Introduction
Phase 4 introduces the technical writing area of lab reports. In this phase, I developed my digital literacy using tools by analyzing search results from Google, Google Scholar, and a college academic database. My lab report below breaks down my results, and discusses the importance of digital and informational literacy in students.
Assignment #21: The Lab Report
Search Engine Showdown: A Competitive Analysis of Search Engine Results
Abstract
This study investigates the impact of information and digital literacy on the quality of research outcomes in engineering. We evaluated three search platforms—Google Search, Google Scholar, and Gale Academic Onefile (Academic Database)—using the CRAAP test to assess their results on “artificial intelligence in business.” Google Search yielded the least dependable results, with an average rating of 7.5 out of 10. With an 8.04/10 rating, Gale Academic Onefile provided diverse scholarly content, however had with some accessibility problems. Google Scholar achieved an 8.2/10 rating, offering the greatest variety of accessible, scholarly sources relevant to the topic. The results provide insight into the need for students to look into scholarly research through scholarly databases, instead of simply utilizing google search. This encompasses the need for students to hone their skills of digital and information literacy as they are essential for navigating the digital information landscape and enhancing their level of research.
Introduction
In the era of digitalization, accessing information has become incredibly convenient. Thanks to numerous search engines, there is endless heaps pieces of information pertaining to virtually any topic ready to be accessed. However, for researchers, this abundance of information poses the challenge of identifying high-quality academic sources. The distinction between general search engines and specialized research databases becomes apparent, highlighting the importance of effective search strategies and proficient use of digital tools. The concepts of information literacy and digital literacy, and their respective differences, are very essential to understand for researchers. Information literacy is the ability to find, evaluate, organize, use, and communicate information in all its various formats, most notably in situations requiring decision making, problem solving, or the acquisition of knowledge. Meanwhile, digital literacy is an individual’s ability to navigate, evaluate, and utilize online media and information. It is critical for students to possess these skills in order to obtain high-quality research. This is especially important for engineering students, as engineering topics are among the most technical in nature and can require a high-level of information and digital literacy in order to navigate towards pertinent information online.
When reviewing relevant literature, it appears that despite students are lacking in their skills of informational and digital literacy. Despite the availability of comprehensive online databases, many students continue to rely primarily on general search engines like Google for their research needs. This is shown in pertinent literature, including Shopova (2014) which highlights the deficiency in students’ digital literacy skills, particularly in the university setting (Shopova, 29). Despite the presence of extensive online databases, many students persist in relying heavily on general search engines like Google for their research, leading to the utilization of non-peer-reviewed and less reliable sources. This reliance on easily accessible but less scholarly resources underscores the importance of addressing digital and informational literacy shortcomings among students (Shopova, 32)
In this lab, we will be testing the research question “What influence do information literacy and digital literacy skills have on the quality of research results?”. To test this research question, this lab goes into assessing the effectiveness of various search platforms of attaining pertinent and reliable material. The 3 search platforms chosen were Google Search, Google Scholar, and CCNY’s Database Gale Academic Onefile. We choose the 10 results for each search platform based on the same phrase. The phrase used was “artifical intelligence in business”, as this is a topic related to the field of engineering and can exhibit a lot of current and academic results. The results include substantial articles, journals, or scholarly peices relevant to the research topic of artificial intelligence in business. Each of these results is evaluated and graded based on its relevance and utility to the topic on a scale of 1-10. I hypothesize that on the scoring scale the Gale Acedmic Onefile database will be the best choice for students to obtain high-quality research information, as it appears to be the most catered to academic research. I believe that Google Scholar will not provide as high-quality results as the database, but still score highly as it may possibly offer more closely relevant results due to the wider range of articles related to the search phrase. I would expect google to have the lowest average score as it’s results are most likely to be less reliable and differing from what I expect as research for the topic.
Materials and Methods
Materials:
Search Engines:
- Google Scholar
- CCNY Database (Gale Academic Onefile)
- Search Phrases
- Interconnected Devices
- Google Form / Spreadsheet
- Ratings / Criteria
- Articles / Search Results
Methods:
- Find a search phrase that I want to use to perform this Lab.
- Select one of the three search engines you have at your disposal.
- Google
- Go to google.com and enter your search phrase in the search bar.
- This will lead to pages of results, from which you would select/open the necessary amount of articles out of the results.
