and systems.After 2000 the key paradigm turned to useware engineering, whichunderlines modeling interactions [9]. Today key issues in e-commerce and e-trade are newapplications and contexts, where applications are used. Today´s engineering systems are simple and not too complex, theyare not based on centralized hierarchies, they allow for areally concurrent engineering by decoupling process,mechanical, electrical, and control design on the basis ofsemantic models, they create and apply standards to alllevels of automation pyramid in order to reduce planningeffort and allow re-use of components, and introducetechnologies and applications for the human being andorganizations [9, p. 137]. As regards to the Internet, itreally has introduced the new institutional revolution in theglobe. As a consequence, the importance of networks andcrowds has increased in relation to market institutions. Inthe future their importance probably increases because ofincreasing coverage of Internet broadband and networks andthe size of population using things and “gadgets” related tothe Internet increases.2 Summing up, the key variables in the2 Internet penetration index will probably increase in the world in nextdecades. In 1990 the number of internet hosts was were small, but today thenumber of internet hosts is considerable and increasing. Current forecastsindicate that internet penetration will slow down towards saturation at

Internet /r/evolution are population size (N), the resourcesfeeding supply sub-system (F) encompassing both the naturalresources and food system, and accumulated technological andscientific knowledge sub-system (K). This N-F-K triangle baseis key platform for Internet r/evolution, human developmentand economic growth [10, pp. 744-745, pp. 750-751]. IoT is a part of latest information infrastructure with cloudcomputing environments, ubiquitous networks, Linked Data Weband autonomous decentralized systems. These new technologiesprovide both computing and communication quantifiableresources that offer flexible levels of business performanceand quality of demand [11, pp. 159-160]. Obviously IoT willhave huge economic and social impacts on business and publicsector management and administration. We´ll discuss theseeffects later on in this paper.It appears that the pre-conditions of data and knowledge management will changefundamentally before 2040. A key aspect of this developmentprocess will be IoT. Also social and economic preconditionsfor crowdsourcing, Big Data and networking are much strongerthan today by year 2040, when Internet saturation phase willbe reached. This means that the so called Internet rush erais turning to Internet saturation era. Internet and itsarchitectural principles were designed in the 1970s, in thebeginning 5th Kondratieff cycle. Now we are starting 6th

Kondratieff cycle, where new technologies of the Internetwill be adopted. Recent analyses reveal that the importanceof the Internet for human society is constantly increasing(e.g. [12]). In last four decades the Internet has moved frombeing a restricted network of computer science researchersinto being the global infrastructure of service economy andinformation society.3

3 The Context: IoT as a Consequence of Technology R/evolution

about 89 % by the year 2040. It will take 45 years for the Internetpenetration process to be globally accomplished. Experts estimate thataverage time lag of development phases of Internet is a four years and theremonths. It is predicted that in 2015 the penetration index should reach 50 %of the world population. From then on it should asymptotically approachsaturation 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. In2010, online retail sale volume was estimated to be $697.8 billion in 2012in the world. Thus, online retail has become an emergent trend throughoutthe world and the e-commerce industry continues to grow significantly acrossthe world. Such issues like importance of convenience, personalinnovativeness, impulsiveness, price consciousness, risk aversion, brandconsciousness, variety seeking, attitudes towards online shopping and onlineadvertising are key issues in the e-commerce market [12].

The IoT is a system that rely on autonomous communication ofa group of physical objects. IoT is an emerging globalInternet-based information architecture facilitating theexchange 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 keyelements of IoT with key realms of multiverse.

Figure 1. Internet of Things, devices and realms ofmultiverse (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 thatleaders, managers, planners – people responsible for runningbusiness – must understand the fundamental nature of threeelements of reality: time, space and matter. [14]

New service designs, architectures and business models areneeded in the multiverse, not only in the universe. What isobvious is that managers must work in order to manage thesecritical eight realms of ubiquitous society.Table 1. Realms in the ubiquitous society and in themultiverse [14, p. 17].

