Demystifying big data: Anatomy of big data developmental process

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Telecommunications Policy

Telecommunications Policy ] (]]]]) ]]]–]]]

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Demystifying big data: Anatomy of big datadevelopmental process

Dong-Hee Shin n

Department of Interaction Science, Sungkyunkwan University, 90327 International Hall, 53 Myungryun-dong 3-ga, Jongro-gu,Seoul 110-745, Republic of Korea

a r t i c l e i n f o

Keywords:Big dataData ecosystemNormalizationNormalization process theoryBig data userBig data user experience

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a b s t r a c t

This study seeks to understand big data ecology, how it is perceived by differentstakeholders, the potential value and challenges, and the implications for the privatesector and public organizations, as well as for policy makers. With Normalization ProcessTheory in place, this study conducts socio-technical evaluation on the big data phenom-enon to understand the developmental processes through which new practices ofthinking and enacting are implemented, embedded, and integrated in South Korea. Italso undertakes empirical analyses of user modeling to explore the factors influencingusers' adoption of big data by integrating cognitive motivations as well as user values asthe primary determining factors. Based on the qualitative and quantitative findings, thisstudy concludes that big data should be developed with user-centered ideas and thatusers should be the focus of big data design.

& 2015 Elsevier Ltd. All rights reserved.

1. Introduction

It is no doubt that big data is a rising trend. Referring to a massive volume of data, big data is growing globally at a nearexponential rate. Big data technology is drastically revolutionizing both markets and society (Chen, Chiang, & Storey, 2012).The unlimited potential of a data-driven economy has been widely recognized, and there is increasing enthusiasm and hypefor the big data potentials (Shin & Choi, 2014).

While big data is certainly a hot topic and a growing development target, most governments and companies are still notactively applying the data analytics. Contemporary discussions concerning big data have been technologically biased andindustry-oriented, leaning toward the technical aspects of design (Eynon, 2013). Most development efforts have beenfocused on the industrialization and commercialization of data technologies and infrastructure, while few efforts haveaddressed the immense repercussions of the social dynamics and organizational, political, and managerial decisionsinherent in the development of big data (Housley, Procter, Edwards, & Burnap, 2014). That is probably because ofwidespread misconception that big data is output rather than process. Like most technologies, it is not the output thatmakes for innovation, it is how people align our society to gain value. Such alignment effort includes how to manageprevailing concerns such as the invasion of privacy, imperfect security, and limited interoperability (Kshetri, 2014a, 2014b).Such issues will be critical to its success as they influence how big data is developed and managed. This study argues that bigdata is a phenomenon rather than a technology. It is a social practice, and as such, its development should be focused ondata integration with the social and cultural milieu.

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This argument is consistent with the normalization perspective that design and analysis of technologies should be basedon contextual understanding, that is, a context-based evaluation that determines the fitness of a technology within aspecific context (May & Finch, 2009; Shin, 2014). May and Finch (2009) argued that normalization is “the work that actorsdo as they engage with some ensemble of activities and by which means it becomes routinely embedded in the matrices ofalready existing, socially patterned, knowledge and practices” (p. 540). The specific concept comes into play when societiesare confronted with change and must find ways of accommodating. This is because changes can sometimes be perceived asdisruptive and may negatively impact the goals and operations of a business. When applying this idea to the field of big data,normalization aims at developing an understanding of the process by which big data are used and accepted. To articulatethe usefulness of normalization in big data, Normalization Process Theory (NPT) is used to closely examine the socialprocesses affecting the acceptance of new ways of working in the Korean context. With normalization in focus, this studyseeks to address both the individually oriented concerns of general users and those of the strategic stakeholders, who areconcerned with the realization of higher-level objectives. As NPT also has some drawbacks, the qualitative analysis of bigdata was also supplemented with a quantitative examination modeling how users accept big data, namely, a big dataacceptance model from the user perspective. This focus on users of big data complements the macro nature of ecologicalframework. By combining macro and micro perspectives, this study seeks an optimal solution for big data consideringappropriate user acceptance, social norms, regulations, industry dynamics, and market receptiveness. The relationshipbetween big data and the surrounding discourse provides insight into the design, framing, and development of big data. TheKorean context offers solid examples of the dynamic interplay of big data, wherein the government has been proactivelyinvolved in the fostering of technological initiatives, which provide key lessons with respect to regulatory regimes,infrastructure development, demand promotion, and institutional configurations conducive to policy execution. Followingquestions guide the study.

RQ: What are the normalization processes of big data at the micro, mezzo, and macro levels in Korea?

–

TaUT

PT

Individually: How do people perceive big data, what cognitive perceptions are fulfilled, and what factors influence theintentions to implement its use?

–
Socially: What obstacles and problems have been encountered with attempts to develop big data in societies? – Nationally: What are the national policies and strategies regarding big data?
These inquiries will lead to the development of a new conceptual framework for a big data system. Following the viewsof Martin (2013) and Kshetri (2014b), the definition of development in this study goes beyond the traditional meaning ofphysical and technological advancement. Development is viewed as the process of economic and social transformation thatis based on complex cultural and environmental factors and their interactions.

2. Theoretical framework

2.1. The UTAUT model

The Unified Technology Acceptance and Usage Theory (UTAUT) aims at explaining user intentions to use technologies, aswell as their subsequent usage behavior (Venkatesh, Morris, Davis, & Davis, 2003). While robust, the model requiresmodification in the case of emerging trends or technologies. Employment of the modified UTAUT model will enable a betterexplanation of big data acceptance and usage behavior (Table 1). The utility of the modified model stems from the fact thatthe field of big data is heavily technology-driven, as well as user-oriented and innovation-focused. Thus, the model is wellsuited to reflect the nature of big data because it addresses the evolutionary progression of a technology and its usagedynamics toward a more innovative and unpredictable service. In application of this modified model to a technology-driven

ble 1AUT and the adapted model.

UTAUT Adapted model Big data

Performance expectancy Relative advantage Competitive advantagePerceived usefulness Big data benefit

Effort expectancy Interoperability Data compatibilityPerceived ease of use Normalized data

Facilitating condition Perceived security Privacy, trust, cyber-securityPerceived quality Integrity, accuracy and reliability

Demographic factors Not included Individual preferencesSocial influence Normative pressure

Behavioral intention Behavioral intention Big data implementation decision

Use behavior Actual implementation and adoption Implementation and use

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environment, the variables of the classical technology acceptance model (TAM) are posited as key drivers of big dataadoption. In consideration of UTAUT, the model also integrated additional key drivers including security, quality andcompatibility. Placing these variables under the nomological structure of the UTAUT and precisely describing theirinterrelationships allows integration into a coherent and parsimonious research model (Tables 2 and 3).

2.2. Normalization process theory

Big data is not a simple discrete technology, but rather a phenomenon resulting from the vast amount of raw informationgenerated across society, and collected by commercial and government organizations. This phenomenon represents both achallenge and an opportunity. This dual aspect presents a series of interesting research topics for Science, Technology andSociety (STS) researchers. Among the theories of STS, NPT can provide a good framework for the case of big data in Korea.The theory was developed by Carl May, and was empirically derived grounded in the theory of STS. The theory fitsparticularly well with macro approaches to innovation, like big data. Further, the situation in Korea, wherein there is acomplex and complicated phenomena regarding IT, the theory poses a solid framework for big data.

Normalization refers to the social processes through which ideas and actions come to be seen as normal and becometaken-for-granted, or natural, in everyday life (Sooklal, Papadopoulos, & Ojiako, 2011). If big data becomes normalized insociety, people will see it as being part of life. Tracing the normalization process can reveal the social processes throughwhich new or modified practices of thinking, enacting, and organizing work are operationalized in institutional settings(Kitchin, 2014a; May & Finch, 2009). Framing big data from the NPT perspective is nicely in line with social discussions of bigdata raised by critical researchers. boyd and Crawford (2012) defined big data as a cultural, technological, and scholarlyphenomenon based on the interplay of technology, analysis, and mythology. Similarly, Faltesek (2013) argued that big data

Table 2Quantitative data of sample demographics (N¼398).

