A Risk and Benefits Behavioral Model to Assess

Intentions to Adopt Big Data

José Esteves

1
and José Curto

1
E Business School, Spain

2
UOC, Spain

Email: jose.esteves@ie.edu jcurtod@uoc.edu

Received October 17, accepted 21 December 2013

ABSTRACT: Everyday a constant stream of data is generated as a result of social interactions, Internet of things, e‐

commerce and other business processes. This vast amount of data should be collected, stored, transformed, monitored and

analyzed in a relatively brief period of time. Reason behind is data may contain the answer to business insights and new

ideas fostering competitiveness and innovation. Big Data technologies/methodologies have emerged as the solution to this

need. However, being a relatively new trend there is still much that remains unknown. This study, based on a risk and

benefits perspective, uses the theory of planned behavior to develop a model that predicts the intention to adopt Big

Data technologies.

KEYWORDS: Big data, perceived benefits, risks, decomposed theory planned behavior, adoption

Introduction

Understanding the adoption of information

technology (IT) innovations continues to be a

challenge for information systems (IS) researchers

(Venkatesh, 2006). Every aspect of society,

including business and culture, is currently in the

midst of a technology‐based phenomenon.
Advances in digital sensors, communications,

mobile networks, storage, processing and cloud

computing have given rise to huge collections of

data, capturing valuable information to business,

science, governments, and society (Bryant et al.

2008, Firestone 2010). By 2020, more than 2.7

zettabytes of data will be created annually

reaching 35 zettabytes (IDC 2011) this will call

into question the ability of firms to analyze

information. Traditional decision‐making systems
are incapable of adequately resolving this

problem. Therefore, companies are starting to roll

out their own Big Data initiatives and building

massive database systems to drive significant new

growth in their business operations (Manyika et al.,

2011).

Although the concept of Big Data exists since

2001 when the META Group analyst Doug Laney

(Laney 2001) defined data growth challenges and

opportunities as being three‐dimensional, i.e.
increasing volume (amount of data), velocity (speed

of data in and out), and variety (range of data types

and sources), only in the last two years Big Data

has become one of the IT industry’s hottest topics.

In the press literature, Big Data is characterized as

the new generation of technologies and

architectures, designed to economically extract

value from very large volumes of a wide variety of

data, by enabling high velocity capture, discovery

and/or analysis (Woo et al. 2011).

Available for free online at https://ojs.hh.se/

Journal of Intelligence Studies in Business 3 (2013) 37-46

mailto:jose.esteves@ie.edu
mailto:jcurtod@uoc.edu
https://ojs.hh.se/

The Big Data market is expanding rapidly since

many firms are expending significant resources

on related projects, or are planning to. According

to IDC (2012), this market is expected to grow

from $3.2 billion in 2010 to $16.9 billion in 2015

based on the premise that these technologies will

improve operational efficiency and drive

innovation.

Software Vendors such as IBM, Oracle,

Microsoft, EMC or SAP, are already providing

Big Data services as a source of competitive

advantage for their customers.

Big Data systems are being implemented in

multiple industries, including commerce, science,

and society (Bryant et al. 2008), but many

companies still are not interested in this new trend.

A Big Data survey conducted in June 2012 by IDC

found that 47% of 502 companies across different

industries think that they do not need Big Data

technologies and 25.8% of them do not see the

value it can generate for their companies. Simon

(2010) provides a sobering statistic: three out of

five Big Data projects do not meet expectations

in terms of cost and performance. The major

implementation costs are incurred during the

integration of Big Data into the existing IT

framework. Also, given the high level of

sophistication required for Big Data projects

(Mckinsey 2011), there are some fears related to the

implementation playing against adoption.

All together, these facts lead to the conclusion

that the market is at an early stage of adoption,

hence only early adopters are betting on these new

technologies.

Overall, Big Data represents a disruption in

decision‐making by enabling business processes to
be effectively based on information. Nonetheless,

the main challenge at this point is not the

deployment of the technology, but rather the

transformation of the culture, processes, and people

within organizations.

The overall purpose of this study is to explore the

impact of Big Data technologies perceived risks

and benefits in the intention to adopt them. Since

behavioral intention may not be reflected in actual

use, this paper also examined the relationship

between intended and actual use.

