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The purpose of research is to enhance society by advancing knowledge through the development of scientific theories, concepts and ideas. A research purpose is met through forming hypotheses, collecting data, analysing results, forming conclusions, implementing findings into real-life applications and forming new research questions.
Simply put, research is the process of discovering new knowledge. This knowledge can be either the development of new concepts or the advancement of existing knowledge and theories, leading to a new understanding that was not previously known.
As a more formal definition of research, the following has been extracted from the Code of Federal Regulations :
While research can be carried out by anyone and in any field, most research is usually done to broaden knowledge in the physical, biological, and social worlds. This can range from learning why certain materials behave the way they do, to asking why certain people are more resilient than others when faced with the same challenges.
The use of ‘systematic investigation’ in the formal definition represents how research is normally conducted – a hypothesis is formed, appropriate research methods are designed, data is collected and analysed, and research results are summarised into one or more ‘research conclusions’. These research conclusions are then shared with the rest of the scientific community to add to the existing knowledge and serve as evidence to form additional questions that can be investigated. It is this cyclical process that enables scientific research to make continuous progress over the years; the true purpose of research.
From weather forecasts to the discovery of antibiotics, researchers are constantly trying to find new ways to understand the world and how things work – with the ultimate goal of improving our lives.
The purpose of research is therefore to find out what is known, what is not and what we can develop further. In this way, scientists can develop new theories, ideas and products that shape our society and our everyday lives.
Although research can take many forms, there are three main purposes of research:
There are 8 core characteristics that all research projects should have. These are:
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Research can be divided into two main types: basic research (also known as pure research) and applied research.
Basic research, also known as pure research, is an original investigation into the reasons behind a process, phenomenon or particular event. It focuses on generating knowledge around existing basic principles.
Basic research is generally considered ‘non-commercial research’ because it does not focus on solving practical problems, and has no immediate benefit or ways it can be applied.
While basic research may not have direct applications, it usually provides new insights that can later be used in applied research.
Applied research investigates well-known theories and principles in order to enhance knowledge around a practical aim. Because of this, applied research focuses on solving real-life problems by deriving knowledge which has an immediate application.
Research methods for data collection fall into one of two categories: inductive methods or deductive methods.
Inductive research methods focus on the analysis of an observation and are usually associated with qualitative research. Deductive research methods focus on the verification of an observation and are typically associated with quantitative research.
Qualitative research is a method that enables non-numerical data collection through open-ended methods such as interviews, case studies and focus groups .
It enables researchers to collect data on personal experiences, feelings or behaviours, as well as the reasons behind them. Because of this, qualitative research is often used in fields such as social science, psychology and philosophy and other areas where it is useful to know the connection between what has occurred and why it has occurred.
Quantitative research is a method that collects and analyses numerical data through statistical analysis.
It allows us to quantify variables, uncover relationships, and make generalisations across a larger population. As a result, quantitative research is often used in the natural and physical sciences such as engineering, biology, chemistry, physics, computer science, finance, and medical research, etc.
Research often follows a systematic approach known as a Scientific Method, which is carried out using an hourglass model.
A research project first starts with a problem statement, or rather, the research purpose for engaging in the study. This can take the form of the ‘ scope of the study ’ or ‘ aims and objectives ’ of your research topic.
Subsequently, a literature review is carried out and a hypothesis is formed. The researcher then creates a research methodology and collects the data.
The data is then analysed using various statistical methods and the null hypothesis is either accepted or rejected.
In both cases, the study and its conclusion are officially written up as a report or research paper, and the researcher may also recommend lines of further questioning. The report or research paper is then shared with the wider research community, and the cycle begins all over again.
Although these steps outline the overall research process, keep in mind that research projects are highly dynamic and are therefore considered an iterative process with continued refinements and not a series of fixed stages.
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Table of contents:-, research report meaning, characteristics of good research report, key characteristics of research report, types of research report, stages in preparation of research report, characteristics of a good report.
A research report is a document that conveys the outcomes of a study or investigation. Its purpose is to communicate the research’s findings, conclusions, and implications to a particular audience. This report aims to offer a comprehensive and unbiased overview of the research process, methodology, and results.
Once the researcher has completed data collection , data processing, developing and testing hypotheses, and interpretation of responses, the next important phase in research is the preparation of the research report. A research report is essential for the communication of research findings to its potential users.
The research report must be free from personal bias, external influences, and subjective factors. i.e., it must be free from one’s liking and disliking. The research report must be prepared to meet impersonal needs.
According to Lancaster, “A report is a statement of collected and considered facts, so drawn-ups to give clear and concise information to persons who are not already in possession of the full facts of the subject matter of the report”.
When researchers communicate their results in writing, they create a research report. It includes the research methodology, approaches, data collection precautions, research findings, and recommendations for solving related problems. Managers can put this result into action for more effective decision making .
Generally, top management places a higher emphasis on obtaining the research outcome rather than delving into the research procedure. Hence, the research report acts as a presentation that highlights the procedure and methodology adopted by the researcher.
The research report presents the complete procedure in a comprehensive way that in turn helps the management in making crucial decisions. Creating a research report adheres to a specific format, sequence, and writing style.
Enhance the effectiveness of a research report by incorporating various charts, graphs, diagrams, tables, etc. By using different representation techniques, researchers can convince the audience as well as the management in an effective way.
Characteristics of a good research report are listed below:
The following paragraphs outline the characteristics of a good research report.
Report information must be accurate and based on facts, credible sources and data to establish reliability and trustworthiness. It should not be biased by the personal feelings of the writer. The information presented must be as precise as possible.
The language of a research report should be as simple as possible to ensure easy understanding. A good report communicates its message clearly and without ambiguity through its language.
It is a document of practical utility; therefore, it should be grammatically accurate, brief, and easily understood.
Jargon and technical words should be avoided when writing the report. Even in a technical report, there should be restricted use of technical terms if it is to be presented to laymen.
The report must be straightforward, lucid, and comprehensive in every aspect. Ambiguity should be avoided at all costs. Clarity is achieved through the strategic and practical organization of information. Report writers should divide their report into short paragraphs with headings and insert other suitable signposts to enhance clarity. They should:
A report should concisely convey the key points without unnecessary length, ensuring that the reader’s patience is not lost and ideas are not confused. Many times, people lack the time to read lengthy reports.
However, a report must also be complete. Sometimes, it is important to have a detailed discussion about the facts. A report is not an essay; therefore, points should be added to it.
A report requires a visually appealing presentation and, whenever feasible, should be attention-grabbing. An effective report depends on the arrangement, organization, format, layout, typography, printing quality, and paper choice. Big companies often produce very attractive and colourful Annual Reports to showcase their achievements and financial performance.
Reports should be clear and straightforward for easy understanding. The style of presentation and the choice of words should be attractive to readers. The writer must present the facts in elegant and grammatically correct English so that the reader is compelled to read the report from beginning to end.
Only then does a report serve its purpose. A report written by different individuals on the same subject matter can vary depending on the intended audience.
Reports should be reliable and should not create an erroneous impression in the minds of readers due to oversight or neglect. The facts presented in a report should be pertinent.
Every fact in a report must align with the central purpose, but it is also vital to ensure that all pertinent information is included.
Irrelevant facts can make a report confusing, and the exclusion of relevant facts can render it incomplete and likely to mislead.
Report writing should not incur unnecessary expenses. Cost-effective methods should be used to maintain a consistent level of quality when communicating the content.
Reports can be valuable and practical when they reach the readers promptly. Any delay in the submission of reports renders the preparation of reports futile and sometimes obsolete.
The points mentioned in a report should be arranged in a step-by-step logical sequence and not haphazardly. Distinctive points should have self-explanatory headings and sub-headings. The scientific accuracy of facts is very essential for a report.
Planning is necessary before a report is prepared, as reports invariably lead to decision-making, and inaccurate facts may result in unsuccessful decisions.
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A research report serves as a means of communicating research findings to the readers effectively.
A well-defined research report must define the what, why, who, whom, when, where, and how of the research study. It must help the readers to understand the focus of the information presented.
The report should strike a balance, being sufficiently brief and appropriately extended. It should cover the subject matter adequately while maintaining the reader’s interest.
The report should be written in an objective style, employing simple language. Correctness, precision, and clarity should be prioritized, avoiding wordiness, indirection, and pompous language.
An excellent report integrates clear thinking, logical organization, and sound interpretation of the research findings.
It should not be dull; instead, it should captivate and sustain the reader’s interest.
Accuracy is paramount. The report must present facts objectively, eschewing exaggerations and superlatives.
Presentation clarity is achieved through familiar words, unambiguous statements, and explicit definitions of new concepts or terms.
The logical flow of ideas and a coherent sequence of sentences contribute to a smooth continuity of thought.
Even technical reports should be easily understandable. Translate technicalities into reader-friendly language.
Follow best composition practices, ensuring readability through proper paragraphing, short sentences, and the use of illustrations, examples, section headings, charts, graphs, and diagrams.
Draw sound inferences and conclusions from statistical tables without repeating them in verbal form.
Footnote references should be correctly formatted, and the bibliography should be reasonably complete.
The report should be visually appealing, maintaining a neat and clean appearance, whether typed or printed.
The report should be free from all types of mistakes, including language, factual, spelling, and calculation errors.
In striving for these qualities, the researcher enhances the overall quality of the report.
Research reports are of the following types:
Technical reports are reports which contain detailed information about the research problem and its findings. These reports are typically subject to review by individuals interested in research methodology. Such reports include detailed descriptions of used methods for research design such as universe selection , sample preparation, designing questionnaire , identifying potential data sources, etc. These reports provide a complete description of every step, method, and tool used. When crafting technical reports, we assume that users possess knowledge of research methodology, which is why the language used in these reports is technical. Technical reports are valuable in situations where there is a need for statistical analysis of collected data. Researchers also employ it in conducting a series of research studies, where they can repetitively use the methodology.
When authors prepare a report with a particular layout or design for publishing in an academic or scientific journal, it becomes a “manuscript for journal articles”. Journal articles are a concise and complete presentation of a particular research study. While technical reports present a detailed description of all the activities in research, journal articles are known for presenting only a few critical areas or findings of a study. The readers or audience of journal articles include other researchers, management and executives, strategic analysts and the general public, interested in the topic.
In general, a manuscript for a journal article typically ranges from 10 to 30 pages in length. Sometimes there is a page or word limit for preparing the report. Authors primarily submit manuscripts for journal articles online, although they occasionally send paper copies through regular mail.
Students working towards a Master’s, PhD, or another higher degree generally produce a thesis or dissertation, which is a form of research report. Like other normal research reports, the thesis or dissertation usually describes the design, tools or methods and results of the student’s research in detail.
These reports typically include a detailed section called the literature review, which encompasses relevant literature and previous studies on the topic. Firstly, the work or research of the student is analysed by a professional researcher or an expert in that particular research field, and then the thesis is written under the guidance of a professional supervisor. Dissertations and theses usually span approximately 120 to 300 pages in length.
Generally, the university or institution decides the length of the dissertation or thesis. A distinctive feature of a thesis or a dissertation is that it is quite economical, as it requires few printed and bound copies of the report. Sometimes electronic copies are required to be submitted along with the hard copy of the thesis or dissertations. Compact discs (CDs) are used to generate the electronic copy.
Along with the above-mentioned types, there are some other types of research reports, which are as follows:
A popular report is prepared for the use of administrators, executives, or managers. It is simple and attractive in the form of a report. Clear and concise statements are used with less technical or statistical terms. Data representation is kept very simple through minimal use of graphs and charts. It has a different format than that of a technical one by liberally using margins and blank spaces. The style of writing a popular report is journalistic and precise. It is written to facilitate reading rapidly and comprehending quickly.
An interim report is a kind of report which is prepared to show the sponsors, the progress of research work before the final presentation of the report. It is prepared when there is a certain time gap between the data collection and presentation. In this scenario, the completed portion of data analysis along with its findings is described in a particular interim report.
This type of report is related to the interest of the general public. The findings of such a report are helpful for the decision making of general users. The language used for preparing a summary report is comprehensive and simple. The inclusion of numerous graphs and tables enhances the report’s overall clarity and comprehension. The main focus of this report is on the objectives, findings, and implications of the research issue.
The research abstract is a short presentation of the technical report. All the elements of a particular technical report, such as the research problem, objectives, sampling techniques, etc., are described in the research abstract but the description is concise and easy.
Research reports result from meticulous and deliberate work. Consequently, the preparation of the information can be delineated into the following key stages:
1) Logical Understanding and Subject Analysis: This stage involves a comprehensive grasp and analysis of the subject matter.
2) Planning/Designing the Final Outline: In this phase, the final outline of the report is meticulously planned and designed.
3) Write-Up/Preparation of Rough Draft: The report takes shape during this stage through the composition of a rough draft.
4) Polishing/Finalization of the Research Report: The final stage encompasses refining and polishing the report to achieve its ultimate form.
Logical understanding and subject analysis.