Sub-category Number

Industry sector Services 81Public sector 79Manufacturing 56Education 74Health 42Financials 52Other 14

Functional area ICT 78Finance 87Marketing/sales 67Operations 76Management 57Other 33

Size Large firm (Conglomerate) 198Medium and small-sizedenterprises

112

Small business 88

Table 3Qualitative data for interpretive analysis.

Sectors Methods Responses

Government Face-to-face 2 20Phone 8Email interview 6Mail survey 4

Industry Content/service provider Face-to-face 3 25Phone 6

System/telecom electronics Email interview 11IT manufacturing Mail survey 5

Academia University Face-to-face 4 20Research institute Phone 8

Email interview 4Mail survey 4

Total 65

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serve as a brand name for the relationship between society, technology, and politics. This view is similar to that ofAndrejevic (2013), who emphasized socio-cultural efforts to understand the social world through big data. The under-standing of big data from the perspective of NPT will facilitate a shift to the implementation of meaningful and sustainablebig data in reference to the human context.

Coincidentally, the term of normalization overlaps in database and NPT. In database, normalization refers to the processof organizing the fields and tables of a relational database to minimize redundancy. The objective is to make the data usableand valuable. In this sense, applying NPT to the case of big data makes good sense. Normalization in database is a systematicapproach of decomposing tables to eliminate data redundancy and undesirable characteristics. This is a multi-step processwhich requires a conscientious effort. Similarly, normalization in NPT refers to the process of the implementation,embedding, and integration of new technologies and organizational innovations. This process is a multi-step phase done toeliminate or minimize challenges with a complex intervention.

3. Hypotheses

This study proposes an adaptation of the UTAUT that incorporates the variables of security, quality, relative advantage,compatibility, perceived usefulness (PU) and perceived ease of use (PEoU) which determine behavioral intent, as well as twoconstructs influencing usage behavior. The big data acceptance/use model proposed herein is presented in Fig. 1.

3.1. Implementation intention and usage

The UTAUT suggests that a person's performance of a specified behavior is determined by his or her behavioral intentionto perform such behavior, while behavioral intention is jointly determined by the person's attitudes and subjective norms.Given the wide applicability of the UTAUT in analysis of emerging technologies, it can be expected that the generalcausalities found therein are applicable to the context of big data. In this study, behavioral intention is represented asimplementation intention in organizations (Fig. 2).

H1. Implementation intention to use big data will have a positive effect on usage behavior concerning big data services.

3.2. PU and PEoU from the TAM

The TAM uses two distinct but interrelated beliefs as the basis for predicting end-user acceptance of technology (Davis,1989). Big data generates potential benefits for companies such as cost control, revenue generation, risk control,improvement of decision-making, etc. Thus, it can be inferred that PU will have a positive effect on intention. Also, usersmay consider the adoption of big data in terms of how easy and convenient it is to use. Big data requires technologies for theprocessing and analysis of large amounts of heterogeneous data. Depending on the degree of knowledge surrounding use oftechnologies, an organization may consider big data more or less easy to use.

H2. PU has a positive effect on the behavioral intention to use big data services.

H3. PEoU has a positive effect on the behavioral intention to use big data services.

H1

H4

H2

H3

Quality

Perceived usefulness

Relative advantage

Inter-operability

Perceivedease of use

Implement Intention

Security

H6

H5

H9

H8

H7

Use behavior

Bigdata system features Perceived value Adoption

Fig. 1. The UTAUT model of big data.





0

2

4

6

8

10Security

Interoperability

Quality

Advantage

Usefulness

Easiness

Intention Usage

Fig. 2. Interest graph of big data.


3.3. Relative advantage

Relative advantage is one of the key concepts in the adoption of new technology. It is defined as the degree to which aninnovation is perceived as being better than the alternative(s) it supersedes (Rogers, 1995). As previous studies have shown,relative advantage and PU are conceptually similar constructs, which are closely related. The relative advantage of big data isits ability to make sense of the ever-changing data landscape in real time (Chen et al., 2012). It may be worthwhile to seehow relative advantage affects the PU of big data. The following hypotheses were proposed to understand the user'sperceptions of the advantages of big data over other available data services.

H4. The relative advantage of big data services has a positive effect on PU.

H5. The relative advantage of big data services has a positive effect on the behavioral intention to use big data.

3.4. Interoperability

Interoperability is important for bringing diverse datasets together and implementing big data. Open standards set aframework for data that can be joined across organizational boundaries. In UTAUT, the issue of interoperability is close tothat of compatibility. Compatibility is defined as the degree to which an innovation is perceived as being consistent withexisting values, past experiences, and the needs of potential adopters (Rogers, 1995). Services compatible with existingstandards and technologies are adopted more rapidly than those which are not. Compatibility issues arise for existinginformation systems when external sources of data are incorporated. Numerous studies have confirmed that compatibility ispositively related to the rate of adoption, while complexity has a negative relation (Shin, 2009). It can be hypothesized thatcompatibility has a positive influence on the adoption of big data.

H6. Interoperability has a positive effect on the PEoU of big data.

H7. Interoperability has a positive effect on the behavioral intention to use big data.

3.5. Perceived quality

Big data is less a matter of data volume than the quality of data to improve quality and efficiency in the delivery ofservices (Kwon, Lee, & Shin, 2014; Tinati, Halford, Carr, & Pope, 2014). The quality aspect of data is considered byorganizations to have relative importance, which is reflected by the amount of investment or policies targeted at itsmaintenance or improvement. In the business arena, it has been rationalized high quality data is a valuable asset, increasingcustomer satisfaction, improving revenues and profits, and offering a strategic competitive advantage (Shin, 2013). Theimportance of data quality in both decision-making and operational processes has also been emphasized by numerousstudies (Cheong & Park, 2005). Data quality is often regarded as the degree to which the data fits its use. DeLone and McLean(2003) argued that only high quality data are fit for their intended uses in operations, decision making and planning. Shin






(2014) defined data quality as the fitness for use of information, determined by whether or not the data meets therequirements of its authors, users and administrators.

H8. Perceived quality has a positive effect on the intention to use big data.

3.6. Perceived security

The security of information held in big data environments poses a significant concern (Paquette, Jaeger, & Wilson, 2010).Private companies as well as government agencies, particularly in defense and law enforcement, may be reluctant to letimportant data flow outside their own firewalls. The use of a service provider residing outside of a company's legal orterritorial jurisdiction may place access or security at risk. Perceived security is defined as the extent to which a personbelieves that use of a particular application will be risk free (Shin, 2013). The current study approached perceived securityfrom a broader perspective, including not only the technical aspects such as confidentiality and authentication (Flavin &Guinaliu, 2006), but also referring to a person's comprehensive sense of security and well-being in the cloud environment(Shin, 2013). A numerous studies have reported that security concerns were a major barrier to adoption of the new big data(Esteves & Curto, 2013; Kshetri, 2014a). It is reasonable to infer that the perceived security of big data will influence theattitude and intentions towards adopting big data.

H9. Perceived security of big data positively influences behavioral intention.

Based on this hypothesis, further questions can be derived as following.

PT

HRQ1: From public perception point of view, what are the trade-off between security and privacy?HRQ2: How differently people make the trade-off decision across different sectors?

4. Methodology

The data used herein were collected from a variety of venues, using multiple methods. Since big data systems areheterogeneous, the process of understanding the phenomena should be diverse and multi-faceted. Multiple triangulationmethods facilitate the validation of data through cross verification from two or more sources. The methodology employed inthis study has two sides: quantitative data based on a survey method, and qualitative data based on an interpretive method.The survey method was based on UTAUT, while the interpretive method was based on normalization process framework.Careful inspection of qualitative and quantitative data allows us to glean genuine insight on user behavior from reams ofbig data.

