Content uploaded by Saheed Adebayo Gbadegeshin
Author content
All content in this area was uploaded by Saheed Adebayo Gbadegeshin on Nov 08, 2021
Content may be subject to copyright.
Content uploaded by Saheed Adebayo Gbadegeshin
Author content
All content in this area was uploaded by Saheed Adebayo Gbadegeshin on Nov 08, 2021
Content may be subject to copyright.
MORE DATA AND MORE DATA: THE MAIN CHALLENGES FOR
SMALL ORGANIZATIONS IN USING BIG DATA ANALYTICS
S.A. Gbadegeshin, A. Al Natsheh, A. Kuoppala, K. Ghafel, A. Gray
Kajaani University of Applied Sciences (FINLAND)
Abstract
With continuous technological advancements and a surge of digitalization, the business ecosystem has
changed. One of the effects of this trend is an increase in the generation of data. Prior studies have
shown that the use of data contributes to the growth of large corporations. These corporations, most
often, have resources to try new innovations or developments. Meanwhile, little is known about the use
of data in small organizations. These organizations are characterized with insufficient resources to utilize
the latest innovations. Therefore, this article investigated how small organizations could use their data
within their budget to improve their operations and maximize their profit. To achieve its goal, the article
employed theory of diffusion as its theoretical framework and a case study method for its research
methodology. The case project consisted of 4 pilots. All documents related to the pilots, such as, meeting
notes, interview transcriptions, pilot reports, scholarly articles, and professional study reports were
analyzed with the content analysis method. Altogether, 121 documents were analyzed. The article found
that lack of business expertise in data analytics teams, lack of business goals in data analytics projects,
and the protection of business secrets were the main challenges facing small organizations in the use
of their big data. These findings revealed that the small organizations considered the use of data
analytics as a technical issue and paid less attention to the business or management aspects of it. The
finding revealed further that the small organizations assembled their data analytics teams with people
that have technical skills. Thus, these results provide insight for scholars and practitioners on their
debates on the use of big data analytics in the small organizations.
Keywords: Big data, Data Analytics, Small Organizations.
1 INTRODUCTION
The use of data has continued to increase since the beginning of the discussions around big data
(Parwez et al., 2017; Misra et al., 2016). The term big data refers to the generation, storage, distribution,
and management of large quantities of various pieces of data (Gandomi & Haider, 2015). Big data are
usually generated through the internet, mobile phone applications, self-quantified, multimedia, social
media, and Internet of Things (Hashem et al., 2016; Bughin, 2016). Thus, the use of data is an
exploration of the big data to deduce meanings and implement them.
With the advent and persistence of Coronavirus (Covid-19), the use of the internet has increased; thus,
more data have been generated. Meanwhile, some scholars, such as Gbadegeshin (2019a, b) and
Parviainen et al. (2017) have been calling for the proper use of digitalization and its influences on
business operations. Similarly, Jose et al. (2017) and Ylijoki and Porras (2016) called for the use of big
data, especially for small and medium-sized enterprises. Malaka et al. (2015) and Mohanty et al. (2013)
stated that very few small organizations make use of their big data. Hashem et al (2016) forecasted that
the total amount of data, in the world, would grow by 40% per year, and by the year 2020, the growth
would be 50 times. All these conditions serve as motivation for this article. They led to this research
question: how can small organizations use their big data within their budget? In answering the
question, the article employed a case study method.
The article focused on small organizations due to their importance to the national economy. Savlovschi
and Robu (2011) and Floyd and McManus (2005) claimed that small organizations contribute
significantly to national and regional economies. Additionally, Parwez et al. (2017),Jose et al. (2017)
and Gao et al. (2015) stated that small organizations are not currently active in the use of their data.
Furthermore, Russom (2016) and Malaka and Brown (2015) pointed out that large corporations are
making use of their data and they continue to invest in it. Thus, small organizations were focused upon.