Theoretical background

The academic literature on Big Data is still

scarce. Recent articles published focus more on

the software, algorithms and hardware needed for

Big Data, especially in techniques such as

Hadoop, while the adoption decision issues remain

unattended.

The initial definition of Big Data was composed of

three‐dimensional characteristics (known as the 3vs
model): volume, variety and velocity. Volume

refers to the need for intensive and complex

processing of data subsets that actually contain

information of value for an organization. Variety

refers to the combination of different types of data

from different sources. The attribute of variety

therefore alludes to the fact that data can come

from inside or outside the organization, and may

also be structured, semi‐structured, or unstructured.
Finally velocity, not all of the data in an

organization has the same urgency of analysis.

There is a full range of velocities: from data that

can be batch processed (as in the case of data

warehousing) to data that must be processed in

real time (when continuous data streams need to be

analyzed). The key to understanding speed in Big

Data is to clearly identify the informational

requirements of the processes and business users.

In 2012, Gartner updated its definition as follows:

"Big Data are high‐volume, high‐velocity, and/or
high‐variety information assets that require new
forms of processing to enable enhanced decision

making, insight discovery and process

optimization." (Laney 2012).

Perceived benefits of big data

There is a fourth characteristic for Big Data: Value.
In the context of Big Data, value refers to: (1) the
cost of the technology, which has dropped to allow
more companies to undertake this type of projects,
and (2) the benefits generated by the use of Big
Data (cost reduction, operational efficiency, and
business improvements and new revenue streams).

Like any other new technologies, Big Data comes
with benefits and drawbacks. Table 1 presents a
list of several key benefits and risks developed by
McKinsey Global Institute (2011).

Benefits Risks

Creating transparency by making data accessible to relevant stakeholders in
a timely manner

Improve operational efficiency (cost, revenue and risk)

Use data and experiments to expose variability and raise performance

Segment populations to customize the way your systems treat people

Use automated algorithms to replace and support human decision

making Innovate with new business models, products, and services

Sector‐specific business value creation

Data quality
Talent scarcity (lack of data

scientists) Privacy and security

concerns Big Data integration

capabilities Decision‐making
Organizational

maturity level

Table 1: Perception of benefits for Big data

Decomposed theory of planned behavior

Decomposed Theory of Planned Behavior (DTPB)

was raised by Taylor and Todd in 1995. DTPB is an

extension of the Theory of Planned Behavior (TPB)

developed by Ajzen (1988, 1991). TPB encompasses

three constructs, the attitude toward the behavior,

subjective norm, and perception of behavioral

control – that when combined form behavioral

intention. Intention is then assumed to be the

immediate antecedent of behavior (Ajzen 2002).

Table 2 presents brief descriptions of the constructs

used in TPB.

H14. Perceived behavioral control has a positive

effect on actual adoption of Big Data. H15. Intention

to adopt Big Data has a positive effect on actual

adoption of Big Data.

Antecedents of big data adoption intention

Based on DTPB, in our research model Big Data

adoption intention is jointly determined by the

individual’s Big Data Attitude, subjective norms, and

Perceived Behavioural Control. Thus we

hypothesize:

Construct Definition

Behavioral
Intention

Refers to individual’s intention to perform a behavior and is a function of Attitude,
Subjective

Norm and Perceived Behavioral Control Attitude Refers to individual’s positive or negative evaluation of the behavior (Ajzen,
1988) Subjective Norm Refers to individual’s “perception of social pressure to perform or not to perform the

behavior” (Ajzen, 1988, p.132)
Perceived

Behavioral control
Refers to the “perceived ease or difficulty of performing the behavior and reflects past

experience as well as anticipated impediments and obstacles” (Ajzen, 1988, p.132)

Table 2: Definitions of predictors of behavior in the theory of planned behavior (TPB)

Taylor and Todd (1995) also specified that, based

on the diffusion of innovation theory, the attitudinal

belief has three salient characteristics that

influence adoption; relative advantage, complexity

and compatibility (Rogers, 1983). Relative

advantage refers to the degree to which an

innovation provides benefits superseding those of its

precursor. This may incorporate factors such as

economic benefits, image, enhancement,

convenience and satisfaction (Rogers 1983).