This initial stage focuses on the subject’s development, which can be achieved through two approaches:
Logical development relies on mental connections and associations between different aspects facilitated by rational analysis. Typically, this involves progressing from simple to complex elements. In contrast, chronological development follows a sequence of time or events, with instructions or descriptions often adhering to chronological order.
This marks the second stage in report writing. Once the subject matter is comprehended, the subsequent step involves structuring the report, arranging its components, and outlining them. This stage is also referred to as the planning and organization stage. While ideas may flow through the author’s mind, they must create a plan, sketch, or design. These are necessary for achieving a harmonious succession to become more accessible, and the author may be unsure where to commence or conclude. Effective communication of research results hinges not only on language but predominantly on the meticulous planning and organization of the report.
The third stage involves the writing and drafting of the report. This phase is pivotal for the researcher as they translate their research study into written form, articulating what they have accomplished and how they intend to convey it.
The clarity in communication and reporting during this stage is influenced by several factors, including the audience, the technical complexity of the problem, the researcher’s grasp of facts and techniques, their proficiency in the language (communication skills), the completeness of notes and documentation, and the availability of analyzed results.
Depending on these factors, some authors may produce the report with just one or two drafts. In contrast, others, with less command over language and a lack of clarity about the problem and subject matter, may require more time and multiple drafts (first draft, second draft, third draft, fourth draft, etc.).
This marks the last stage, potentially the most challenging phase in all formal writing. Constructing the structure is relatively easy, but refining and adding the finishing touches require considerable time. Consider, for instance, the construction of a house. The work progresses swiftly up to the roofing (structure) stage, but the final touches and completion demand a significant amount of time.
The rough draft, whether it is the second draft or the n th draft, must undergo rewriting and polishing to meet the requirements. The meticulous revision of the rough draft is what distinguishes a mediocre piece of writing from a good one. During the polishing and finalization phase, it is crucial to scrutinize the report for weaknesses in the logical development of the subject and the cohesion of its presentation. Additionally, attention should be given to the mechanics of writing, including language, usage, grammar, spelling, and punctuation.
Good research possesses certain characteristics, which are as follows:
1. Empirical Basis: It implies that any conclusion drawn is grounded in hardcore evidence collected from real-life experiences and observations. This foundation provides external validity to research results.
2. Logical Approach: Good research is logical, guided by the rules of reasoning and analytical processes of induction (general to specific) and deduction (particular to the public). Logical reasoning is integral to making research feasible and meaningful in decision-making.
3. Systematic Nature: Good research is systematic, which adheres to a structured set of rules, following specific steps in a defined sequence. Systematic research encourages creative thinking while avoiding reliance on guesswork and intuition to reach conclusions.
4. Replicability: Scientific research designs, procedures, and results should be replicable. This ensures that anyone apart from the original researcher can assess their validity. Researchers can use or replicate results obtained by others, making the procedures and outcomes of the research both replicable and transmittable.
5. Validity and Verifiability: Good research involves precise observation and accurate description. The researcher selects reliable and valid instruments for data collection, employing statistical measures to portray results accurately. The conclusions drawn are correct and verifiable by both the researcher and others.
6. Theory and Principle Development: It contributes to formulating theories and principles, aiding accurate predictions about the variables under study. By making sound generalizations based on observed samples, researchers extend their findings beyond immediate situations, objects, or groups, formulating generalizations or theories about these factors.
1. What are the key characteristics of research report?
Scope of Business Research
Data Collection
Questionnaire
Difference between questionnaire and schedule
Measurement
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Published on July 12, 2022 by Eoghan Ryan . Revised on November 20, 2023.
Research objectives describe what your research is trying to achieve and explain why you are pursuing it. They summarize the approach and purpose of your project and help to focus your research.
Your objectives should appear in the introduction of your research paper , at the end of your problem statement . They should:
What is a research objective, why are research objectives important, how to write research aims and objectives, smart research objectives, other interesting articles, frequently asked questions about research objectives.
Research objectives describe what your research project intends to accomplish. They should guide every step of the research process , including how you collect data , build your argument , and develop your conclusions .
Your research objectives may evolve slightly as your research progresses, but they should always line up with the research carried out and the actual content of your paper.
A distinction is often made between research objectives and research aims.
A research aim typically refers to a broad statement indicating the general purpose of your research project. It should appear at the end of your problem statement, before your research objectives.
Your research objectives are more specific than your research aim and indicate the particular focus and approach of your project. Though you will only have one research aim, you will likely have several research objectives.
Research objectives are important because they:
Once you’ve established a research problem you want to address, you need to decide how you will address it. This is where your research aim and objectives come in.
Your research aim should reflect your research problem and should be relatively broad.
Break down your aim into a limited number of steps that will help you resolve your research problem. What specific aspects of the problem do you want to examine or understand?
Once you’ve established your research aim and objectives, you need to explain them clearly and concisely to the reader.
You’ll lay out your aims and objectives at the end of your problem statement, which appears in your introduction. Frame them as clear declarative statements, and use appropriate verbs to accurately characterize the work that you will carry out.
The acronym “SMART” is commonly used in relation to research objectives. It states that your objectives should be:
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If you want to know more about the research process , methodology , research bias , or statistics , make sure to check out some of our other articles with explanations and examples.
Methodology
Statistics
Research bias
Research objectives describe what you intend your research project to accomplish.
They summarize the approach and purpose of the project and help to focus your research.
Your objectives should appear in the introduction of your research paper , at the end of your problem statement .
Your research objectives indicate how you’ll try to address your research problem and should be specific:
Once you’ve decided on your research objectives , you need to explain them in your paper, at the end of your problem statement .
Keep your research objectives clear and concise, and use appropriate verbs to accurately convey the work that you will carry out for each one.
I will compare …
A research aim is a broad statement indicating the general purpose of your research project. It should appear in your introduction at the end of your problem statement , before your research objectives.
Research objectives are more specific than your research aim. They indicate the specific ways you’ll address the overarching aim.
Scope of research is determined at the beginning of your research process , prior to the data collection stage. Sometimes called “scope of study,” your scope delineates what will and will not be covered in your project. It helps you focus your work and your time, ensuring that you’ll be able to achieve your goals and outcomes.
Defining a scope can be very useful in any research project, from a research proposal to a thesis or dissertation . A scope is needed for all types of research: quantitative , qualitative , and mixed methods .
To define your scope of research, consider the following:
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Section 1- Evidence-based practice (EBP)
Components of a research report.
Partido, B.B.
Elements of research report
Introduction | What is the issue? |
Methods | What methods have been used to investigate the issue? |
Results | What was found? |
Discussion | What are the implications of the findings? |
The research report contains four main areas:
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Understanding research reports, financial analyst research reports, research report impact, conflicts of interest.
James Chen, CMT is an expert trader, investment adviser, and global market strategist.
A research report is a document prepared by an analyst or strategist who is a part of the investment research team in a stock brokerage or investment bank . A research report may focus on a specific stock or industry sector, a currency, commodity or fixed-income instrument, or on a geographic region or country. Research reports generally, but not always, have actionable recommendations such as investment ideas that investors can act upon.
Research reports are produced by a variety of sources, ranging from market research firms to in-house departments at large organizations. When applied to the investment industry, the term usually refers to sell-side research, or investment research produced by brokerage houses.
Such research is disseminated to the institutional and retail clients of the brokerage that produces it. Research produced by the buy-side, which includes pension funds, mutual funds, and portfolio managers , is usually for internal use only and is not distributed to external parties.
Financial analysts may produce research reports for the purpose of supporting a particular recommendation, such as whether to buy or sell a particular security or whether a client should consider a particular financial product. For example, an analyst may create a report in regards to a new offering being proposed by a company. The report could include relevant metrics regarding the company itself, such as the number of years they have been in operation as well as the names of key stakeholders , along with statistics regarding the current state of the market in which the company participates. Information regarding overall profitability and the intended use of the funds can also be included.
Enthusiasts of the Efficient Market Hypothesis (EMH) might insist that the value of professional analysts' research reports is suspect and that investors likely place too much confidence in the conclusions such analysts make. While a definitive conclusion about this topic is difficult to make because comparisons are not exact, some research papers do exist which claim empirical evidence supporting the value of such reports.
One such paper studied the market for India-based investments and analysts who cover them. The paper was published in the March 2014 edition of the International Research Journal of Business and Management. Its authors concluded that analyst recommendations do have an impact and are beneficial to investors at least in short-term decisions.
While some analysts are functionally unaffiliated, others may be directly or indirectly affiliated with the companies for which they produce reports. Unaffiliated analysts traditionally perform independent research to determine an appropriate recommendation and may have a limited concern regarding the outcome.
Affiliated analysts may feel best served by ensuring any research reports portray clients in a favorable light. Additionally, if an analyst is also an investor in the company on which the report is based, he may have a personal incentive to avoid topics that may result in a lowered valuation of the securities in which he has invested.
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A research report is one big argument how and why you came up with your conclusions. To make it a convincing argument, a typical guiding structure has developed. In the different chapters, distinct issues need to be addressed to explain to the reader why your conclusions are valid. The governing principle for writing the report is full disclosure: to explain everything and ensure replicability by another researcher.
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Hunziker, S., Blankenagel, M. (2021). Writing up a Research Report. In: Research Design in Business and Management. Springer Gabler, Wiesbaden. https://doi.org/10.1007/978-3-658-34357-6_4
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Hannah, D.R. and Lautsch, B.A. (2010) ‘Counting in Qualitative Research: Why to Conduct it, When to Avoid it, and When to Closet it’, in Journal of Management Inquiry , 20(1): 14–22.
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This survey investigates artificial intelligence (AI) literacy among academic library employees, predominantly in the United States, with a total of 760 respondents. The findings reveal a modest self-rated understanding of AI concepts, limited hands-on experience with AI tools, and notable gaps in discussing ethical implications and collaborating on AI projects. Despite recognizing the benefits, readiness for implementation appears low among participants. Respondents emphasize the need for comprehensive training and the establishment of ethical guidelines. The study proposes a framework defining core components of AI literacy tailored for libraries. The results offer insights to guide professional development and policy formulation as libraries increasingly integrate AI into their services and operations.
In a world increasingly dictated by algorithms, artificial intelligence (AI) is not merely a technological phenomenon, it is a transformative force that redefines our intellectual, social, and professional landscapes (McKinsey and Company, 2023). The rapid integration of AI in our everyday lives has profound implications for higher education, a sector entrusted with preparing individuals to navigate, contribute to, and thrive in this AI-driven era. From personalized learning environments to automated administrative tasks, AI’s influence in higher education is omnipresent and its potential boundless. However, this potential can only be harnessed effectively if those at the frontline of academia—our educators, researchers, administrators, and, notably, academic library employees—are equipped with the necessary AI literacy (UNESCO, 2021). Without an understanding of AI’s principles, capabilities, and ethical considerations, higher education risks falling prey to AI’s pitfalls rather than leveraging its benefits.
The potential risks and benefits underscore a pressing need to scrutinize and elevate AI literacy within the higher education community—a task that begins with understanding its current state. As facilitators of information and knowledge, academic library employees stand at the crossroads of this AI revolution, making their AI literacy an imperative, not a choice, for the future of higher education.
In an era marked by exponential growth in digital technology, the concept of literacy has evolved beyond traditional reading and writing skills to encompass a wide array of digital competencies. One such competency, which is gaining critical importance in higher education, is AI literacy. With AI systems beginning to permeate every facet of university operations—from learning management systems to research analytics—the ability to understand and navigate these AI tools has become an essential skill for academic library employees.
AI literacy, a subset of digital literacy, specifically pertains to understanding AI’s principles, applications, and ethical considerations. It involves not only the ability to use AI tools effectively, but also the capacity to evaluate their outputs critically, to understand their underlying mechanisms, and to contemplate their ethical and societal implications. AI literacy is not just for computer professionals; as Lo (2023b) and Cetindamar et al. (2022) emphasize, operationalizing AI literacy for non-specialists is essential.
The significance of AI literacy in higher education is underscored by several contemporary trends and challenges. Companies and governments globally are engaged in fierce competition to stay at the forefront of AI integration. Concurrently, the rapid proliferation of AI is giving rise to a host of ethical and privacy concerns that require informed stewardship (Cox, 2022). Furthermore, the COVID-19 pandemic has accelerated the digital transformation of higher education, leading to an increased reliance on AI technologies for remote learning and operations. This reliance further points to the necessity of AI literacy among academic library employees, who play a pivotal role in facilitating online learning and research.
As artificial intelligence proliferates across higher education, developing AI literacy is increasingly recognized as a priority to prepare students, faculty, staff, and administrators to harness AI’s potential, while mitigating risks (Ng et al., 2021). Hervieux and Wheatley’s (2021) 2019 study (n=163) found that academic librarians require more training regarding artificial intelligence and its potential applications in libraries. The U.S. Department of Education’s recent report (2023) on AI emphasizes the growing importance of AI literacy for educators and students, highlighting the necessity of understanding and integrating AI technologies in educational settings. This report aligns with the broader discourse on AI literacy and emphasizes the need to equip library professionals with skills needed to evaluate and utilize AI tools effectively (Lo, 2023a).