Therefore, this article analysed empirical research data from a project case that aimed to investigate
how small organizations could use their data. The project also aimed to develop a business concept for
the use of data analytics. It was a two-year project. There were four pilots in the project. These pilots
focused on the implementation of data analytics in the participating small organizations. The case also
Proceedings of ICERI2021 Conference
8th-9th November 2021
ISBN: 978-84-09-34549-6
0461
consisted of the review of several scholarly articles, white papers, and professional reports. The rest of
the article is as follows: research background, research methodology, findings, and conclusions.
2 RESEARCH BACKGROUND
Big data analytics is an umbrella term that incorporates methods and technologies, hardware, and
software for collecting, managing, and analyzing large scale data (both structured and unstructured
data) in real-time. It works on entire data as opposed to only sample data in conventional data analytics
schemes (Parwez et al., 2017). Analyzed data are used to do business processes or activities
optimization, fraud management, market watch, diagnosis or fault management, forecasting and
monitoring, customization, marketing activities, error reduction, new product development, site planning,
and value optimization (Russom, 2016). Similarly, the analyzed data are used to identify current trends
and synthesize the behavior of customers, as well as to coordinate internal activities, such as financial
management, inventory and procurement management, supply chain management, and resource
management (Jose et al., 2017).
Additionally, the analyzed data are used to make decisions and to innovate and create new values
(Ylijoki & Porras, 2016). In fact, the use of big data has an impact of 5–6 % increment of total productivity
for some companies and yields a 3% return on investments in other enterprises (Mohanty et al., 2013).
Some companies shared that they had better customer service experience with use of their data (Malaka
& Brown, 2015).
In respect to the benefits of the use of data, there are many organizations employing data analytics
experts to utilize their available data. Most of these organizations are large corporations and are largely
multinational (Mohanty et al., 2013). These large companies are engaging in retailing, pharmaceuticals,
insurance, banking and financial services, and telecommunications. These corporations experience
positive results and impacts on the use of data for their businesses (Malaka & Brown, 2015).
Furthermore, the data analytics has been applied in other areas, such as education and training (e.g.
Marttila-Kontio et al., 2014) and business model (e.g. Huhtala et al., 2017; Lindman et al., 2014). The
results from these areas are positive.
Meanwhile, an important employer of a large workforce, small organizations, do not use their data
analytically and efficiently. Only a few of these companies are currently making use of their data (Malaka
& Brown, 2015; Mohanty et al., 2013). They are not engaging in the use of their big data due to their
limited resources for acquiring experts and their limited knowledge on the use of data (Experian, 2018;
Jose et al., 2017; Jiang et al., 2017; Gao et al., 2015).
The scholars identified several challenges that small organizations were facing in the use of data
analytics. As examples, Coleman et al. (2016) listed that lack of understanding of data analytics, a focus
on specific industry (small organizations usually dominate a certain industry), cultural barriers, lack of
an in-house data analytics expert, lack of success stories on the use of data analytics, lack of data
analytics services, a non-transparent software market for data analytics, management and organization
problems, data security and privacy issues, and financial challenges. Ardagna et al. (2016) added that
technology opacity, data diversity, security and privacy compliance, and legal hurdles are the obstacles
facing small organizations in using their data. Additionally, Iqbal et al. (2018) stated that lack of
awareness and lack of business concept of data analytics posed obstacles for the small organizations.
Shah et al. (2017) summarized that these challenges could be grouped into technical, management and
technical obstacles.
In ascertaining the use of the data analytics in small organizations, a diffusion theory was employed.
Hoffmann (2011) and Rogers (2003) explained that diffusion is a communication process of certain
benefits of an innovation to its potential beneficiaries. These scholars also explained that the
communication takes time. They added that the diffusion contains certain elements - innovation,
communication channel, time and users. These scholars outlined a diffusion process which entails
having knowledge on the innovation, motivating the users to try the innovation, getting decision from the
users (either to accept or reject the innovation), the testing of the innovation by the users, and confirming
the promising values of the innovation.