The application are of the IoT are numerous, basicallymeaning smart things and smart systems such as smart homes,smart cities, smart industrial automation and smart services.IoT systems provide better productivity, efficiency andbetter quality to numerous service providers and industries.Iot is based on social, cultural and economic trust andassociated trust management skills, which broadly speakingmean developed security services and antifragilityoperations. Critical issues of IoT security field are [15, p.1505]: trusted platforms, low-complexity, encryption, accesscontrol, secure data, provenance, data confidentiality,authentication, identity management, and privacy enhancingtechnologies (PETs). Security of IoT requires data confidentiality, privacy andtrust. These security issues are managed by distributedintelligence, distributed systems, smart computing andcommunication identification systems. [15, p. 1505, p. 1508].Finally, in Figure 2 we have figured out the functioningpattern of markets networks and crowds. IoT can be foundbetween these key systems of global economy. Probably thereis a lot of potential for smartness between these keysystems. Data, information and knowledge about communicationand interaction of these systems will be vital issue for thefuture of management.

Figure 2. The functioning pattern of markets networks and crowds.Especially IoIt, Internet of Intelligent Things, as someexperts emphasize smart Machine-to-Machine communication,provides much potential for crowdsourcing of markets andnetworks. IoIT provides also much potential for smartnetworking (between markets and networks and between variousnetworks). We expect that one obvious consequence of IoITwill be the broader scope of deliberate democracy. Finally,the legal framework of IoT/IoIT is very vague, or it does notexist. Such issues like standardization, service designarchitecture, service design models, data privacy and datasecurity create management and governance problems, which arenot totally solved inside current service architectures. IoThas also become subject to power politics because of risks ofcyber 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 willbe central for the collection of BigData. BigData will becollected from the (1) environment, (2) from human beings and(3) from robots and AI applications.

Figure 3. A global reference scenario for IoT aided roboticsand 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 andleaders in many ways.

4. The Second Coming of Knowledge Based Decision-making?

Seminal contributions by Simon [18] and Choo [19] and manyothers have showed that organizations use information andknowledge both for improving the quality of decisions and forlegitimizing decisions including also those decisions made bypoor knowledge. Feldman and March [20] have written one ofthe most persuasive articles explaining why organizationsfail to use information in effective way in decision-making.According to them, organizations’ knowledge behavior israther perverse. By this they mean that althoughorganizations “systematically gather information moreinformation than they use, yet [they] continue to ask formore”. The oversupply of information happens due to severalreasons. The main reason is that organizations incentives forinformation are biased in sense that they tend tounderestimate the costs of information gathering relative toits benefits. Typically, decisions about information are madein a different part of organization than where the actualinformation gathering is carried out. This division of usingand gathering information enable decision-makers to launchinformation gathering process which may has value for them,albeit from the organizational perspective create more costs

than benefits. This kind of behavior is rational forindividual decision-maker as it creates an illusion ofmanaging uncertainty. It is rational because “an intelligentdecision maker knows that a decision made in the face ofuncertainty will almost always be different from the choicethat would have been made if the future had been preciselyand accurately predicted” [20, p. 175]. Rationality of information oversupply relates also tostrategic value of information. This manifests itself, forexample, in cases where information is not, in the firstplace, used for doing sound decisions, but for persuadingsomeone to do something. In organizational life, informationis seldom neutral. Instead most information is subject tomisrepresentation [Ibid, p. 176]. Worth noting is thatinformation not only unveils some aspect of the issue at thestake, but also hides other aspects of the same issue.Feldman and March [20 p. 176] concluded that “it is betterfrom the decision maker’s point of view to have informationthat is not needed [in decision making] than not to haveinformation might be needed”. Eventually, knowledge based decision-making can be seen as awidely repeated truism – a statement of obvious truth withoutany spesific meaning. This is because it is quite difficultto imagine what else than knowledge, could provide soundbasis for organisation’s decisions. Although beliefs,intuitions, and sometimes pure guesses may play important ineveryday decision-making, organisations’ strategic andoperative choices cannot in the first place be based on them.An organization that openly admits that its’ decisions aremainly pulled out of the hat does not attract trust within oroutside of its borders. Knowledge and information have probably played a criticalrole in organisational decision-making for as long as man hastrusted on organisations, however, it was not early than thebeginning of 1990 when the theory knowledge-basedorganization were developed. However, Grant [21] (1996) andSpender [22] (1996) laid down the cornerstone, which becameknown as the knowledge-based view of the firm. As an example of theincreasing interest in knowledge as organisational resourcesprovides the rapid growth of academic papers which usedknowledge management (KM) in their theoretical lenses. Infive years period just before (1990–1995) Grant’s andSpender’s articles, the number of papers which touched uponthe knowledge management issues in peer-reviewed journalsfound 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 andSpender’s papers (1996–2001) the number had grown to 2435.

