Exploring Big Data Analytics Adoption using Affordance Theory
Veena Bansal and Shubham Shukla
Indian Institute of Technology Kanpur, Kanpur-208016, India
Keywords:
Big Data Analytics Adoption, Affordance Theory, Adoption and Usage, Adoption Framework.
Abstract:
This research explores big data analytics adoption in organisations using affordance theory. Big data analytics
are a set of tools and techniques that help companies to get useful business insights from the data. Adoption
of big data analytics is a challenging task. Affordance theory has been used to study usage and effect of infor-
mation technology. In this work, we have modified the affordance theory framework to study adoption of big
data analytics. The framework takes into account characteristics of the technology, the goal and characteris-
tics of the organisation. Organisation achieve different outcomes based on their goals and characteristics. We
have used case study method to verify efficacy of the adopted framework. The results clearly show that the
framework is effective in studying the adoption of big data analytics.
1 INTRODUCTION
Today, a large number of organisations produce, ob-
tain and store data about their business. These organ-
isation want to extract valuable knowledge from this
data using big data analytics (Dhar, 2013). Many or-
ganisations are in the process of adopting big data an-
alytics (BDA) in order to make data driven decisions.
Data analytics is the key differentiator in the compet-
itive position of organizations. In 2012, 2.5 exabytes
10
9
GB) of data was generated every day and this
rate doubles every 40 months (McAfee et al., 2012).
This data is generated in real time, has varied form
and is voluminous. Such kind of data is called Big
Data and big data cannot be handled using traditional
techniques. Analytics is becoming an integral part
of organisations and business processes (Davenport,
2013). However, only 15 percent of the organisation
who intended to adopt BDA actually adopted analyt-
ics (Strauss and Hoppen, 2019). Data and technology
are important for big data analytics. Some other im-
portant factors are- understanding of the management
of the potential of analytics, skills of the employees,
data policy (LaValle et al., 2011).
An organisation may have a business case and an
associated objective. In order to achieve the objective,
the organisation may require to adopt big data analyt-
ics. The organisation needs to check its readiness for
big data analytics. An adoption framework may help
the organisation to proceed in a systematic manner.
Theory of affordance provides a potential big data an-
alytics adoption framework. Section 2 provides a
review of the relevant literature including theoretical
framework. We have identified factors for each phase
and discussed them in section 3. We have verified our
framework using two case studies that are presented
in Section 4. We close the paper with a discussion
and conclusion presented in Section 5.
2 LITERATURE REVIEW
Big data analytics or simple big data has been de-
fined as a holistic approach to manage, process and
analyse 5 Vs (i.e., volume, variety, velocity, veracity
and value) in order to create actionable insights for
sustained value delivery, measuring performance and
establishing competitive advantages (Wamba et al.,
2015). Big data analytics may involve sophisticated
computational methods that are applied to massive
data. BDA can help firms in decision making. Also,
BDA helps in customer segmentation, fraud detec-
tion, quantifying the risks and forecasting sales, etc.
(Russom, 2011). Companies can enhance their ef-
ficiency, improve the profitability and their competi-
tiveness (Alharthi et al., 2017). Top-performing com-
panies are known to use data analytics twice than
lower-performing companies (Grimaldi et al, 2019).
However, big data analytics (BDA) adoption is a
project whose outcome is anything but deterministic.
According to Adaptive Structuration Theory, outcome
of an IT project depends on many factors (DeSanc-
tis and Gallupe, 1987). IT artefacts are visualised
Bansal, V. and Shukla, S.
Exploring Big Data Analytics Adoption using Affordance Theory.