The diffusion theory is accompanied by its twin, which is known as the theory of adoption. According to
Straub (2009), adoption theory is a micro perspective of acceptability of an innovation. This scholar
buttressed that the diffusion theory is a macro point of view of innovation acceptability. This scholar
combined both theories and renamed them as “diffusion-adoption” theory. The scholar defined diffusion
0462
as a process of spreading good information on an innovation, while adoption as a process of interpreting
the information and making a decision to accept or reject it.
Hoffmann (2011; 37) explained further that diffusion and adoption depends on the benefits, compatibility,
complexity, trialability and observability of the innovation. Both Hoffmann and Roger (2003) grouped the
innovation adopters into – innovator, early adopter, early majority, late majority, and laggards. Notably,
the innovator, here, refers to the first set of buyers of an innovation.
The theory of diffusion-adoption is well explained by Straub (2009), Venkatesh et al. (2003), and Rogers
(1995, 2003). These scholars outlined the theoretical assumptions of the theory that include: there
should be an innovation that needs to be diffused to a specific group of users, there should be
information on it that needs to be spread through communication, there should be relevant details of the
innovation in the communication, the target users should be able to analyze communicated information
and decide, and there should be a decision of either adopting the innovation or rejecting it.
The above-mentioned assumptions served as the theoretical framework for this article. It was assumed
that the use of data analytics is an innovation that small organizations should benefit from. This
assumption was based on the work of Elgendy and Elragal (2016), Russom (2011) and LaValle et al.
(2011) who claimed that data analytics are beneficial for business enterprises. It was also assumed that
there has been communication on the use of data analytics. Similarly, it was assumed that the messages
on the use of data analytics contained important details. Additionally, it was assumed that small
organizations had access to the information, and they were able to analyze it. It was finally assumed
that small organizations made decision to use or not use data analytics in accordance to their
understanding of the received information. In testing these assumptions, a case study research method
was employed, and it is presented in the next section.
3 RESEARCH METHODOLOGY
The case study research method was used purposely to have mutual understanding for the use of data
analytics among the small organizations. The method was also used to outline how these organizations
could use data analytics for the betterment of their operations. These reasons were initially stated by
Ellis and Levy (2009) and Järvinen (2004). These scholars argued that the method is useful for
investigating real-life activities. Supportably, Creswell (2009) and Eriksson and Kovalainen (2008)
argued that the case study method is suitable to any empirical research. Yin (2003) and Shank (2002)
added that the method is relevant when a certain issue needs to be examined.
Additionally, Creswell (2009), Eriksson and Kovalainen (2008) and Yin (2003) noted that the case study
must follow certain procedure in order to make its process scientific. These scholars proffered
guidelines. Their guidelines were summarized as defining criteria for selecting a case, collecting the
data, analyzing them, and presenting results. These steps were duly followed in this article. The criteria
were developed for selecting a case, and they included personal involvement, real-life pilot, accessibility,
and usability of case data. After this, all available materials on the selected project were collated. The
materials consist of scholarly articles, white paper reports, professional reports, meeting notes, project
plan, and interview transcriptions of the case project. Table 1 shows the quantity of the materials.
Table 1. List of Research Materials
Material
Quantity
1
Scholarly articles
45
2
White paper reports
38
3
Professional reports
24
4
Meeting notes
10
5
Interview transcriptions
3
6
Case project plan
1
The collated materials were anonymized, especially the meeting notes and interview transcriptions. The
materials were also organized sequentially to reflect the project implementation activities. Then, the
materials were analyzed. A content analysis technique was used. This technique is described as a
0463
method of reducing bulking contents to meaningful pieces of information. This analysis was done
according to the recommendations of Silverman (2011) and Miles and Huberman (1994). Firstly, all
materials were read carefully and key pieces of information on these three points: use of the data
analytics, benefits of using, and the challenges. These points were based on the theoretical framework
and the assumptions of the diffusion-adoption theory as propounded by Rogers (1995, 2003). This first
set of analysis showed that scholarly articles, white papers and professional reports discussed these
points, but they paid less attention to small organizations. It also showed that the challenges of data
usage(?) were the larger focus. On the hand other, the case project-related materials focused on the
application of data analytics and the difficulties experienced during the project implementation. Then,
the authors of this article decided to focus on the “challenges” that the small organizations faced. The
first set results were fully dissected as the second phase of analysis. The results of the second set were
outlined. These results were summarized and defined as the final findings of this article. They are
presented and discussed in the next sections.