While efforts to promote AI literacy are growing, the required content for different target groups remains ambiguous. Some promising measurement tools have been proposed, such as Pinski and Benlian’s (2023) multidimensional scale assessing perceived knowledge of AI technology, processes, collaboration, and design. However, further validation of AI literacy assessments is required. Developing rigorous definitions and measurements is crucial for implementing effective AI literacy initiatives.
Ridley and Pawlick-Potts (2021) put forth the concept of algorithmic literacy, involving understanding algorithms and their influence, recognizing their uses, assessing their impacts, and positioning individuals as active agents rather than passive recipients of algorithmic decision-making. They propose libraries can contribute to algorithmic literacy by integrating it into information literacy education and supporting explainable AI.
Ocaña-Fernández et al. (2019) argued curriculum and skills training changes are critical to prepare students and faculty for an AI future, though also warn about digital inequality issues. Laupichler et al.’s (2022) scoping review reveals efforts to teach foundational AI literacy to non-specialists are still in formative stages. Proposed essential skills vary considerably across frameworks, and robust evaluations of AI literacy programs are lacking. Findings indicate that carefully designed AI literacy courses show promise for knowledge gains; however, research substantiating appropriate frameworks, core competencies and effective instructional approaches for diverse audiences remains an open need.
Within libraries, Heck et al. (2019) discussed the interplay of information literacy and AI. They propose that AI could aid information literacy teaching through timely feedback and tracking skill development, but note that common evaluation approaches would need establishing first. Information literacy empowers learners to actively engage with, not just passively consume from, AI systems. Lo (2023c) proposed a framework to utilize prompt engineering to enhance information literacy and critical thinking skills.
Oliphant (2015) examined intelligent agents for library reference services. The analysis found they rapidly retrieve information but lack human evaluation abilities. Findings suggest librarians will need to guide users in critically evaluating AI-generated results, indicating that information literacy instruction remains crucial. Furthermore, Lund et al. (2023) discuss the ethical implications of using large language models, such as ChatGPT, in scholarly publishing, emphasizing the need for ethical considerations and the potential impact of AI on research practices.
While research is still emerging, initial findings highlight the need for rigorous, tailored AI literacy initiatives encompassing technical skills, critical perspectives, and ethical considerations. As AI becomes further entwined with education and work, developing validated frameworks, assessments, and instructional approaches to enhance multidimensional AI literacy across contexts and roles is an urgent priority. This study seeks to contribute by investigating AI literacy specifically among academic library employees.
The rapid pace of AI development and integration in higher education heightens the need to address this research gap. As AI continues to evolve and permeate further into academic libraries, the demand for AI-literate library employees will only increase. Failure to understand the current state of AI literacy, and to identify the gaps, could result in a significant skills deficit that would impedes the effective utilization of AI in academic libraries.
In light of this, the purpose of this study is to embark on an investigation of AI literacy among academic library employees. The study seeks to answer the following critical research questions:
By addressing these questions, this study aims to fill a research gap and provide insights that can inform policy and practice in higher education. It strives to shed light on the competencies that academic library employees possess, identify the gaps that need to be addressed, and propose strategies for enhancing AI literacy among this essential group of higher education professionals.
The Technological Pedagogical Content Knowledge (TPACK) framework developed by Mishra and Koehler (2006) serves as the theoretical foundation for this study. TPACK has also been advocated as a useful decision-making structure for librarians evaluating instructional technologies (Sobel & Grotti, 2013).
Mishra and Koehler (2006) explain that TPACK involves flexible, context-specific application of technology, pedagogy, and content knowledge. It goes beyond isolated knowledge of the concepts to an integrated understanding. TPACK development requires moving past viewing technology as an “add-on” and focusing on the connections between technology, content, and pedagogy in particular educational contexts.
In the context of this study, the researcher applied the TPACK framework to examine AI literacy specifically among academic library professionals. The three key components of the TPACK framework are interpreted as:
This tailored application of the TPACK framework will allow a multidimensional assessment of AI literacy among academic library employees. It facilitates examining employees’ understanding of AI as a technology (TK), perceptions of how AI can enhance library services (PK), and the potential impact of AI on the library’s content and services (CK).
The significance of this study lies in its potential to contribute to academic library policy, practice, and theory in several ways. Firstly, it utilizes the TPACK framework to evaluate AI literacy among academic library employees, identifying competencies, gaps, and necessary strategies. This insight is crucial for designing effective professional development programs, as well as for resource allocation. Secondly, it adds to the discourse on digital literacy in higher education by specifically focusing on AI literacy, aiding in understanding its role and implications. Thirdly, the study provides insights into the ethical, practical, and opportunity dimensions of AI technology integration in libraries, informing best practices and guidelines for its responsible use. Lastly, by applying the TPACK framework to AI literacy in libraries, the study expands its theoretical applications and offers a robust basis for future research in technology integration in academic settings.
Research design.
This study employs a survey-based approach to explore AI literacy among academic library employees, chosen for its ability to quickly gather extensive data across a geographically diverse group. The method aligns with the TPACK framework, highlighting the integration of technological, pedagogical, and content knowledge. Surveys facilitate the collection of standardized data, allowing for comparisons across different roles and demographics. This design is particularly effective for descriptive research in higher education, making it suitable for assessing the current state of AI literacy in academic libraries.
The researcher utilized a comprehensive approach to recruit a diverse group of academic library employees for the survey. This involved posting on professional listservs across various roles and regions in librarianship (Appendix A), as well directly contacting directors of prominent library associations: the Association of Research Libraries (ARL), the Greater Western Library Alliance (GWLA), and the New Mexico Consortium of Academic Libraries (NMCAL). These organizations represent a broad spectrum of academic libraries in terms of size, location, and type. The directors were requested to share the survey with their staff, thus ensuring a wide-reaching and representative sample for the study.
Data collection was facilitated through a custom-designed survey instrument, which was built and administered using the Qualtrics platform (Appendix B). The survey itself was developed to address the study’s research questions and was structured into four main sections, each focusing on a specific aspect of AI literacy among academic library employees.
The first section sought to capture respondents’ understanding and knowledge of AI, including their familiarity with AI concepts and terminology. The second section focused on respondents’ practical skills and experiences with AI tools and applications in professional settings. The third section aimed to identify areas of AI literacy where respondents felt less confident, signaling potential gaps in knowledge or skills that could be addressed through professional development initiatives. Finally, the last section explored respondents’ perspectives on the ethical implications and challenges presented by AI technologies in the library context.
The survey employed a mix of question types to engage respondents and capture nuanced data. These included Likert-scale questions, multiple choice, and open-ended questions. Prior to the full-scale administration, the survey was pilot-tested with a small group of academic library employees to ensure clarity, relevance, and appropriateness of the questions.
The survey questions were designed to tap into different dimensions of the TPACK framework. For instance, questions asking about practical experiences with AI tools and self-identified areas of improvement indirectly assess the intersection of technological and pedagogical knowledge (TPK), as they relate to AI.
Upon finalizing the survey, an invitation to participate, along with a link to the survey, was distributed via the listservs and direct outreach methods. The survey remained open for two weeks, with reminders sent out at regular intervals to maximize the response rate.
While the study offers insights into AI literacy among academic library employees, it is crucial to acknowledge its limitations. Firstly, given the survey’s self-report nature, the findings may be subject to social desirability bias, where respondents might have over- or under-estimated their knowledge or skills in AI.
Secondly, despite best efforts to reach a wide range of academic library employees, the sample may not be entirely representative of the population. The voluntary nature of participation, coupled with the distribution methods used, may have skewed the sample towards those with an existing interest or engagement in AI.
Moreover, while the use of professional listservs and direct outreach to library directors helped widen our reach, this strategy might have excluded those academic library employees who are less active, or not included, in these communication channels. The inclusion of Canadian libraries through the Association of Research Libraries suggests a small number of non-U.S. respondents.
Finally, the rapidly evolving nature of AI and its applications in libraries means that our findings provide a snapshot at a specific point in time. As AI continues to advance and integrate more deeply into academic libraries, the landscape of AI literacy among library employees is likely to shift, necessitating ongoing research in this area.
These limitations, while important to note, do not invalidate our findings. Instead, they offer points of consideration for interpreting the results and highlight areas for future research to build on our understanding of AI literacy among academic library employees.
Descriptive statistics.
The survey drew a diverse response: 760 participants started the survey, 605 completed it. The participants represented a cross-section of the academic library landscape, with the majority (45.20%) serving in Research Universities. A significant proportion also hailed from institutions offering both graduate and undergraduate programs (29.64%) and undergraduate-focused Colleges or Universities (10.76%). Community Colleges and specialized professional schools (e.g., Law, Medical) were represented as well, albeit to a lesser extent.
Over half of the respondents (61.25%) were from libraries affiliated with the Association of Research Libraries (ARL), signifying an extensive representation from research-intensive institutions. Respondents were predominantly from larger academic institutions. Those serving in institutions with enrollments of 30,000 or more made up the largest group (30.67%), closely followed by those in institutions with enrollments ranging from 10,000 to 29,999 (34.66%).
As for professional roles, the survey drew heavily from the library specialists or professionals (60.99%) who directly support the academic community’s research, learning, and teaching needs. Middle (20.00%) and senior (9.09%) management personnel were also well-represented, providing a leadership perspective to the survey insights.
Table 1 | ||
Role or Position in Organization | ||
Role or Position in Organization | Percentage of Respondents | Number of Respondents |
Senior management (e.g. Director, Dean, associate dean/director) | 9.09% | 55 |
Middle management (e.g. department head, supervisor, coordinator) | 20.00% | 121 |
Specialist or professional (e.g., librarian, analyst, consultant) | 60.99% | 369 |
Support staff or administrative | 8.93% | 54 |
Other | 0.99% | 6 |
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Most of the respondents were primarily involved in Reference and Research Services (25.17%) or Library Instruction and Information Literacy (24.34%)—two areas integral to the academic support infrastructure.
In terms of professional experience, participants exhibited a broad range, from novices with less than a year’s experience (2.81%) to seasoned veterans with over 20 years in the field (22.68%).
Table 2 | ||
Primary Work Area in Academic Librarianship | ||
Primary Work Area in Academic Librarianship | Percentage of Respondents | Number of Respondents |
Administration or management | 10.93% | 66 |
Reference and research services | 25.17% | 152 |
Technical services (e.g., acquisitions, cataloging, metadata) | 8.11% | 49 |
Collection development and management | 4.64% | 28 |
Library instruction and information literacy | 24.34% | 147 |
Electronic resources and digital services | 4.30% | 26 |
Systems and IT services | 3.64% | 22 |
Archives and special collections | 3.31% | 20 |
Outreach, marketing, and communications | 1.66% | 10 |
Other | 13.91% | 84 |
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Table 3 | ||
Years of Experience as a Library Employee | ||
Years of Experience as a Library Employee | Percentage of Respondents | Number of Respondents |
Less than 1 year | 2.81% | 17 |
1–5 years | 21.19% | 128 |
6–10 years | 19.54% | 118 |
11–15 years | 19.04% | 115 |
16–20 years | 14.74% | 89 |
More than 20 years | 22.68% | 137 |
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The survey group was highly educated, with most holding a master’s degree in library and information science (65.51%), and a significant number having completed a doctoral degree or a master’s in another field.
The survey also collected demographic information. A substantial majority identified as female (71.97%), and the largest age group was 35–44 years (27.97%). While the majority identified as White (76.11%), other ethnicities, including Asian, Black or African American, and Hispanic or Latino, were also represented.
This diverse participant profile offers a broad-based view of AI literacy in the academic library landscape, setting the stage for insightful findings and discussions.
Table 4 | ||
Level of Understanding of AI Concepts and Principles | ||
Level of Understanding of AI Concepts and Principles | % of Respondents | Number of Respondents |
1 (Very Low) | 7.50% | 57 |
2 | 20.13% | 153 |
3 (Moderate) | 45.39% | 345 |
4 | 23.29% | 177 |
5 (Very High) | 3.68% | 28 |
At a broad level, participants expressed a modest understanding of AI concepts and principles, with a significant portion rating their knowledge at an average level. However, the number of respondents professing a high understanding of AI was quite small, revealing a potential area for further training and education.
A similar pattern was observed when participants were queried about their understanding of generative AI specifically. This suggests that while librarians have begun to grasp AI and its potential, there is a considerable scope for growth in terms of knowledge and implementation (Figure 1).
Figure 1 |
Understanding of Generative AI |
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Regarding the familiarity with AI tools, most participants had a moderate level of experience (30.94%). Only a handful of participants reported a high level of familiarity (3.87%), signaling an opportunity for more hands-on training with these tools.
In examining the prevalence of AI usage in the library sector, the researcher found a varied landscape. While some technologies have found significant adoption, others remain relatively unused. Notably, Chatbots and text or data mining tools were the most widely used AI technologies.
Participants’ understanding of specific AI concepts followed a similar trend. More straightforward concepts such as Machine Learning and Natural Language Processing had a higher average rating, whereas complex areas like Deep Learning and Generative Adversarial Networks were less understood. This trend underscores the need for targeted educational programs on AI in library settings.
