On the Adoption of Big Data Analytics: Interdependencies of

Contextual Factors

Anke Schüll and Natalia Maslan

Department of Business Information Systems, University of Siegen, Kohlbettstr. 15, Siegen, Germany

Keywords: Big Data Analytics, BDA Adoption, TOE Framework, Dynamic Capabilities Theory.

Abstract: Even though the number of papers on the adoption of big data analytics (BDA) has increased, the literature

still only scratches the surface in terms of understanding the influential factors of BDA adoption. To cope

with the complexity of these factors, this paper focuses on the influence of some of the most important factors

regarding BDA and their interrelations. We followed the technology, organization, and environment

framework (TOE framework), which is frequently used to explain the process of technology adoption, to

examine the context of the decision-making process and combined it with insights from dynamic capability

theory. This paper contributes to BDA research by extending the TOE framework towards a dynamic

capability view. It assists in the decision-making process regarding the development of BDA capabilities by

determining the most influential factors and their side effects, thereby helping to prioritize these factors and

to encourage investments accordingly.

1 INTRODUCTION

Big data refers to large sets of structured, semi-

structured or unstructured data, which are obtained

from different unrelated resources; examples include

sensor data and content that is extracted from social

media (Malaka and Brown, 2015). As the processing

of big data is beyond the abilities of conventional

software tools (Manyika et al., 2011), decisions on

investments in big-data-related technology must be

faced. However, big data are not without benefit: As

an asset, big data can “improve competitiveness,

innovation and efficiencies in organizations”

(Braganza et al, 2017).

The term big data analytics (BDA) covers

advanced analytical techniques and technologies that

operate on big data to obtain enhanced insights and

improve the decision-making process (Malaka and

Brown, 2015). Chen et al. (2016) understand BDA as

a “unique information processing capability that

brings competitive advantage to organizations” and is

expected to improve performance (Kwon et al.,

2014).

Deeply rooted in business intelligence (BI), BDA

“reawakens” an interest in mathematics, statistics and

quantitative analysis (Braganza et al., 2017), but adds

some specific requirements. Because the objective of

BDA is to answer highly specific questions, its

solutions must be tailored to this context, which

requires sound methodological skills (Debortoli et al.,

2014).

Competencies on BI and BDA can be categorized

into three waves, which are characterized by DBMS-

based, structured content (1

wave), web-based, user

generated, unstructured content (2

wave), and

mobile- and sensor-based content (3

wave) (Chen et

al., 2012). BDA capabilities can be understood as

dynamic capabilities, which require a “delicate

mixture of management, talent and technology”

(Akter et al., 2016). As these capabilities are tailored

to suit a highly specialized question (Debortoli et al.

2014), they are context-specific (Chen et al., 2016).

BDA adoption requires investments in costly

technology, which is rapidly changing and offering

new opportunities for information processing at

increasing speeds. It requires investments in the

development of analytical skills that are pinpointed to

a context-specific task, and intensified data collection

and storage, which are connected with issues

regarding data quality, IT security, and privacy

concerns. These factors are closely entangled and

influence decisions on BDA adoption in different

ways. The goal of this paper is to shed light on their

influence on BDA adoption, to inform the decision-

Schüll, A. and Maslan, N.

On the Adoption of Big Data Analytics: Interdependencies of Contextual Factors.

DOI: 10.5220/0006759904250431

In Proceedings of the 20th International Conference on Enterprise Information Systems (ICEIS 2018), pages 425-431

ISBN: 978-989-758-298-1

425

making process and to assist in prioritizing these

factors and in encouraging investments accordingly.

2 THEORETICAL

BACKGROUND

2.1 TOE Framework

To understand the contextual factors regarding BDA

adoption, we base our work on the TOE framework.

This framework identifies factors that are related to

the adoption of technological innovations in the

technological, organizational, and environmental

contexts (Oliveira and Martins, 2011). Building upon

diffusion of innovation theory (DOI), this framework

is well accepted and frequently used to explain

specific aspects of the adoption of BDA (Table 1).