Despite of increasing academic, as well as, practicalefforts, the consensus related to knowledge in decision-making is nowhere in sight. From this paper´s view, a maindivide is, whether knowledge is seen as a static asset ownedby organization or as a social construction emerged frominteraction. Static view on knowledge implies themanagebiality of knowledge, where as social view emphasizesthat knowledge cannot be managed, only enabled. Worth notingis that different approaches have different practicalimplications related to the role of information technology.Static view on knowledge has contributed “IT-track KM”, whilesocial view on knowledge has brought “People-track KM”. “IT-track KM” treats knowledge as object that can be identifiedand handled in information systems. “People-track KM” deemsthe role of IT as useful but not critical because itemphasizes assessing, changing and improving human individualskills and/or behaviour. Related to differences in the roleof IT, the two views on knowledge have also contributed twodifferent knowledge management strategic. According to Hansenet al. [23] (1999) organisations rely on (consciously orunconsciously) either codification or personalisationknowledge management strategies. Codification strategy restson explicit knowledge, i.e. knowledge that can be easilycaptured, organised and communicated [24], whereaspersonalisation strategy deals with tacit knowledge, i.e.knowledge that cannot be extracted from individuals [25].Hansen et al. (1999) [23] concluded that organisations that tryto exploit both strategies risk the failure of both. As anapproximate division, they suggest an 80–20 split: 80 % ofthe organisation’s knowledge practices follows one strategy,20 % the other. From this paper´s perspective, the most interesting questionis not, however, the division of KM strategies. Instead, theidentified two views on KM and the role of IT in them begs toquestion what possibilities come along with the emergence ofBig Data. Does Big Data lay down a basis for more smart,intelligence and even wise decision-making? Does Big Databring knowledge based decision-making into higher level?In order to reflect the question, we need to analyse thefunctions of knowledge and information in decision-making.One possible useful approach to analysing decision-making isdefining it as a moment which divides time into two eras,before and after decision. Broadly adapting Andersen [26], itcan be argued that knowledge shapes the distinctionfixed/open contingency concerning social operations (Figure4).

Figure 4. Decision as a dividing system [25].It is important to recognize that while decisions fulfillexpectations they simultaneously produce insecurity in thesense that “it becomes obvious that a different decisioncould have been reached” [25]. To manage uncertainty relateddecision-making organizations’ need information and knowledgeto convince internal and external stakeholders that choicesare made rationally. Although, conflicting interests andproblems of gathering the all relevant information means thatrationality in decision-making is only bounded [18], [19]Choo (1996), for example, has suggested that by informationand knowledge, however, it is possible to create animpression of rational and reasoned behavior, which, in turn,contributes to internal trust and to preserve externallegitimacy [19, pp. 329-330]. This means that sound knowledgebefore decision also helps the implementation of decisions.It is also good to understand that the problem of boundedrationality is key motivation for organizational foresightactivities. Brunsson [26] (1985), for example, has arguedthat successful management has more to do with the ability tomotivate people and to create organizational culture thanmaking rational decisions. According to Brunsson [26, p. 4]“organization´s main problem is not choosing, but it istaking organized action.” Seemingly, what matters is notknowledge as “universal truth” but as “serviceable truth”[6]. The above discussion shows that information is gathered andknowledge used both for improving the quality of decisionsand for attaining potential decision consequences.Occasionally organization’s knowledge behavior is based onrationalistic ideal, whereas sometimes it is highly symbolic.Adopting the conventional view of Big Data [6], it issuggested that the true value of Big Data in decision makinglies on its’ ability to simultaneously promote (bounded)rational behavior (i.e. provide the best possibleinformation) 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 hypothesizedthat Big Data predicts the renaissance of knowledgemanagement. Perhaps, the division of KM strategies intocodification and personalization strategies should also bereconsidered.