Table 5 | |
Understanding of Specific AI Concepts | |
AI Concept | Average Rating |
Machine Learning | 2.50 |
Natural Language Processing (NLP) | 2.38 |
Neural Network | 1.93 |
Deep Learning | 1.79 |
Generative Adversarial Networks (GANs) | 1.37 |
Notably, there was almost a nine percent drop in responses from the previous questions to the questions that asked about the more technical aspects of AI. This could signify a gap in knowledge or comfort level with these topics among the participants.
In the professional sphere, AI tools have yet to become a staple in library work. The majority of participants do not frequently use these tools, with 41.79% never using generative AI tools and 28.01% using them less than once a month. This might be attributed to a lack of familiarity, resources, or perceived need. However, for those who do use them, text generation and research assistance are the primary use cases.
Concerns about ethical issues, quality, and accuracy of generated content, as well as data privacy, were prevalent among the participants. This finding indicates that while there’s interest in AI technologies, the perceived challenges are significant barriers to full implementation and adoption.
In their personal lives, AI tools have yet to make a significant impact among the participants. The majority (63.98%) reported using these tools either ‘less than once a month’ or ‘never.’ This could potentially reflect the current state of AI integration in non-professional or leisurely activities, and may change as AI continues to permeate our everyday lives.
A chi-square test of independence was performed to examine the relation between the position of the respondent and the understanding of AI concepts and principles. The relation between these variables was significant, χ 2 (16, N = 760) = 26.31, p = .05. This means that the understanding of AI concepts and principles varies depending on the position of the respondent.
The distributions suggest that—while there is a significant association between the position of the respondent and their understanding of AI concepts and principles—the majority of respondents across all positions have a moderate understanding of AI. However, there are differences in the proportions of respondents who rate their understanding as high or very high, with Senior Management and Middle Management having higher proportions than the other groups.
There is also a significant relation between the area of academic librarianship and the understanding of AI concepts and principles, χ²(36, N = 760) = 68.64, p = .00084. This means that the understanding of AI concepts and principles varies depending on the area of academic librarianship. The distributions show that there are differences in the proportions of respondents who rate their understanding as high or very high, with Administration or management and Library Instruction and Information Literacy having higher proportions than the other groups.
Furthermore, a Chi-Square test shows that the relation between the payment for a premium version of at least one of the AI tools and the understanding of AI concepts and principles is significant, χ²(4, N = 539) = 85.42, p < .001. The distributions suggest that respondents who have paid for a premium version of at least one of the AI tools have a higher understanding of AI concepts and principles compared to those who have not. This could be because those who have paid for a premium version of an AI tool are more likely to use AI in their work or personal life, which could enhance their understanding of AI. Alternatively, those with a higher understanding of AI might be more likely to see the value in paying for a premium version of an AI tool.
It’s important to note that these findings are based on the respondents’ self-rated understanding of AI, which may not accurately reflect their actual understanding. Further research could involve assessing the respondents’ understanding of AI through objective measures. Additionally, other factors not considered in this analysis, such as the respondent’s educational background, years of experience, and exposure to AI in their work, could also influence their understanding of AI.
In this section, the researcher delved deeper into the gaps in knowledge and confidence among academic library professionals regarding AI applications. These gaps highlight the urgent need for targeted professional development and training in AI literacy.
The survey data pointed to moderate levels of confidence across a spectrum of AI-related tasks, indicating room for growth and learning. For evaluating ethical implications of using AI, a modest 30.12% of respondents felt somewhat confident (levels 4 and 5 combined), while 29.50% were not confident (levels 1 and 2 combined), and the largest group (39.38%) remained neutral.
Discussing AI integration revealed similar patterns. Here, 31.1% reported high confidence, 34.85% expressed low confidence, and the remaining 33.06% were neutral. These distributions suggest an overall hesitation or lack of assurance in discussing and ethically implementing AI, potentially indicative of inadequate training or exposure to these topics.
When it came to collaborating on AI-related projects, fewer respondents (31.39%) felt confident, while 40.16% reported low confidence, and 28.46% chose a neutral stance. This might point to the necessity of not only individual proficiency in AI but also the need for collaborative skills and shared understanding among teams working with AI.
Troubleshooting AI tools and applications emerged as the most significant gap, with 69.76% rating their confidence as low and only 10.9% expressing high confidence. This highlights an essential area for targeted training, as troubleshooting is a fundamental aspect of successful technology implementation.
Table 6 | |||||
Confidence Levels in Various Aspects of AI | |||||
Aspect | % at Confidence Level 1 | % at Confidence Level 2 | % at Confidence Level 3 | % at Confidence Level 4 | % at Confidence Level 5 |
Evaluating Ethical Implications of AI | 12.48% | 17.02% | 39.38% | 24.64% | 6.48% |
Participating in AI Discussions | 13.29% | 21.56% | 33.06% | 20.75% | 11.35% |
Collaborating on AI Projects | 15.77% | 24.39% | 28.46% | 21.63% | 9.76% |
Troubleshooting AI Tools | 41.79% | 27.97% | 19.35% | 9.76% | 1.14% |
Providing Guidance on AI Resources | 25.65% | 24.51% | 25.81% | 20.13% | 3.90% |
Approximately one-third of survey participants have engaged in AI-focused professional development, showcasing several key themes:
The findings emphasize the multifaceted nature of AI in libraries, underlining the need for ongoing, comprehensive professional development. This includes addressing both technical and ethical aspects, equipping librarians with practical AI skills, and fostering a supportive community of practice.
A Chi-square test examining the relationship between the respondents’ positions and their participation in any training focused on generative AI (χ²(4, N = 595) = 26.72, p < .001) indicates a significant association. Upon examining the data, the proportion of respondents who have participated in training or professional development programs focused on generative AI is highest among those in Senior Management (47.27%), followed by Specialist or Professional (37.40%), Middle Management (29.75%), and Other (16.67%). The proportion is lowest among Support Staff or Administrative (3.70%).
This suggests that individuals in higher positions, such as Senior Management and Specialist or Professional roles, are more likely to have participated in training or professional development programs focused on generative AI. This could be due to a variety of reasons, such as these roles potentially requiring a more in-depth understanding of AI and its applications, or these individuals having more access to resources and opportunities for such training. On the other hand, Support Staff or Administrative personnel are less likely to have participated in such programs, which could be due to less perceived need or fewer opportunities for training in these roles.
These findings highlight the importance of providing access to training and professional development opportunities focused on AI across all roles in an organization, not just those in higher positions or those directly involved in AI-related tasks. This could help ensure a more widespread understanding and utilization of AI across the organization.
Despite these efforts, many participants did not feel adequately prepared to utilize generative AI tools professionally. A notable 62.91% disagreed to some extent with the statement: “I feel adequately prepared to use generative AI tools in my professional work as a librarian,” underscoring the need for more effective training programs.
Interestingly, the areas identified for further training weren’t just about understanding the basics of AI. Participants showed a clear demand for advanced understanding of AI concepts and techniques (13.53%), familiarity with AI tools and applications in libraries (14.21%), and addressing privacy and data security concerns related to generative AI (14.36%). This suggests that librarians are looking to move beyond a basic understanding and are keen to engage more deeply with AI.
Preferred formats for professional development opportunities leaned towards remote and flexible learning opportunities, such as online courses or webinars (26.02%) and self-paced learning modules (22.44%). This preference reflects the current trend towards digital and remote learning, providing a clear direction for future training programs.
Notably, almost half of the participants (43.99%) rated the need for academic librarians to receive training on AI tools and applications within the next twelve months as ‘extremely important.’ This emphasis on urgency indicates a significant and immediate gap to be addressed.
In summary, a deeper analysis of the data reveals a landscape where academic librarians possess moderate to low confidence in understanding, discussing, and handling AI-related tasks, despite some exposure to professional development in AI. This finding indicates the need for more comprehensive, in-depth, and accessible AI training programs. By addressing these knowledge gaps, the library community can effectively embrace AI’s potential and navigate its challenges.
The comprehensive results of our survey, as illustrated in Table 7, offer a detailed portrait of librarians’ perceptions towards the integration of generative AI tools in library services and operations.
Table 7 | |||||
Perceptions Towards the Integration of Generative AI Tools In Library Services | |||||
Statement | 1 | 2 | 3 | 4 | 5 |
To what extent do you agree or disagree with the following statement: “I believe generative AI tools have the potential to benefit library services and operations.” (1 = strongly disagree, 5 = strongly agree) | 3.32% | 10.96% | 35.88% | 27.91% | 21.93% |
How important do you think it is for your library to invest in the exploration and implementation of generative AI tools? (1 = not at all important, 5 = extremely important) | 7.24% | 15.95% | 29.93% | 28.78% | 18.09% |
In your opinion, how prepared is your library to adopt generative AI tools and applications in the next 12 months? (1 = not at all prepared, 5 = extremely prepared) | 32.28% | 37.75% | 23.84% | 4.80% | 1.32% |
To what extent do you think generative AI tools and applications will have a significant impact on academic libraries within the next 12 months? (1 = no impact, 5 = major impact) | 2.81% | 20.03% | 36.09% | 26.16% | 14.90% |
How urgent do you feel it is for your library to address the potential ethical and privacy concerns related to the use of generative AI tools and applications? (1 = not at all urgent, 5 = extremely urgent) | 2.15% | 5.46% | 18.05% | 29.47% | 44.87% |
When considering the potential benefits of AI, the responses indicate a degree of ambivalence, with 35.88% choosing a neutral stance. However, when we combine the categories of those who ‘agree’ and ‘strongly agree,’ we see that a significant portion, 49.84%, view AI as beneficial to a certain extent. Similarly, on the question of the importance of investment in AI, there is a notable inclination towards agreement, with 46.87% agreeing that investment is important to some degree.
However, this optimism is juxtaposed with concerns about readiness. When asked how prepared they feel to adopt generative AI tools within the forthcoming year, 70.03% of respondents (those who ‘strongly disagree’ or ‘disagree’) admit a lack of preparedness. This suggests that despite recognizing the potential value of AI, there are considerable obstacles to be overcome before implementation becomes feasible.
The uncertainty surrounding AI’s impact on libraries in the short-term further illuminates this complexity. A significant proportion of librarians (36.09%) chose a neutral response when asked to predict the impact of AI on academic libraries within the next twelve months. Nonetheless, there is a considerable group (41.06% who ‘agree’ or ‘strongly agree’) who foresee significant short-term impact.
A key finding from the survey was the collective recognition of the urgency to address ethical and privacy issues tied to AI usage. In fact, 74.34% of respondents, spanning ‘agree’ and ‘strongly agree,’ underscored the urgent need to address potential ethical and privacy concerns related to AI, highlighting the weight of responsibility librarians feel in maintaining the integrity of their services in the age of AI (Figure 2).
Figure 2 |
Perceived Urgency for Addressing Ethical and Privacy Concerns of Generative AI in Libraries |
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The qualitative responses provide a rich understanding of the perceptions of generative AI among library professionals and the implications they foresee for the library profession. The responses were categorized into several key themes, each of which is discussed below with relevant quotes from the respondents.
A significant theme that emerged from the responses was the ethical and privacy concerns associated with the use of generative AI tools in libraries. Respondents expressed apprehension about potential misuse of data and violations of privacy. As one respondent noted, “Library leaders should not rush to implement AI tools without listening to their in-house experts and operational managers.” Another respondent cautioned, “We need to be cautious about adopting technologies or practices within our own workflows that pose significant ethical questions, privacy concerns.”
The need for education and training on AI for librarians was another prevalent theme. Respondents emphasized the importance of understanding AI tools and their implications before implementing them. One respondent suggested: “quickly education on AI is needed for librarians. As with anything else, there will be early adopters and then a range of adoption over time.” Another respondent highlighted the need for an AI specialist, stating, “I also think it would be valuable to have an AI librarian, someone who can be a resource for the rest of the staff.”
Respondents expressed concern about the potential for misuse of AI tools, such as generating false citations or over-reliance on AI systems. They emphasized the importance of critical thinking skills, and cautioned against replacing human judgment and learning processes with AI. As one respondent put it, “Critical thinking skills and learning processes are vital and should not be replaced by AI.” Another respondent warned: “there are potential risks from misuse such as false citations being provided or too much dependence on systems.”
Several respondents expressed doubts about the ability of libraries to quickly and effectively implement AI tools. They cited issues such as frequent updates and refinements to AI tools, the need for significant investment, and the potential for AI to be used in ways that do not benefit the library or its users. One respondent noted, “the concern I have with AI tools is the frequent updates and refinements that occur. For libraries with small staff size, it seems daunting to keep up.”
Some respondents suggested specific ways in which AI could be used in libraries, such as for collection development, instruction, and answering frequently asked questions. However, they also cautioned against viewing AI as a panacea for all library challenges. One respondent stated: “using them for FAQs will be more useful than answering a complicated reference question.”