The survey method consisted of four phases. First, individual in-depth interviews were conducted with possiblecustomers. Ten college students were asked to explain their experiences with and opinions of big data. Second, six focusgroups were organized and group interviews were conducted, in which groups of 4 to 6 individuals discussed how theycurrently use big data services and/or what factors would influence their use of such services in the future. Third, based onthe focus group sessions, the final survey questionnaire was developed through several comment rounds of an expert panelconsisting of professors and researchers, as well as data experts. Prior to use, the questionnaire was tested by administrationof a pilot survey among possible users, who provided a comprehensive review of their individual responses to the pretestsurvey. The pilot test analysis consists of a detailed comparison of the data for each of the pretest survey participants withother responses. The pilot test included twenty students who had previous experience with big data, and the tests weregiven at a three-week interval. The participants were familiar with big data, and, prior to answering the questionnaire, werestrictly instructed to ask the experimenter any questions about questionnaire items that they didn't fully understand.Finally, to reduce possible ambiguities in syntax and semantics in the questions, a final pilot test was performed with 22respondents self-selected from the mobile community.

The finished survey was administered by a marketing firm specializing in survey development and analyses. Thecompany administered the survey to managers involved in big data adoption decisions and usage, such as CIOs, marketingdirectors, and business analytics managers. Based on the list of the Korean Industry Association, the survey companycontacted the respondents through phone calls, emails, and social network services. There were two parts to thequestionnaire. The first contained demographic information with control variables such as the job role of the participant,size of the company, and existence of a data mining center. The second included the measurement questions. To avoidcommon method bias, data were gathered for the independent variables and dependent variables using various differentmethods, sources, and times.

In addition to the primary data collected through survey, secondary data were used as a major source. Specifically,archival materials such as industry reports, government publications, technical reports, and design, planning, anddevelopment-related materials were utilized as secondary data. Content analysis was employed to analyze the secondarydata obtained from the literature. The data collected were analyzed through thematic analysis using the Atlas.ti program.The coded texts were then extracted in a systematized form to determine the developmental processes of big data. Theprinted transcripts were read thoroughly, and then re-read for line-by-line coding. The codes from the line-by-line codingprocess were then summarized into a coding book. Based on the coding, categorization was performed by the researchers

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through analysis of both the content and context. After all the collected data were coded into Atlas.ti, patterns of experiencewere listed based on the ecology model components, that is, according to networks, applications, services, and users. Thenext step in the thematic analysis was to identify all data relating to the pre-classified patterns. All discussions which fit aspecific pattern were identified and placed within the corresponding pattern. Analysis of the documents, in turn, revealedexplanations of the activities, events, and outcomes through interviews with the individuals involved in the activities.Interviews were conducted with respondents from the government (Ministry of Security and Public Administration-MOSPA,Ministry of Science, ICT and Future Planning-MSIP, National Information Security Agency-NIA), industry, universities, andgovernment-run research institutes. The interviews revealed the negotiation and inscribing of different business realitiesand interests in big data project development.

The analysis was refined after the collection of data by focusing on the social and cultural factors involved in big dataproject development, the demand and supply roles in the ecology model, the roles of governments and industry, and playerinvolvement in big data development. The collected data were used for triangulation of the findings. Different data from thevarious methods contributed to various aspects of big data, whereas certain methods were used for specific aspects.

4.1. Measurement instrument

Chronbach's alpha was also employed to test the reliability and content validity of each construct. Most of the scoreswere above the acceptable level of 0.70. The variables in this study, derived from the existing literature, exhibited strongcontent validity. The convergent and discriminant validity of the model were examined using the procedure suggested byFornell and Larcker (1981), who recommend measuring the reliability of each measure and construct, and the averagevariance was extracted (AVE) for each construct. The reliability of each item was examined using a principle componentsfactor analysis. Most items were found to have factor loadings higher than 0.7, which was considered by Fornell and Larcker(1981) to be very significant. Each item loaded significantly on its underlying construct (po0.001 in all cases). Therefore, allconstructs in the model had adequate reliability and convergent validity. To examine discriminant validity, comparison ofthe shared variance among constructs with the AVE from the individual constructs was carried out.

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Criteria and results

Internal consistency
Cronbach's αZ0.9 (the correlations between different items on the same test) Content validity Existing literature, similar other studies Convergent validity Loading coefficient from principle component factor analysis (above 0.7) Discriminant validity AVE (the shared variance between constructs was lower than the AVE from the individual constructs)
4.1.1. Structural modelStructural equation modeling was used to analyze the data. The overall fit of the model was satisfactory, demonstrating

relevant goodness of fit indices greater than 0.90 in all cases. The GFI was 0.95, the AGFI was 0.89, and the TLI was 0.91.Similarly, no evidence of misfit was observed, with the RMSEA showing a very satisfactory level of 0.067, which favorablycompares to the benchmarks by Joreskog and Sorbom , who suggested that values of 0.06 or more reflect close fit. Thestandardized RMR was also fairly good at 0.027, well below the threshold for a good overall fit. The normed chi-square value(a chi-square divided by the degrees of freedom) of 1.98, a value that was appropriately below the benchmark of three,provided another positive test statistic to indicate good overall performance of the model.

5. Results

5.1. Structural paths and hypotheses tests

To test the structural relationships, the hypothesized causal paths were estimated. All ten hypotheses were found to besupported with satisfactory levels of significance (Table 4). The results generally supported the proposed model, illustratingnew dimensions of big data user acceptance. The specified relationship between PU and relative advantage (H4) wassupported by the data, as indicated by a significant critical ratio (CR¼3.109). In addition, the relationship between PEoU andcompatibility (H6) was also supported by the significant critical ratio (CR¼3.416). The PEoU of big data, which is enhancedby compatibility (H6; CR¼3.416), was related to reaching a higher level of positive intention (H3; CR¼4.094). The usefulnessof big data, which was influenced by its relative advantage over the other available data services, significantly affected theintention to adopt (H2; CR¼3.218). Aside from PU and PEoU, perceived security was the strongest determinant of userintentions for big data (H9; CR¼3.925). The high effect of security is in line with other significant effects (relative advantageand compatibility). Given that the factors are related concepts, hidden relations among switching cost, relative advantageand compatibility may be inferred. Quality was also found to be a significant factor determining user intention (H8;CR¼3.530). Compared with perceived value, the impact of quality on intention was generally greater. This is in line with the

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Table 4A summary of hypothesis tests.

Hypothesis Standardized coefficient S.E. C.R. Support

H1: Intention-Usage 0.54 0.014 2.122* YesH2: PU-Intention 0.39 0.103 4.094** YesH3: PoEU-Intention 0.22 0.016 3.113** YesH4: Relative advantage-PU 0.21 0.091 3.218** YesH5: Relative advantage-Intention 0.38 0.459 4.013** YesH6: Interoperability-PEoU 0.40 0.218 3.416** YesH7: Interoperability-Intention 0.38 0.130 2.356** YesH8: PQ-Intention 0.23 0.013 3.530** YesH9: PS-Intention 0.42 0.231 4.925** Yes

S.E. is an estimate of the standard error of the covariance.C.R. is the critical ratio obtained by dividing the covariance estimate by its standard error.n Critical ratios values exceeding 1.96 are significant at the 0.05 confidence level.nn Critical ratios exceeding 2.32 are significant at the 0.01 confidence level.


notion that even though value may be more important in the initial decisions of consumers, quality still plays an importantrole throughout the adoption process (Shin, 2013).