4 RESEARCH FINDINGS
4.1 Lack of business expertise on the data analytics team
Although some scholarly articles and white reports explained that the lack of business cases was one
of the key challenges for the use of data analytics, the article found that the data analytics team appeared
to be the main obstacle. This finding was often cited and discussed in the professional reports. It also
emerged as the first challenge that the pilot organizations experienced. As Iqbal et al. (2018), Ardagna
et al. (2016) and Coleman et al. (2016) argued, when data analytics is discussed, the focus usually went
to the technical teams. This notion made the small organizations considered the use of data analytics
as a technical issue and paid less attention to the business or management aspects of it. The small
organizations assembled their data analytics teams with people that have technical skills and ignored
the business “side” of the outcome of team’s activities. This problem was evident in the piloted cases.
All the data analytics teams were made up of technical people.
4.2 Lack of business goals in the data analytics projects
Iqbal et al. (2018), Shah et al. (2017) and Coleman et al. (2016) affirmed that the absence of business
concepts in data analytics projects posed a serious challenge for small organizations. These scholars
explained that having a business goal would make small organizations consider the use of data analytics
as a business investment, not a technical project. This problem was obvious in the piloted cases. The
participating organizations did not establish a specific business goal for the use of data, though they had
vague “hopes”. It is called vague hopes because these organizations could not state clearly what they
wanted to use their data for. When this finding was analysed in relation to the diffusion-adoption theory,
it was learned that the small organizations might not have received information on the business “values”
of the use of data analytics. The professional and white paper reports emphasized that the use of data
analytics facilitate business operation; meanwhile, the question “how does it facilitate a specific small
organization’s operation?” seemed to not have been explained. This unanswered question appears to
cause the decision-makers of the small organizations to not consider the use of data analytics as a
business project. Thus, the assumption of the diffusion-adoption theory seems to be relevant in this
context.
4.3 Protecting of business secrets
Most of the discussions on the use of data analytics in small organizations centred on the specific-
industry dominance. The scholars, such as Iqbal et al. (2018), Shah et al. (2017) and Coleman et al.
(2016), stated that most small organizations are specialists in their specific industry. This made it difficult
for them to engage in the use of data analytics (due to extra investment). Furthering this challenge, this
article found that many small organizations do not want to use or implement data analytics operations
because they were trying to protect their business secrets. This problem was accompanied by a lack of
trust or a misunderstanding of the added value of data analytics. Several scholarly articles and
professional reports explained that some small organizations are industrial-based, and entrepreneur-
centered. These kinds of small organizations have a fear of losing their unique competitive advantage
if they tried to use their data. Again, when this result was compared to the diffusion-adoption theory, it
was learned that benefits of an innovation might be well-communicated, but the user might reject it due
to privacy and secrets.
0464
5 DISCUSSION AND CONCLUSION
Despite the fact that there are several articles on the use of data analytics in small organizations, these
works have not yet employed the theory of diffusion-adoption. One of possible reasons might be that
data analytics seems to not be considered as an innovation or technology. For instances, some scholars
described it as an investment (e.g., Coleman et al. 2016; Malaka & Brown, 2015; Mohanty et al., 2013),
an innovation tool (e.g., Jose et al., 2017; Ylijoki & Porras, 2016; Russom, 2016) and technology (e.g.,
Huhtala et al., 2017; Marttila-Kontio et al., 2014; Lindman et al., 2014). Meanwhile, if some works were
considered, such as, Iqbal et al. (2018), Shah et al. (2017), Coleman et al. (2016) and Ardagna et al.