Some respondents expressed concern that the use of AI could lead to job displacement or a devaluation of the human elements of librarianship. They suggested that AI should be used to complement, not replace, human librarians. One respondent expressed that, “I could see a future where only top research institutions have human reference librarians as a concierge service.”
Respondents emphasized the need for critical evaluation of AI tools, including understanding their limitations and potential biases. They suggested that libraries should not rush to implement AI without fully understanding its implications. One respondent advised: “the framing of AI usage as a forgone conclusion is concerning. It’s a tool, not a solution, and should not be implemented without due consideration.”
Some respondents suggested that libraries have a role to play in teaching AI literacy to students and other library users. They emphasized the importance of understanding how AI tools work and how to use them responsibly. One respondent stated: “I think we need to teach AI literacy to students.” Another respondent echoed this sentiment, saying, “it is essential that we prepare our students to use generative AI tools responsibly.”
The perceptions of generative AI among library professionals are multifaceted, encompassing both the potential benefits and challenges of these technologies. While there is recognition of the potential of AI to enhance library services, there is also a strong emphasis on the need for ethical considerations, education and training, critical evaluation, and responsible use of these tools. The implications for the library profession are significant, with concerns about job displacement, the need for new skills and roles, and the potential for changes in library practices and services. These findings highlight the need for ongoing dialogue and research on the use of generative AI in libraries.
While library employees acknowledge the potential advantages of AI in library services, they also express concerns regarding readiness, and emphasize the urgency to address ethical and privacy considerations. These findings indicate the need for support systems, training, and resources to address readiness gaps, alongside rigorous discussion, and guidelines to navigate ethical and privacy issues as libraries explore the possibilities of AI integration.
The survey results cast light on the current state of artificial intelligence literacy, training needs, and perceptions within the academic library community. The findings reveal a landscape of recognition for the potential of AI technologies, yet, simultaneously, a lack of in-depth understanding and preparedness for their adoption.
A detailed examination of the data reveals that a considerable number of library professionals self-assess their understanding of AI as sitting around, or below, the middle. While this does suggest a basic level of familiarity with AI concepts and principles, it likely falls short of the proficiency required to navigate the rapidly evolving AI landscape confidently and competently. This gap in understanding holds implications for the library field as AI continues to infiltrate various sectors and increasingly permeates library services and operations.
Moreover, an analysis of the familiarity of library professionals with AI tools lends further credence to this call for more comprehensive AI education initiatives. An understanding of AI extends beyond mere theoretical comprehension—it necessitates hands-on familiarity with AI tools and the ability to use and apply them in practice. Direct interaction with AI technologies provides an avenue for library professionals to bolster their practical understanding and thus equip them to incorporate these tools into their work more effectively.
However, formulating training initiatives that address these gaps is a multifaceted task. The AI usage in libraries is as diverse as the scope of AI applications themselves. From customer service chatbots, and text or data mining tools, to advanced technologies like neural networks and deep learning systems—each offers unique applications and therefore requires distinct expertise and understanding. Accordingly, training programs must be flexible and comprehensive, encompassing the full range of potential AI applications while also delving deep enough to provide a solid grasp of each specific tool’s functionality and potential uses.
The study also sheds light on the varying degrees of understanding across different AI concepts. Participants generally exhibited a higher level of comprehension for simpler AI concepts. However, their understanding waned when it came to more complex concepts, often the bedrock of cutting-edge AI applications. This variation in comprehension underscores the need for a stratified approach to AI education. Such an approach could start with foundational concepts and gradually progress towards more advanced topics, providing a scaffold on which a deeper understanding of AI can be built.
Addressing the AI literacy gap in the library sector thus requires a concerted approach—one that offers comprehensive and layered educational strategies that bolster both theoretical understanding and practical familiarity with AI. The aim should not only be to impart knowledge, but to empower library professionals to confidently navigate the AI landscape, to adopt and adapt AI technologies in their work effectively and—crucially —responsibly. Through such training and professional development initiatives, libraries can harness the potential of AI, ensuring they continue to be at the forefront of technological advancements.
As the focus shifts to the professional use of AI tools in libraries, the data reveal that their adoption is not yet commonplace. The use of AI tools—such as text generation and research assistance—are most reported, reflecting the immediate utility these technologies offer to librarians. However, a significant proportion of participants do not frequently use AI tools, indicating barriers to adoption. These barriers could include a lack of understanding or familiarity with these tools, a perceived lack of necessity for their use, or limitations in resources necessary for implementation and maintenance. To overcome these barriers, the field may need more than just providing education and resources. Demonstrating the tangible benefits and efficiencies AI tools can bring to library work could play a pivotal role in their wider adoption.
The data show a strong enthusiasm among librarians for professional development related to AI. While introductory training modalities are popular, the findings reveal a demand for more advanced, hands-on training. This need aligns with the complexity and rapid evolution of AI technologies, which require a deeper understanding to be fully leveraged in library contexts.
Furthermore, the findings highlight the importance of ethical considerations and the potential benefits of fostering communities of practice in AI training. With the increasing integration of AI technology into library services, the issues related to AI ethics will likely become more complex. Proactively addressing these concerns through in-depth, focused training can help libraries continue to serve as ethical stewards of information. Communities of practice provide a platform for shared learning, mutual support, and the pooling of resources, equipping librarians to better navigate the intricacies of AI integration.
Importantly, the data show that the diversity in librarians’ roles and contexts necessitates a tailored approach to AI training. Libraries differ in their services, target audiences, resources, and strategic goals, and so do their AI training needs. A one-size-fits-all approach to AI training may fall short. Future AI training could therefore take these variations into account, offering specialized tracks or modules catering to specific roles or institutional contexts.
Likewise, the perceptions surrounding the use of generative AI tools in libraries are intricate and multifaceted. While the potential benefits of AI are acknowledged and the importance of investing in its implementation recognized, there is also a pronounced lack of readiness to adopt these tools. This readiness gap could stem from various factors, such as a lack of technical skills, insufficient funding, or institutional resistance. Future research should delve into these possibilities to better understand and address this gap.
Library professionals express uncertainty about the short-term implications of AI for libraries. This could reflect the novelty of these technologies and a lack of clear use cases, or it could echo the experiences of early adopters. The findings also emphasize a heightened sense of urgency in addressing the ethical and privacy concerns associated with AI technologies. These concerns underline the necessity for ongoing dialogue, education, and policy development around AI use in libraries.
The results reveal an intricate landscape of AI understanding, usage, and perception in the library field. While the benefits of AI tools are acknowledged, a comprehensive understanding and readiness to implement these technologies remain less than ideal. This reality underlines the pressing need for an investment in targeted educational strategies and ongoing professional development initiatives.
Crucially, the wide variance in AI literacy, understanding of AI concepts, and hands-on familiarity with AI tools among library professionals points towards the need for a stratified and tailored approach to AI education. Future training programs must aim beyond just knowledge acquisition—they must equip library professionals with the capabilities to apply AI technologies in their roles effectively, ethically, and responsibly. Ethical and privacy concerns emerged as significant considerations in the adoption of AI technologies in libraries. Our findings reinforce the crucial role that libraries have historically played, and must continue to play, in advocating for ethical information practices.
The readiness gap in AI adoption uncovered by the study suggests a disconnect between understanding the potential of AI and the ability to harness it effectively. This invites a deeper investigation into potential barriers, including technical proficiency, resource allocation, and institutional culture, among others.
This study presents a framework for defining AI literacy in academic libraries, encapsulating seven key competencies:
This multidimensional definition of AI literacy for libraries provides a foundation for developing comprehensive training programs and curricula. For instance, the need to understand AI system capabilities and limitations highlighted in the definition indicates that introductory AI education should provide a solid grounding in how common AI technologies like machine learning work, where they excel, and their constraints. This conceptual comprehension equips librarians to set realistic expectations when evaluating or implementing AI.
The definition also accentuates that gaining practical skills to use AI tools appropriately should be a core training component. Hands-on learning focused on identifying appropriate applications, utilizing AI technologies effectively, and critically evaluating outputs can empower librarians to harness AI purposefully.
Moreover, emphasizing critical perspectives and ethical considerations reflects that AI training for librarians should move beyond technical proficiency. Incorporating modules examining biases, privacy implications, misinformation risks, and societal impacts is key for fostering responsible AI integration.
Likewise, the collaborative dimension of the definition demonstrates that cultivating soft skills for productive AI discussions and teamwork should be part of the curriculum. AI literacy has an important social element that training programs need to nurture.
Overall, this definition provides a skills framework that can inform multipronged, context-sensitive AI training tailored to librarians’ diverse needs. It constitutes an actionable guide for developing AI curricula and professional development that advance both technical and social aspects of AI literacy.
Based on the findings and limitations of the current study, the following are specific recommendations for future research:
By pursuing these avenues for future research, we can continue to deepen our understanding of AI literacy in the library profession, inform strategies for enhancing AI literacy, and promote the effective and ethical use of AI in libraries.
Cetindamar, D., Kitto, K., Wu, M., Zhang, Y., Abedin, B., & Knight, S. (2021). Explicating AI literacy of employees at digital workplaces. IEEE Transactions on Engineering Management , 68(5), 1259–1271.
Cox, A. (2022). The ethics of AI for information professionals: Eight scenarios. Journal of the Australian Library and Information Association , 71(3), 201–214.
Heck, T., Weisel, L., & Kullmann, S. (2019). Information literacy and its interplay with AI . In A. Botte, P. Libbrecht, & M. Rittberger (Eds.), Learning Information Literacy Across the Globe (pp. 129–131). https://doi.org/10.25656/01:17891
Hervieux, S., & Wheatley, A. (2021). Perceptions of artificial intelligence: A survey of academic librarians in Canada and the United States. The Journal of Academic Librarianship , 47(1), 102270.
Laupichler, M. C., Aster, A., Schirch, J., & Raupach, T. (2022). Artificial intelligence literacy in higher and adult education: A scoping literature review. Computers and Education: Artificial Intelligence , 3, 100101. https://doi.org/10.1016/j.caeai.2022.100101
Lo, L. S. (2023a). An initial interpretation of the U.S. Department of Education’s AI report: Implications and recommendations for Academic Libraries. The Journal of Academic Librarianship , 49(5), 102761. https://doi.org/10.1016/j.acalib.2023.102761
Lo, L. S. (2023b). The art and science of prompt engineering: A new literacy in the information age. Internet Reference Services Quarterly , 27(4), 203–210. https://doi.org/10.1080/10875301.2023.2227621
Lo, L. S. (2023c). The clear path: A framework for enhancing information literacy through prompt engineering. The Journal of Academic Librarianship , 49(4), 102720. https://doi.org/10.1016/j.acalib.2023.102720
Lund, B. D., Wang, T., Mannuru, N. R., Nie, B., Shimray, S., & Wang, Z. (2023). ChatGPT and a new academic reality: artificial intelligence‐written research papers and the ethics of the large language models in scholarly publishing. Journal of the Association for Information Science and Technology , 74(5), 570–581. https://doi.org/10.1002/asi.24750
McKinsey & Company. (2023). The state of AI in 2023 : Generative AI’s breakout year . McKinsey & Company. https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/the-state-of-ai-in-2023-generative-ais-breakout-year
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Mishra, P. (2019). Considering contextual knowledge: The TPACK diagram gets an upgrade. Journal of Digital Learning in Teacher Education , 35(2), 76–78. https://doi.org/10.1080/21532974.2019.1588611
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Ocaña-Fernández, Y., Valenzuela-Fernández, L., & Garro-Aburto, L. (2019). Artificial intelligence and its implications in higher education. Propósitos y Representaciones , 7(2), 536–568. https://doi.org/10.20511/pyr2019.v7n2.274
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Survey flow.
Standard: Block 1 (1 Question)
Block: Knowledge and Familiarity (12 Questions)
Standard: Perceived Competence and Gaps in AI Literacy (5 Questions)
Standard: Training on Generative AI for Librarians (6 Questions)
Standard: Desired Use of Generative AI in Libraries (7 Questions)
Standard: Demographic (10 Questions)
Standard: End of Survey (1 Question)
Start of Block: Block 1
Dr. Leo Lo from the University of New Mexico is conducting a research project. You are invited to participate in a research study aiming to assess AI literacy among academic library employees, identify gaps in AI literacy that require further professional development and training, and understand the differences in AI literacy levels across different roles and demographic factors. Before you begin the survey, please read this Informed Consent Form carefully. Your participation in this study is voluntary, and you may choose to withdraw at any time without any consequences.
Artificial Intelligence (AI) refers to the development of computer systems and software that can perform tasks that would typically require human intelligence. These tasks may include problem-solving, learning, understanding natural language, recognizing patterns, perception, and decision-making
You are being asked to participate based of the following inclusion and exclusion criteria:
The purpose of this study is to evaluate the current AI literacy levels of academic librarians and identify areas where further training and development may be needed. The findings will help inform the design of targeted professional development programs and contribute to the understanding of AI literacy in the library profession.
If you agree to participate in this study, you will be asked to complete an online survey that will take approximately 15–20 minutes to complete. The survey includes questions about your AI knowledge, familiarity with AI tools and applications, perceived competence in using AI, and your opinions on training needs.