Complexity represents the degree to which an

innovation is perceived to be difficult to understand,

learn or operate (Rogers, 1983). The complexity

construct is extremely similar, although it is

conceived in the opposite direction as ‘‘perceived

ease of use’’ (Technology acceptance model, Davis

1989). Innovative technologies that are perceived

to be easier to use and less complex have a higher

possibility of acceptance and use by potential

users. Thus, complexity would be expected to

have negative relationship to attitude. Complexity

(and its corollary, ease of use) has been found to be

an important factor in the technology adoption

decision (Davis et al. 1989).

Theoretical model and research hypotheses

Synthesizing the theoretical background, we

propose the following model (see figure 1) based

on DTPB for understanding factors influencing Big

Data adoption.

Antecedents of Big Data Adoption

Based on DTPB, the adoption adopt Big Data will

be determined by intention to adopt Big Data and

perceived behavioral control. As a consequence, we

hypothesize:

Figure 1: The proposed research model and research hypotheses

H11. Attitude towards Big Data has a positive

effect on intention to adopt Big Data. H12.

Subjective norm has a positive effect on intention

to adopt Big Data.

H12.1. Media has a positive effect on intention to

adopt Big Data. H12.2. Social influence has a

positive effect on intention to adopt Big Data.

H13. Perceived behavioral control has a positive

effect on intention to adopt Big Data.

Antecedents of attitude

Big Data requires of technologies that process and

analyze large amounts of heterogeneous data within

the right scope of time. These technologies includes

A/B testing, association rule learning, classification,

cluster analysis, crowdsourcing, data fusion and

integration, ensemble learning, genetic algorithms,

machine learning, natural language processing,

neural networks, pattern recognition, predictive

modeling, regression, sentiment analysis, signal

processing, supervised and unsupervised learning,

simulation, time series analysis and visualization,

Massively Parallel‐Processing (MPP) databases,

search‐based applications, data‐mining grids,

distributed file systems, distributed databases, cloud

computing platforms, the Internet, and scalable

storage systems. Depending on the degree of

knowledge of these technologies, an organization

may consider that Big Data is more or less easy to

use.

It is reasonable to infer that the perceived ease of use

positively influence the company’s perceived

usefulness and intention to adopt Big Data.

Therefore, we hypothesize that:

H7. Perceived ease of use has a positive effect on

attitude towards Big Data.

Perceived Usefulness is defined as the degree to

which a person believes that adopting Big Data

would enhance his or her job performance (Davis

1989). Therefore, we hypothesize that:

H6. Perceived usefulness has a positive effect on

attitude towards Big Data

Also, as previously discussed, there are three main

reasons to Big Data adoption, namely: volume,

variety and velocity. Thus we hypothesize:

H1. Volume has a positive effect on perceived

usefulness towards Big Data H2. Variety has a

positive effect on perceived usefulness towards

Big Data H3. Velocity has a positive effect on

perceived usefulness towards Big Data.

As discussed in section 2.1, Big Data generates

many potential benefits for companies such as cost

control, revenue generation, risk control, decision‐

making improving, etc. Therefore, it is reasonable

to infer that Big Data Technologies perceived

benefits positively influence the company’s attitude

and intention to adopt Big Data.

H5. Perceived benefits have a positive effect on

attitude towards Big Data.

Similarly, it is reasonable to infer that the

perceived risks of Big Data negatively influence

the company’s attitude and intention to adopt Big

Data. Among them: Talent scarcity, organization

maturity, Big Data internal capabilities and data

quality.

H4. Perceived risk has a negative effect on

attitude towards Big Data.

Compatibility is the degree to which the innovation

fits with the potential adopter’s existing values,

previous experience and current needs (Rogers,

1983). Tornatzky and Klein (1982) found that an

innovation is more likely to be adopted when it is

compatible with the job responsibilities and value

system of the individual. Therefore, it may be

expected that compatibility has a positive influence

on Big Data adoption. The existence of information

systems such as e‐commerce platforms, Enterprise

Resource Planning (ERP), Business Intelligence

(BI), Customer Relationship Management (CRM) or

product lifecycle management (PLM), external

sources of information and the need to make

decision near real‐time are factors that generate

Big Data situations. It is reasonable to infer that

compatibility has a positive influence on attitude

towards Big Data. Hence, we hypothesize:

H8. Compatibility has a positive effect on attitude

towards Big Data.

