Article Review Example
Time to write an article review?
Do you have the requisite skills on how to write an article review?
What about you reference a well-written article review example?
Article reviews are common assignment undertakings in learning institutions.
Note that different from article critiques, article reviews do not require an opinion from an expert.
All you need to do is examine how well key sections of an article have been written.
This is where an article review example comes in.
Purposes of an Article Review Example
An example of an article review acts as a guide on how to execute different sections of an article review.
Areas covered include:
- Introduction
- Review
- Conclusion
- Formatting
Below is a good article review example.
Article Review Example
Article reviewed:
Oshodin, O., Molla, A., Karanasios, S., & Ong, C.E. (2017). Is FinTech a Disruption or a new Eco-System? An Exploratory Investigation of Bank’s Response to FinTech in Australia, ACIS 2017 Proceedings, 95.
Introduction
Gomber et al. (2018) observe that the global business environment is experiencing an industrial revolution driven by technology and the internet. This revolution has had a major impact on operations in different industries and sectors, including banking. The banking industry has been forced to play catch up in the adoption of technology in it business processes. Previously, the industry has been accused of limited enthusiasm towards technology. Technological innovations such as credit/debit cards, mobile-banking, and internet banking have posed new challenges to the banking industry. There is need to embrace technology to enhance efficiency in service provision (Al-ghanemi, 2017).
FinTech is viewed as a major game changer in the finance and banking sector. It has caused a storm by unsettling and reshaping the financial industry across the globe. Digital disruption is changing the way financial services are accessed and offered (Mackenzie 2015). Sadly, this has been happening outside the confines of conventional financial institutions. This understanding has led to studies such as Is FinTech a Disruption or a new Eco-System? An Exploratory Investigation of Bank’s Response to FinTech in Australia, by Oshodin et al. Per se, this report encompasses an appraisal of the above study. This introduction section is followed by critical review, literature gap, and conclusions sections respectively.
Research Area and Scope
The premise that FinTech is causing a revolution in the financial ecosystem forms the foundation for this article. Researchers advances that previous literature on this subject covers areas such as FinTech evolution, understanding the phenomenon, banks’ opportunities recognition, and start-up development exploration. The objectives of this study were: 1) contributing to information systems research, 2) explore the innovative activities of current institutions in the FinTech ecosystem, and 3) explore areas that require further research. It was noted that although the study analysed how four traditional financial institutions banks in Australia sensed and responded to challenges and opportunities within the FinTech ecosystem, this could be extrapolated to other banks in the country.
Relevant Theories
Theoretical frameworks encompass key research maps in studies (Strayhorn, 2013). Nonetheless, this research paper does not expressly provide its theoretical background. Its main paradigm is founded on the argument that the FinTech environment is experiencing serious disruptions resulting into start-up partnerships, adoption of pro start-ups technology, and increased FinTech start-ups investment. In this, the researchers examine the traditional banks’ abilities to secure adequate knowledge promptly and the requisite changes as well as their willingness to incorporate technology-based transformation. Generally, sensing and responding are borrowed from the control theory. It is therefore accurate to conclude that this study was founded on the control theory.
Dependent Variables
Dependent variables change with the introduction of the independent variables (Mangal & Mangal, 2013). In this article, sensing by traditional banks within the FinTech ecosystem is the dependent variable. As such, the reaction of the banks to the disruptions caused by the emergent of financial technology is determined by the banks’ ability to acknowledge relevant changes and the will to act appropriately. The “ability to sense” has the power to change variables such as appropriate response. Response entail a multiple of variables that include development of deep interactions with customers, technology scanning, FinTech ideas crowdsourcing, attempts to attract outside FinTech knowledge, and active monitoring of FinTech players.
Population
The research in this paper utilized a case study in its research design. It studied four banks in Australia. The researchers collected secondary data review of banks’ websites and databases through Google search to secure information on initiatives that translated into FinTech ecosystem sensing and responding acts. Notably, the decision to rely solely on secondary may attract some criticism. Goodwin (2012) notes that numerous biases are associated with secondary data. Further, some scholars would argue that this study should have assumed a more qualitative approach. In this, relying on secondary data that was more quantitative in nature may have limited the research results.
