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

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

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.

ICEIS 2021 - 23rd International Conference on Enterprise Information Systems

132

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.

ICEIS 2021 - 23rd International Conference on Enterprise Information Systems

134

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.

ICEIS 2021 - 23rd International Conference on Enterprise Information Systems

136

• 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.

REFERENCES

Alharthi, A., Krotov, V., and Bowman, M. (2017). Ad-

dressing barriers to big data. Business Horizons,

60(3):285–292.

Behl, A., Dutta, P., Lessmann, S., Dwivedi, Y., and Kar,

S. (2019). A conceptual framework for the adoption

of big data analytics by e-commerce startups: a case-

based approach. Information Systems and e-Business

Management, 17(2):285–318.

Bernhard, E., Recker, J., and Burton-Jones, A. (2013).

Understanding the actualization of affordances: A

study in the process modeling context. In 34th In-

ternational Conference on Information Systems (ICIS

2013), pages 1–11. Association for Information Sys-

tems (AIS).

Davenport, T. (2013). Analytics 3.0. Harvard business re-

view, 91(12):64–72.

DeSanctis, G. and Gallupe, R. (1987). A foundation for the

study of group decision support systems. Management

Science, 33(5):589–609.

Dhar, V. (2013). Data science and prediction. Communica-

tions of the ACM, 56(12):64–73.

Gao, J., Koronios, A., and Selle, S. (2015). Towards a pro-

cess view on critical success factors in big data ana-

lytics projects. In 21st Americas’ Conference on In-

formation Systems, page 1–14.

Gibson, J. (1977). The theory of affordances. Hilldale,

USA.

Grimaldi, D., Fernandez, V., and Carrasco, C. (2019). Ex-

ploring data conditions to improve business perfor-

mance. Journal of the Operational Research Society,

pages 1–11.

Gupta, M. and George, J. (2016). Toward the development

of a big data analytics capability. Information & Man-

agement, 53(8):1049–1064.

H.O. Awa, O. U. and Igwe, S. R. (2017). Revisiting

technology-organization-environment (toe) theory for

enriched applicability. The Bottom Line.

Hoffman, S. and Podgurski, A. (2013). Big bad data: law,

public health, and biomedical databases. The Journal

of Law, Medicine & Ethics, 41:56–60.

Hutchby, I. (2001). Echnologies, texts and affordances. So-

ciology, 35(2):441–456.

Ji-fan Ren, S., Wamba, S. F., Akter, S., Dubey, R., and

Childe, S. (2017). Modelling quality dynamics, busi-

ness value and ﬁrm performance in a big data analyt-

ics environment. International Journal of Production

Research, 55(17):5011–5026.

Kangelani, P. and Iyamu, T. (2020). April. a model for eval-

uating big data analytics tools for organisation pur-

poses. In Conference on e-Business, e-Services and

e-Society, pages 493–504. Springer.

LaValle, S., Lesser, E., Shockley, R., Hopkins, M., and Kr-

uschwitz, N. (2011). Big data, analytics and the path

from insights to value. MIT sloan management review,

52(2):21–32.

Leonardi, P. (2011). When ﬂexible routines meet ﬂexible

technologies: Affordance, constraint, and the imbrica-

tion of human and material agencies. MIS quarterly,

pages 147–167.

Majchrzak, A. and Markus, M. (2012). Technology af-

fordances and constraints in management information

systems (mis). In Encyclopedia of Management The-

ory. Sage Publications.

Markus, M. (2015). New games, new rules, new score-

boards: the potential consequences of big data. Jour-

nal of Information Technology, 30(1):58–59.

McAfee, A., Brynjolfsson, E., Davenport, T. H., Patil, D. J.,

and Barton, D. (2012). Big data: the management

revolution. Harvard business review, 90(10):60–68.

Exploring Big Data Analytics Adoption using Affordance Theory

137

Nam, D., Lee, J., and Lee, H. (2019). Business analytics

adoption process: An innovation diffusion perspec-

tive. International Journal of Information Manage-

ment, 49:411–423.

Pozzi, G., Pigni, F., and Vitari, C. (2014). Affordance the-

ory in the is discipline: A review and synthesis of the

literature. In AMCIS 2014.

Rackoff, N., Wiseman, C., and Ullrich, W. (1985). Informa-

tion systems for competitive advantage: implementa-

tion of a planning process. MIS quarterly, pages 285–

294.

Russom, P. (2011). Big data analytics. Technical report,

TDWI best practices report, fourth quarter.

Strauss, L. M. and Hoppen, N. (2019). A framework to

analyze affordances when using big data and analyt-

ics in organizations: A proposal. RAM. Revista de

Administrac¸

ao Mackenzie, 20(4).

Trelles, O., Prins, P., Snir, M., and Jansen, R. (2011). Big

data, but are we ready? Nature Reviews Genetics,

12(3):224–224.

Wamba, S., Gunasekaran, A., Akter, S., Ren, S., Dubey,

R., and Childe, S. (2017). Big data analytics and ﬁrm

performance: Effects of dynamic capabilities. Journal

of Business Research, 70:356–365.

Wamba, S. F., Akter, S., Edwards, A., Chopin, G., and

Gnanzou, D. (2015). How big data can make big im-

pact: Findings from a systematic review and a longitu-

dinal case study. International Journal of Production

Economics, 165:234–246.

ICEIS 2021 - 23rd International Conference on Enterprise Information Systems

138