Antecedents of perceived behavioral control

According to Ajzen (1988), Perceived Behavioral

Control reflects beliefs regarding access to the

resources and opportunities needed to perform

behavior, or alternatively, to the internal and

external factors that may impede performance of

the behavior. This notion encompasses the

component of “facilitating conditions” (Triandis

1980) and self‐efficacy (Bandura 1982). In this

research, we define Perceived Behavioral Control as

the degree to which external and internal factors

influence, knowledge‐seeking behavior in an EKR.

Thus, we hypothesize:

H9. Self‐efficacy has a positive effect on

Perceived behavioral control to adopt Big Data.

H10. Facilitating conditions have a positive

effect on Perceived behavioral control to adopt

Big Data.

Research methodology

Data for this study was collected using an online

survey questionnaire. The participants in the survey

were managers involved in Big Data adoption

decision and usage such as CIOs, marketing

directors, and business analytics managers.

Based on the list of the top 100 Spanish

companies firms, we contacted the users through

email and/or Linkedin. The questionnaire has two

parts. The first considers demographic information

with control variables such as the job role of the

participant, size of the company, and existence of a

data mining data center. The second part considers

the theoretical model. The measurement items in the

questionnaire were developed for the decision

variables of attitude, perceived behavioral control,

intention to adopt, and actual adoption by adapting

the measures proposed and validated by Azjen

(2002) to fit the Big Data context. The total number

of answers was 53. Table 3 reports the demographic

breakdown of the research sample.

Variable Sub‐category Number (n=53) %
Business sector Services 15 28.3

Public sector 11 20.75
Manufacturing 2 3.77
Education 2 3.77
Health/Pharmaceutical 3 5.66
Banking/Finance 7 13.21
Other 13 24.53

Functional Technology 27 50.94

Area Marketing/sales 7 13.21
Operations 4 7.55
Finance 3 5.66
Top management 3 5.66
Other 8 15.09

Annual >10 million euros 13 24.53

Revenue 10 to 50 million euros 5 9.43
>50 million euros 35 66.04

Table 3: Research sample demographics

A SEM technique was used to examine the

relationships among the constructs. The Partial Least

Squares (PLS) approach was chosen for its

capability to accommodate small‐sized samples

(Chin 1998). Further, PLS recognizes two

components of a causal model: the measurement and

the structural model. Additionally, PLS is especially

suitable for exploratory research focusing on

explaining variance. Given the aforementioned PLS

seemed particularly relevant for this exploratory

study – one that is limited by sample size.

Construct reliability and validity

Table 4 shows the factor loadings, Cronbach’s

alphas (A), Average variance extracted (AVE), and

R
2

values. All Cronbach’s alphas exceeded the

recommended minimum value of 0.7 with the

exception of perceived risks variable and, all of the

observed construct reliabilities (C.R.) were higher

than 0.8 (Fornell and Lacker 1981) with the

exception of perceived risks variable. All construct

loadings were found to be significant at greater

than the recommended p‐value of 0.05 (Gefen and

Straub 2005) and typically exceeded the

recommended

threshold value of 0.707 (Barclay et al. 1995) with

the exception of perceived risk, perceived benefits

and behavioral intention that were inferior in some

constructs. Average variance extracted (AVE) was

found to account for a minimum of 50 percent of

the variance in each construct and the square root

of AVE for each construct was much larger than

the construct’s correlation with every other

construct (Barclay et al. 1995; Gefen and Straub

2005). Measurement items loaded on their

respective constructs at a value of at least 0.1

greater than their loading on other constructs

(Barclay et al. 1995; Gefen and Straub 2005) and

all items loaded higher on their intended construct

than on any other construct. Hence, it was

concluded that the construct measurement items

were consistent and exhibited a substantial degree

of convergent and discriminant validity.