The variance in big data use explained by the model was 59 percent, which is fairly high given that numerous factors mayaffect the acceptance of and intention to use the service. The results showed that a large proportion of the variance inindividual intentions toward accepting big data services can be explained by the differences in the PU and PEoU, along withthe switching cost. Thirty-six percent of the variance was explained by behavioral intention to use big data services.

5.2. Interest graph

Based on the model and survey, an interest graph could be drawn. An interest graph is a graphic representation of theuser model in which an individual is interested. Interest graphs have perceived utility, and value that people's interests are amajor aspect of how they see big data. The graph of big data largely supported the model, and was in line with userresponses. For usage, users showed higher interest in utility (usefulness and easiness) than in features (security, quality,interoperability, and advantage). For intention, on the other hand, users showed higher interest in features than utility.Among the features, security was shown the highest interest, which is consistent with the SEM findings. From the graph, itcan be inferred that users are directly influenced by utility during actual usage and continuous use, while they tend to seethe features of big data as important in their decision of future intentions. This inference makes sense and is in line with theUTAUT studies, because people generally tend to assess features first, which then influence the utility of big data. Theusefulness and ease of use of the big data are seen in the usage behavior of the users.

5.3. Trade-off privacy and security

The questions of trade-off between privacy and security are answered through in-depth interviews of the respondentsselected from the survey administration. The interview findings show that respondents' attitudes significantly affect theirpreferences in relation to privacy and security. Moreover, these attitudes are influenced by age, gender and income, as wellas context specific factors, such as organization resources; time spent on the Internet and current technology condition.Overall, the results indicate that respondents' preferences related to security and privacy are much more complicated thanthe simplistic inverse relationship between security and privacy that is often assumed; an important finding from a policymaking perspective.

Also, based on the finding, it can be inferred that we need to shift the conventional privacy-security trade-off model(having more security leads to less privacy and vice versa) to a more evidence-based model for the complex relationshipbetween privacy and security. It seems that the public's perception of security and privacy is reflected and reinforcedthrough mass media. Their role in reconstructing images, perceptions and beliefs is crucial; media express and at the sametime shape public opinion. The public in general perceives privacy and security as highly abstract topics and thus first-handexperience is not available for many people. It is important to offer to the public important information on what peopleactually are able to know about privacy and security, on which issues the two concepts are bound, their respective notionsand instances of use and their framing.

As to the HRQ2, the respondents were asked to rank/score (7-Likert scale) industry sector in relation to privacy andsecurity. There are clear differences in preferences for attributes are found across the sectors as respondents make trade-offsbetween their needs for privacy, security and surveillance in each setting. In general, public sector show higher securitypreferences than private sector. That is, the respondents are willingly to sacrifice privacy over security in the public sectorservices as they tend to give more trust public sector than private sector. On the other hand, the respondents reveal morestrict cautions in the private sector services by prioritize security over piracy. The result can be tabularized in Table 5.





Table 6NPT and application.

NPT Process Description Application in this study

Processproblems

The implementation of new ways of thinking, acting and organizingin health care.

Infrastructure, software and technologies, service andapplications, and big data initiative.

Structuralproblems

The integration of new systems of practice into existingorganizational and professional settings.

Standards, usage and users, capacity, social and culturalfactors, government and governance.

Table 5Trade-off between Privacy and Security (N¼398).

Privacy Security

Services 6.471 4.239Manufacturing, industry/consumer goods

6.362 4.136

Technology 6.354 4.248Financials 6.285 5.123Healthcare 4.252 6.421Education 4.174 6.332Government agency 4.217 6.326Research 5.153 6.234


6. Big data as a normalization process

Based on the significant results of the big data adoption model, it is worthwhile to extend the discussion of big data tothe macro level, which includes diffusion and policy issues. Using the NPT framework, big data was conceptualized as acomponent of the ecological ecosystem; that is, within the networks of technology, government, industry, markets, users,and society. The framework focused on the interactions with the larger ecology of markets, organizations, usage, culture,and the production of services (May & Finch, 2009). The ecological view is in line with that of Hampton et al. (2013), whoemphasized data ecology, wherein it is important to consider all aspects that constitute a socio-technical system in theanalysis. Based on the previous studies (May & Finch, 2009; Sooklal et al., 2011), the NPT can be broken down into twoaspects (Table 6).

6.1. Implementation

Overall, the implementation of big data has been performed smoothly and successfully. A solid infrastructure has beenbuilt, and related technologies such as distributed computing have been operating stably.

6.1.1. InfrastructureThe processing big data requires powerful computation and the possibility of enforcing data/dataset policies such that

the data can only be processed on trusted systems and/or in compliance with other requirements (Roski, Bo-Linn, &Andrews, 2014). The Korean efforts to develop big data have, thus far, focused on and remained in the form of infrastructureimplementation. The MSIP established a new big data center in 2014, and further plans exist to rebuild a real-time collectionand analytics system that can collect and analyze the data from text-based social networks (Shin, 2014). Additionally, thenew system will upgrade the real-time data analytics infrastructure to include the streaming of audio and video data anddata from the Internet of Things. These initiatives have been designed to expand connections among the government,industry, and social agency organizations. For example, the online platform of the center is connected with a governmentdata portal service that stores national statistics and other public information. This infrastructure must therefore ensuredata security and data ownership protection (Assuncao, Calheiros, Bianchi, Netto, & Buyya, in-press; Hashem et al. 2015;Kshetri, 2014b).

6.1.2. Software and technologiesWhile software and technologies are as significant as the infrastructure, most efforts in Korea so far have been

concentrated on the development of crowdsourcing, data fusion and integration, genetic algorithms, machine learning,natural language processing, signal processing, simulation, time series analysis, and visualization (Kwon et al., 2014).Additional technologies applied to big data include massive parallel-processing databases, search-based applications, data-mining grids, distributed file systems, distributed databases, cloud-based infrastructure (applications, storage, andcomputing resources), and the Internet.






6.1.3. Service and applications: Songdo smart cityBig data technology provides an infrastructure for transparency in the manufacturing industry, which facilitates the

unraveling of uncertainties such as inconsistent component performance and availability. One compelling and ongoingapplication is Songdo city, a smart city application that has been currently developing and using in South Korea (Kshetri,Alcantara, & Park, 2014). Particularly, Songdo International Business District is a new Smart City or Ubiquitous City built fromscratch on 1500 acres (610 ha) of reclaimed land along Incheon's waterfront, 40 miles (65 km) southwest of Seoul, SouthKorea and connected to Incheon International Airport by a 7.4 mile (12.3 km) reinforced concrete highway bridge, calledIncheon Bridge.

The core concept is of a connected city with a very big data center, which is the brains of the city. Individual apartmentsfeature panels in each room that control lighting, temperature and access to media. The preferences and settings are storedin the central data center, which is also responsible for maintenance. In addition, 20,000 units feature Cisco's home versionof telepresence. Cisco has supplied routers and other networking equipment for Songdo smart city. It has spent millionscreating wired, intelligent urbanism from the ground up, where roads talk to traffic managers and the electrical grid isdigitally adjusted according to peak demand.

The city government is making special efforts to enhance utilities integration and security system – a smart city feature thatenable seamless connectivity and better efficiency for city authorities to manage facilities and utilities, as well as for futureresidents to have easier access to information such as traffic and weather updates or even security alerts on their smart phones.In 2011, the city adopted an intelligent context-aware surveillance system. Closed-circuit televisions that can recognize criminals'vehicles are installed at two bridges which one must pass over to get in or out of the city. Once the CCTVs recognize them, thepolice immediately move in. This is a part of Songdo's U-Life visions that takes into account the overall process of constructingand operating a ubiquitous computing environment. Through organic connections to a central control center in charge of theintegrated management and monitoring of diverse services, a wide range of services including facilities management, IT service,security and healthcare will be provided on a one-stop basis (Kshetri et al., 2014).