There are no known risks or discomforts associated with participating in this study. Some questions might cause minor discomfort due to self-reflection, but you are free to skip any questions you prefer not to answer. Benefits While there are no direct benefits to you for participating in this study, your responses will help contribute to a better understanding of AI literacy among academic librarians and inform the development of relevant professional training programs.
Your responses will be anonymous, and no personally identifiable information will be collected. Data will be stored securely on password-protected devices or encrypted cloud storage services, with access limited to the research team. The results of this study will be reported in aggregate form, and no individual responses will be identifiable. Your information collected for this project will NOT be used or shared for future research, even if we remove the identifiable information like your name.
Your participation in this study is voluntary, and you may choose to withdraw at any time without any consequences. Please note that if you decide to withdraw from the study, the data that has already been collected from you will be kept and used. This is necessary to maintain the integrity of the study and ensure that the data collected is reliable and valid.
If you have any questions or concerns about this study, please contact the principal investigator, Leo Lo, at [email protected] . If you have questions regarding your rights as a research participant, or about what you should do in case of any harm to you, or if you want to obtain information or offer input, please contact the UNM Office of the IRB (OIRB) at (505) 277-2644 or irb.unm.edu
By clicking “I agree” below, you acknowledge that you have read and understood the information provided above, had an opportunity to ask questions, and voluntarily agree to participate.
I agree (1)
I do not agree (2)
Skip To: End of Survey If Q1.1 = I do not agree
End of Block: Block 1
Start of Block: Knowledge and Familiarity
(AI) refers to the development of computer systems and software that can perform tasks that would typically require human intelligence. These tasks may include problem-solving, learning, understanding natural language, recognizing patterns, perception, and decision-making
Please rate your overall understanding of AI concepts and principles (using a Likert scale, e.g., 1 = very low, 5 = very high)
Q2.2 On a scale of 1 to 5, how would you rate your understanding of generative AI ? (1 = not at all knowledgeable, 5 = extremely knowledgeable)
Q2.3 Rate your familiarity with generative AI tools (e.g., ChatGPT, DALL-E, etc.) (using a Likert scale, e.g., 1 = not familiar, 5 = very familiar)
Q2.4 Which of the following AI technologies or applications have you encountered or used in your role as an academic librarian? (Select all that apply)
Q2.5 For each of the following AI concepts, indicate your understanding of the concept by selecting the appropriate response.
I don’t know what it is (1) | I know what it is but can’t explain it (2) | I can explain it at a basic level (3) | I can explain it in detail (4) | |
Machine Learning (1) | ||||
Natural Language Processing (NLP) (2) | ||||
Neural Network (3) | ||||
Deep Learning (4) | ||||
Generative Adversarial Networks (GANs) (5) |
Q2.6 Which of the following generative AI tools have you used at least a few times? (Select all that apply)
Display This Question:
If If Which of the following generative AI tools have you used at least a few times? (Select all that a… q://QID5/SelectedChoicesCount Is Greater Than 0
Q2.7 Have you ever paid for a premium version of at least one of the AI tools (for example, ChatGPT Plus; or Mid Journey subscription plan, etc.)
Q2.8 How frequently do you use generative AI tools in your professional work? (Select one)
Several times per week (2)
A few times per month (4)
Monthly (5)
Less than once a month (6)
Q2.9 For what purposes do you use generative AI tools in your professional work? (Select all that apply)
Q2.10 On a scale of 1 to 5, how would you rate how reliable generative AI tools have been in fulfilling your professional needs? (1 = not at all reliable, 5 = extremely reliable)
Please explain your choice.
1 (1) __________________________________________________
2 (2) __________________________________________________
3 (3) __________________________________________________
4 (4) __________________________________________________
5 (5) __________________________________________________
Q2.11 What level of concern do you have for the following potential challenges in implementing generative AI technologies in academic libraries? (Rate each challenge on a scale of 1 to 5, where 1 = not at all concerned and 5 = extremely concerned)
1 (1) | 2 (2) | 3 (3) | 4 (4) | 5 (5) | |
Obtaining adequate funding and resources for AI implementation (1) | |||||
Ethical concerns, such as bias and fairness (2) | |||||
Intellectual property and copyright issues (3) | |||||
Staff resistance or lack of buy-in (4) | |||||
Quality and accuracy of generated content (5) | |||||
Ensuring accessibility and inclusivity of AI tools for all users (6) | |||||
Potential job displacement due to automation (7) | |||||
Data privacy and security (8) | |||||
Technical expertise and resource requirements (9) | |||||
Other (please specify) (10) |
Q2.12 How frequently do you use generative AI tools in your personal life ? (Select one)
End of Block: Knowledge and Familiarity
Start of Block: Perceived Competence and Gaps in AI Literacy
Q3.1 On a scale of 1 to 5, how confident are you in your ability to evaluate the ethical implications of using AI in your library? (1 = not at all confident, 5 = extremely confident)
Q3.2 On a scale of 1 to 5, how confident are you in your ability to participate in discussions about AI integration within your library? (1 = not at all confident, 5 = extremely confident)
Q3.3 On a scale of 1 to 5, how confident are you in your ability to collaborate with colleagues on AI-related projects in your library? (1 = not at all confident, 5 = extremely confident)
Q3.4 On a scale of 1 to 5, how confident are you in your ability to troubleshoot issues related to AI tools and applications used in your library? (1 = not at all confident, 5 = extremely confident)
Q3.5 On a scale of 1 to 5, how confident are you in your ability to provide guidance to library users about AI resources and tools ? (1 = not at all confident, 5 = extremely confident)
End of Block: Perceived Competence and Gaps in AI Literacy
Start of Block: Training on Generative AI for Librarians
Q4.1 Have you ever participated in any training or professional development programs focused on generative AI?
If Q4.1 = Yes
Q4.2 Please briefly describe the nature and content of the training or professional development program(s) you attended.
________________________________________________________________
Q4.3 To what extent do you agree or disagree with the following statement: “ I feel adequately prepared to use generative AI tools in my professional work as a librarian .” (1 = strongly disagree, 5 = strongly agree)
Q4.4 In which of the following areas do you feel the need for additional training or professional development related to AI? (Select all that apply)
Q4.5 What types of professional development opportunities related to AI would be most beneficial to you? (Select all that apply)
Q4.6 How important do you think it is for academic librarians to receive training on generative AI tools and applications in the next 12 months ? (1 = not at all important, 5 = extremely important)
End of Block: Training on Generative AI for Librarians
Start of Block: Desired Use of Generative AI in Libraries
Q5.1 To what extent do you agree or disagree with the following statement: “ I believe generative AI tools have the potential to benefit library services and operations .” (1 = strongly disagree, 5 = strongly agree)
Q5.2 How important do you think it is for your library to invest in the exploration and implementation of generative AI tools ? (1 = not at all important, 5 = extremely important)
Q5.3 If you have any additional thoughts or suggestions on how your library could or should use (or not use) generative AI tools, please share them here.
Q5.4 How soon do you think your library should prioritize implementing generative AI tools and applications? (Select one)
Immediately (1)
Within the next 6 months (2)
Within the next year (3)
Within the next 2–3 years (4)
More than 3 years from now (5)
Not a priority at all (6)
Q5.5 In your opinion, how prepared is your library to adopt generative AI tools and applications in the next 12 months? (1 = not at all prepared, 5 = extremely prepared)
Q5.6 To what extent do you think generative AI tools and applications will have a significant impact on academic libraries within the next 12 months ? (1 = no impact, 5 = major impact)
Q5.7 How urgent do you feel it is for your library to address the potential ethical and privacy concerns related to the use of generative AI tools and applications? (1 = not at all urgent, 5 = extremely urgent)
End of Block: Desired Use of Generative AI in Libraries
Start of Block: Demographic
Q6.1 In which type of academic institution is your library located? (Select one)
Community college (1)
College or university (primarily undergraduate) (2)
College or university (graduate and undergraduate) (3)
Research university (4)
Specialized or professional school (e.g., law, medical) (5)
Other (please specify) (6) __________________________________________________
Q6.2 Is your library an ARL member library?
Q6.3 Approximately how many students are enrolled at your institution? (Select one)
Fewer than 1,000 (1)
1,000–4,999 (2)
5,000–9,999 (3)
10,000–19,999 (4)
20,000–29,999 (5)
30,000 or more (6)
Q6.4 What is your current role or position in your organization? (Select one)
Senior management (e.g. Director, Dean, associate dean/director) (1)
Middle management (e.g. department head, supervisor, coordinator) (2)
Specialist or professional (e.g., librarian, analyst, consultant) (3)
Support staff or administrative (4)
Other (please specify) (5) __________________________________________________
Q6.5 In which area of academic librarianship do you primarily work? (Select one)
Administration or management (1)
Reference and research services (2)
Technical services (e.g., acquisitions, cataloging, metadata) (3)
Collection development and management (4)
Library instruction and information literacy (5)
Electronic resources and digital services (6)
Systems and IT services (7)
Archives and special collections (8)
Outreach, marketing, and communications (9)
Other (please specify) (10) __________________________________________________
Q6.6 How many years of experience do you have as a library employee?
Less than 1 year (1)
1–5 years (2)
6–10 years (3)
11–15 years (4)
16–20 years (5)
More than 20 years (6)
Q6.7 What is the highest level of education you have completed? (Select one)
High school diploma or equivalent (1)
Some college or associate degree (2)
Bachelor’s degree (3)
Master’s degree in library and information science (e.g., MLIS, MSLS) (4)
Master’s degree in another field (5)
Doctoral degree (e.g., PhD, EdD) (6)
Other (please specify) (7) __________________________________________________
Q6.8 What is your gender? (Select one)
Non-binary / third gender (3)
Prefer not to say (4)
Q6.9 What is your age range?
Under 25 (1)
65 and above (5)
Q6.10 How do you describe your ethnicity? (Select one or more)
End of Block: Demographic
Start of Block: End of Survey
Q7.1 Thank you for participating in our survey!
Your input is incredibly valuable to us and will contribute to our understanding of AI literacy among academic librarians. We appreciate the time and effort you have taken to share your experiences and opinions. The information gathered will help inform future professional development opportunities and address potential gaps in AI knowledge and skills.
We will carefully analyze the responses and share the findings with the academic library community. If you have any further comments or questions about the survey, please do not hesitate to contact us at [email protected].
Once again, thank you for your contribution to this important research. Your insights will help shape the future of AI in academic libraries.
Best regards,
University of New Mexico
End of Block: End of Survey
* Leo S. Lo is Dean, College of University Libraries and Learning Sciences at the University of New Mexico, email: [email protected] . ©2024 Leo S. Lo, Attribution-NonCommercial (https://creativecommons.org/licenses/by-nc/4.0/) CC BY-NC.
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As record numbers of workers quit their jobs, companies are busy trying to figure out how to make working conditions at their organization more attractive and more sustainable. Many companies boast flexible hours, good benefits, and, of course, higher pay. And some go further, looking closely at how roles in the organization can fulfill people’s psychological needs.
Business leaders recognize these emotional needs—whether it is the sense of reward workers have when they accomplish something, the frustration they feel when being micromanaged, the anger they experience after being treated unfairly, the longing they feel to be part of a group, or the desire they have for their work to be interesting and meaningful.
Yet many leaders mistakenly believe that only other professionals who have enjoyed similar success—and the financial rewards that come with it—truly value the nonfinancial aspects of their work. As we show in this article, that is simply not true.
People in lower-paying jobs also want their psychological needs at work to be satisfied. Yet data show that those needs are typically going unmet, far more often than is the case for higher earners.
Some of this may be unavoidable: for example, there is only so much autonomy one can feasibly grant a production line worker, while the job of a truck driver may be inherently lacking in social contact. However, most jobs could be enhanced to provide a much greater degree of psychological satisfaction.
In this article, we share novel data and analysis that illustrate the premium placed by all workers on psychologically satisfying work and how current work practices appear to be exacerbating existing inequalities. We also look at what business leaders can do to address the psychological needs of their lower-earning employees.
The good news is that, for the most part, companies have direct control over actions that can improve matters. Moreover, many of the practices that are needed—while requiring some time and effort—do not typically call for direct cash outlays. In fact, better satisfying workers’ psychological needs tends to correlate with higher revenues and profits.
For thousands of years, philosophers have argued about what constitutes a “good life”—a life with more progress, pleasure, or purpose. Now, modern sciences—neuroscience, endocrinology (hormones), psychology, anthropology, and evolutionary biology, among others—have caught up. All agree: there is much more to being a human than surviving and procreating. 1 Admittedly, the underlying motivators of human behavior—needs, desires, and preferences—may be evolutionary. In other words, they may be serving the goal of survival and procreation. Nevertheless, in modern societies, these needs, desires, and preferences include a large social and psychological component—for example, the need for belonging, friendship, and love. If these needs are not met, people’s reactions can be just as visceral as if their physical safety is threatened.