5. Big Data Revolution and Smart OrganizationsNext we discuss the role of Big Data with regard toorganizations 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 inLondon in 1996 to collect match data for the English PremierLeague. The management consultancy´s main aim was to build abrand by creating soccer rankings. Soon The Premier League´smain sponsor paid for the so-called Opta-index and thereafterclubs and media – thus the football enthusiasts as well – gotthe data gathered buy OPTA for free. For instance, clubsstarted to learn fact they had never contemplated before: howmany kilometres each player ran per match, how many tacklesand passes he made, from which part of the pitch the goalswere scored, and the like. The numbers revolution has beengoing on in football, as far as Kuper and Szymanski [28] areconcerned, since twenty years now. This development hasresulted to the fact, and almost unseen by fans, that themajority of the (big) clubs (at least) have arrived atstatistical insights that are incrementally changing thewhole nature of the game. [28, pp. 147-148, pp. 154-155].Football clubs are organizations per se. The developmentstaken in using big data in the area of football indicate thatyou definitely need data to get ahead. If you study figures,you will see more and win more, that is. The point fromfootball is that the beneficaries of big data are twofold:the spectators and fans on the other hand, and the clubs onthe other. This section of our paper discusses the role of big datarevolution vis-á-vis organizational intelligence. Att heouset, we argue that Big Data private and publicorganizations many ways. First, it can mean new businesspossibilities (for private business/companies) and betterlegitimacy and accountability(public policies and publicservices) at various levels. Secondly, it affects services –they can be better since the knowledge base makes it possibleto access services easier or the knowledge-base can providebetter focus to (co-)product services appropriately. Thirdly,it causes – if and when organizations base their actions onbusiness intelligence - better production logic. In praticethis happens as transformation from mass-production tocustomized service-dominant –logic. Finally, Big Data affectsorganizations brand (in the case of private