Table 1: Some recent studies on BDA adoption based on

the TOE framework (ordered by year and name).

Reference Focus Research

Metho

(Debortoli

et al., 2014)

Competencies and

skills in BI and

BDAs

Text mining of

job

advertisements

(Agrawal,

2015)

BDA adoption in

firms from China

and India

Data collection

(106

anizations

)

(Malaka

and Brown,

2015)

Challenges of BDA

adoption

Interpretive

study, single-

organization

case stud

(Nam et al.,

2015)

Influences of

perceived benefit,

financial readiness,

IS competence, and

industrial pressure

on BDA adoption

Online

questionnaire

survey

(Chen et

al., 2016)

Key antecedents of

organizational-

level BDA usage

and the effect on

value creation

Survey data

(161 U.S.-based

companies)

Domain: supply

chain

management

(Salleh and

Janczewski,

2016)

Security and

privacy issues

related to BDA

ado

tion

Anonymous

online

questionnaire

surve

Chen et al. (2016) identified two limitations of the

TOE framework. The first limitation is the

assumption of the model, that contextual factors

directly affect the decision to adopt a technological

innovation. They argue that the idealization of the

decision-making process as a fully rational process

cannot hold true in practice. The second limitation is

that contextual factors can affect this decision in ways

that are not covered by the TOE framework.

Therefore, combining the TOE framework with one

or more theoretical models is recommended (Low et

al., 2011).

2.2 Dynamic Capability Theory

The TOE framework provides an overview of

contextual factors of BDA adoption, but it’s not the

adoption of BDA as such, that provides competitive

advantage. As part of the dynamic capabilities of a

firm, BDA capabilities enhance the potential to

improve the performance of a firm and to adapt to the

challenges of turbulent environments. Therefore, we

complement the TOE framework with the dynamic

capability theory (DCT). DTC offers additional

explanations for gaining competitive advantage out of

the adoption of BDA, as several recent publications

have shown (Table 2).

Table 2: Theories related to BDA adoption in some recent

publications (ordered by year and name).

Reference Theor

(Esteves and

Curto, 2013

)

Decomposed Theory of Planned

Behavio

(Debortoli et al.,

2014)

Resource-Based View

(Akter et al.,

2016)

Resource-Based View, IT

Capability Theories, Concept of

Sociomaterialit

(Chen et al.,

2016)

Dynamic Capability Theory

(Gupta and

Geor

e, 2016

)

Resource-Based View,

Knowled

e-Based View

(Prescott, 2016) Resource-Based View, Dynamic

abilit

Theor

(Braganza et al.,

2017)

Resource-Based View,

Knowledge-Based View,

namic Ca

abilit

Theor

(Côrte-Real et

al., 2017)

Resource-Based View,

Knowledge-Based View,

Dynamic Capability Theory

(Gunasekaran et

al., 2017

)

Resource-Based View

(Mikalef et al.,

2017

)

Resource-Based View; Dynamic

abilit

Theor

As an extension of the resource-based view (RBV),

the DCT is closely connected to RBV. Resources

refer to the tangible, intangible and human resources

of a firm that, bundled together, influence the

performance outcomes. Capabilities can be

understood as subsets of these resources that are non-

transferable, have a direct or indirect impact on the

ICEIS 2018 - 20th International Conference on Enterprise Information Systems

426

performance of a firm, and are influenced by

environmental conditions (Gunasekaran et al., 2017).

Dynamic capabilities enable a firm to adapt to

changing requirements (Mikalef et al., 2017). They

refer to the ability to configure and reconfigure the

resources of a firm to maintain competitive advantage

in turbulent environments (Prescott, 2016; Côrte-Real

et al., 2017). El Sawy and Pavlou (2008) identified

four dimensions: sensing the environment, learning,

integrating knowledge and coordinating activities.