Results
Key demographics such as year were critical mediating variables when searching for relevant data. The concepts of sensing and responding were used as a guide when conducting a thematic analysis. Moreover, NVivo 11 software was relied upon when identifying themes. Results indicated that four main responding initiatives were dependent on eight sensing initiatives. The responding initiatives entailed partnership, investment, setting up innovation laboratories, and platform design and development. On the other hand, sensing initiatives included technology scanning, customer deep engagement, FinTech ideas crowdsourcing, inbound FinTech knowledge channels, and FinTech players monitoring. Findings showed that the sensing initiatives influenced the responding initiatives.
Research Novelty
As noted by Lam (n.d.), novelty is an obligatory element in research. The study by Oshodin et al. is instrumental in filling the research gap on the disruption of the conventional financial environment by FinTech. Findings from the study add value to the existing literature since disruption of the financial industry is one of the major effects if FinTech. This study therefore deviates from the usual studies that focus primarily on topics like recognition of opportunities in FinTech adoption by banks, and FinTech evolution. The researchers argue that the area on disruption of the financial environment has been neglected by previous scholars. The article suggests the need for further research in areas like management of wide-range ideas sources for banks.
Literature Gap
Oshodin et al. cover a key subject area on the impact of FinTech within the traditional banking environment. Their approach is quite utilitarian as it reviews the context in which FinTech and banking industry interact, an area that other researchers had ignored. Nonetheless, various issues arise on the research study’s inclusiveness. One such issue includes the extensive scope of the research study. While the study focuses on arguably to main issues, FinTech elements that banking firms need to constantly check and the respective FinTech approaches they should adopt, the latter issue is extremely wide. As pointed out by Uhlig (2012), the research subject should narrowed down to manageable levels to allow an in-depth evaluation of the phenomena being studied.
Generally, sampling is expected to generate results that can be generalized to the whole population understudy. Fox, Hunn and Mathers (2007) observes that for the generalizations to be accurate, it is important to ensure sampling method and the sample size used are appropriate. This would be key in making sure that results generated are representative and entailed statistics can discern differences or associations in study results. With this in mind, the sample of four banks used in this study could be considered as less representative of the population, which in return constrains the generalizability of the results. As noted by “How Large a Sample” (n.d.), the sample size should be increased as much as possible to enhance the benefit of sample size and additional sampled unit cost are equal.
Accordingly, the use of secondary data in this research is suitable due to the nature of data collected in this study. Boslaugh (n.d.) notes that secondary data is economical in collection, available in breadth and the data is usually reliable since in most cases it had been collected by experts. Nonetheless, secondary data comes along with numerous challenges. One such challenges encompasses the inappropriateness of the data. As advanced by Tripathy (2013), secondary data present serious challenges like relevancy. It is observed that secondary data in question may not have been collected the research questions in the current study. It relevancy may be affected by issues such as accuracy, collection methodology, purpose of collection, collection period and its content.
Lastly, it is notable the study has an expansive conceptual framework. This conceptual framework provides a solid background for the understanding of the prevailing issues. However, the study lacks a theoretical framework section. As observed by Grant and Osanloo (2014), the theoretical framework is the most essential aspect in research. It serves as the scheme for the entire research inquiry. Without it, it is impossible to clearly identify the methodological, philosophical, epistemological and analytical approaches adopted in this research. It is therefore important for all research studies to stipulate with clarity the theoretical framework assumed by a particular study.
Conclusions
This literature review indicate that there are numerous elements of sensing of and responding to FinTech disturbances in the traditional financial environment. For sensing, banks could embark on technological scanning, deep customer engagement, FinTech ideas crowdsourcing, opening FincTech inbound knowledge channels and actively monitoring FinTech players’ activities. Responding would entail activities such as developing innovation labs, partnering with FinTech start-ups, investing in FinTech start-ups and designing and developing digital platforms. This literature review indicate that the researchers were able to realize their objects. Nonetheless, research gaps like the wide scope of the responding elements makes it impossible to exhaustively examine the entailed phenomena (Mertler, 2009).