Townsend (2013), on the other hand, considers Songdo's case as not the best practice as it is top-down, technocraticdevelopments. Per Townsend's prediction (2013), Songdo might end up as gated residential communities or businessdistricts rather than the model smart cities they were envisioned to become. While Songdo has completed physically andtechnically (classrooms, hospital rooms, and apartments were pre-equipped with video screens and telecommunicationlines. Roads, water, and electrical lines were also built with sensors, so traffic, and water and electricity use can bemonitored and controlled digitally), it is not clear how successful it will be. Big multinational corporations are not comingyet and there is also the risk that the city's digital infrastructure, some of it planned in rudimentary form a decade ago, willquickly become outmoded, given the rapid pace of change in the tech world. For cities, what these and other companiesoffer is a juicy apple promising services and expertise at an affordable cost. Yet they also may lock cities into proprietarysystems that reduce incentives to cultivate in-house expertise (Townsend, 2013). Lastly, Songdo smart-city initiativesrevolve around collecting information about the moving parts of a system in real time to allow a central operator morecontrol. This raises the question: who will have that control? Also, it is not yet clear how more data will make cities smarter.

Please cite thTelecommunicat

is article as: Shin, D.-H. Demystions Policy (2015), http://dx.doi.org/10

Songdo smart city
Ideal features
Change
Transformational innovation Incremental improvement Big data Focusing on the collection/processing of big
data
Focusing on the use of big data. Traffic statistics, energy consumption rates and GPSmapping
Innovationtypology

Top-down
Juxtaposition of top-down and bottom-up
Standard
Proprietary technology Open standard Industry A few firms (Cisco, LG, IBM) Multi-players for diverse layers Focus Technological focus. Spatial-oriented Everyday social lives. Ecological-oriented Recenttechnology
Internet of things

6.1.4. Korea's big data initiativeSince 2013, the Korean government has initiated explicit support of a big data policy (Table 7). The government selected big data

as a strategic area for government and industry focus during the next ten years (Ministry of Science, ICT and Future Planning, 2014),and plans to double the big data market by 2017 by boosting the development of related technologies and increasing theinfrastructure and number of available services. According to the MSIP, the government is set to assist in the creation of over tenleading big data businesses and in the development of up to 5000 professional experts in a bid to seal Korea's position as a leader inthe international standardization of big data technologies. The ministry also has a plan to invest 462 million USD in the newmarketthrough 2016, but has temporarily suspended all related projects. The government's move to ease the regulations concerning onlinefinancial transactions to bolster its creative economic initiative will also involve the development of public services by connectingmassive data from the public and private sectors. To improve Korea's competitiveness in the big data sector, the MSIP, inconjunction with the NIA, will develop a big data service model which combines public and private data with creative ideas(National Information Agency, 2014). It is expected that the pilot project will lead to the emergence of an intelligent service model

ifying big data: Anatomy of big data developmental process..1016/j.telpol.2015.03.007i




Table 7Major events in data infrastructure development.

Time 1999–2002 2002–2006 2004–2006 2006–2007 2007–2012 2012-presnet

Title Cyber Korea 21Initiative

e-KoreaVision 2006

IT839 u-IT839 Cyber-infrastructure

Songdo smart city

Underlyingtechnologies

ATM, ADSL, cablemodem

VDSL, FTTB,FTTH, CDMA,

VDSL, FTTB, FTTH,WiBro, W-CDMA,HSDPA

FTTH, WiBro, W-CDMA, HSDPA

Grid, ubiquitouscomputingnetworks

Internet of Things,Big data

Supporting agency MIC and NCA MIC and NCA MIC with industrycoalition

MIC KCC IncheonMunicipality

Total investment(millions USD)

2101 1982 2800 2291 N/A Ongoing

Components of the ecosystemOverall vision andstrategy

Upgrade backbone andaccess networks

Promoteinformationsociety

Firstcomprehensiveinformationstrategy

The fastest and mostextensive wirelessnetwork in the world

Futurebroadbandsmart GRIDrollout

A hyper-connectedcity with a very bigdata center

Services Standardize andmonitor service quality

Providebroadbandnetworks toschools,government

Create an enablingenvironment forintra- and inter-modal competition

Ensurenondiscriminatoryaccess for service,application, andcontent providers

Expandinguniversal serviceobligation toincludebroadband

Intelligent trafficsystem, smart grid,Seamlessconnectivity andbetter efficiency

Applications Facilities-basedservices

Developadvanced e-governmentprograms

Convergenceservices

Ubiquitouscomputingapplication/services

Advanced GRIDservices

Intelligent context-aware surveillance

Users/usage User accessibility/affordability (universalservice)

Promotecreation ofdigital content

Content/mediapromotion

Government-leddemand aggregation

Demandfacilitation

Big data usage/capacity

NCA: National Computerization Agency.


wherein public and private data, including location and payment information, will converge. A total of 1.4 billion USD, with eachtask receiving from 20 to 40 million USD, has been allocated to the project, and will be provided in the form of fund matching afterthe selection of approximately four consortiums. Any institutions which possess data, or use or develop services, will be eligible toparticipate in the consortium. Through this project, the MSIP aims to identify services for users and generate a synergy effect byencouraging the active disclosure and sharing of data between the public and private sectors. In subsequent years, theMSIP plans todevelop and build a real-time collection and analytics system that will include the streaming of audio and video data as well as datafrom the Internet of Things.

In 2015, Korean government has announced six public-private Big Data projects; all managed by a combination of publicinstitutes and private company cooperation. The six projects cover health, transportation and business sectors. In the healthsector, one project will focus on linking big data from the national health database with social media information to createan epidemic disease warning system. Another will analyze big data for predictive medical services, and the third will createan early warning system for medical safety issues. Public transport in will benefit by connecting data from multiple sourcesto plan new midnight bus routes most efficiently, while big data will help the business sector by providing intelligent datasearching tools and by a project aiming to help small businesses by providing historical small business data analysis.

6.2. Integration

Integration involves more complicated operations and sophisticated processes than implementation. Accordingly,examination of the Korean case revealed that more problems are embedded in integration than implementation.

6.2.1. Standard issuesAlthough the number of big data applications in the government and research areas is growing rapidly, there are not yet

any official, mature big data standards in Korea. Harmonization of the emerging open-source industry standards and officialstandard processes is required. As an increasing number of big data applications are introduced, interoperability standardsfor big data are required to cope with the challenges including clear provenance of data, reliable research standards for theexchange of data between multiple domains, enabling of data combination and analysis, and security measures to facilitateresearch while ensuring the protection of sensitive data (Kitchin, 2014a).

6.2.2. Usage and usersAlthough users are the central element of big data systems, they are often the most neglected. This trend is no exception

in Korea. As the current big data efforts have been focused on physical implementation to improve data collection, bothusage and user aspects have been neglected. A survey showed that the majority of users consider big data to be






inconvenient. A great number of users indicated that big data is disorganized, and substantial work is required to obtainusable resources. The survey conducted in this study showed that 68% of executives expect that their organizations willinvest more than one million USD in big data in 2015; this amount is expected to rise steadily to 88% by 2017. Big datainvestments greater than 10 million USD are projected to rise sharply from 19% in 2013 to 50% by 2016, with additionalexpectation that the investments greater than 50 million USD will more than double from 6% in 2013 to 14% by 2016. At bothorganizational and individual levels, there has been significant hype about big data, but with little corresponding activity.The influence of big data with respect to its impact on the public, organizations, and daily life must be assessed realistically.