DOI: 10.5220/0010509801310138
In Proceedings of the 23rd International Conference on Enterprise Information Systems (ICEIS 2021) - Volume 2, pages 131-138
ISBN: 978-989-758-509-8
Copyright
c
2021 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
131
in terms of their technical specifications and affor-
dances they offer to goal oriented and experienced
users (Markus, 2015). There is another dimension
that deals with intent and values of the IT artefact that
is referred to as symbolic expression or spirit of the
system. In this article we will not concern ourselves
with this dimension. Affordances (Gibson, 1977) are
what a group of users may be able to do with IT arte-
facts given their goal and experience. Some organ-
isations have adopted and integrated BDA across a
wide array of functions. Such organisations have been
termed as transformed organisation (LaValle et al.,
2011). On the other end of the spectrum are the as-
pirational organisations that barely use data analytics
to guide their decisions. Experienced organisations
are the ones that fall in the middle and have started
using data analytics for decision making but it is not
integrated into their business processes. Each of these
types of organisations have different level of experi-
ence with data analytics. Their goals may also be dif-
ferent for a data analytics project.
Affordances emerge from the interaction between
the actor and objects (Bernhard et al., 2013) (Pozzi
et al., 2014). The information about affordances be-
comes available when interaction between actors and
objects take place. In addition, information may also
be available from external sources. Affordance is a
pre-condition for an action, but existence of an affor-
dance does not mean that an action will happen. Infor-
mation about affordances must be available to the ac-
tors for them to perceive affordances (Bernhard et al.,
2013). The actor will perceive only a limited set of af-
fordances from the entire set of affordances available.
The perception may be correct, false or a combina-
tion of the two. The perceived affordances may be
actualised by actualisation effort. The actualisation
effort is dependent on the perceived difficulty of the
affordances. The actualised affordances will create
an effect. However, the actualised affordances may or
may not have desired effect. There are three different
use cases for IT artefacts adoption, use and effect
(result). The studies available in literature mostly fo-
cus on use or effect (Strauss and Hoppen, 2019). In
this article, we have focused on adoption.
2.1 Affordance Theory Framework
The theory of affordance is the base for our frame-
work that is adopted from (Bernhard et al, 2013).
In this framework, constituents of an organisation
who have potential to interact with technology are ac-
tors. The big data analytics technology is the artefact.
Big data analytics technology consists of BDA tools,
hardware and software (Strauss and Hoppen, 2019).
The framework consists of four phases (shown in Fig-
ure 1 that are explained below.
2.1.1 Affordance Emergence
Organisation and technology need to interact in or-
der for technology affordances to emerge (Hutchby,
2001) (Majchrzak and Markus, 2012). The affor-
dances emerge in the form of information about affor-
dances. In case of adoption, the technology doesn’t
yet exist in the organisation. Therefore, affordances
cannot emerge through this channel. As an alterna-
tive, the organisation may gather information from ex-
ternal sources. Another possibility is that the organ-
isation asks one or more vendors to make their tech-
nology available. The actors interact with the ven-
dor technology with a goal in sight. The information
thus generated and gathered results in emergence of
affordances- a possibility for available action. Prior
experience with technology plays an important role.
Affordances have dual-functional nature- enabling
and constraining nature. The actor will perceive af-
fordances as enabling or constraining based on his ca-
pabilities and goals (Pozzi et al, 2014). Affordance
emergence is the first construct of the affordance the-
ory framework. Affordances that emerge may be dif-
ferent for aspirational, experienced, and transformed
organisations. Transformed organisations may be
able to see different or more affordances than an as-
pirational organisation. It is the responsibility of the
organisation to be aware of the goal and discover rel-
evant affordance available (Bernhard et al, 2013).
2.1.2 Affordance Perception
In IS literature, it is argued that affordances exist ir-
respective of actors, their goals and experience (Pozzi
et al, 2014). Therefore, affordance perception is the
first process instead of affordance emergence. Af-
fordance perception is the process of recognition of
affordances. The affordances related information af-
fects the affordance perception and gives clues to the
user that affordances exist. Factors such as actor’s
goals and capability, and features and information
about object are important factors for affordance per-
ception. We take a position that affordance emergence
and perception are two different processes in the con-
text of IS adoption. The emerged affordances from
the technology that is being considered for adoption
may be more than what the organisation is interested
in. Perceived affordance can be different from affor-
dances that emerged. Actor may recognise only a sub-
set of all affordances that emerge.