- Google Scholar
- Go to scholar.google.com and enter your search phrase in the search bar.
- This will lead to pages of results, from which you would select/open the necessary amount of articles out of the results.
- CCNY Database (Gale Academic Onefile)
- Go to library.ccny.cuny.edu and go through the colleges A-Z databases and find any that may be relevant to the topic of your search phrase.
- Upon finding a preferred database (Gale Academic Onefile), clicking on it will take you to a log in page from which you will need to enter your city mail email and password. Entering these will take you to the database’s page.
- On this page the data base will provide you with a search bar, in which you would enter your search phrase.
- This will lead to pages of results, from which you would select/open the necessary amount of articles out of the results.
- Choose 10 of the first results from each search engine using your search phrase.
- Fill out google form to rank each article you have viewed using the following categories:
- Access
- Currency
- Relevance
- Authority
- Expectancy
- Choose 2 articles from each search engine, to eliminate those that were not needed and instead leave those that are strong pieces of evidence.
Results and Analysis
APPLYING THE PRINCIPLES OF INFORMATION LITERACY
In order to evaluate my search results, I utilized the “CRAAP” test to score each search result based on 5 criteria. For the purposes of this lab, the criteria include access, currency, relevance, authority, and expectancy.
| Serach Result Number | Result Name |
| 1 | How Artificial Intelligence Will Transform Businesses |
| 2 | How Businesses Are Using Artificial Intelligence In 2024 |
| 3 | How Do Businesses Use Artificial Intelligence? |
| 4 | 10 Examples of Artificial Intelligence in Business |
| 5 | Artificial Intelligence for the Real World |
| 6 | How Is Artificial Intelligence Used in Business? |
| 7 | What is artificial intelligence (AI) in business? |
| 8 | The role of artificial intelligence in business in 2024 |
| 9 | How Artificial Intelligence Impacts Business |
| 10 | Artificial intelligence in business: State of the art and future research agenda |
| SEARCH RESULT | ACCESS | CURRENCY | RELEVANCE | AUTHORITY | EXPECTANCY | SEARCH RESULT AVERAGE |
| 1 | 10 | 9 | 8 | 7 | 9 | 8.6 |
| 2 | 9 | 8 | 9 | 8 | 9 | 8.6 |
| 3 | 9 | 7 | 8 | 8 | 8 | 8 |
| 4 | 10 | 4 | 7 | 7 | 6 | 6.8 |
| 5 | 10 | 6 | 5 | 6 | 5 | 6.4 |
| 6 | 6 | 8 | 6 | 4 | 6 | 6 |
| 7 | 7 | 10 | 7 | 8 | 7 | 7.8 |
| 8 | 10 | 10 | 8 | 5 | 7 | 8 |
| 9 | 8 | 7 | 6 | 5 | 2 | 5.6 |
| 10 | 10 | 7 | 9 | 10 | 10 | 9.2 |
| CATEGORY AVERAGE | 8.9 | 7.6 | 7.3 | 6.8 | 6.9 | 7.5 |
Google Search Result #10: “Artificial intelligence in business: State of the art and future research agenda” Overall Score 9.2/10
Access received a 10/10 as the document was fully accessible. Currency scored an 7 as the paper was only published a few years ago (2020). Relevance was scored a 9/10 as well, as the paper explores trends of AI in business. This was an expectable result based on what I was looking for, expectancy was scored with a 10/10 as well. Authority was scored a 10/10 as all authors had their distinctions listed, including their respective universities/titles, all being relevant to the topic.
Google Search Result #5: “Artificial Intelligence for the Real World” Overall Score 6.4 /10
Access received a 10/10 as the article was fully accessibble. Currency scored an 6 as the article is not very recent (2018). Relevance was scored a 5/10 as well, as the information in the article is not very pertinent outside of a base evel. Expectancy with a 5/10 as well, as this type of discussion is not what I expected necessarily however the result wasn’t surprising in hindsight. Authority was scored a 6/10 as the article was published by the Harvard Business Review, which is a fairly relevanty resource however there are no references listed.