Almost all of these dimensions can be leveraged by

BDA.

3 RESEARCH MODEL AND

CONSTRUCT MEASURES

Informed by recent literature, we have identified

several contextual factors that are crucial for the

adoption of BDA. Information on how these factors

influence the adoption of BDA and how they are

interrelated can assist in prioritizing the different

aspects of BDA investments. As it is impossible to

cover all contextual factors that are relevant for the

decision-making process, we adapted contextual

factors according to previous research.

The technological context covers relative

advantage, complexity and compatibility. It refers to

relevant internal and external technologies (Borgman

et al., 2014). The integration of internal and external

data and prior IT experiences with BDA-related

technologies was considered the most relevant

technological factor. Thus, we posit that levels of

experience with data usage from external sources,

internal sources and big-data-related technology each

have a significant positive effect on the adoption of

BDA (H1-H3). Security and privacy issues can be

obstacles to the adoption of BDA technologies;

therefore, we postulate that experiences with security

mechanisms have a significant positive effect on

BDA adoption (H4).

The organizational context refers to descriptive

measures of the organization regarding scope, size,

and managerial structure (Oliveira and Martins,

2011). Successful deployment of BDA is almost

impossible without the appropriate analytical skills;

therefore, we posit that BDA skills have a significant

positive effect on the adoption of BDA (H5). In the

telecom industry, Bughin (2016) found evidence that

a good part of the returns could be explained by the

capabilities to effectively manage big data projects;

thus, we postulate that management support has a

significant positive effect on BDA adoption (H6).

As dynamic capabilities enable a firm to evolve

according to the requirements of a changing

environment, market pressure (H7) is expected to

have a positive impact on BDA adoption.

Competitive pressure is an important external driver

for the adoption of innovations (Agrawal, 2015);

therefore, we postulate that competitive pressure to

use BDA has a significant positive effect on BDA

adoption (H8). With these hypotheses, we intend to

confirm the results of previous research and extend

the previous research to an analysis of the factors’

interrelated effects. As gaining competitive

advantage is at the core of developing dynamic

capabilities, we postulate that BDA adoption will

have a positive effect on market performance (H9).

Where possible, constructs were adapted from

existing research.

Figure 1: Research model.

The research model covers technological factors that

are relevant for assessing experiences with data from

external or internal sources and with big data

technology. We follow the argument of Kwon et al.

(2014) that “expanded IT capability in data

management and utilization is expected to become a

virtuous force in furthering adoption of new data-

related IT capability” (e.g., BDA). As privacy and

security issues can affect the (perceived) complexity

(Borgman et al. 2014), these were included,

according to Salleh and Janczewski (2016).

Big data capabilities cover tangible resources

(e.g., technology, data and financial resources),

human skills (e.g., technical skills and managerial

skills) and intangible resources (e.g., organizational

learning and data-drive culture) (Mikalef et al. 2017).

On the Adoption of Big Data Analytics: Interdependencies of Contextual Factors

427

Table 3: Technology Context: Constructs.

Usage experience with data from external sources

(Kwon et al., 2014):

To predict demand;

To facilitate understanding of market conditions;

To facilitate understanding of customer demands;

Quality and reliability evaluation of external (data)

(N);

Usage of social media data (N).

Usage experience with data from internal sources

(Gupta and George, 2016):

Integration of data from multiple internal data sources

into a data warehouse;

Access to very large, unstructured, or fast-moving

data for analysis;

Analysis of Cookies, Logfiles, App-data (N);

Anal

sis of sensor data

(

)

;

Experience with big-data-related technology (Gupta

and George, 2016):

Parallel computing approaches;

Different visualization tools;

Cloud-based services for data processing and

analysis;

Open-source software for big data analytics;

New forms of data storage;

Near-real-time or real-time analysis (N);

Event-driven decision automation

(

)

;

Privacy and security (Salleh and Janczewski, 2016):

Security requirements for BDA are compatible with

existing information security infrastructure.