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Figure 1: Affordance Theory Framework; a rectangle represents what must exist at the beginning, rounded corder rectangle
represents what is made available; an oval indicates outcome of a phase; the indicators are in italics text.
2.1.3 Affordance Actualisation
In case of technology adoption, actualisation involves
actions to make technology affordances ready to use.
Actualisation is a goal-oriented and iterative process
(Leonardi, 2011). The actualisation effort is a collec-
tive activity done at the level of the organisation. The
degree of effort may vary a lot in actualisation—the
amount of action required by an actor to realise the
possible results. The actualisation effort and its out-
come depend on many factors (Pozzi et al., 2014). For
technology adoption, among all the factors, the actu-
alisation of previous affordances is particularly inter-
esting. This particular factor captures experience of
the actor. If an organisation has prior experience with
a predecessor of the present technology, the organ-
isation may have required ability and understanding
of the technology under consideration (Bernhard et
al, 2013). The organisation may have environmental
structures to support adoption. A transformed organ-
isation is likely to have less difficulty in adopting a
new big data analytics technology compared to an as-
pirational organisation. The technology configuration
and technology features are also important factors in
actualisation. Skills and knowledge of the employees
of the organisation also play a critical role in actual-
isation. The willingness to change behaviour is con-
sidered important in affordance actualisation (Pozzi
et al, 2014). The effort may also depend on the af-
fordances selected to be actualised. An organisation
may perceive false or wrong affordances (Bernhard
et al., 2013). During the actualisation phase, wrong
or false affordances will become apparent. Most of
the factors that are considered important during ac-
tualisation phase are consistent with factors that are
considered critical from a project management per-
spective (LaValle et al., 2011) (Gao et al., 2015).
2.1.4 Affordance Actualisation Effects
Effect of affordance actualisation are of two types
based on time perception of the actor: short term ef-
fect and long-term effect. Short term effects are called
immediate concrete outcomes. Immediate concrete
outcomes guide the actualisation and help in predict-
ing the long-term effect (Strauss and Hoppen, 2019).
Actualisation effect of big data analytics adoption is
different from the effect of using big data analytics.
Immediate concrete outcome may be that adopted big
data analytics tool performs as expected on the test
data. Long-term effect may include improvement in
the business.
3 FACTORS FOR AFFORDANCES
EMERGENCE, PERCEPTION
AND ACTUALISATION PHASES
In the emergence phase, interaction between IT arte-
facts and actors take place. Affordances appear when
there is an interaction between actors and IT arte-
facts. This interaction happens more specifically be-
tween capabilities and characteristics of actors and IT
Exploring Big Data Analytics Adoption using Affordance Theory
133
artefacts. In the perception phase, information that
emerges from emergence phase is utilised to perceive
affordances. Finally, in the actualisation phase, the
chosen affordances are realised. There are indicative
factors for each phase. The health of these factors
are indicative of preparedness of the organisation for
adopting big data analytics.
3.1 Relevant Factors for Affordances
Emergence
3.1.1 Data and Its Properties
Data and its properties are important for affordance
emergence (Russom, 2011) (Alharthi et al., 2017)
(McAfee et al., 2012). 4Vs (volume, velocity, vari-
ety, veracity) of the data is the starting point for big
data analytics (Grimaldi et al., 2019). An organisa-
tion that has big data and is desirous of drawing value
(fifth V) from the data is a candidate for adopting big
data analytics. The organisation should also have a
data management policy in place. Without a policy
data that is collected may be poorly understood and
may not be good enough for generating knowledge.
Data policy deals with data access, security and pri-
vacy issues.
3.1.2 Data Analytics Tool
Experience in data analytics tool may be helpful
in selecting big data analytics tools and technology
(Grover and Kar, 2017). A wide range of tools are
available; some are proprietary and others are open
source (Srinivasan and Kumari, 2018). There are
many ways of selecting the right BDA tool, and what
tools are selected depends on the need of the organisa-
tion, budget, and available skill set. An organisation
may have a selection process in place.