Factor Item Loadings AVE Cronbach Composite Reliability R2

ATT

ATT1

ATT2

ATT3

0.972

0.976

0.970

0.946

0.712

0.981

0.407
AA ‐ ‐ 1.000 1.000 1.000 0.568

PB1 0.838

0.475

0.812

0.859

‐

PB2 0.524
PB3 0.653
PB4 0.459
PB5 0.609
PB6 0.848
PB7 0.790

BI1

BI2 BI3

0.975

0.976

0.660

0.952

0.95

0.975

0.476

COM

0.833

0.918

0.769

0.707

0.869

‐

MI1

MI2 MI3

0.889

0.896

0.868

0.782

0.862

0.915

PBC

PBC1

PBC2

0.859

0.898

0.772

0.706

0.871

0.706

PEOU

PEOU1

PEOU2

PEOU3

0.897

0.936

0.972

0.875

0.933

0.954

‐

PU1 0.861

0.789

0.933

0.949

0.283

PU2 0.926
PU3 0.897
PU4 0.931
PU5 0.823

PR1 0.632

0.286

0.30

0.194

‐

PR2 0.010
PR3 0.790
PR4 0.122
PR5 ‐0.627

SE1

SE2 SE3

0.827

0.965

0.950

0.84

0.904

0.94

‐

SI1

SI2 SI3

0.954

0.935

0.840

0.83

0.896

0.936

‐

FC1

FC2

0.912

0.911

0.83

0.797

0.908

‐

VLCTY

VLC1

VLC2

0.756

0.885

0.68

0.535

0.807

‐

VLM

VLM1

VLM2

0.895

0.751

0.68

0.548

0.8101

‐ VRT VRT1 1.000 1.000 1.000 1.000 ‐

Table 4: Convergent, discriminant validity and reliability of measurements

Path analysis

SmartPLS (Version 2.0.M3) (Ringle et al. 2005)

was used to evaluate the statistical significance and

relative salience of the research hypotheses. Results

of model testing indicated that the constructs

included in the research model accounted for

approximately 47.6 percent of the variance in the

intention to adopt Big Data and 56.8 percent of the

variance in actual use of Big Data (Figure 2). Chin

(1998) notes that path coefficient values between

0.20 and 0.30 are adequate for meaningful

interpretations. Thus, in particular, the results

provided support for the significance of eleven

research hypotheses. R
2

values, which indicate the

predictive power of the model, ranged from 0.28 to

0.7, indicating that the fit of the research model was

acceptable.

Figure 2: Main study path model results

Discussion

Adding to previous literature on Big Data, the first

contribution of this study is the recognition that

volume and velocity are the key aspects in Big

Data adoption and they have a significant impact

in the intention to adopt these technologies.

Although, Variety seems not having still such

effect, it is expected to become an important factor

in determining adoption. The logic behind is that

the more heterogeneous and unstructured the data

is, the higher the barriers to capture and analyze

data. What is clear is as corporate systems are

built into Database Management Systems

(DMBS), companies perceive volume and

velocity as more urgent matters than variety.

Also, companies have traditionally focused more

on numerical and structured data rather than

working with different types of data. However,

with the increasingly diversity of data, being able

to manage that aspect will play a key part in

companies´ data strategy.

Even though the traditional definition of perceived

usefulness does not have an impact on the attitude

toward Big Data, our model shows that perceived

benefits have a significant impact on behavior.

Thus, in the subsequent/confirmatory study we

plan to use perceived benefits as the construct

that replaces perceived usefulness.

Regarding perceived risks, the exploratory results

suggest that perceived risks variable and

measurements need to be re‐defined. Construct

loadings are not statistically relevant, so we need

to adjust the constructs definition. Hence, the

definition of the potential Big Data risks needs to

be reviewed and perhaps extended with more

risks. However, the results lead to the belief that

perceived risks might have a moderate effect on

the attitude towards Big Data adoption. Finally, our

results suggest that Media and press news about

Big Data have a stronger impact on the decision to

adopt Big Data than social influences (friends

and/or colleagues suggestion to adopt Big Data).

Therefore the results indicate that specific

opportunities as well as challenges exist in Big

Data technologies adoption.