(2016), it can be deduced that data analytics is a technology or innovation. Similarly, some scholars,
who explained the benefits of the data analytics, such as Elgendy and Elragal (2016), Russom (2011)
and LaValle et al. (2011), described it as an innovation. Therefore, employing the diffusion-adoption
theory seems to be relevant here.
The assumptions of the diffusion-adoption theory were tested in this article (as they outlined in the
research background section). The first assumption that the use of data analytics is an innovation was
confirmed to be true because several case materials discussed it as either an innovation or technology.
The second assumption was also confirmed to be true. This assumption states that there should be
communication on the benefits of an innovation. The scholarly articles, white papers and professional
reports present and expatiate the values of the use of data analytics for both large and small enterprises.
Contrarily, the third assumption appeared to be not true in the current case. This assumption states that
communication messages should contain important details. This article found that specific details on the
use of data analytics are not yet well-communicated to small organizations. The possible reason might
be the channel of communication. Small organizations appear to be “one-man or few people show”
according to the literature. These people are always busy and do not have the time or motivation to read
scholarly articles and lengthy reports. This seems to lead to poor knowledge on the use of data analytics,
as it was explained by Iqbal et al. (2018), Shah et al. (2017), Coleman et al. (2016) and Ardagna et al.
(2016). The fourth assumption is about information accessibility. As it was just mentioned, small
organizations and their managers or owners seem to have limited access to the information. This
hindered their knowledge and understanding on the use of data analytics. Hence, this assumption
appears to be true in this context. The last assumption is about the decision of small organizations to
accept or reject. This assumption seems to be relevant here because most of the small organizations
still decided to engage in the use of data analytics. Their reasons for such decision are the findings of
this article.
Therefore, it can be concluded that the lack of detailed information on the benefits of the use of data
analytics had led to insufficient knowledge on the topic in the small organizations. This situation led the
small organizations to not include business experts in their analytics team, a lack business goals in their
projects, and to have limited scope on the added values of the data analytics. Similarly, it can be
concluded that the diffusion-adoption theory would be effective when essential information is provided
and well-communicated to the target users of a certain innovation. The lack of such details would lead
to breakdown of diffusion-adoption theory assumptions, thereby make it irrelevant. Furthermore, it can
be concluded that all assumptions of the diffusion-adoption theory are relevant for modern innovation.
For instance, in the current case, the theory did not state anything relating to teams that are developing
or implementing an innovation. Meanwhile, these teams are highly important in the modern business
operations.
Thus, this article contributes to big data analytics and the theory of diffusion-adoption. It informs scholars
that the use of data analytics can be examined from another theoretical point of view. It also shed light
on other latent challenges of the use of data analytics. The article also has practical implications. It
specifically enlightens practitioners on the key challenges that the small organizations are facing on the
use of data analytics. This enlightenment will assist business advisors, entrepreneurs, and management
consultants on how to support and motivate the small organizations in using their bid data. However,
this article has a limitation. Its research data were based on a project case and review of collected
scholarly articles and reports. This implies the article could not be generalized. Notably, this limitation
does not affect the quality of the information of the article.
ACKNOWLEDGEMENTS
The authors of this article thank the Foundation for Economic Education (Finland) for financing this
project. This article is one of the outputs of the project.
0465
REFERENCES
[1] C.A. Ardagna, P. Ceravolo, E. Damiani, “Big Data Analytics as-a-Service: Issues and challenges”,
In Proceedings of the 3rd International Workshop on Privacy and Security of Big Data (PSBD 2016),
Washington, VA, USA, December 2016.
[2] J. Bughin, “Reaping the Benefits of Big Data in Telecom”. Journal of Big Data, vol 3, no 1, pp 14,
2016.
[3] S. Coleman, R. Göb, G. Manco, A, Pievaltolo, X. Tort-Martorell, M. Seabra, “How
[4] can SMEs benefit from big data? Challenges and a path forward”, Quality and Reliability
Engineering International, vol 6, no 32, pp 2151–2164, 2016.