Google Scholar
| Search Result Number | Result Name |
| 1 | Artificial Intelligence for Business |
| 2 | Artificial Intelligence and Business Value: a Literature Review |
| 3 | Leveraging Artificial Intelligence in Business: Implications, Applications and Methods |
| 4 | Artificial intelligence and business models in the sustainable development goals perspective: A systematic literature review |
| 5 | The Impacts of Robotics, Artificial Intelligence On Business and Economics |
| 6 | ARTIFICIAL INTELLIGENCE IN BUSINESS AND ECONOMICS RESEARCH: TRENDS AND FUTURE |
| 7 | Artificial Intelligence in Business: From Research and Innovation to Market Deployment |
| 8 | Applications of artificial intelligence in business management, e-commerce and finance |
| 9 | Artificial Intelligence and Business Strategy towards Digital Transformation: A Research Agenda |
| 10 | Artificial intelligence and business models in the sustainable development goals perspective: A systematic literature review |
| SEARCH RESULT | ACCESS | CURRENCY | RELEVANCE | AUTHORITY | EXPECTANCY | SEARCH RESULT AVERAGE |
| 1 | 9 | 6 | 9 | 9 | 9 | 8.4 |
| 2 | 10 | 6 | 9 | 9 | 10 | 8.8 |
| 3 | 1 | 7 | 9 | 9 | 7 | 6.6 |
| 4 | 10 | 7 | 9 | 9 | 9 | 8.8 |
| 5 | 9 | 5 | 7 | 8 | 9 | 7.6 |
| 6 | 8 | 8 | 9 | 9 | 9 | 8.6 |
| 7 | 9 | 8 | 8 | 9 | 10 | 8.8 |
| 8 | 9 | 8 | 9 | 9 | 9 | 8.8 |
| 9 | 9 | 8 | 8 | 9 | 9 | 8.6 |
| 10 | 10 | 7 | 6 | 9 | 6 | 7.6 |
| CATEGORY AVERAGE | 8.333333333 | 7.111111111 | 8.222222222 | 8.888888889 | 8.666666667 | 8.244444444 |
Google Scholar Search Result #8: “Applications of artificial intelligence in business management, e-commerce and finance” Overall Score: 8.8/10
Access received a 9/10 as the document was easily accessible, and the entire document could be viewed including the abstract. I dropped the score 1 point off of a perfect 10 as the page included “section snippets”, and I felt it would have been better if more of the sections were shown. Currency scored an 8 as even though the paper was only published a year ago (2023), in the area of artificial intelligence this is a more significant time frame than it would be in regards to other areas of study. While much of the material is still highly relevant, more additions would surely be made due to developments in the last year if it were instead published today. Relevance was scored a 9/10 as well, as its contents were highly relevant to the search phrase as the paper discussed the different areas of business, such as e-commerce and finance, and how AI has been implemented in regard to these areas of business. This is partially why I also scored expectancy with a 9/10 as well, as this type of discussion is what I expected from the search phrase. Authority was scored a 9/10 as all 6 authors had their distinctions listed, including their respective universities/titles. However, I was expecting more expertise about both AI and business with respect to the authors, as only 4/6 authors appeared to have authority in these 2 categories.
Google Scholar Search Result #3: “Leveraging Artificial Intelligence in Business: Implications, Applications and Methods” Overall Score: 6.6/10
Access received a 1/10 as the document was not accessible at all, and was behind a paywall. Only the abstract was visible. Currency scored an 7 as the paper was only published a few years ago (2020). Relevance was scored a 9/10 as well, as the abstract highlights how the paper is a crafted literature review of how AI has played its part in business. This is also why I also scored expectancy with a 9/10 as well, as this type of discussion is what I expected from the search phrase. Authority was scored a 9/10 as all authors had their distinctions listed, including their respective universities/titles.