6.2.3. Social and cultural factorsBig data should bridge multiple cultures and address various social issues and problems (Tinati et al., 2014). For big data

to be effective, it should (1) be accessible as a public good, (2) be sustainable, (3) provide interoperability, (4) facilitatecollaboration, and (5) support experimentation. Contrary to these requirements, however, development of the Korean dataecosystem has focused only on building technological infrastructure and technical equipment for big data. The tendency inKorea thus far has been to consider the impact of big data on society and culture as merely an added benefit to the efforts ofpracticing scientists (Shin, 2014). Korea may have overestimated the near-term effects of big data while underestimating thephenomena's long-term consequences. Although big data has brought positive functions to and had beneficial impacts onsociety, negative functions have also emerged, such as lack of cyber-security and increased invasion of privacy. Each day,Koreans create personal data that is collected, analyzed, and used by private and public entities. Such data reveal therelationships and personal information of users, from shopping choices to health-care information, and are also used bycommercialized services (Shin, 2014). In addition, there is a constant concern that the country is under-prepared for theever-growing scale of cyber-attacks (Oboler, Welsh, & Cruz, 2012). Consequently, in realization of the seriousness of illicitacts and the misuse of big data, the government initiated reform of its big data regulations, including tougher penalties forcyber libel. In response to a series of problems, the Korea Communications Commission released big data privacy guidelinesin 2014 2ewhich allow industries to collect and use the private information and history of individuals without their consent.

6.2.4. Government and governanceThe Korean government has taken proactive actions and played a significant role in development of the big data market of the

future (Shin, 2014). To accomplish this, it launched a series of technological initiatives for big data (Table 8). Korea's big datainitiatives were shaped by a plan implemented by the new government in 2013, named the “Creative Economy.” The CreativeEconomy plan features ambitious technology-driven programs that have already led to a number of remarkable R&D andtechnology innovations. In December 2013, the government published the “Big Data Industry Development Strategy,” and begansupporting the use of big data in earnest.

The government has laid the foundation for a technological approach to complete big data systems and networks. In parallel,a number of operators have developed a comprehensive strategy toward big data development. Their big data visions tend to bemore in line with the original vision of development as the next generation of a ubiquitous computing environment. Such visionsfocus more on increasing the technological capacity through the development of new systems or data server components, andless on the applications and social services applied through infrastructure (Shin, 2014). The country envisions the government astaking an active role in encouraging domestic manufacturers to set early big data standards.

Table 8Korea's actions regarding big data.

Time Regulation

2002 General guidelines of security and privacy regulations introduced for e-commerce2004 A bill regarding customer relations management proposed2005 Customer data management regulation introduced2006 Regulations on mobile commerce data management imposed2006 Privacy and Data Protection Act2007 Revised data protection rules2007 A general guideline of customer privacy protection2008 Data regulatory compliance and standard requirementsAugust 2009 Songdo's Big Data ProjectMarch 29, 2011 Personal Information Protection Act promulgatedDec. 2012 National Master Plan on Big DataApril 2013 Korea Government 3.0 Vision is releasedMarch 23, 2013 First amendment to Act No. 11690Dec. 2013 A Master Plan of Cyber Warfare is announced.August 6, 2013 Second amendment to Act No. 11990Feb. 2014 A bill proposing tighter security and stiff penalties for data leaksMarch 2014 Big Data Institute is establishedJan. 2015 Six public-private big data projects are launched






The Korean government has also sponsored active research activities through the Electronic Telecom Research Institute(ETRI), which is conducting cooperative research with the involvement of universities, IT service operators, and domesticmanufacturers (Ministry of Science, ICT and Future Planning, 2014). This cooperative activity is led by the ETRI withcontributions from many participating organizations and collaborative international partnerships. The goals of Korea's bigdata R&D activities represent global leadership in data computing. Korea is establishing the world's first big data repository-based infrastructure, which can collect more than 90 billion pieces of data each day from a variety of sources and analyzethem on a real-time basis. To support such research activities and establish the vision and strategies of big data, the KoreanBig Data Committee and Big Data Forum were organized in March 2013 and January 2014, respectively. The Big Data Forumwas organized to respond to increasing interest in future ubiquitous computing and the need for formation of aninternational organization focusing on big data research. The objectives of the forum were to analyze the technical trends,establish vision, facilitate international cooperation, support advanced research and development strategies, and study thespectrum of use.

The government has also invested 58.4 million USD over the next three years into the advancement of informationinfrastructure and concurrent big data projects. Korea Big Data is one of the milestone projects recently initiated by Korea(Kwon et al., 2014). The Korean government has fully supported the project, recognizing the potential magnitude and scopeof the impact of big data. Extensive financial investment (over 70 billion USD) has been committed to the initiative, andsubstantial regulatory support has been provided to the industries involved.

In March 2014, the government declared a national plan, “Government 3.0” vision, which allows wider public access togovernment data. This initiative was a drive to boost transparency, information sharing, communication, and cooperation inoverall state affairs management. MOSPA's target is to open 60% of government data sets by 2016. It has secured significantmoney to improve the quality of government data ($2bn USD over 5 years). Change is progressing through leadership fromPresident Park. Her ‘Government 3.0' initiative seeks to provide customized services for the citizen through utilization oftechnology and open data. The government is to apply the vision to all forms of administrative information in all stages ofthe policy-making process, from preparation to implementation. Under this initiative, the government will extend the scopeof public data disclosure to 6150 items by 2017 from the existing 2260 items.

South Korean government has engaged in cyber warfare against North Korea. From South Korea's standpoint, a keyconcern has been North Korea's advanced cyber warfare capabilities and alleged involvement of its substantial workforce inthe Internet's dark side activities (Kshetri, 2014a). North Korea's cyber warfare capabilities are on the rise despite beingentrenched in ageing infrastructure and dampened by a lack of foreign technology. South Korea views the regime's cybercapabilities as a terroristic threat, and has prepared for a multifaceted attack in the future. South Korea estimates that NorthKorea's premier hacking unit, Unit 121, is behind the US and Russia as the world's third largest cyber unit.

With ongoing tensions with North Korea, South Korea has strived to improve cyber-defense strategies in hopes ofpreparing itself from possible cyber-attacks. In March 2013, South Korea's major banks as well as many broadcasting stationswere hacked and more than 30,000 computers were affected; it is one of the biggest attacks South Korea has faced in years.Although it remains uncertain as to who was involved in this incident, there has been immediate assertions that NorthKorea is connected, as it threatened to attack South Korea's government institutions, major national banks and traditionalnewspapers numerous times. Kshetri (2014b) views the cyber-attacks in reaction to the sanctions it received from nucleartesting and South Korea's annual joint military exercise with the United States. North Korea's cyber warfare capabilities raisethe alarm for South Korea, as North Korea is increasing its manpower through military academies specializing in hacking.Current figures state that South Korea only has 400 units of specialized personnel, while North Korea has more than 3000highly trained hackers; this portrays a huge gap in cyber warfare capabilities and sends a message to South Korea that it hasto step up and strengthen its Cyber Warfare Command forces.

6.2.5. IndustryThe creation of data infrastructure and the IT industry in Korea has led it to gain a reputation as a technically shrewd

leader in the area of global information technology. The unique relationship between industry and government is notable.Korea's ICT sector is a heavily regulated industry, even with respect to market entrance. In the early stages of broadbandinfrastructure development, Internet services, electric communication lines, and rental equipment services were the focus,whereby qualified service providers were able to build a broadband network for commercial services due to governmentpromotion of an effective competitive structure through self-regulation. Based on this structure, the government introduceda number of supporting industrial policies to encourage R&D in ICT, provide incentives for joint international research aswell as tax and rent reductions for emerging Internet sectors, deregulation for high technology startups, the promotion ofoverseas IT market penetration, the promotion of greater ICT use in traditional industries such as agriculture and fisheries,and measures to facilitate standardization. In addition to providing frameworks and supporting market developmentinitiatives, the government's supply-side role has extended to the implementation of competition policies and regulatoryframeworks (National Information Agency, 2014).

