jteixeir – José A. Apolinário Teixeira

Crafting a literature review on “How do young people evaluate information pertaining sports and fitness” with the help of jenni.ai

It’s finally time to do a literature review on how do young people evaluate information about sports and fitness? – it is not for publication yet, instead to show my colleagues in the department, so we discuss the use of AI generative systems in research.

I have already done a Google search (hit many lib. guides), and my Google Scholar search, from where I found around 20 publications (some recent others highly cited).
I already imported some of those references in my reference manager – In my case are plain files of .bib files that I host in a Git repository. I have better results with it over Zotero or Mendley, mostly because I use Linux, Latex, write both at home and office computers, and change or upgrade my computers often.
I printed, seat down and read a few of those.
From the references list of the first printed papers, I highlighted many must-read references. It is the so called snowball effect.
I paid already a month’s subscription to Jenni AI, as I found the system quite powerful. And I want to use its full potential. Quite expensive subscription!!
I do know there is almost no research on the specific topic. I know however that there is a lot of research in evaluating online resources.
I already found some quotes that can be used to say that this research is important.

As stated in some Lib. Guides online in public domain such as this one from the University of Reading …

One common way to approach a literature review is to start out broad and then become more specific. Think of it as an inverted triangle:

So my literature review will be broken down into some mini-literature reviews with different research questions (that will be the prompts for Jenni AI) that go from general to specific.

For the Introduction section:

1. What is information evaluation?
2. What other concepts are used to refer to the evaluation of information?
3. Why is information evaluation important in general?
4. Why is information in the particular context of sports and fitness important?

For the theoretical background section:

1. What disciplines or subjects research the evaluation of information?
2. What models and theories in information behavior address the evaluation of information?
3. What models and theories in information literacy address the evaluation of information?
4. Was most of the research on information evaluation conducted in the educational context?
5. How to evaluate information from online resources?
6. How do young people evaluate information in everyday life?
7. How do young people evaluate information about sports and fitness?

Note I am going from general to specific, all towards showing that I am aware of existing research and motivate my empirical research.

So it’s now time to go Jenni.ai and finally prompt “What is information evaluation?” …

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Learning about the use of AI generative tools for literature reviews

I have already written and published several literature reviews. Some were pure review papers, but most were empirical research papers that reviewed extant theory before reporting methods and results.
I have been more lucky to publish systematic literature reviews than with critical literature reviews (have one that has been bouncing back with rejects already 3 times). Besides my experiences, I feel that my students know more about using some online available tools for literature reviews than me.

I have a very carefully curated collection of references organized in .bib files, but my students might write faster literature reviews even if they did not read, digested or organized hundreds of papers as I did before. I learned during my master thesis, that you (1) get an article, (2) read a sentence (3) imagine that you throw the sentence written on paper to the garbage, (4) write in your words sentence and cite the source. However, now as a teacher, I came across students that did not get the articles, barely read them and go to one of the recent AI generative tools that can write and format the literature review for them as in a quite advanced state.

Whether is ethical or not, whether it leads to plagiarism or not, it’s productive. Still, literature reviews generated with those systems will not lead to serious scientific advances per se. But I think in future, many great scientific discoveries will be communicated and integrated with a literature review that leveraged the power of AI generative tools or AI “co-pilots”.

It is a hot topic, last December when attending the International Conference on Information Systems, many panels were discussing the use of generative AI systems in teaching and research. A panel on the use of AI generative systems in papers submitted to top journals led to a lot of discussion and disagreements.

It’s time to learn about this things, and here is a good place to share my lessons learned. I started learning about this on YouTube. I ended up following the videos of academics such as @DrAndyStapleton, @profdavidstuckler and @DrMatJ.

They introduced me to a good few tools and processes that should help in the process. It’s time now to test them and see if they can help me with my current research on how students evaluate information in the context of sports and fitness. At the moment, I already know about a few “must cite” papers. Would AI tools find them? Will I get plagiarized content? Would it be worth learning of these tools? Or it is faster to just do it the good old way? So far is seems a very interesting and a creative process to combile all those different tools.