In a way, Maslow’s famous hierarchy of needs 2 Abraham Maslow, “A theory of human motivation,” Psychological Review , July 1943, Volume 50, Number 4. was both right and wrong at the same time. On the one hand, it recognized that people have many desires in addition to basic bodily needs such as water, food, and shelter. On the other hand, it assumed a fixed hierarchy where psychological needs—such as belonging and self-esteem—became relevant only after basic physical and safety needs were met. However, modern research has shown that these needs exist in parallel and that a person’s well-being can be enhanced—for example, by good social relationships— even if their basic physical and safety needs are not completely fulfilled. 3 Ed Diener and Louis Tay, “Needs and subjective well-being around the world,” Journal of Personality and Social Psychology , August 2011, Volume 101, Number 2.
It is no longer a surprise that people seek more from their employers than just a paycheck and a safe place to work. A preponderance of evidence suggests that “good work” also means satisfying employees’ psychological needs.
At all levels of income, the most important drivers of people’s job satisfaction were interpersonal relationships and having an interesting job.
One of the most prominent models of human motivation, extensively applied to organizational and employment research, is the self-determination theory by psychologists Richard Ryan and Edward Deci. 6 Delia O’Hara, “The intrinsic motivation of Richard Ryan and Edward Deci,” American Psychological Association, December 18, 2017. According to this theory, as well as a large body of empirical evidence, all employees have three basic psychological needs—competence, autonomy, and relatedness—and satisfying these needs promotes high-quality performance and broader well-being. 7 Edward L. Deci et al., “Self-determination theory in work organizations: The state of a science,” Annual Review of Organizational Psychology and Organizational Behavior , 2017, Volume 4. Additional studies, including McKinsey’s own research, have also found a link between positive outcomes (for both employer and employee) and employee engagement, 8 Jan-Emmanuel de Neve et al., “Employee well-being, productivity, and firm performance: Evidence and case studies,” in Global Happiness Policy Report , edited by Global Council for Happiness and Wellbeing, New York, NY: Sustainable Development Solutions Network, 2019. often embodied in questions about the degree to which employees consider their work to be interesting, and purposeful .
Drawing on this literature, as well as a large global data set generated by the International Social Survey Programme, 9 ISSP Research Group (2017), “International Social Survey Programme: Work Orientations IV - ISSP 2015.” we looked at how well employees’ psychological needs are satisfied in different types of occupations, ranging from managerial and professional jobs to lower-paid roles, such as those in customer service, cleaning, and waste disposal. Given the data available, we focused on five psychological needs: competence (related to the concept of mastery), autonomy (related to control and agency), relatedness (including positive relationships), meaning (proxied by how interesting individuals find their jobs), and purpose (proxied by how proud individuals are of their organizations).
The results are fascinating (Exhibit 2). First, the good news: on a net basis (deducting those who “disagree” or “strongly disagree” from those who “agree” or “strongly agree”) across all occupations, a greater proportion of workers feel that their psychological needs are satisfied. Even for those with the worst net score—plant and machine operators and assemblers who were asked about feelings of competence—around 48 percent said that they could use “almost all” or “a lot” of their past experience and skills, versus 23 percent who said that they could use “almost none” of their skills on the job. Similarly, while 23 percent of workers in elementary occupations (such as cleaners, couriers, and waiters) didn’t find their jobs to be interesting, more than half did.
In absolute terms, more global workers—whatever their role—feel more positive than negative about the degree to which their psychological needs are met.
The bad news, however, is that this is far less true for individuals employed in lower-paying, and often lower-skilled, jobs. The differences between, say, managers and people in elementary occupations are particularly large in terms of competence (the ability to use experience and skills) and meaning (how interesting the job is). In this sense, current work practices globally seem to be exacerbating inequalities rather than ameliorating them.
The data indicate that not all of this is inherent to, or directly determined by, the characteristics of each role. After all, some people in even the most manual, routine, repetitive, or poorly paid jobs still indicate that their work is meaningful, that they are proud of the organization they work for, and that their role enables them to express and satisfy their needs for competence, autonomy, and relatedness.
Indeed, the potential for any job to inspire is illustrated powerfully by the classic story of the three bricklayers working at St Paul’s Cathedral in London. Christopher Wren, one of the most highly acclaimed English architects in history, had been commissioned in the late 17th century to rebuild the cathedral. One day, he noticed three bricklayers on a scaffold, each of whom appeared to have very different levels of motivation and speed. He asked each of them the same question: “What are you doing?”
The first bricklayer, seemingly the least satisfied with his position, said, “I’m a bricklayer. I’m working hard laying bricks to feed my family.” The second bricklayer, slightly more engaged, replied, “I’m a builder. I’m building a wall.” The third bricklayer, who seemed to be working with the greatest amount of purpose, said, “I’m a cathedral builder. I’m building a great cathedral to The Almighty.” 10 Jim Baker, “The story of three bricklayers—a parable about the power of purpose,” Sacred Structures, April 9, 2019. In the modern workplace, great managers and leaders can elicit a sense of meaning by emphasizing, and reflecting with employees on, the ultimate contribution that their organization is making to society.
McKinsey research suggests that society is a key source of meaning for employees, along with company, customer, team, and individual. Together, they make up a collective, integrated whole that leaders can address. If average job satisfaction is weaker for lower-earning roles despite the many lower-paid individuals who do have their psychological needs met, organizations must be overlooking opportunities to do better. Luckily, they have many ways to refocus and improve their efforts.
Any organization claiming to be a good employer would want to address the imbalances highlighted above, as much as is operationally feasible. As we have written previously , positive and negative experiences at work—beyond pay and rations—have significant spillover consequences for people’s personal lives. 11 Diego Cortez et al., “Revisiting the link between job satisfaction and life satisfaction: The role of basic psychological needs,” Frontiers in Psychology , May 9, 2017, Volume 8, Article 680. For example, one study showed that a mother’s dissatisfaction with her job can contribute to her children’s behavioral problems. 12 Julian Barling and Karyl E. MacEwen, “Effects of maternal employment experiences on children’s behavior via mood, cognitive difficulties, and parenting behavior,” Journal of Marriage and Family , August 1991, Volume 53, Number 3.
However, in addition to the moral case for equalizing the scales on psychological well-being, there is also a strong business case. A comprehensive evidence base shows that higher employee satisfaction is associated with higher profitability 13 James K. Harter et al., “Business-unit-level relationship between employee satisfaction, employee engagement, and business outcomes: A meta-analysis,” Journal of Applied Psychology , April 2002, Volume 87, Number 2. and that this phenomenon is not confined to a company’s higher-earning roles. Consider the case of frontline customer service staff: one experiment showed that weekly sales for call center operators increased by 13 percent when the operators’ happiness increased by one point on a scale of one to five. 14 Clement Bellet et al., “Does employee happiness have an impact on productivity?,” Saïd Business School working paper 2019-13, October 17, 2019. Worker satisfaction and customer satisfaction tend to go hand in hand. 15 “Business-unit-level relationship between employee satisfaction, employee engagement, and business outcomes,” April 2002.
Another direct link from employee satisfaction to the business bottom line is through employee turnover. In the wake of the COVID-19 pandemic, more people than ever are leaving their jobs voluntarily , both in the United States and in other developed economies .
And while the competition for talent is heated among professionals such as software engineers and medics, vacancy rates in many low-paying jobs are also sky-high. Across the United States, the United Kingdom, and the European Union, unfilled roles abound in the hospitality, entertainment, and logistics sectors, among others. 16 McKinsey analysis based on data from the US Bureau of Labor Statistics, Eurostat, and the UK Office for National Statistics, accessed on May 11, 2022. For businesses, losing personnel means costly and time-consuming recruitment and retraining , not to mention lost output and productivity.
Psychological well-being at work is one of the most important factors in employees’ decisions to stay or to go . Regardless of income level, workers who “strongly agreed” that they were proud of the organization they worked for were significantly more likely also to say that they would turn down a job at another organization, even if it offered higher pay. Granted, people in higher-earning roles tended to be more loyal, but the difference in loyalty between staff who felt proud and staff who did not was dramatic across all income categories.
Whether motivated by equity considerations or bottom-line benefits, employers would do well to consider ways they can improve the working experience for lower earners.
To get started, leaders can think of this as a journey with six steps:
The best suggestions for how to redesign jobs or processes, or how to make the workplace more psychologically satisfying, will almost certainly come from workers themselves. Indeed, the process of discussing issues and opportunities and listening to employees’ daily experiences is itself a core part of creating positive change. Many businesses already routinely talk to their workers about employee engagement and satisfaction.
The best suggestions for how exactly to redesign jobs or processes, or make the workplace more psychologically satisfying, will almost certainly come from staff themselves.
However, it is vitally important to base these discussions on more than workers’ fundamental needs, such as physical safety and pay. The style of conversation should focus on both what people think about work and how they feel about work. Such discussions are likely to unleash a range of responses—both positive and negative—which leaders will need to harness both respectfully and skillfully.
In addition to intensive employee engagement processes, there are a number of practical behaviors that leaders can encourage through mindsets, communication, role modeling, training, and performance-management processes. For lower-earning employees, the actions and behaviors of immediate line managers can make an enormous difference. Some of the practices that have positive returns in almost every situation include the following:
Recognize competence: Frequently review a day’s work (with no judgment or blame) and ask what you as the manager or leader can do to make the next day easier. Thank and praise people for a job (well) done. Make the most of individuals’ skills through delegation. Provide regular, strength-based feedback oriented toward problem-solving.
For example, the plant and machine operators in Exhibit 2 who said that they were able to utilize their skills may still have had production line tasks that were fairly prescribed. But their factory organized short two-way briefings at every shift change, allowing workers to help make decisions about how operations are carried out.
Grant autonomy: Focus on the end goal of what is to be achieved and why and let employees decide—or at least give them a voice in—how to get there. Give frontline workers discretion over appropriate decisions. Ask employees how they feel about work and really listen to their answers.
For example, retail assistants who are given the discretion to accept customer returns or hand out vouchers in specific situations are more likely not only to make customers happier and more confident but also to feel better themselves.
Build connections: Set up regular (for example, daily) meetings at the beginning of each day (or shift) and allow time for socializing. Create regular breaks or events that help build social connections. Act decisively to eradicate any bullying or harassment. Praise and promote compassionate leaders .
For example, one skin care company whose sales agents work exclusively from home managed to maintain high levels of staff satisfaction by orchestrating regular one-on-one catch-ups, as well as virtual group get-togethers, throughout the COVID-19 pandemic, which allowed people to feel more connected to their colleagues. 17 Tera Allas et al., “Lessons on resilience for small and midsize businesses,” Harvard Business Review , June 3, 2021.
Instill meaning: Always explain the “why” behind tasks and link the reason to goals that go beyond making money (for example, being proud of the organization’s product or service). Help make work more interesting by upskilling people to be able to perform more complex or varied tasks. Simply ask people what would make their jobs more interesting.
For example, the workers in elementary occupations in Exhibit 2 who said that they still found their jobs meaningful may well have benefited from the same attitude that met President John F. Kennedy when he visited NASA in 1962. When the president came across a janitor in the hallway and asked him what his role was, the janitor replied, “I’m helping put a man on the moon.”
Discuss purpose: Set aside time for teams to reflect on the impact the company has on the world. Use one-on-one conversations to better understand workers’ individual sense of purpose and discuss how they can act on it in their work setting.
For example, for a worker at a clothing manufacturer, a manager can make the role more fulfilling by regularly sharing positive messages, photos, or videos from smiling customers wearing the company’s garments.
This advice may sound basic. We all know how to meet the psychological needs of the people in our lives—our children, our partners, our friends. We might even compliment, thank, and empathize with strangers.
We need to take these positive behaviors and apply them in the workplace as well—not only with peers but with employees at all levels of the organization. However routine their tasks, we can stop treating workers as cogs in a machine and start treating them as the wonderful human beings they are.
The authors wish to thank Jacqueline Brassey and Marino MB for their contributions to this article.
This article was edited by Rick Tetzeli, an executive editor in the New York office.
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Luxury yacht finds purpose and passion in scientific research.
Humpback Whales, Megaptera novaeangliae, provided the reason for a recent expedition to the Silver ... [+] Bank area of the Dominican Republic Marine Sanctuary. Made possible by a Yachts For Science match with Bering Yachts, with assistance from the government of the Dominican Republic, Blue Marine Foundation, Mission Blue, BOAT International, and additional nonprofits, the five day trip collected information valuable to the protection of humpback whale populations on board a private Bering yacht.
Imagine: One boat. Five days. Thirteen people. Thousands of humpback whales.
Nothing compares. Just ask Alexei Mikhailov, Founder and CEO of luxury superyacht builder Bering Yachts , who recently teamed up with Mission Blue through Yachts For Science , two nonprofits, for five days of research on humpback whales in the Silver Bank calving zone.
The Bering 92 Papillon as it prepared to carry the team to the research area of the Dominican ... [+] Republic's Silver Bank Marine Sanctuary to photo ID individual whales and collect eDNA as it was shed by passing cetaceans.