3.1.3 IT Infrastructure
IT infrastructure is an essential factor in adopting big
data analytics (Behl et al., 2019) (Nam et al., 2019)
(Gupta and George, 2016). If an organisation has big
data, it must have infrastructure and processes to col-
lect and store data. Big data analytics may require
specialised hardware and software to be able to pro-
cess big data. An organisation may not have IT infras-
tructure for big data analytics (Wamba et al., 2017).
Procurement of required IT infrastructure is part of
affordances actualisation phase. Some organisations
use third party IT infrastructure or shared IT services
and may need to expand it (Behl et al., 2019).
3.1.4 Organisational Characteristics
The attitude of top management towards Big Data
Analytics plays an important role in adoption (Behl
et al., 2019) (Russom, 2011) (Nam et al., 2019)
(McAfee et al., 2012) (Gupta and George, 2016).
Chief data/technology/information officer must be in-
terested and committed to adopting data analytics.
Lack of management bandwidth is a major issue
that stops organisations from taking up data analyt-
ics project (LaValle et al., 2011). The management
may have a goal (Ji-fan Ren et al., 2017) (Gupta and
George, 2016) to be achieved by adopting big data
analytics. If the top management takes a strategic de-
cision to adopt BDA, the project is more likely to suc-
ceed (Behl et al., 2019).
Data analytic experience and skills of employees
is another important factor (Hoffman and Podgurski,
2013) (Behl et al., 2019) (Gupta and George, 2016)
(Alharthi et al., 2017). An organisation that has been
using data analytics will be better prepared to adopt
big data analytics. If the organisation has actualised
affordances in the past, employees will have the skill
set to carry on the BDA project (Behl et al., 2019).
Skills such as data preparation, data visualisation, do-
main knowledge, problem-solving ability, and quan-
titative aptitude for solving a new problem are rele-
vant. Another possibility is that the organisation has
been using data analytics but the project was out-
sourced. In such a situation, an in-house team would
have been involved. This team will be able to lead
the big data adoption project whether done in-house
or out-sourced.
3.2 Relevant Factors for Emergence
Perception
The information that is gathered during affordance
emergence serves as the basis for this phase. The in-
formation may reveal affordances or may shield af-
fordances. For instance, if the organisation has a data
policy in place, data access emerges. If the organi-
sation has no data policy, data access is shielded. A
selection and purchase process, vendor and product
selection emerge. An absence of such a process re-
veals a need for creating a purchase process. The
organisation will need to identify criteria (Kangelani
and Iyamu, 2020) for selecting the most suitable BDA
tool. Prior to affordance actualisation, the organisa-
tion may want to experiment with select few products
(Strauss and Hoppen, 2019). The vendors may pro-
vide demonstration version to the organisation.
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3.3 Relevant Factors for Affordance
Actualisation
Affordances actualisation is a team effort. Actualisa-
tion has to be managed as a project. Composition and
abilities of the team are important for affordances ac-
tualisation. There are two possibilities- an in-house
team may be capable of executing the project, or an
implementation partner may be required. The in-
house team will identify and engage an implementa-
tion partner. The in-house team will plan affordance
actualisation. Planning (Steven Ji-fan et al, 2017)
is a systematic process to use resources to accom-
plish the goal. Planning provides a structure to ac-
tivities, their sequencing, their time and resource re-
quirements. Planning reduces risks (Rackoff et al.,
1985). The factors that were important during emer-
gence phase are important for actualisation. Com-
mitment of the management, skills of the employ-
ees, data policy etc. all come together in affordance
actualisation phase. In addition, budgeting (Trelles
et al., 2011), affordance actualisation team compo-
sition (Goldsmith et al, 2000) and characteristics of
the selected technology (Behl et al., 2019) (Gupta and
George, 2016) play important role. Change manage-
ment and employee training (Behl et al., 2019) (Gupta
and George, 2016) are also required for adoption to
succeed (Bernhard et al., 2013).
3.4 Relevant Factors for Actulisation
Effect
The effect of affordances actualisation is deployment
of big data analytics technology in the organisation
to achieve the decided goal (Ji-fan Ren et al., 2017).