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How to introduce the open source concept to a more general audience

As early put by Marc Andreessen (2011), a well-respected serial entrepreneur and venture capitalist who co-founded Netscape and later sold to AOL for $4.2 billion, “software is eating the world”. Every industry ranging from oil, gas, financial services, healthcare, education, and even defense are increasingly powered by software.

Software matters for our modern societies. There should be no doubts about it. Still, few people realize the importance of open-source software. From my experiences of talking with highly educated professionals without formal computer-related education, many do not know what open-source means. Neither they can give examples. This is even if they use it (without realizing it) on their Apple computer, their BMW car, their Android-based smartphone, or their Samsung TV. Worst than that, many associate it directly with software that does not cost (that you don’t need to buy).

To me this is surprising, one of the latest industry reports points out that:

99% of new software projects rely on open-source components;
78% of companies use open source software over proprietary software;
96% of applications have at least one open-source component;
Open source makes up over 80% of the software code in use in modern applications;
The top contributor to open source projects on GitHub (a popular hosting service for open-source software projects) is Microsoft;
Over 70% of developers said that working on open-source software projects helped improve their skills;

Still, it is easy to spot active university students, lawyers, politicians, nurses, doctors and professors who don’t know what open-source software is, nor have “never heard about it”. How can something so impactful in our society be not even noticed by the general population? Maybe this lack of awareness explains why my research proposals related to open-source software don’t get more funding. After, most software runs in the hardware, being often invisible for the common users.

The point is software is very important, open-source software is also very important. This is because most software is either: (1) open-source per se, or (2) largely dependent on it. Still, people do not understand the meaning of open-source software. Many never hear about it. Maybe they heard about Linux, Android, or Firefox, but not open-source software. Often misunderstanding the concept with the concept of freeware.

Due to my research and past professional experience, I got invited a few times of times to give a talk about open-source software. Often to a young audience at universities (i.e., guest lectures) or to local businesses in Finland (e.g., training, seminars, tech Tuesdays, among other internal events in companies). Here I share my approach to talking about open-source software in a more general audience:

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Four methodological books I recommend to Master Students

For the ones doing their master thesis within a business school, or for the ones looking for an introductory book on (1) qualitative research, (2) quantitative research, (3) case study research, or (4) social network analysis. Here are my recommendations. Those would be the “first reader” to the different methodologies. I read myself all of them. But I might have missed the most recent editions.

Book cover of Doing Qualitative Research by David Silverman (6th edition) as on amazon.com

1) Silverman, D. (2022). Doing Qualitative Research (6h Edition). Sage.

Written in a easy and accessible style, the book provides a step-by-step guide to a first qualitative research project. This was my main book during the first year of my doctoral studies. It is a very easy to read and very well structured. Full of examples.

Book cover of Quantitative Social Science Data with R: An Introduction (2nd edition) by Brian J Fogarty as on Amazon.com

2) Fogarty, B. J. (2023). Quantitative social science data with R: an introduction (2nd edition). Sage.

I am a very big fan of R. Mastering is a very valuable competence to have in both industry and academia. Being able to load data into R and perform visualizations and statistical analyses using some of the hundreds of modules available is a good way to master quantitative research. R works with both small and big data. I value this book because introduces the methodology with “hands-on” examples on my favorite open-source statistical platform. There are many books out there on R and quantitative methods. I think this is a fun one for a master student that wants to go quantitative.

Book cover from Case Study Research and Applications: Design and Methods 6th edition as in Amazon.com

3) Yin, R. K. (2009). Case study research: Design and methods (6th Edition). Sage.

It is one of the most cited methodology books in the social sciences. It was my main reference for my own Master’s Thesis where I interviewed many people at Nokia to understand “Why do mobile devices vendor co-produce a mobile platform in an open-source means?”

Book cover of What is Social Network Analysis? by John Scott as in bloomsbury.com

4) Scott, J. (2012). What is social network analysis? Bloomsbury Academic.

While there are better reference books on Social Network Analysis (SNA), this one introduces the principal ideas, nature and purpose of the method to non-specialist readers. It touches theory, methods and tools. While reading it, we also learn about topics on across many disciplines. To get it, you do not even need to go to library. You can just download it by open-access manners via https://library.oapen.org/handle/20.500.12657/58730.