Information security mechanisms for BDA are

com

atible with existin

stems

(

)

;

(A) adapted, (N) new

Debortoli et al. (2014) observed that a big data project

is often more comparable to a research project, as it

requires better methodological skills than traditional

BI and requires some learning intensity (Gupta and

George, 2016). As capabilities cannot provide

competitive advantage by themselves, management

plays a crucial role in capacity building, by

structuring and orchestrating resources (Gunasekaran

et al., 2017).

Table 4: Organizational Context: Constructs.

BDA skills: (Gupta and George, 2016)

Providing BDA training for employees;

Hiring new employees with BDA skills;

Using external experts to bring in BDA expertise (N);

Suitable education or work experience of BDA staff

(

)

Management Support: (Gupta and George, 2016)

Having a good sense of where to apply BDA (A);

Having clear expectations related to the outcomes and

enefits of BDA (A).

(A) adapted, (N) new

Côrte-Real et al. (2017) used the construct “market

pressure” and two other items to measure

organizational agility. These items are market-driven;

thus, they are environmental contextual factors. As

the readiness of competitors to deploy BDA is

expected to influence BDA adoption (Chen et al.,

2016), Big Data pressure is included in the

environmental context using constructs that were

adapted from Agrawal (2015).

Table 5: Environmental Context: Constructs.

Market pressure: (Côrte-Real et al., 2017)

Necessity of responding to changes in consumer

demand (A);

Necessity of reacting to new product or service

launches by competitors;

Necessity of adopting new technologies to produce

better, faster, cheaper products and services (due to

market demands);

Big Data pressure (Agrawal, 2015):

Perceived competition intensity to implement BDA

(A);

Risk of competitive disadvantage if BDA is not

ado

ted.

(

)

(A) adapted, (N) new

Because the dynamic capabilities are orchestrated to

gain competitive advantage, measurement of the

market performance has been included in the model.

Since this advantage will not materialize

immediately, the time since the adoption of BDA was

required as additional information (Gupta and

George, 2016).

Table 6: Market Performance: Construct.

Market Performance (Gupta and George, 2016):

Time needed to introduce new products or services into

the market compared to competitors;

Success rate of new product or services launches

compared to competitors;

Market share com

ared to com

etitors

(

)

(A) adapted, (N) new

4 DATA ANALYSIS AND

RESULTS

To test our hypotheses, we conducted an anonymous

online survey, addressing the top management of

German companies. The addresses were acquired

using the Hoppenstedt database. 138 German

companies took part in this survey, which had been

pre-tested in a pilot study. After data sets with

missing values on BDA usage were filtered out, 46

data sets from organizations of different sizes

ICEIS 2018 - 20th International Conference on Enterprise Information Systems

428

(turnover from 0-1 million Euros up to 1000 million

Euros per year) remained for further analysis.

According to their answers, 30% belong to the 1

wave of BI and BDA competencies, 46% to the 2

wave, and 24% to the 3

1-5 Likert scales were used to measure DI, DE,

ITS, BDS, MSu, MP, BDP and P. T is a measure, that

covers six widespread big data technologies, which

were adopted from Gupta and George (2016). For our

analysis, we used IBM SPSS Statistics 25.

Cronbach’s alpha was used to assess the reliability

of scales. A confirmatory factor analysis was

conducted for deleting items that did not contribute

strongly to the scales. All items of each final scale

loaded on a single factor. With the exception of ITS

and DI, all cronbach’s alpha coefficients are above

0.80 which are excellent values. DI with 0.707 is a

commonly acceptable value (Hair et al. 2006). Hair et

al. (2006) argued that Cronbach’s alpha values may

decrease to .60 and still be acceptable, especially in

exploratory studies. Thus, we accept the cronbach’s

alpha of 0.652 for ITS. The Kaiser-Meyer-Olkin

(KMO) Measure of Sampling Adequacy meets the

minimum criteria of 0.5 and Bartlett's test of

sphericity is significant for each construct (Field

2013). Table 7 lists the Cronbach’s Alpha scores,

KMO values and Barlett’s test significance levels for

DI, DE, ITS, BDS, MSu, BDP, MP and P.