Effect of actualisation can be judged with respect to
goal using historical data. The long term effect of
deployment depends on many other factors. Success
of long term usage of the technology may be judged
using corresponding framework (Pozzi et al., 2014)
(Bernhard et al., 2013) (Strauss and Hoppen, 2019).
4 CASE STUDY
We have validated our framework using case study
methodology. We have analysed two organisations.
Both organisations are very different.
4.1 Case Study 1
About the Organisation Artificial Limbs Manufactur-
ing Corporation of India (ALIMCO) is a 100% Gov-
ernment of India owned central public sector enter-
prises. This is a Mini Ratna organisation. ALIMCO
was established in 1972 and started manufacturing of
hearing, mobility, vision and other rehabilitation aids
in 1976. It’s headquarter is situated in Kanpur, Uttar
Pradesh, India. ALIMCO is governed by the Min-
istry of Social Justice & Empowerment, Department
of Empowerment of Persons with Disabilities, India.
It manages sales, promotion, distribution of its prod-
ucts. ALIMCO has four auxiliary production centres
located in four different cities across India. The en-
terprise also has four marketing centres in across In-
dia. Findings: We visited ALIMCO Kanpur Centre
and interviewed their IT head. Our findings are as
follows. Data- The total data of all ALIMCO cen-
tres put together is less than 15 GB. The data pri-
marily consists of employee data, payroll data, pro-
duction data, inventory data and sale transaction data.
IT Infrastructure- All IT functions of ALIMCO head-
quarter located at Kanpur are conducted from a small
room with outdated systems. Systems are connected
through Wi-Fi technology and can share information
within a centre. All ALIMCO centres are not inte-
grated. Information of one centre is not readily acces-
sible to the other centres. The current IT infrastruc-
ture is not flexible and cannot be upgraded easily. Pro-
cessing of a request takes time from couple of hours to
a day. The attitude of top management towards BDA-
Top management is focused on production and related
activities. They have not concerned themselves with
data analytic yet and have not figured out potential of
data analytics. There are only two employees in the
IT section.
4.1.1 Comments and Findings
ALIMCO is using a transactional processing system
(TPS) and is in the process of graduating to using
management information system (MIS). ALIMCO is
also considering implementing an ERP system in near
future. They have a relational database management
system. With some effort, they can use the existing
data for forecasting and production planning. AL-
IMCO is less than an aspirational organisation as far
as big data analytics adoption is concerned.
4.2 Case Study 2
4.2.1 About the Organisation
XYZ is a multinational investment bank and financial
services company. It is the sixth-largest bank in the
world and largest bank in Europe, with 2.715 trillion
US dollars assets. XYZ formally started operation
more than 150 years ago. XYZ provides services in
Exploring Big Data Analytics Adoption using Affordance Theory
135
retail banking, corporate banking, investment bank-
ing, mortgage loans, private banking, wealth man-
agement, credit cards, finance, and insurance. It has
around 4000 offices in more than 65 countries and
has around 38 million customers. The company has
around 2,50,000 employees, more than half are fe-
male. The revenue of the company is 56 billion US
dollars.