Anchoring eighty miles off the northeast shore of the Dominican Republic, the team quickly got to work as they were surrounded by humpback whales coming to Silver Bank to mate and birth calves. Learning, discovering, sharing academic as well as cultural knowledge, there was great communication among the scientists and crew.
Mikhailov described the profuse conversation and exchange of information while on board for the expedition, noting that the thirteen people represented nine different nationalities with a wealth of knowledge from previous work.
"I got first hand experience of how to be on board a yacht in collaboration with the expedition," Mikhailov said, emphasizing that "The amount of information, the density of information, the value of this information, was incredible." He added that not a moment was wasted. People eagerly shared their expertise with one another about various expeditions and research findings, along with details relating to culture and family. All that while surrounded by thousands of whales in this protected calving area was "Marvelous," Mikhailov exclaimed.
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The team of international researchers and crew aboard the Papillon for the five-day expedition to ... [+] the humpback whale breeding ground 80 miles from the northeast coast of the Dominican Republic.
Not your everyday superyacht experience, there is increasing interest in the use of superyachts as vehicles for research. Organizations like Yachts For Science specialize in matching marine research projects to superyachts, creating expeditions that explore everything from coral reefs and marine flora to whales and microalgae.
It takes a village. The long list of those who worked to make the expedition possible includes the ... [+] Government of the Dominican Republic, Mission Blue, Blue Marine Foundation, Blue Nature Alliance, Wyss Foundation, and Caribbean Cetacean Society. Bering Yachts connected through the Yachts for Science initiative, made possible by the Ocean Family Foundation, BOAT International, Nekton, EYOS, and others.
According to Mikhailov, the first four days were a little rough as the boat left from Freeport in the Bahamas en route to the Dominican Republic, experiencing rough weather with 25 knot winds. Many on board agreed that most research boats might not be very comfortable under those conditions. Papillon , the Bering 92 the group was on, was built for just such conditions. With its steel hull and two stabilizers, Papillon made walking, cooking, sleeping, even reading, comfortable with no issues.
The Silver Bank area of the Dominican Republic Marine Sanctuary is visited by as many as 3,000 ... [+] whales each year. By contrast, the privilege of visiting the whales in the sanctuary is extended to just 500 people annually. Alexei Mikhailov, Founder and CEO of Bering Yachts is bullish on getting Bering yacht owners engaged to experience what it's like to be a part of unique scientific expeditions, offering the use of their superyachts as a research base.© MAXBELLO
The government of the Dominican Republic has committed to protect 30 percent of its ocean areas within the Exclusive Economic Zone by 2030. The protected area would include coral reefs, deep-sea corals, seamounts, whale aggregations, and a section of the deepest zone of the Atlantic Ocean, the Puerto Rico Trench. This expedition set out to photo ID individual whales and collect eDNA samples to determine which species have visited the area.
Humpback fluke as the whale completes a visit to the surface.
Mikhailov is ready to continue to partner with such expeditions in the future, hoping that his participation will encourage others, triggering a robust Caribbean effort. Bering has compiled footage for a video recording highlights of the expedition in anticipation of completing many more in additional locations from the Mediterranean to the Antarctic.
The Bering 92 Papillon measures 29.08 meters with a 6.74 meter beam and a 1.85 meter draft. She carries a 3.6 meter tender, ten guests, and four crew across three decks. Equipped with two Cummins QSM engines and 1220 hp, she has a range of 3500 nautical miles at cruise speed with a maximum speed of 13 knots. Five solar panels, a saloon, a formal dining and entertainment area, an aft al fresco cockpit, swim platform, wet bar with grill, jetski, and spacious crew area, Papillon is engineered and outfitted for a combination of safety and comfort.
Designed to weather the storm, Bering yachts are built to be safe and capable for both owners and passengers. Sturdy, modern designs minimize noise and vibration, reduce fuel consumption, and engage alternative energy solutions. Active participants in conservation efforts, not just for videos and marketing, but with genuine commitment, Bering is positioning itself as a steward of the environment it operates in to improve the future for a thriving marine ecosystem.
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A new report by 451 Research, part of S&P Global Market Intelligence, finds that computing in the cloud is five times more energy efficient than on-premises data centers in the Asia Pacific region.
1. data center infrastructure designed to increase efficiency, 2. improving how we cool our facilities.
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Countries across the OECD are facing long-term fiscal pressures in areas such as health, ageing, climate change, and defence. At the same time, governments must grapple with mounting debt levels, rising interest rates and high levels of uncertainty. In this increasingly constrained fiscal environment, reconciling new and emerging spending pressures with already stretched public finances requires high-quality budget institutions and processes.
Key messages, oecd countries are facing long-term fiscal pressures..
The long-term fiscal pressures associated with climate change and reducing greenhouse gas emissions; ageing populations and shrinking labour supply; and rising health care and social care costs continued to mount. Interest expenditures are now increasing significantly. The current geopolitical tensions are adding further new spending pressures, including in the defence area, as well as greater economic uncertainty.
Budgets are about more than money. They are a statement of a nation’s priorities. Engagement and oversight of the budget process by Independent Fiscal Institutions, parliaments and the public is fundamental to democratic governance and trust in government. Empowering the public to understand fiscal challenges is essential for generating the will to solve them
Governments must have credible public financial management frameworks to build trust in budgetary governance and maintain enough fiscal space to be able to finance crisis responses when needed.
Each of the crises of recent years has shown the importance of preserving the resilience of public finances; countries need to be able to finance large and unexpected expenditures, such as in the aftermath of major natural disasters, to support a distressed sector or to address the consequences of a major pandemic. However, debt levels in OECD countries have risen significantly in recent years.
Between 2019 and 2021 general government expenditures as a percentage of GDP increased by 5.4 percentage points, from 40.9% in 2019. This increase is largely explained by the COVID-19 pandemic, which led to significant economic disruption. This prompted large-scale fiscal stimuluses, including increased spending on healthcare, social welfare programmes, and support for businesses and individuals affected by the pandemic, while at the same time GDP was falling.
The fiscal balance is the difference between a government’s revenues and its expenditures. It signals if public accounts are balanced or if there are surpluses or deficits. Recurrent deficits over time imply the accumulation of public debt and may send worrying signals to consumers and investors about the sustainability of public accounts which, in turn, may deter consumption or investment decisions. Nonetheless, if debt is kept at a sustainable level, deficits can help to finance necessary public investment, or in exceptional circumstances, such as unexpected external shocks (e.g. pandemics, wars or natural disasters), can contribute to maintaining living conditions and preserving social stability.
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Purpose of Research Report. The purpose of a research report is to communicate the results of a research study to a specific audience, such as peers in the same field, stakeholders, or the general public. The report provides a detailed description of the research methods, findings, and conclusions. Some common purposes of a research report include:
A research report is a well-crafted document that outlines the processes, data, and findings of a systematic investigation. It is an important document that serves as a first-hand account of the research process, and it is typically considered an objective and accurate source of information.
Research reports are recorded data prepared by researchers or statisticians after analyzing the information gathered by conducting organized research, typically in the form of surveys or qualitative methods. A research report is a reliable source to recount details about a conducted research. It is most often considered to be a true testimony ...
The process of writing a report. Analyse the assignment task. Establish the purpose and scope of the report and identify audience. Prepare a draft plan using headings. Investigate prior research. Design and plan your research. Conduct your research. Analyse the results. Write first draft.
A research report is a collection of contextual data, gathered through organized research, that provides new insights into a particular challenge (which, for this article, is business-related). Research reports are a time-tested method for distilling large amounts of data into a narrow band of focus. Their effectiveness often hinges on whether ...
The purpose of research can vary depending on the field of study, the research question, and the intended audience. In general, research can be used to: Generate new knowledge and theories. Test existing theories or hypotheses. Identify trends or patterns. Gather information for decision-making. Evaluate the effectiveness of programs, policies ...
An abstract is a concise summary that helps readers to quickly assess the content and direction of your paper. It should be brief, written in a single paragraph and cover: the scope and purpose of your report; an overview of methodology; a summary of the main findings or results; principal conclusions or significance of the findings; and recommendations made.
There are three main forms of reports: factual, instructional and persuasive; each has a different purpose and will require different arguments and evidence to achieve that purpose. It will help you write good reports if you know what you are trying to achieve before you start your report. Factual. Instructional. Persuasive.
There are five MAJOR parts of a Research Report: 1. Introduction 2. Review of Literature 3. Methods 4. Results 5. Discussion. As a general guide, the Introduction, Review of Literature, and Methods should be about 1/3 of your paper, Discussion 1/3, then Results 1/3. Section 1: Cover Sheet (APA format cover sheet) optional, if required.
A research report is one big argument about how and why you came up with your conclusions. To make it a convincing argument, a typical guiding structure has developed. ... It might be a good idea to read through the following chapters about writing a research report, look at the purpose of each chapter, and then come back to this section.
Step 4: Organizing Research and the Writer's Ideas. When your research is complete, you will organize your findings and decide which sources to cite in your paper. You will also have an opportunity to evaluate the evidence you have collected and determine whether it supports your thesis, or the focus of your paper.
Use the section headings (outlined above) to assist with your rough plan. Write a thesis statement that clarifies the overall purpose of your report. Jot down anything you already know about the topic in the relevant sections. 3 Do the Research. Steps 1 and 2 will guide your research for this report.
Research Report Definition. According to C. A. Brown, "A report is a communication from someone who has information to someone who wants to use that information.". According to Goode and Hatt, "The preparation of report is the final stage of research, and it's purpose is to convey to the interested persons the whole result of the study, in sufficient detail and so arranged as to enable ...
Abstract. This guide for writers of research reports consists of practical suggestions for writing a report that is clear, concise, readable, and understandable. It includes suggestions for terminology and notation and for writing each section of the report—introduction, method, results, and discussion. Much of the guide consists of ...
The purpose of research is to further understand the world and to learn how this knowledge can be applied to better everyday life. It is an integral part of problem solving. Although research can take many forms, there are three main purposes of research: Exploratory: Exploratory research is the first research to be conducted around a problem ...
A research report is a document that conveys the outcomes of a study or investigation. Its purpose is to communicate the research's findings, conclusions, and implications to a particular audience. This report aims to offer a comprehensive and unbiased overview of the research process, methodology, and results.
A research aim is a broad statement indicating the general purpose of your research project. It should appear in your introduction at the end of your problem statement, before your research objectives. Research objectives are more specific than your research aim. They indicate the specific ways you'll address the overarching aim.
1. As an investigative process, it originates with a question. It attempts to satisfy an unanswered question that is in the mind of a researcher. 2. Research demands a clear articulation of a goal, and a clear statement of the problem is a pre-condition of any research. 3.
The research report contains four main areas: Introduction- What is the issue? What is known? What is not known? What are you trying to find out? This sections ends with the purpose and specific aims of the study. Methods- The recipe for the study. If someone wanted to perform the same study, what information would they need? How will you ...
A purpose statement clearly defines the objective of your qualitative or quantitative research. Learn how to create one through unique and real-world examples.
Research Report: A research report is a document prepared by an analyst or strategist who is a part of the investment research team in a stock brokerage or investment bank . A research report may ...
A research report is one big argument how and why you came up with your conclusions. To make it a convincing argument, a typical guiding structure has developed. ... In the following chapter, we present the purpose of each report section and provide some guidance how to write them. 4.4.1 Management Summary. The management summary is the ...
Report of a collaborative workshop in the UK to discuss social research priorities on visual impairment', British Journal of Visual Impairment, 25(2): 178-189. Hannah, D.R. and Lautsch, B.A. (2010) 'Counting in Qualitative Research: Why to Conduct it, When to Avoid it, and When to Closet it', in Journal of Management Inquiry, 20(1): 14 ...
College & Research Libraries (C&RL) is the official, bi-monthly, ... The U.S. Department of Education's recent report (2023) on AI emphasizes the growing importance of AI literacy for educators and students, highlighting the necessity of understanding and integrating AI technologies in educational settings. ... The purpose of this study is to ...
Yet companies do a better job of addressing the psychological needs of higher-earning employees than lower-earning colleagues. One of the most prominent models of human motivation, extensively applied to organizational and employment research, is the self-determination theory by psychologists Richard Ryan and Edward Deci. 6 Delia O'Hara, "The intrinsic motivation of Richard Ryan and Edward ...
Many on board agreed that most research boats might not be very comfortable under those conditions. Papillon, the Bering 92 the group was on, was built for just such conditions. With its steel ...
Local, state, and federal government websites often end in .gov. Commonwealth of Pennsylvania government websites and email systems use "pennsylvania.gov" or "pa.gov" at the end of the address.
A new report by 451 Research, part of S&P Global Market Intelligence, finds that computing in the cloud is five times more energy efficient than on-premises data centers in the Asia Pacific region. ... These purpose-built accelerators enable AWS to efficiently execute AI models at scale, reducing the carbon footprint for similar workloads ...
Public finance is the economic field focusing on the financial activities of government entities at various levels. Our work examines government expenditures, including public services, infrastructure, social welfare, defence, education, healthcare, and more. These are outlined in the national budget, reflecting financial commitments to meet obligations and provide essential services. Our ...