business/companies) and trustworthiness (in the case ofpublic policies and public services). As follows, we will argue that there are a number of factorsaffecting how the possibilities of Big Data are enhanced atorganizational level. According to our view, there are numberof possible drivers and possible dysfunctions that eitherenhance or hinder the possibilities offered by Big Data.These factors relate to the operating environment, agency,accountability, organizational coping mechanisms leadershipmodel, information flows, innovation philosophy, productionlogic, and change philosophy.As a whole, today´s organizations and their operatingenvironments are complex entities and research-wiseconstantly ´on the move´. This has brought about the need tomanage organizations as complex systems and to understand thelogic of organizational learning organizational-wise. Giventhe salient nature of current economic constraints,tightening competition in business sector, problems withpublic sector spending and productivity, and ever growingcustomer demandes, the need to analyse organizationalintelligence seems all the greater. In a word, organizations,dependent on the sector they operate, need to functionsmarter than they used to be. Basically, the overall structure of an organization consistsof leadership, strategy and foresight, people, partnershipsand resources, as well as organizational processes [see e.g.29]. This means that the modus operandi of any intelligentorganisation can be defined by using these organizationalfeatures and adding the intelligent modes of action based onthese elements. The conceptual idea with regard tointelligent organization needs to be clarified here. Namely,the intelligence of organization refer, to put it bluntly, totwo 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 forwardpreviously in desribing the nature of business successcriteria. For intance, Peters and Waterman, Jr. [31, pp. 8-11] argued in their Magnum Opus that the success criteria fora successful organisation consist of various elements. Theseinclude, strategy, skills, shared values, structure, systems,style and staff (see also [32]). Currently, based on theevolvoing understanding with regard to organizations and toincorporate modern systems theory and open systems view inparticular, the logic of intelligence has evolved as well andresulted in a new view to understand the role of knowledgeflows in-between organizations and the role of customer needsas a foundation of service-dominant –logic. Given this,according to modern systems theory, organizations are viewedas 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 adistributed knowledge system or sense-making community to putthe idea forward by the terminology by Tsoukas [35] (2005)and Choo [36] (1998). This view holds that the resources thegive 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 andTakeuchi [39] (1995) have described as a process during whichtacit knowledge is converted into explicit knowledge withinthe structures of a given organisation. As knowledge becomesan asset in terms of organizational competitiveness,mechamisms of learning, unlearning and competence buildingbecome incalculably valuable features (e.g. [40]). This meansthat the traditional views of well-being at work andmotivation theories with regard to work (e.g. [41] [42]) haveto be re-thought and complemented with knowledge generatedwith regard to organisational learning and individualcompetencies. Research literature indicates that performance measurementought to be multi-dimension (e.g. [43]). Research literaturealso suggests that performance measurement does notnecessarily mean that organizational decision-making isapproproate or evidence-based (e.g. [44, pp. 6-12]:Consequently, organizations may end up in casualbenchmarking, doing what seems to have worked in the past,and to follow deeply held yet uneximined ideologies. Lookingfrom the public sector, public policy evaluation and publicsector accountability point of view, the causal relationbetween implementation of public policies and programmes andtheir effects are far from self-evident (e.g. [45]; [48]).We argue that there are two kind of societal effects of thedeployment of Big Data when we look at the matter from theorganizations´ point of view. First, there are the effectsrelated to the objectives organizations try to achieve, i.e.services, products and manufactured goods which are their keymandates in the market. These effects can be pinpointed bothto private and public sector, but from a bit different angle– namely, these effects include new business possibilities(private business/companies) and better legitimacy (publicpolicies and public services), as well as better services forcustomers and service users. Secondly, there are certaineffects which concern organizations themselves. Big Dataenables organizations to construct their strategies onknowledge which consequently mean that they possess betterforesight know-how to understand the profound changes intheir operating environments. It also pave way for betterproduction logic which incorporates the shift from mass-

production to customized service-dominant –logic (e.g. [47]),which eventually means better brand and trustworthiness forthe organizations as a whole. Therefore, it is noteworthy tosay, that in organizational terms information – e.g. Big Datain particular – and technology are arguably one of the mostimportant systemic changes factors, which affectorganizations and organizational life. In Table 2, we haveput forward nine organizational dimensions (left column)through which we we try to make sense of the possible driversand possible dysfunctions at organizational level with regardto Big Data.Table 2. Organizational dimensions as possible drivers and dysfunctions enhancing/limiting the use of Big Data.

Dimension Possibledrivers

enhancing BigData

utilization

Possibledysfunctionslimiting BigData utilization

Interpretationof operatingenvironment

Open system Closedsystem

Agengy NetworkOrganizations

asinformation

flows

Hierarchy Single

organizations

Accountability Horizontal +vertical

Vertical

Organizational coping

mechanism

Foresight-based

resilience

Retrospective analysis–basedrigidity

Leadership Businessintelligence

Conventionalmanagement

andleadership

Informationflows

Intra-organizationa

l

Inter-organizati

onalInnovation Open Closed

philosophy

Productionlogic

Service-dominant –logic,

“customersfirst”

Taylorianproduction

ideal“productivity first”