Six Misconceptions About Writing a Master’s thesis

Writing a Master’s thesis varies a lot from country to country, university to university, faculty to faculty, discipline to discipline, and supervisor to supervisor.

Master thesis at ÅAU ASA huset — Master thesis at ÅAU. For many it was the main “book” of their lives.

I am fortunate to have experienced the Portuguese, the French, the Dutch and the Finnish way of writing a Master’s thesis as a Erasmus Mundus student. I still remember that a Master’s thesis at a business school can be very different from place to place. Some are more oriented towards practice and act almost like an internship or action report, others are more oriented towards making a theoretical contribution to the existing scientific literature. Writing a thesis at a Finnish business school is more of the latter – therefore much of the student efforts are concentrated on reviewing the literature, and somehow extend it by scientific manners. The use of in-line citations, and a comprehensive reference list are expected – all in a congruent citation style.

At the Faculty of Social Sciences, Business and Economics, and Law (FSEJ) of Åbo Akademi, it is well documented in the evaluation criteria that you are expected to:

Formulate a topic and motivate it in relation to the existing literature;
Formulate the aim and research question(s);
Identify, evaluate and review relevant literature;
Chose a research method and argue for its choice;
Independently apply the chosen method to create new knowledge;

Note that while on 1, 2, 3 and 4 the literature is your main point of departure, in 5 you are supposed to contribute back to literature. Corroborating, extending or criticizing extant theory are all good and welcomed contributions to a Master thesis.

For the ones starting the Master’s thesis process, the task seems daunting – after all many master theses take years to be completed (note this is exceptional, not the most common way). After supervising some master’s thesis at Turku University and Åbo Akademi, I would like to point out some misconceptions that will ease the life of the ones starting the Master’s thesis process at a Finnish Business school. Please note these are my own views and my own takes. They approximate the views of my colleagues supervising Master’s thesis at the Information Studies unit @ Åbo Akademi where I work – so take everything with a grain of salt and clarify pending issues with your own supervisor.

First misconception – You need to collected your own data

The first false assumption that students often take is that they need to provoke research data. To my view this is false. While collecting your own data from scratch is a merit, finding and combining research data collected by others can be a merit as well.

For example, Statistics Finland produces official statistics on Finnish society as well as Eurostat does the same at the European Union level that are often used in Master’s theses from public administration to national economics. If you are more into qualitative research, you can turn to the Finnish Social Science Data Archive which provides access to a wide range of digital research data or search for equivalent organizations in other EU countries.

It’s rare, but it is possible to buy data-sets, or pay somebody to collect data on your behalf or simply source it in a crowdsourcing platform. I discorate all these ways of getting data, as a master thesis should be a very individual project where both theory and your own “brain” should shape the data collection and analysis.

You do not always need to provoke your own data (e.g., experiments, surveys or interviews). There is so much naturally occurring digital trace data by publicly available data on the Internet these days. For example, you can study collaboration in open-source communities, study information contagions on Twitter, study the bidding mechanisms for ads on YouTube, study the evolution of co-citation networks in a given discipline, among many other things by simply leveraging data that already exists on the Internet.

In addition, please do not disregard the use of archives. For example, the Åbo Akademi University Library has an outstanding archive of documentation that goes back centuries. You can do a thesis on Finance by analyzing newspaper ads by banks in old newspapers, you can do a thesis on public administration by analyzing the evolution of old policy guidelines, or you can do research in information studies by reporting on how the library itself organizes its historical and cultural heritage.

Finally, I must remark that it is easier to collect and analyse your data than to grab large unstructured or undocumented data sets from the Internet. As you collect your data, you are at the same learning from it and getting familiar with it. Furthermore, data collection is often a creative process. You can do it in a “social” oriented way by conducting interviews or designing an online form to collect data from a targeted social group. You can also be more technical-oriented by screen-scrapping Internet pages, getting data via APIs (e.g., SOAP, REST, and GraphQL among other ways), or combining several data sets into a single curated one. Both social and technical-oriented skills can be valued by future employers.

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