Table 7: Cronbach’s Alpha scores, KMO values and

Barlett’s test significance levels.

Construct Cronbach’s

Alpha

KMO Barlett’s test

DI .707 .500 Si

(

0.001

)

DE .884 .846 Si

(

0.000

)

ITS .652 .500 Si

(

0.004

)

BDS .900 .822 Sign (0.000)

MSu .951 .500 Sign (0.000)

BDP .891 .500 Sign (0.000)

MP .816 .687 Si

(

0.000

)

P .833 .692 Si

(

0.000

)

BDA adoption was measured as a dichotomous

variable, which resulted in two groups: BDA adopters

and BDA non-adopters. Therefore, a t-test analysis

(independent sample test) was conducted to test

hypotheses H1-H9 (Figure 1). The t-test assesses

whether the means of two groups are significantly

different from each other.

There is sufficient evidence to suggest that the

values of DE, DI, T, ITS, BDS, MSu, and BDP are

higher in organizations that adopt BDA than in those

that do not (Table 8 and 9). Thus, H1-H6 and H8 are

supported, but H7 is not supported.

Table 8: Group statistics.

BDA Mean Std. Dev Std. Error Mean

DE no 2.8389 .93580 .19102

es 3.6147 1.26142 .30594

DI no 2.1818 1.17053 .24956

yes 3.2941 1.43678 .34847

T no 2.3750 1.31256 .26793

es 4.3125 1.81544 .45386

ITS no 2.7105 1.03166 .23668

es 3.6563 .87023 .21756

BDS no 2.1146 1.26901 .25904

yes 3.7396 .96555 .24139

MSu no 2.5455 1.46311 .31194

es 3.5938 .98689 .24672

MP no 3.5797 1.03581 .21598

yes 3.6979 .98924 .24731

BDP no 2.5750 1.19511 .26723

yes 3.9688 1.10255 .27564

P no 2.9091 1.23091 .26243

es 3.2083 1.25831 .31458

The hypothesis that BDA adoption has a positive

effect on market performance (H9) could not be

supported either.

Table 9: Independent Sample Test.

Levene's Test fo

ualit

of Variances

t-test for Equality o

Means

F Si

. t Si

(

2-tailed

)

DE EVA 1.740 .195 -2.263 .029

EVNA -2.151 .040

DI EVA .641 .428 -2.665 .011

EVNA -2.595 .014

T EVA 3.491 .069 -3.921 .000

EVNA -3.676 .001

ITS EVA .324 .573 -2.898 .007

EVNA -2.942 .006

BDSEVA 3.236 .080 -4.345 .000

EVNA -4.589 .000

MSuEVA 4.252 .046 -2.480 .018

EVNA -2.636 .012

MP EVA .011 .916 -.357 .723

EVNA -.360 .721

BDPEVA .687 .413 -3.597 .001

EVNA -3.630 .001

P EVA .103 .750 -.733 .468

EVNA -.730 .470

(EVA) Equal variances assumed, (EVNA) Equal variances

not assumed

It is a remarkable result that technology is not among

the most important internal factors that influence

BDA adoption, but BDA skills and usage of internal

data are. Among the environmental factors,

competitor pressure has a stronger impact on BDA

adoption than market pressure.

On the Adoption of Big Data Analytics: Interdependencies of Contextual Factors

429

Table 10 represents the correlations of the constructs.

There are significant relationships between MSu and

DE, DI, BDS, BDP and ITS, with p (2-tailed) < 0.01.