Changephilosophy

Immanent,emergent,cyclical

Phase-based,linear

Based on Table 2, we argue that there definitely are certainorganizational drivers which enhance Big Data utilization insociety. As organizations operate in open system as networks,the role of information becomes truly valuable commodity.Knowledge, based on information intra-organizationalinformation flows, and incorporated to organizational lifethrough the mechanisms of foresight and planning, is thecornerstone of business intelligence. This calls for newunderstanding on the organizations´ accountability function(e.g. [48, 49]) – putting the emphasis on measuring andanalysing accountability both vertically (reporting about theoutputs and outcomes of an organization from bottom-up) andhorizontally (reporting to customers, citizens, media, andthe like). And it is important to see, that not onlyaccountability aspects are at stake here. This new understanding ´requirement´ concern also innovationand change philosophy organization possesses. Innovationparadigm opens up because of the availability of information– tomorrows strategies and innovations are orchestrated´together´ instead of organizational siloes. We have arguedearlier [3] that traditional change management models have toa certain extent come to impasse. Traditional top-down changemanagement models do not function anymore because – to usethe expression of Kets de Vries [50, p. 1] – organisationsare like automobiles. They do not run themselves, exceptdownhill. They need people to make them work. In fact, thiscalls for psycho-dynamic-systemic way of looking at people inorganisations and a new focus on elusive micro-processes thattake place in organisations. This is precisely why changemanagement ought to be conceived two-dimensionally – itconcerns individuals working within an organisation as wellas the organisation which is about to change (e.g. [51, 52,35]). As a whole, Big data also strengthens thetransformation from mass-production logic towards morecustomized and personalized production-logic. In order tokeep ´fit´ in the tightening competion, more focus should beput on both products and services organizations aredelivering.

We have indicated possible dysfunctions of the Big Datautilization in the third column in Table 2. Hierarchicalthinking, vertical accountability philosophy, the non-existence of modern foresight procedures, conventionalmanagement 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 ofdysfunctions which can be detected when and if thepossibilities 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 taskof building more resilient organizations is complicated by aninability to translate the concept of resilience intotangible working constructs for organizations. In fact,resilience is often considered to be a crisis or emergencymanagement issue and the link between creating resilient day-to-day operations and having a resilient crisis response andrecovery is typically not well understood by organizations.We would like to add that resilience can be defined as anorganization’s capacity to anticipate disruptions, adapt todisruptive events, and create lasting value in a turbulentenvironment (e.g. [3]). Organizational resilience is thus theability of an organization to overcome an internal orexternal shock and to return to a stable state [54]. Needlessto say, resilience is the key feature of smart organization.The main point is the resilience does not occur by accidentor by chance. It is the effect of smart actions and smartleadership. The capacity of resilience must be developed bysmart organizational decisions.

6. Reflections on knowledge management inorganizationsIoT and Big Data are key drivers for change whenorganizations re-organize their knowledge-managementpractices. Tsoukas [35, pp. 110-111] emphasizes to importantthings in this respect. According to him, organizations aredistributed knowledge systems, which means that organizationshave to take a system-based view if they intend to meet thechallenges of the IoT and Big Data. On the other hand,Tsoukas underlines the fact knowledge truly is a resource fororganizations – it is a resource, which is neither given, nordiscovered, but actually created. This means that knowledgemanagement practices should be developed with a system-basedholistic view. The heart of this renewal of knowledgemanagement practices lies at learning and competence-building[55, pp. 8-10]. This means that organizations need to payattention how they compete, make decisions, apply theprinciples of organizational learning, connect and relate new

and existing information, and finally how they monitor theirsuccess and effectiveness. The above discussion shows that information is gathered andknowledge used both for improving the quality of decisionsand for attaining potential decision consequences.Occasionally organization’s knowledge behavior is based onrationalistic ideal, whereas sometimes it is highly symbolic.Adopting the conventional view of Big Data [56, 6], it issuggested that the true value of Big Data in decision makinglies on its’ ability to simultaneously promote (bounded)rational behavior (i.e. provide the best possibleinformation) and to limit symbolic use of information (i.e.oversupply of information that have no value in improvingdecision’s quality). Big Data creates value for knowledgemanagement particularly as it provides transparency inorganizational decisions (e.g. [56]). It makes informationaccessible across organization and therefore significantlyreduces information search and processing time. At best BigData promotes information gathering and its conversion intoorganizational knowledge assets. More generally, it can behypothesized that Big Data predicts the renaissance ofknowledge management. Perhaps, the division of KM strategiesinto codification and personalization strategies should alsobe reconsidered. Moreover, it is reasonable to speak ofparadigmatic changes if we consider IoT and Big Data as thedrivers of this paradigmatic change. In order to understandthe paradigmatic changes that will take place in knowledgemanagement practices, we would like to think of it really asa paradigm shift, even though it is always rathercontroversial to speak about paradigms as clear-cut epochs.The paradigm definitions are usually far from clear, precise,and determined, and of course there is a lot of room forscholarly hair-splitting and confusion here. We think thatorganizations are, in terms of knowledge managementpractices, moving from knowledge management based oninformation storage & decision making paradigm towards moredetailed and sophisticated knowledge management practiceswhich will be based on value-creation and business processrenewal [55, 56, 57]. Whereas the older paradigm prevailed inthe world of closed systems and semi-technological operatingenvironment, the value-based knowledge utilization paradigmencapsulates the multi-dimensional aspects of the IoT and BigData, such as the knowledge architecture, heterogeinity,scalability, look up, dynamic mashup, security and privacy,communication protocols, social networking identification,social networking management, and trustworthiness issues[58]. The evolving new knowledge management paradigm is basedon the fact that the IoT and Big Data have an enormous effecton organizations´ business processes and businessarchitectures aiming at applying value-based approaches to