Gale Academic Onefile
| Search Result Name | Result Name |
| 1 | Beyond the Business Case for Responsible Artificial Intelligence: Strategic CSR in Light of Digital Washing and the Moral Human Argument. |
| 2 | Business intelligence; artificial intelligence in business, industry and engineering; proceedings |
| 3 | The Role of Artificial Intelligence Technology in Predictive Risk Assessment for Business Continuity: A Case Study of Greece. |
| 4 | urrent and Future Artificial Intelligence (AI) Curriculum in Business School: A Text Mining Analysis. |
| 5 | Governance of artificial intelligence applications in a business audit via a fusion fuzzy multiple rule-based decision-making model. |
| 6 | OECD Business and Finance Outlook 2021: AI IN BUSINESS AND FINANCE. |
| 7 | Artificial intelligence in business |
| 8 | THE ADVANTAGES OF INTEGRATING ARTIFICIAL INTELLIGENCE IN BUSINESS PROCESSES. |
| 9 | Organizational Processes for Adopting Breakthrough Technology: Text Mining of AI Perception among Japanese Firms. |
| 10 | Using artificial intelligence for hiring talents in a moderated mechanism. |
| SEARCH RESULT | ACCESS | CURRENCY | RELEVANCE | AUTHORITY | EXPECTANCY | SEARCH RESULT AVERAGE |
| 1 | 10 | 10 | 7 | 8 | 7 | 8.4 |
| 2 | 0 | 2 | 7 | 4 | 8 | 4.2 |
| 3 | 10 | 10 | 9 | 9 | 9 | 9.4 |
| 4 | 10 | 8 | 7 | 8 | 6 | 7.8 |
| 5 | 10 | 9 | 10 | 10 | 10 | 9.8 |
| 6 | 10 | 8 | 7 | 9 | 8 | 8.4 |
| 7 | 7 | 7 | 7 | 8 | 6 | 7 |
| 8 | 9 | 8 | 8 | 8 | 8 | 8.2 |
| 9 | 10 | 10 | 7 | 9 | 7 | 8.6 |
| 10 | 10 | 10 | 8 | 9 | 6 | 8.6 |
| CATEGORY AVERAGE | 8.6 | 8.2 | 7.7 | 8.2 | 7.5 | 8.04 |
Gale Academic Onefile Search Result #5: “Governance of artificial intelligence applications in a business audit via a fusion fuzzy multiple rule-based decision-making model.” Overall Score: 9.8/10
Access received a 10/10 as the document was fully accessible.Currency scored an 9 as even though the paper was only published recently (2023). Relevance was scored a 10/10 as well, as the paper highlights how AI operates within auditorial work. The specificity of this paper regarding the role of AI in a more narrow form of business is what i wanted so expectancy was scored with a 10/10 as well. Authority was scored a 10/10 as all authors had their distinctions listed, including their respective universities/titles, all being relevant to the topic.
Gale Academic Onefile Search Result #2: “Business intelligence; artificial intelligence in business, industry and engineering; proceedings” Overall Score: 4.2/10
Access received a 0/10 as the document was not accessible at all, and was behind a paywall. There was not an even an abstract to access. Currency scored an 2 as the paper was published a very long time ago, especially in regards to the scope of the field few years ago (2009). Relevance was scored a 7/10 as well, as based on the little information to draw conclusions on the paper it appeared to disguise the different AI models used in business. This is fairly expectable, leading to the expectancy with a 8/10 as well, as this type of discussion is what I expected from the search phrase. Authority was scored a 4/10 as while there were citations the authors credibility could not be found.
Discussion
The results of this study offer a comprehensive analysis of the effectiveness of different search platforms—Google, Google Scholar, and Gale Academic Onefile—when it comes to obtaining high-quality research information on the topic of artificial intelligence in business. By applying the CRAAP test, I evaluated each search result based on access, currency, relevance, authority, and expectancy. The findings present significant differences in the quality and reliability of the information retrieved from these platforms, which largely align with my initial hypothesis, and reflects recommendations for students going forward.
Google Search
Google Search, as anticipated, provided the least scholarly results, with an average overall score of 7.5/10. The platform scored high in accessibility (8.9/10), reflecting the ease with which users can access content. However, it performed lower in terms of authority (6.8/10) and expectancy (6.9/10), suggesting that while information is readily available, it often lacks the scholarly integrity and the overall material expected for academic research. This is evident in results like “Artificial Intelligence for the Real World,” which, despite being fully accessible and published by a reputable source, was lacking in detailed references and was only moderately relevant to the given research topic.
Google Scholar
Google Scholar averaged the highest overall score, an average score of 8.2/10. It provided a balance between accessibility (8.3/10) and scholarly content, with high scores in relevance (8.2/10), authority (8.9/10), and expectancy (8.7/10). Notably, Google Scholar results such as “Applications of artificial intelligence in business management, e-commerce and finance” scored well due to their comprehensive and largely current coverage of the topic, despite minor issues with some documents being behind paywalls. Additionally, the sources found the Google Scholar were not as current as the other two engines which is an issue as currency is especially important for engineering based topics. Yet, Google Scholar exceeded my personal expectations overall, as I anticipated its results to be more lacking in each respective category. This platform clearly benefits from its focus on academic sources, and is a very solid option for the research purposes of students.