The strongest significant relationships are those

between BDS and MSu (r = 0.747), DI and MSu (r =

0.673) and BDP and MSu (r = 0.597), with p (2-

tailed) < 0.01. It is interesting to note that BDP is

strongly and significantly related to DI (r = 0.750) and

to BDS (r = 0.714), and DI is also strongly and

significantly related to BDS (r = 0.657), with p (2-

tailed) < 0.01.

The correlations indicate that the perceived

competition intensity to implement BDA and the risk

of competitive disadvantage are highly correlated

with learning activities regarding BDA skills and

usage of internal data.

Table 10: Pearson Correlations.

DE DI BDS MP BDP ITS MSu

T .457

.425

.560

.049 .435

.410

.273

DE .617

.589

.223 .549

.369

.415

DI .657

.377

.750

.289 .673

BDS .198 .714

.510

.747

MP .351

.120 .119

BDP .507

.597

ITS .446

**Correlation is significant at the 0.01 level (2-tailed)

*Correlation is significant at the 0.05 level (2-tailed)

Competitive pressure has a stronger effect on BDA

adoption than market pressure and has a strong

correlation to management support. That competitive

pressure to use BDA is positively associated with

management support is further confirmation of the

results of Chen et al. (2016).

Correlations of MSu with BDS, DI, ITS, DE and

T indicate strong differences, the strongest being the

one with BDS, followed by the correlation with

internal data usage. Taking the three waves of BI and

BDA competencies into consideration, it is

reasonable that internal data usage has a higher

correlation with MSu than external data usage. That

there is no strong correlation between technology and

MSu indicates that technology is not fueling the

expectations that are related to BDA in the same way

as BDA skills or data usage.

Developing new knowledge and skills is

fundamental for exploiting the potential of BDA,

which results in improved operational capabilities (El

Sawy and Pavlou 2008). Gupta and George (2016)

emphasize that the development of firm-specific

BDA capabilities will not be rewarding if “an

organization lacks learning intensity”. They

identified the need to adopt a culture where

“decisions are made based on people’s opinions.” The

strong correlation between BDA skills and

management supports can be explained by this kind

of a culture: having a clear expectation on where to

apply BDA and what outcomes and benefits to expect

could indicate that management is well advised.

5 CONCLUSIONS

The main focus of this work was on highlighting the

entanglement of the contextual factors. Enriching the

TOE framework with insights from dynamic

capabilities provided additional information on how

the BDA capabilities are orchestrated according to a

specific task to be accomplished by BDA.

As the sample size is too small to provide strong

evidence, most of this paper is argumentative. The

results of the survey are used as indications; however,

a more extensive survey is required to confirm the

results. Nonetheless, as the argument is in line with

previous research, it contributes to the discussion on

interrelated effects regarding the contextual factors of

BDA.

We identified BDA skills and internal data usage

as the most influential factors, both of which have a

strong correlation to management support. This gives

skill development high priority in regard to

channeling BDA investments.

That perceived competition intensity to

implement BDA and the risk of competitive

disadvantage (if BDA is not adopted) have a strong

effect on BDA adoption, does not come as a surprise.

However, we did expect the market pressure to have

some influence on BDA adoption. As the lack of

influence was rather unexpected, further research is

necessary to confirm the results or to adopt constructs

and variables.

We could not find evidence for a link between

BDA adoption and firm performance, but we expect

that the time since adoption would need to be taken

into consideration. Due to missing values, we had to

omit the time since BDA adoption from our analysis.

As a positive influence on market performance would

be a sustained effect, one explanation for this could

be the recency of BDA investments. Assessing this

relationship over an extended period of time could be

an interesting direction for further research.

The lack of a significant effect is in line with the

results of Chae, Koh et al. (2014), who could not

confirm a relationship between IT capabilities and

firm performance. We follow their suggestion to

further investigate constructs and variables that take

into consideration that the role of IT has changed over

time (Chae et al. 2014).

ICEIS 2018 - 20th International Conference on Enterprise Information Systems

430

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