4.2.2 Information Gathered
XYZ started business intelligence (BI) implementa-
tion more than 20 years ago.. The bank has a core
business end and an IT/operation end. Analytics
needs are raised by the business end. The IT depart-
ment implements and deploys solutions to analytics
requirements. We contacted a senior analyst who is
a part of the data analyst team for four years. We
interviewed him that took around 40 minutes. XYZ
currently applied data analytics in fraud analysis, risk
calculation, money laundering and data security. The
organisation wants to excel in these fields through
data analytics. Data security is still a challenge for
the bank. Apart from that, bank also aspires to au-
tomate all its operations. The bank also wants to en-
courage use of cryptocurrency and digital money. The
bank already have data, data policy and IT infrastruc-
ture in place. The management is well aware of the
potential of data analytics. Employees have experi-
ence with business intelligence. and skills makes it a
possible, achievable business goal for XYZ. The bank
has more than 100 TB for analytics purposes. Data is
in a well-defined structured form. The data is col-
lected from may sources. New data is generated and
become part of the data store every second. The bank
owns latest IT infrastructure. The IT infrastructure
can be easily expanded to increase storage and com-
puting capacity. A request is processed mostly in real
time. Specialised requests are processed in less than
one hour. Data analytics tools and platforms include
python, SAS, R, R Shiny and Tableau. Previous data
analytics affordances actualisations have been com-
pleted on time. Employees have domain knowledge
and statistical knowledge. IT team is equipped with
coding skills. Managers understand the business and
the role of the technology well. They are well versed
with IT services of the organisation. They understand
the future needs. Regular meetings of the functional
team, analytics team and business team are help ev-
ery week. Discussions during these meetings help in
identifying new opportunities. Every affordances im-
plementation is planned meticulously. The team is
built according to the skills and experience required
for a project. Skill building training is organised for
emerging technology. Training is either delivered by
an in-house team or a specialised third party. The
bank already has loan prediction model, risk analysis
model and data security model in place. According
to the interviewee, for a bank, data security is of ut-
most importance. A bank may compromise and not
deploy latest technology. But the bank can not com-
promise with data security. A single data breach may
bring the credibility of the bank down. Data analytics
has reduces incidences of data breach and has made
transactions secure. The bank has been able to reduce
the manpower by 30% due to technology. The bank
wants to become a self-serviced bank and do away
with direct service employees.
4.2.3 Analysis
We now map our findings from the interview to our
proposed framework.
Data and Its Properties. The bank has volume, ve-
locity and variety of good quality data. The bank
has been using business intelligence for drawing
value out of the data.
IT Infrastructure. The bank has reliable, flexible
and integrated IT infrastructure. IT infrastructure
is accessible to employees. IT is used for running
applications, accessing data and for data analytics.
Organisation Characteristics. The present top
management has been part of the data analytics
team in the past. They know various data analytics
applications that have been deployed in the organ-
isation. They are familiar with the data analytics
strategy and its objective. The management may
not have technical know how, but they certainly
know its capabilities. Consequently, management
takes data analytics initiatives and sets goals for
the initiative. Due consideration is given to the
budget and benefits while deciding a goal.
The Management Motivates Employees to Adopt
Data Analytics. The management is aware of the
potential challenges and plans accordingly. The
bank has been using business intelligence and data
analytics for last 30 years. Employees and the IT
team have been working together on data analyt-
ics projects. The IT team understands the busi-
ness and is equipped to manage and execute data
analytics projects. Employees of the organisation
are also well aware of potential of data analytics.
They have skills to work with IT team on data an-
alytics projects. Employees have no hesitation in
adopting data analytics.
The organisation has all required characteristics
to generate and gather information for affordance
emergence and perception.
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Affordance Perception. The bank has data policy
in place. They also have processes for selecting
technology and vendors. Their prior experience
helps them to make right choices.
Affordance Actualisation. We learnt that the team
plans actualisation and sticks to the plan during
actualisation effort to the extent possible. Man-
agement regularly provides advice and provide
training to employees. The previous affordance
existence, perception and actualisation inspire the
bank to explore advanced affordances.
Affordance Effect. The bank has successfully ac-
tualised and deployed data analytics.
4.2.4 Comments
The bank is a transformed organisation. Data analyt-
ics has become integral part of the organisation. The
company has been drawing value from big data ana-
lytics.
5 CONCLUSIONS
Big data analytics has potential to help organisations
to gain useful business insights. An organisation must
have a clear goal leading to a business case while
adopting data analytics. Adoption of big data ana-
lytics require preparedness of the organisation. Af-
fordance theory provides a framework to check pre-
paredness of an organisation for adoption of big data
analytics. The framework also guides actualisation
of identified affordances. The framework has three
phases, namely affordance emergence, perception and
actualisation phases. We have adopted affordance
theory framework for big data analytics adoption. We
have included organisational and technological fac-
tors that play important role in the framework. We
have not included factors that are part of external en-
vironment such as government policies, market etc.
TOE (Technology Organisation Environment) theory
(H.O. Awa and Igwe, 2017) considers environment in
addition to technological and organisational factors.
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