Gale Academic Onefile
Gale Academic Onefile had an average overall score of 8.04/10, slightly falling short of Google Scholar. Despite this, the quality of individual results varied significantly, highlighting the platform’s specialized nature. For example, “Governance of artificial intelligence applications in a business audit via a fusion fuzzy multiple rule-based decision-making model” scored a near-perfect 9.8/10, due to its high accessibility, recent publication date, and strong relevance and authority. However, some results, like “Business intelligence; artificial intelligence in business, industry and engineering; proceedings,” scored poorly due to accessibility issues and outdated content. While there were what could be considered outliers, it still is discouraging that there are articles that aren’t accessible at all through the database especially when considering colleges pay for their students to be able to access them. Overall, however, the results still reinforce the sentiment that databases are generally a source for high-quality results for students to research through.
Reflecting on Hypotheses
The hypothesis that Gale Academic Onefile would provide the highest quality research information was largely confirmed by the data. While it was still outperformed by Google Scholar on the CRAAP test, the platform’s focus on peer-reviewed and scholarly articles still likely makes it a superior resource for academic research compared to more general search tools. Google Scholar, while very useful for academic research, does not necessarily provide the same level of scholarly integrity and varies in the depth of information provided. As expected, Google Search, while being the most accessible, frequently returns less reliable and less academically rigorous results and is a much weaker option for academic research than the other two engines.
Reflecting on Research Strategy
The research strategy of using the CRAAP test to evaluate search results proved effective in systematically assessing the quality of information retrieved from each platform. The criteria of access, currency, relevance, authority, and expectancy allowed for a comprehensive evaluation, highlighting the strengths and weaknesses of each platform. Especially considering, the use of a 10 point scale to average out results, this research strategy was very strong for the task at hand.
Reliability of Sources
The reliability of sources varied significantly between platforms. Gale Academic Onefile consistently provided peer-reviewed, authoritative sources, affirming its reliability for academic research. Google Scholar offered a mix of reliable sources, though accessibility issues occasionally undermined its utility. In contrast, Google often returned less reliable sources, demonstrating the need for careful scrutiny of search results from general search engines.
Contribution to Existing Literature
These findings align with existing literature, which highlights that using Google for research, as the existing literature points as being the main method for students, is not the best research strategy. There are many databases on the internet, such as Gale Academic Onefile, which may be free provided through institutions and are significantly stronger assets for research than a simple google search.
Conclusion
This lab investigation highlights the crucial role of information and digital literacy in enhancing the quality of research outcomes for students, especially in engineering. This study compared Google Search, Google Scholar, and Gale Academic Onefile, using the CRAAP test to assess the relevance and reliability of search results on the engineering topic “artificial intelligence in business.” Google Search, while highly accessible, provided less reliable sources, scoring an average of 7.5/10. Google Scholar balanced accessibility and scholarly content, scoring 8.2/10, but had some currency issues. Gale Academic Onefile closely followed with an 8.04/10, offering highly relevant and authoritative sources, confirming the hypothesis that specialized databases yield high-quality academic material.
The study emphasizes that proficiency in information and digital literacy is crucial for producing high-quality research, as students with higher proficiency in these areas are more likely to produce high-quality research. Effective use of specialized databases like Gale Academic Onefile, or even using Google Scholar can significantly improve research outcomes when compared to only utilizing search engines like Google. Future research should explore comparing Google and Google Scholar to other databases, as one limiting factor of this paper was the use of only Gale Academic Onefile.
In conclusion, fostering information and digital literacy among student researchers is principal for advancing knowledge and innovation. Searching for information through engines specialized for scholarly research provide more reliable and pertinent material than general search engines, accentuates the need for effective search strategies and digital tool proficiency in the digital age.
References
Shopova, T. (2014) ’DIGITAL LITERACY OF STUDENTS AND ITS IMPROVEMENT AT THE UNIVERSITY’, Journal on Efficiency and Responsibility in Education and Science, vol. 7, no. 2, pp. 26–32. https://doi.org/10.7160/eriesj.2014.070201
Machin-Mastromatteo, J. D. (2021). Information and digital literacy initiatives. Information Development, 37(3), 329-333. https://doi.org/10.1177/02666669211031695