[5] J. W. Creswell, “Research Design: Qualitative, Quantitative, and Mixed Methods Approaches”, 3rd
Edition. UK, London: Sage Publication Ltd, 2009.
[6] N. Elgendy, A. Elragal, “Big Data Analytics in Support of the Decision Making Process”, Procedia
Computer Science, vol 100, pp 1071 – 1084, 2016.
[7] S. LaValle, E. Lesser, R. Shockley, M. S. Hopkins, N. Kruschwitz, “Big data, analytics and the path
from insights to value”, MIT Sloan Manag Rev., vol 52, pp 20-32, 2011.
[8] T. J. Ellis, Y. Levy,” Towards a Guide for Novice Researchers on Research Methodology: Review
and Proposed Methods”, Issue s in Informing Science and Information Technology, vol 6, pp 323-
337, 2009.
[9] P. Eriksson, A. Kovalainen, “Qualitative Methods in Business Research”, 1st Edition, London: Sage
Publications Ltd, 2008.
[10] Experian, “The 2018 global data management benchmark report. Retrieved from
https://www.experian.com.vn/wp-content/uploads/2018/02/2018-global-data-management-
benchmark-report.pdf
[11] D. Floyd, J. McManus, “The role of SMEs in improving the competitive position of the European
Union”, European Business Review, vol. 17, no 2, pp 144-50, 2005.
[12] A. Gandomi, M. Haider, “Beyond the hype: Big data concepts, methods, and analytics”, International
Journal of Information Management. vol 35, no 2, pp 137–144, 2015.
[13] J. Gao, A. Koronios, S. Selle, “Towards a process view on critical success factors in big data
analytics projects”, Proceedings of the Twenty-First Americas Conference on Information Systems,
Puerto Rico, August 13–15, 2015.
[14] S. A. Gbadegeshin, “The Eff"ect of Digitalization on the Commercialization Process of High-
Technology Companies in the Life Sciences Industry”, Technology Innovation Management
Review, vol 9, no 1, pp 49-63, 2019a.
[15] S. A. Gbadegeshin, “The Commercialization Process of High Technologies: Case Studies of High
Technologies from ICT, Cleantech and Life Sciences Industries, Doctoral Dissertation”, Turku
School of Economics, University of Turku, Finland, 2019b.
[16] I. A. T. Hashem, A. Gani, S. Mokhtar, E. Ahmed, N.B. Anuar, A. V. Vasilakos, Big data: From
beginning to future”, International Journal of Information Management, vol 36, no 6, pp 1231-
1247, 2016.
[17] V. Hoffmann, (Ed.). “Knowledge and Innovation Management. Module Reader. Hohenheim
Universit” 2011.
[18] T. Huhtala, M. Pikkarainen, S. Saraniemi, ”Transformation of the Business Model in an Occupational
Health Care Company Embedded in an Emerging Personal Data Ecosystem: A Case Study in
Finland”, World Academy of Science, Engineering and Technology, International Journal of Social,
Behavioral, Educational, Economic, Business and Industrial Engineering, Vol 9, pp 3469–3477, 2017.
[19] M. Iqbal, A. R. Soomrani, S. H. Butt, “A study of big data for business growth in SMEs: Opportunities
& challenges”. In Proceedings of the International Conference on Computing, Mathematics and
Engineering Technologies (iCoMET), Sukkur, Pakistan, 3–4 May, 2018.
[20] P. Järvinen, “On Research Methods”. Finland: University of Tampere Press, 2004.
0466
[21] J. Jiang, V. Sekar, I. Stoica, H. Zhang, “Unleashing the potential of data-driven networking”. In
Proceedings of 9th International Conference on COMmunication Systems & NETworkS
(COMSNET), 2017.
[22] B. Jose, T. R. Ramanan, S. D. M. Kumar, “Big data provenance and analytics in telecom contact
centers”. TENCON 2017 - IEEE Region 10 Conference, 2017.