With this background, the nation's leading conglomerates are expanding their investments to win private firm ordersand government-organized business projects focusing on big data. The companies Samsung SDS, LG CNS, and SK C&C areseeking to improve their competitive edge in this potentially lucrative market by creating sophisticated analytical solutionsand data management platforms. LG CNS first established an independent analytics division in July 2014. It currently has200 consultants allocated to big data, including experienced Hadoop engineers and analysts. LG CNS is also attempting to






boost the marketability of its big data-related solutions outside the Korean peninsula; however, SDS is quickly gainingground on LG CNS. This Samsung affiliate pilots projects through Samsung's technology affiliates, including SamsungElectronics and Samsung Life Insurance. Samsung SDS has won projects for big data development, and provides consultationto state-run firms, financial institutions, and even manufacturing companies other than Samsung units. SK C&C, the systemintegration affiliate of the SK Group, recently launched a so-called “big data business task force.” While the increase indeveloping economic big data markets will further enhance overall market growth, organizations in Korea tend to see bigdata as game changers of existing market and services.

6.3. A complex intervention of big data in Korea

As shown, companies and governments need to address considerable challenges if they are to capture the full potential ofbig data. These challenges and the actions needed are called intervention in NPT, which is defined as a deliberately initiatedattempt to introduce new, or modify existing, patterns of collective action (May & Finch, 2009). Complex interventionsconsist of multiple behavioural, technological, and organizational components. However, they pose special problems forevaluation because their components may act either independently or interdependently, often making it difficult to teaseout the relationships between them. A complex intervention has three types of components (actors, objects, and contexts),which are explained with the example of Korean big data (Table 9).

7. Discussion: normalization of big data

Big data is increasingly seen as the one dominant strategy in a smart society. Despite the obvious potential, there arerelatively few well-established services in routine practice as of yet. Providing interpretation through NPT, it becomes clearthat the development of big data should embrace a socio-technical ecology within which big data is valued for itstransformative effect rather than its materialistic value, and should be made accessible to the communities which itpositively transforms. The transformation process should be viewed from the normalization of data implementation,embedding, and integration with a focus on the users and people in society. This turns into a classical axiom that there is noone-size-fits-all approach in ICT governance. That is, where specific country can develop a more tailor-made normalizationapproach, rather than a one-size-fits-all approach, there is a greater chance of achieving successful outcomes, based on theresources and scale of the country.

Korea currently offers the most favorable conditions for big data to flourish (Shin, 2014) by virtue of its globally superiornetwork infrastructures and the immense amount of data consequently produced. If opportunities to creatively connectpublic and private data are provided through a pilot project, big data will contribute to the establishment of a creativeeconomy that can generate high-quality jobs and new businesses. However, such potential cannot be fully realized withouta cautious and deliberate normalization process.

From the perspective of big data normalization, the Korean model has room for improvement considering the country'sparticular cultural, political, and institutional milieu (Table 10). The design process itself, rather than the final status of bigdata, is of greater significance because big data continues to evolve. Thus, how to align the concept of big data to ongoinginfrastructure projects, which are in a similar state of evolution as the ecosystem, is of great relevance. This question isinherently related to socio-technical inquiries and normalization questions. Although the development of big data has beenactively carried out in Korea over the last decade, there are significant obstacles to its continued growth which requireintervention. The question remains as to how to maintain the momentum and decide the future direction of big data.Among competing demands for a limited budget, how can big data be justified? Can the budget be expanded, particularly in

Table 9A complex intervention of big data normalization in Korea.

Components Intervention Aim Phase

Actors Government Change people's attitude and itsintended outcomes

ImplementationIndustry Implementation

and integrationUsers of big data Integration

Objects Procedures, protocols, hardware, software, application and services ofbig data

Change actors' action and physicalperformance (big data collection andanalysis)

Mainlyimplementation

Contexts Organizational, institutional, and social structures that enable andconstrain, and resource and realize big data

Change the ways actors' behavior intheir normal routine life (normalization)

Mainly integration





Table 10Big data ecosystem architecture (big data as a service).

Function Description Type

Layer 6 Governance Regulation, rules, policy, etc. Informatics Integration NormalizationLayer 5 Usage Content, literacy, user capacity ValueLayer 4 Application Service, process, standards, etc. IntelligenceLayer 3 Platform Middleware, data commons, protocols, etc. Knowledge ImplementationLayer 2 Data

equipmentAnalytic technologies, Relational Databases, SQL, XML, etc. Information

Layer 1 Physicalinfra

Data infrastructure, Hadoop, data warehouse, cloud system,distributed servers, etc.

Bit, Data

Big data as a service


light of public criticism concerning previous failures of infrastructure projects? These questions are related to the existingtendency in Korea to discuss big data development and the belief that infrastructures can be built strictly from a plan, in ahighly conscious, carefully controlled, and fully directed manner.

In light of the obstacles and interventions, big data presents both promises and threats to Korean society. Big datatechnology offers opportunities for policymaking and the implementation of more citizen-focused systems that consider theneeds, preferences, and experiences of citizens with respect to public services. In the same way that citizens express theiropinions regarding government policies on social networking sites, they can also rate or rank the services or agencies usinggovernment applications. Users generate a range of data that could be exploited by government agencies for productive use.Policy makers also have access to substantial data on public behavior patterns that are recorded digitally whenever citizensinteract with government administrations or undertake acts of civic engagement such as signing petitions. Data mined fromsocial media or administrative operations provide new information that can enable government agencies to monitor – andimprove – their own performance.

With respect to threats, big data systems present technological challenges for governments and new moral and ethicaldilemmas for policy makers (Kitchin, 2014b). Large-scale information projects are particularly troublesome. Governmentshave long suffered from shortages in information technology skills and literacy, and the complex skill sets required for bigdata analytics pose an acute challenge. Even in the corporate sector, over one-third of the survey respondents claimed “lackof big data specialists and quality data” as their primary concern regarding the use of big data software. Related to thiscomment, big data poses imminent questions, such as how should governments conduct a census or produce meaningfulstatistics in the age of big data. Fundamentally, policy makers must respond quickly to any backlash, and have no choice butto tackle the rising concerns and social challenges. The challenges include those of scale and heterogeneity, lack of structure,governance, privacy, timeliness, provenance, and visualization at all stages of the analysis pipeline from data acquisition tothe interpretation of results. Given the obstacles, what Korea needs is not big data, but broad social data (Housley et al.,2014) that is contextualized to create value for the people in society. For example, although big data is very good at detectingcorrelations, especially Korea where boasts the power of modern computing with the plentiful data of the digital era, itrarely shows which correlations are meaningful. This is why contextual normalization approach is needed in Korea.

Appropriate interventions have been applied, though most have been ineffective and unsuccessful thus far. The technicalchallenges are common across a large variety of application domains, and are therefore not cost-effective to address in thecontext of one domain alone. Moreover, the challenges will require transformative solutions, which cannot be addressednaturally through simple technical advancement of industrial products.

8. Duality of big data: suggestions for a Korean big data initiative

Based on the analysis and findings, practical suggestions to the Korean government are proposed in this section for future bigdata initiatives. Given the global trends of big data, these suggestions can be applied to governments around the world. The firstrecommendation affirms the continuity of investment in the core data infrastructure, the foundation for advanced data, and,significantly, the design of future big data, technological functions, and rationales translated through a contextual aboriginallanguage. It should seek to address the initial set of use cases by augmenting current IT investments, but do so with an eye toleveraging these investments for inevitable expansion to support far wider use cases in subsequent phases of deployment.Development should be focused on how to stimulate both supply and demand on open data to create user-centered big datamarket. A lack of normalization and contextualized understanding in the planning and design of computing can lead to intrusivetechnologies with an over-emphasis on infrastructure (Batty, 2013).

