Thesis: Understanding The Factors That Influence The Adoption of Big Data by Small and Medium-sized Enterprises (Grade: 90/100)
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Course
Thesis
Institution
Hogeschool Van Amsterdam (HvA)
How can SMEs in Europe adopt and successfully realize the benefits from implementing big data analytics?
In order to answer this main research question, primary research method in the form of semistructured interviews with four interviewees from different European countries and a secondary resea...
UNDERSTANDING THE FACTORS THAT
INFLUENCE THE ADOPTION OF BIG DATA BY
SMALL AND MEDIUM-SIZED ENTERPRISES
A study on how SMEs can adopt and successfully realize the benefits from
implementing big data analytics
Marcella Yoong – 500755925
Supervised by Mrs. Alicia Román Alonso
Bachelor of Business Administration
Amsterdam School of International Business
August 2020 – January 2021
,UNDERSTANDING THE FACTORS THAT INFLUENCE
THE ADOPTION OF BIG DATA BY SMALL AND
MEDIUM-SIZED ENTERPRISES
A study on how SMEs can adopt and successfully realize the benefits from
implementing big data analytics
Author: Marcella Yoong
Assessor: Dr. Mike Russell
Supervisor: Mrs. Alicia Román Alonso
August 2020 – January 2021
Amsterdam, The Netherlands
International and Management Studies
Bachelor of Business Administration
Research Lab Honours Thesis
Amsterdam University of Applied Sciences (AUAS)
Faculty of Business and Economics (FBE)
Amsterdam School of International Business (AMSIB)
Centre for Applied Research on Economics & Management (CAREM)
Centre of Entrepreneurial Dynamics and International Strategy (CEDIS)
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,Executive Summary
Small and medium-sized enterprises (SMEs) are the backbone of most economies in Europe. They
represent more than 99% of all businesses in the European Union and is therefore, high priority for
many European governments. Growth in data is a consequence of the growth of SMEs and creates
opportunities and challenges for enterprises to make proper use of that data and creates business value.
Big data is becoming a significant potential for SMEs, allowing companies to provide solutions for its
problems in every type of industry. SMEs have the advantage of flexibility to change in continuously
changing markets but requires a strategy of doing so.
The aim of this thesis is to understand the factors that influence the adoption of big data by SMEs, the
opportunities and potentials big data adoption can create and to provide recommendations on how SMEs
could successfully adopt big data in order to realize the benefits from it. This research utilizes the
Technology – Organization – Environment (TOE) model integrated with the Diffusion of Innovation
(DOI) theory in order to understand the different factors that impact the adoption of big data technology.
In order to answer the main research question: “How can SMEs in Europe adopt and successfully realize
the benefits from implementing big data analytics?”, a primary research method in the form of semi-
structured interviews with four interviewees from different European countries and a secondary
research method in the form of a systematic literature review that consists of a final portfolio of 18
empirical journal articles were conducted. Outcomes have shown that that the most impactful factor is
organizational readiness, which highlights the lack of financial, human and technical resources. This is
followed by benefit / relative advantage, compatibility, complexity, management support, risks and
insecurity, external support, trialability, competitive pressure, government regulations and observability
as the least significant factor on the adoption of big data. The opportunities and potentials that can be
generated through the adoption of big data lies in the form of strategic value, informational value,
transactional value and transformational value.
In accordance with the factors stated above, it can be concluded that there are multiple factors that affect
the adoption of big data in European SMEs and the requirements for successfully adopting big data are
complex. Top management support and organizational readiness are two critical factors as they are key
to the whole implementation process of big data. Management should cultivate a data-driven culture in
the organization and ensure that the required resources are available. SMEs could opt for external
support such as cloud-based managed services which provides solution to the problem of lack of
financial, human, and technical resources. This allows SMEs to assess its compatibility with the
organizational goals, assess if it meets the value that is expected to be created and to remain agile for
changing solutions or to create decisions on whether to continue to utilize big data or not.
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, Table of Contents
Executive Summary .............................................................................................................................. 3
Chapter 1: Introduction ....................................................................................................................... 6
1.1 Background of the Research ......................................................................................................... 6
1.2 Company Description ................................................................................................................... 6
1.3 Problem or Opportunity Analysis ................................................................................................. 7
1.4 Main Research Question ............................................................................................................... 8
1.5 Organization of the Report ............................................................................................................ 9
Chapter 2: Theoretical Framework .................................................................................................. 10
2.1 Introduction ................................................................................................................................. 10
2.2 Theoretical Perspectives on the Topic ........................................................................................ 10
2.2.1 Big Data ............................................................................................................................... 10
2.2.2 Changing the Traditional Role of IT .................................................................................... 10
2.2.3 The Difference in Realizing Synergies between SMEs and Large Firms............................ 11
2.2.4 The Big Data Technologies Adopted ................................................................................... 11
2.2.5 Models of Big Data Adoption by SMEs .............................................................................. 12
2.2.6 Technological Context (DOI Theory) .................................................................................. 12
2.2.7 Organizational Context ........................................................................................................ 13
2.2.8 Environmental Context ........................................................................................................ 14
2.2.9 Realizing Value from Big Data Adoption............................................................................ 14
2.3 Selection and Justification of Theory to be Applied ................................................................... 15
2.4 Limitations of the Selected Theory ............................................................................................. 16
2.5 Conceptual Model ....................................................................................................................... 16
2.6 Sub-Research Questions ............................................................................................................. 18
Chapter 3: Research Methodology .................................................................................................... 19
3.1 Introduction ................................................................................................................................. 19
3.2 Primary Qualitative Data Collection Method: Structured in-depth Interviews .......................... 19
3.2.1 Data Analysis ....................................................................................................................... 19
3.3 Secondary Research: A Systematic Literature Review............................................................... 20
3.3.1 Protocol Development.......................................................................................................... 20
3.3.2 Data Sources and Search Strategy ....................................................................................... 20
3.3.3 Inclusion and Exclusion Criteria .......................................................................................... 21
3.3.4 Study Quality Assessment ................................................................................................... 22
3.3.5 Data Analysis ....................................................................................................................... 22
3.4 Summary ..................................................................................................................................... 22
3.5 Ethical Considerations ................................................................................................................ 22
Chapter 4: Data Findings and Analysis ............................................................................................ 23
4.1 Introduction ................................................................................................................................. 23
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