[23] J. Lindman, T. Kinnari, M. Rossi, “Industrial open data: Case studies of early open data
entrepreneurs”. System Sciences (HICSS), 2014 47th Hawaii International Conference on System
Science. pp 739-48, 2014.
[24] I. Malaka, I. Brown, “Challenges to the Organisational Adoption of Big Data Analytics”. Proceedings
of the 2015 Annual Research Conference on South African Institute of Computer Scientists and
Information Technologists - SAICSIT 2015..
[25] M. Marttila-Kontio, M. Kontio, V. Hotti, ”Advanced data analytics education for students and
companies”. In Proceedings of the 2014 conference on Innovation and technology in computer
science education (ITiCSE ’14). ACM, 2014.
[26] M. B. Miles, A. M. Huberman, “From Qualitative Data Analysis: An Expanded Sourcebook”. 2nd
Edition. USA: Sage publications, 1994.
[27] R. Misra, B. Panda, M. Tiwary, “Big data and ICT applications: A study”, In Proceedings of the
Second International Conference on Information and Communication Technology for Competitive
Strategies (pp. 1-6)., 2016.
[28] S. Mohanty, M. Jagadeesh, H. Srivatsa, “Big Data Imperatives: Enterprise “Big Data” Warehouse,
“BI” Implementations and Analytics”, Apress, 2013.
[29] P. Parviainen, M. Tihinen, J. Kääriäinen, S. Teppola, S. “Tackling the Digitalization Challenge: How
to Benefit from Digitalization in Practice”. International Journal of Information Systems and Project
Management, Vol 5, no 1, pp 63–77, 2017.
[30] M. S. Parwez, D. B. Rawat, M. Garuba, “Big data analytics for user-activity analysis and user-
anomaly detection in mobile wireless network”. IEEE Transactions on Industrial Informatics, vol 13,
no 4, pp 2058-2065, 2017.
[31] E. Rogers, “Diffusion of innovations”. 4th Edition. New York: Free Press, 1995.
[32] E. Rogers, “The Diffusion of Innovations”. 5th Edition. New York: The Free Press, 2003.
[33] P. Russom, “Big Data Analytics Guidebook”. TMforum Research, (May):73 -76 (2016).
[34] P. Russom, “Big Data Analytics,” TDWI Best Practices Report, Fourth Quarter, 2011. Retrieved
from https://vivomente.com/wp-content/uploads/2016/04/big-data-analytics-white-paper.pdf
[35] L. Savlovschi, N. Robu, “The Role of SMEs in Modern Economy”, Economia. Seria Management,
vol 14, no 1, pp 277–281, 2011.
[36] S. Shah, C. B. Soriano, A.D. Coutroubis, “Is bg data for everyone? The challenges of big data adoption
in SMEs”, In Proceedings of the 2017 IEEE International Conference on Industrial Engineering and
Engineering Management (IEEM), Singapore, 11–13 December 2017; pp. 803–807
[37] G. Shank, “Qualitative Research: A Personal Skills Approach”. USA: Merril Prentice Hall, 2002.
[38] D. Silverman, “Interpreting Qualitative Data: a guide to the principles of qualitative research.” 4th
Edition. UK: SAGE Publications Ltd, 2011.
[39] E. Straub, “Understanding technology adoption: Theory and future directions for informal
[40] learning”, Review of Educational Research, vol 79, pp 625-649, 2009.
[41] V. Venkatesh, M. Morris, G. B. Davis, F. D. Davis, “User acceptance of information technology:
Toward a unified view”. MIS Quarterly, vol 27, no 3, pp 425-478, , 2003.
[42] R. K. Yin, “Case Study Research: Design and Method”, 2nd Edition. USA: Sage Publications Inc.,
2003.
[43] O. Ylijoki, J. Porras, “Conceptualizing big data: Analysis of case studies”. Intell. Syst. Account.
Financ. Manag, vol 23, pp 295–310, 2016.
0467