Second, big data in Korea has highlighted another significant challenge, the social practice of usage, which is a criticalcomponent in normalization. Currently, the majority of big data applications address specific business solutions rather thanactual usage in the everyday lives of people. While big data is transformative, the journey towards becoming big datacapable will be iterative and cyclical, versus revolutionary. Thus, governments must engage in a micro-normalization






process in order to embed big data into societies and cultures. Based on NPT, it was determined that the big data designprocess must constructively involve technical, legal, policy, and usage communities. While big data technology may bebeneficial for development initiatives, it also carries serious risks that are often underestimated (Crawford, Miltner, & Gray,2014). In pursuit of the potential social benefits, it is critical that fundamental user rights and ethical values not be sacrificed(Kemp, 2014; Zwitter, 2014). What matters in Korea is the appropriate use of big data, rather than its availability oraccessibility. The success of transitioning society toward big data ecology depends on the attitudes and motivations of thepublic toward the normalization of big data. While the government promotes big data, individuals must embrace it andinformation knowledge in their everyday lives. People must willingly allow the permeation of such ideologies into the coreof their private domains and the inclusion of ICT ideology within their daily lives, affecting both patterns of behavior andcognition. The use of big data is intrinsically connected to ethical values, which implies that the starting point should be thedevelopment of guidelines governing the access to, and an analysis of, data on individuals. As security, quality andcompatibility were found to be the three most important factors in the user acceptance model, governance features shouldfocus on these aspects accordingly (Crawford et al., 2014).

Third, Korean policy makers and the IT industry alike may undergo a socio-technical normalization through thedevelopment of such a large project. The government has been slow to recognize the social normalizations associated withbig data. Policy makers and strategy formulators need to integrate themselves into the lives of everyday people to graspcertain challenges. In reference to NPT, it is more pivotal to determine why and how to create a framework, rather than thespecific data infrastructure to build. The maximization of the social and cultural benefits provided by big data is moresignificant than the manufacture of technical gadgets and a technological infrastructure. This perspective is consistent withthe metaphor of the data ecosystem as a distributed, adaptive, and open socio-technical system with the capabilities of self-organization, scalability, and sustainability.

Finally, big data development should be planned and evaluated from an objective point of view. Much of the Koreangovernment has hyped big data as the next big thing without properly defining the concept or specifying actionable stepsthat businesses can take to allow effective utilization. What the business press needs to do is to better explain the rewards,risks, implications, and opportunities associated with big data and advance a larger dialogue about its economic impact andsocial implications. Business leaders and entrepreneurs should continue to contribute to this discussion by detailing howthey put big data to work for their organizations. While the significance of big data has been well established, it is worthreemphasizing that without vision and careful planning, even a treasure trove of big data will only serve to distract, ratherthan drive success (Batty, 2013).

Against policy implications that can be derived from the qualitative and quantitative findings, current practices andpolicies related to data use, access, sharing, privacy, and stewardship need to be newly created or revised. From the findingsof the qualitative study, policy recommendations can be drawn as follows.

Please cite thTelecommunicati

Domain
Policy recommendations
Access
A legal framework and system to facilitate data sharing and access should be established in order to ensure effective sharing ofbig data among agencies.
Security and datause

A flexible policy framework should be formed to enable the flow of data while ensuring legitimate user protection includingsecurity, privacy, and copyright protection.

Governance
Big data should be democratized in order to broaden its potential impact and value through the adoption and accessibility ofappropriate support tools.
Governance
Suitable knowledge of the data-driven economy should be created for all users in the data ecosystem, and the literacy required tofuel and support the data-driven economy needs to be developed.
From the underlying data issues, the problems of quality, standards and security still need to be recognized andaddressed. From the model of the key features of big data, the following suggestions can be summarized.

Domain
Interoperability
Cooperation channels and mechanisms among stakeholders, such as the government, enterprises and academia need to beestablished to collect and provide a wide variety of qualified data sets. Where standards are closed and analyses rely too heavily onproprietary data formats and software packages, the risk arises that there will be limited potential for imaginative use of the data inthe public sector.
Security
Strict data security practices must be adhered to and maintained in line with technological developments. This is partly related tohardware and also about people. Where individuals and groups work with sensitive data, the culture and incentives must becompatible with proper data security.
Privacy
User privacy rights need to be enhanced. It is essential that policy concerning privacy protection should address the purpose (thewhat) rather than prescribing the mechanism (the how). The government should take appropriate steps to advance user rights.
Quality
For big data to provide benefits, governments need to fully realize the benefits of bringing diverse data sets together, and individualdata owners must be given incentives to manage the quality of data collection and data capture across the board.
User awareness
Governments should promote big data features in order to increase user awareness. The graph showed that users are influenced byusefulness and ease of use in usage decision, whereas they see the big data features (security, quality, etc.) in future intention.
Usage andcapacity

Policy attention should focus more on the actual uses of big data and less on its collection and analysis.

is article as: Shin, D.-H. Demystifying big data: Anatomy of big data developmental process.ons Policy (2015), http://dx.doi.org/10.1016/j.telpol.2015.03.007i




From an ecological view, the following can be suggested.


Please citeTelecommunic

Domain
Datamanagement

Data should be organized and preserved for posterity and social well-being.

Governance
A legal framework and system to facilitate data sharing and access should be established in order to ensure effective sharing of bigdata among agencies.
Access
Data should be shared through publically accessible databases. Data sharing Collaboration is needed in networks where data are shared and utilized. Governance Issues of data management should be closely addressed with people in society.
9. Implications of practice and theory: big data as a service

Understanding and assessing the conditions in which complex interventions of big data can be introduced andnormalized in society is important to people, industry, and governments.

It is also important to researchers across a range of disciplines who are undertaking trials and other evaluations of suchinterventions. The use of NPT offered a window for conceptualizing the complex interventions in practice regarding big data.It focused on interactions within and between the processes of practice at the micro, mezzo, and macro levels. The modelenabled an understanding and evaluation of the conditions of normalization and of the complex interventions by which it ismediated. In this light, the findings of this study provide implications for both business practices and academia. Forgovernments, practical guidelines were provided, including insight into big data strategies and market structuring at anational level, as the findings addressed an interrelated set of technology, geography, cost, and demand factors. This studyshowed the potential for a new infrastructure and also presented a number of challenges that will be apparent with suchtechnology. While the findings implied that the current Korean model of big data can be viable and sustainable for next-generation ICT ecology, the social and cultural side is not always equivalent to the technological side. This aspect is nicelyaligned with the idea of Big Data as a Service that big data should be viewed and approached more than a tool or vehicle todeliver data, but considered as a service to users that is social threads to enhance the social fabric of the society to providemore and better interactions among members of the society, thus weaving the threads together and they can make moremeaningful connections, be more involved, be more willing to participate.

In addition to practical contributions, theoretical contributions were also made. This study contributes to the interpretiveapproach of STS by investigating the big data phenomenon in a particular context at diverse levels. Whereas STS hastypically been applied in an organizational context to provide organizational management and design implications, such aframework has rarely been applied at the societal or macro level for the thorough analysis of emerging technologies. Limitedresearch has been done to investigate the interactions of the theory components. Substantial research using the socio-technical systems theory investigated components in a discrete way, and has thus somewhat neglected the integratedperspective and the interactions of the components themselves. Attempting the use of NPT, one of the emerging theories inSTS, is very worthwhile particularly in the big data case. Though NPT is still a middle range theory, its conceptualization of abig data was nicely aligned with the socio-technical systems discussion because the concept addresses the social aspects ofindividuals and society with the technical aspects of systems and technology. Given their individual features, the twoconcepts complement each other. The normalization procedural perspective on socio-technical systems theory is aneffective tool in the examination of dynamic ICT development and situations where sustainable technology developmenttakes place. Socio-technical systems conceptualize big data as a normalization, which can further show specific complexitybecause of the hybrid nature of such systems, which consist of various elements that interact with each other.

Acknowledgements

This work was supported by the National Research Foundation of Korea Grant funded by the Korean Government (NRF-2014S1A5B1014964).

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Demystifying big data: Anatomy of big data developmental process