The wide scope as a major literature gap challenge overshadows limitations like overly small sample size and lack of a theoretical framework. In line with arguments advanced by King and Horrocks (2010), assuming a broader approach towards key elements in the study denies researchers the opportunity to thoroughly scrutinise key study issues. Particularly, numerous areas that banks can focus on in response to industry disruptions by FinTech have been remotely examined. This makes it necessary for future research to focus expansively on specific responding elements to help generate and encompassing image of the issue under study.
Reference List:
Al-ghanemi, M. (2017). The fintech revolution: disruptor or innovator? Islamic Business & Finance, 104, 20-22.
Boslaugh, S. (n.d.). An Introduction to Secondary Data Analysis. http://www.langtoninfo.com/web_content/9780521870016_excerpt.pdf
Fox, N., Hunn, A., & Mathers, N. (2007). Sampling and Sample Size Calculation, National Institute for Health Research. http://www.webpages.uidaho.edu/ed571/571-Modules/M3/NIHS-Sampling_Sample_Size_calculation.pdf
Gomber, P., Kauffman, R., Parker, C., & Weber, B. (2018). On the Fintech Revolution: Interpreting the Forces of Innovation, Disruption, and Transformation in Financial Services, Journal of Management Information Systems, 35(1), 220-265.
Goodwin, J. (2012). SAGE secondary data analysis Volume 1. Los Angeles: SAGE.
Gozman, D., Liebenau, J., & Mangan, J. (2018). The Innovation Mechanisms of Fintech Start-Ups: Insights from SWIFT’s Innotribe Competition, Journal of Management Information Systems, 35, (1), 145-179.
Grant, C. & Osanloo, A. (2014). Understanding, selecting, and integrating a theoretical framework in dissertation research: Creating the blueprint for your “house”. https://files.eric.ed.gov/fulltext/EJ1058505.pdf
How Large a Sample. (n.d.). Issues in determining sample size. http://www.resample.com/content/text/28-Chap-24.pdf
King, N. & Horrocks, C. (2010). Interviews in qualitative research. London: SAGE.
Lam, Wei-Huar. (n.d.). 1st Lesson of PhD Research. Dubai: Lulu.com.
Mackenzie, A. (2015). The FinTech Revolution. London Business School Review, 26(3), 50-53.
Mangal, S. & Mangal, S. (2013). Research Methodology in Behavioural Sciences. New Delhi: Prentice-Hall.
Mertler, C.A. (2009). Action research: teachers as researchers in the classroom. Los Angeles: Sage.
Oshodin, O., Molla, A., Karanasios, S., & Ong, C.E. (2017). Is FinTech a Disruption or a new Eco-System? An Exploratory Investigation of Bank’s Response to FinTech in Australia, ACIS 2017 Proceedings, 95.
Strayhorn, T.L. (2013). Theoretical frameworks in college student research. Lanham: University Press of America.
Tripathy, J.P. (2013). Secondary Data Analysis: Ethical Issues and Challenges. Iranian Journal of Public Health, 42(12), 1478–1479.
Uhlig, L.H. (2012). Choosing a Successful Paper Topic, Georgetown University Law Center. https://www.law.georgetown.edu/academics/academic-programs/legal-writing-scholarship/writing-center/upload/Uhlig-Choosing-a-Successful-Paper-Topic.pdf
Vasiljeva, T. & Lukanova, K. (2016). Commercial Banks and FinTech Companies in the Digital Transformation: Challenges for the Future, Journal of Business Management, 11, 25-33.
Weichert, M. (2017). The future of payments: How FinTech players are accelerating customer-driven innovation in financial services. Journal of Payments Strategy & Systems, 23-33.