develop business modelling of new solutions based on the IoTin particular (see also [59]). One of the most promisingapproaches in this field is to connect SECI approach andActivity Theory via cascading modes of communication [60].

7. ConclusionsBearing in mind their importance already today, IoT and BigData most definitely are key factors affecting societaldevelopment in the future. Private and public organizationshave begun to gain critical insights from the Big Data andubiquitous technology through various management systems.Basically, the issue at stake here is the fact that it is notjust the question how to manage and control the technologicalpossibilities. The development also concern leadershipfunctions. Namely, taking seriously Internet of Things andubiquitous technology may lead towards the revolution ofdigitalization which effects on management processes inorganizations. The deployment of on-going key processes callfor leadership. Both the utilization and the development oftechnologies are the key challenges in the revolution. Toconclude, the key aspects of digital revolution in managementprocess are to be considered as smart solutions in thefuture. Organizational processes form the base for theknowledge-based decision-making. Developing and utilizingsmart solutions – like the utilization of Big Data –emphasize the importance of open system thinking. Digitalizedservices can for instance create new interfaces betweenservice providers and users. Service users create socialvalue while they are participating in co-producingactivities. Hence, the IoT (or in some contexts IoIT) and BigData undoubtedly strengthen the role of participation inservice production, service economy, innovativeness in-between organizations (as a joint processes) and leadershipmodels incorporated in service-dominant –logic.IoT, Big Data, and especially digitalization bring about therenaissance of knowledge in decision-making. Atorganizational level, smart organizations do no rely onknowledge production, but focus on knowledge integrationinstead. Knowledge integration becomes a key part ofmanagement systems. This also means that seminal theorieswith regard to decision-making and knowledge management donot suffice anymore. What is needed a new understanding oforganizations functioning in the framework of open systems.

Open systems are interlinked with each other by boundariesconstituted and manifested by knowledge. Managing theseboundaries require that knowledge is exchanged, traded andmade understandable in organizations (e.g. [60]). Moreover,the true value of Big Data in decision-making and inorganizational terms lies on its’ ability to simultaneouslypromote (bounded) rational behavior (i.e. provide the bestpossible information) and to limit symbolic use ofinformation (i.e. oversupply of information that have novalue in improving decision’s quality). This also affectsorganizations´ ability for resilience. We think thatresilience should be seen as an organization’s capacity toanticipate disruptions, adapt to disruptive events, andcreate lasting value in a turbulent environment. “Built tolast” and “built to be changed in modular way” are broadermanagement issues, which should be planned carefully todevelop visionary organizations. To conclude, looking frommanagement point of view, there is a growing need to developabilities to act in changing, not easy to forecasted and non-linear situations due to the complexity related toutilization and developing digitalization. Authentic andclinical leadership involves components such as awareness,unbiased processing, action, and relations ([61], [62], [63],[64]). Authentic leaders are deeply aware of how they thinkand behave and are perceived by others as being aware oftheir own and others’ values/moral perspectives, knowledge,and strengths, and aware of the context in which they operate[65].

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