Considerations and future work

This research‐in‐progress contributes to the

existing body of knowledge on Big Data by

developing a theoretical model to explore and

predict the intention to adopt Big Data technology.

By extending the theory of planned behavior with

the concepts of perceived benefits, risks and

perceived usefulness of Big Data, we seek to

understand the adoption of Big Data. Overall, our

exploratory results suggest that the proposed model

is a first fruitful step to design a theoretical model

to predict Big Data adoption.

Also, our exploratory model provides insightful

evidence to further research and analysis, especially

in terms of perceived risks and the variables that

impact on the attitude to adopt Big Data such as

velocity and volume.

As a future work, we will review the literature on

Big Data risks and redesign the perceived risks

construct and then conduct a confirmatory study

with a bigger sample size.

References

Ajzen, I. (2002). Perceived behavioral control,

self‐efficacy, locus of control, and the
theory of planned behavior. Journal of

Applied Social Psychology, 32, pp 665–

683.

Ajzen, I. (1991). The theory of planned behavior,

Organizational Behavior and Human

Decision Processes, vol. 50, pp 179‐211.
Ajzen, I. (1988). Attitudes, personality and

behavior. Milton Keynes: Open University

Press.

Bandura A. (1982). Self‐Efficacy Mechanism in
Human Agency. American Psychologist,

No. 37, pp 122‐147.
Bantleman, J. (2012, April 16). The big cost of Big

Data. In E. Savitz, CIO network: Insights and

ideas for technology leaders [Web log post].

Forbes Magazine. Retrieved October 4, 2012

from

http://www.forbes.com/sites/ciocentral/2012/0

4/16/the‐big‐cost‐of‐big‐data/ .

Barclay, D. W., Higgins, C. A., &

Thompson, R. (1995). The Partial

Least Squares (PLS) Approach to

Causal Modeling: Personal

Computer Adaptation and Use as an

Illustration, Technology Studies,

2(2), 285‐309.
Benjamin Woo, Dan Vesset, Carl W.

Olofson, Steve Conway, Susan

Feldman, Jean S. Bozman. (2011).

Worldwide Big Data

Taxonomy, IDC report.

Bryant, R. E., Katz, R. H., & Lazowska, E. D.

(2008). Big‐data computing: Creating

revolutionary breakthroughs in commerce,

science and society. Computing Research

Association.

Chin, W. (1998). Issues and opinion on structural

equation modeling, MIS Quarterly, 22(1), 7‐

16.

Dan Vesset, Benjamin Woo, Henry D.

Morris, Richard L. Villars, Gard

Little, Jean S. Bozman, Lucinda

Borovick, Carl W. Olofson, Susan

Feldman, Steve Conway, Matthew

Eastwood, Natalya Yezhkova. (2012).

Worldwide Big Data Technology and

Services 2012–2015 Forecast, IDC.

Davis, F.D. (1989). User Acceptance of

Computer Technology: A Comparison

of Two Theoretical Models.

Management Science, No. 35, pp 982‐
1003.

Deloitte (2012). Billions and billions: Big

Data becomes a big deal, Deloitte,

http://www.deloitte.com/view/en_GX/gl

obal/industries/technology‐media‐
telecommunications/tmt‐predictions‐
2012/technology/index.htm

Firestone, C. (2010). Foreword. In D. Bollier, The

promise and peril of Big Data (pp. vii ‐ ix),

Washington, DC: The Aspen Institute,

https://www.c3e.info/uploaded_docs/aspenbig

_data.pdf .

Gefen, D., & Straub, D. (2005). A Practical

Guide to Factorial Validity Using PLS‐

Graph: Tutorial and Annotated

Example, Communications of the

Association for Information Systems,

16(1), 91‐109.

Hurwitz, J. (2012, Apr 30). The big deal about

Big Data. Business Week, , 1.

http://www.businessweek.com/articles/20

12‐ 04‐23/the‐big‐deal‐about‐big‐data .
Kiron, D. (2012). All fired up in massachusetts: The

states new wave of Big Data companies. MIT
Sloan Management Review, Vol. 53, No. 3, pp
1‐3.

Lamont, J. (2012). Big Data has big implications for
knowledge management. KM World, 21(4), 8‐
11.

Laney D. (2001). 3D Data Management:

Controlling Data Volume, Velocity and

Variety, http://blogs.gartner.com/doug‐

laney/files/2012/01/ad949‐3D‐Data‐

Management‐Controlling‐Data‐Volume‐

Velocity‐and‐Variety.pdf

Laney D. (2012). Douglas, Laney. The

Importance of 'Big Data': A Definition.

http://www.forbes.com/sites/ciocentral/2012/04/16/the-big-cost-of-big-data/
http://www.forbes.com/sites/ciocentral/2012/04/16/the-big-cost-of-big-data/
http://www.deloitte.com/view/en_GX/global/industries/technology-media-telecommunications/tmt-predictions-2012/technology/index.htm
http://www.deloitte.com/view/en_GX/global/industries/technology-media-telecommunications/tmt-predictions-2012/technology/index.htm
http://www.deloitte.com/view/en_GX/global/industries/technology-media-telecommunications/tmt-predictions-2012/technology/index.htm
http://www.deloitte.com/view/en_GX/global/industries/technology-media-telecommunications/tmt-predictions-2012/technology/index.htm
https://www.c3e.info/uploaded_docs/aspenbig_data.pdf
https://www.c3e.info/uploaded_docs/aspenbig_data.pdf
http://www.businessweek.com/articles/2012-04-23/the-big-deal-about-big-data
http://www.businessweek.com/articles/2012-04-23/the-big-deal-about-big-data
http://www.businessweek.com/articles/2012-04-23/the-big-deal-about-big-data
http://blogs.gartner.com/doug-laney/files/2012/01/ad949-3D-Data-Management-Controlling-Data-Volume-Velocity-and-Variety.pdf
http://blogs.gartner.com/doug-laney/files/2012/01/ad949-3D-Data-Management-Controlling-Data-Volume-Velocity-and-Variety.pdf
http://blogs.gartner.com/doug-laney/files/2012/01/ad949-3D-Data-Management-Controlling-Data-Volume-Velocity-and-Variety.pdf
http://blogs.gartner.com/doug-laney/files/2012/01/ad949-3D-Data-Management-Controlling-Data-Volume-Velocity-and-Variety.pdf

Gartner.

http://www.gartner.com/resId=2057415

Manyika, J., Chui, M., Brown, B., Bughin, J.,

Dobbs, R., Roxburgh, C., & Byers, A.

(2011). Big Data: The next frontier for

innovation, competition, and

productivity, McKinsey Global

Institute.

Mathieson, K. (1991). Predicting user

intentions: Comparing the technology

acceptance model with the theory of

planned behavior, Information Systems

Research, vol. 2, No. 3, pp 173‐191.

Ringle, C. M., Wende, S., & Will, A. (2005).

SmartPLS 2.0 (M3) Beta, Hamburg,

Germany: University of Hamburg

(http://www.smartpls.de).

Rogers E. M. (1983). Diffusion of

Innovations (3rd edition). London: The

Free Press.

Simon, P. (2010). Why new systems fail.

Boston, MA: Course Technology, a part

of Cengage Learning. Strenger, L.

(2008). Coping with Big Data Growing

Pains. Business Intelligence Journal,

vol. 13, No. 4, pp 45‐52.

Taylor, S. & Todd, P. (1995). Decomposition

and crossover effects in the theory of

planned behavior: A study of consumer

adoption intentions. International

Journal of Research in Marketing, 12,

137‐156.

Tornatzky, L.G., & Klein N. (1982).

Innovation characteristics and

innovation adoption implementation: A

meta‐analysis, IEEE Transactions on

Engineering Management, 29, pp 28‐45.

Triandis, H.C. (1980). Beliefs, Attitudes and

Values. University of Nebraska Press,

Lincoln, NE.

Venkatesh, V. (2006). Where to go from

here? Thoughts on future directions for

research on individual‐level technology

adoption with a focus on decision‐

making. Decision Sciences, vol. 37, No.

4, pp 497–518.

White, M. (2011). Big Data‐Big Challenges.

Econtent, vol. 34, No. 9, pp 21.

http://www.gartner.com/resId%3D2057415
http://www.smartpls.de/