review of literature in business research

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Business Research Methods

• Literature Review
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What is a literature review

“A literature review is a description of the literature relevant to a particular field or topic. It gives an overview of what has been said, who the key writers are, what are the prevailing theories and hypotheses, what questions are being asked, and what methods and methodologies are appropriate and useful" (Emerald Insight).

A literature review  is not  just a summary of everything you have read on the topic.  It is a critical analysis of the existing research relevant to your topic, and you should show how the literature relates to your topic and identify any gaps in the area of research. Our Learning Hub has lots of useful guidance for carrying out a  Literature Review .

How is it different?

It's on a much larger scale from your research for previous modules.

You may need to devise new ways of searching and managing your results.

Think about:

• Using RefWorks to manage your references
• Setting up alerts to retrieve new results for your searches

How to carry out a review

• Devise a search strategy
• Search systematically
• Read critically – i.e. deconstruct the material
• Put it all back together – reconstruct

1. Devise a search strategy

Think about the sort of research that would help your project.

1. What subject areas does you topic fall into?

2. What possible sources could you use? Think broadly, for example:

• Company reports
• Industry profiles
• Market research
• Financial reports
• Newspaper articles
• Journal articles

3. What don't you want?  What are the limits? For example, geographical restrictions or time periods.

2. Search systematically

• Plan your search first, thinking about your keywords
• Use the pages on this LibGuide to identify quality resources
• Use the tutorials and advice on those pages to improve your searches
• Use the  Inter Library Loans service  to borrow books or to obtain copies of papers which aren't in the library
• Speak to the Business Librarians for help with your searches, or to recommend new items for library stock
• Look at the programme of  Succeed @ Tees workshops , and attend any which are relevant.

3. Read critically - i.e. deconstruct your results

Read critically, argument: .

• What is the main argument?
• Is the main argument clear and logical?
• What is the evidence?
• Is the evidence valid?
• Does the evidence support the conclusions?

4. Put it all back together – reconstruct

• Group your topic areas – develop themes
• Briefly summarise key findings

- See Phrasebank for suggestions of how to phrase your sentences.

• Use the academic papers as examples of the style of academic writing as well as for their content
• Check your referencing

Succeed@Tees Workshops: Writing a Literature Review

The following workshop will help you to develop your skills in writing a literature review :

Writing a literature review

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• Last Updated: Apr 23, 2024 11:08 AM
• URL: https://libguides.tees.ac.uk/business_research

• UConn Library
• Literature Review: The What, Why and How-to Guide
• Introduction

Literature Review: The What, Why and How-to Guide — Introduction

• Getting Started
• How to Pick a Topic
• Strategies to Find Sources
• Evaluating Sources & Lit. Reviews
• Tips for Writing Literature Reviews
• Writing Literature Review: Useful Sites
• Citation Resources
• Other Academic Writings

What are Literature Reviews?

So, what is a literature review? "A literature review is an account of what has been published on a topic by accredited scholars and researchers. In writing the literature review, your purpose is to convey to your reader what knowledge and ideas have been established on a topic, and what their strengths and weaknesses are. As a piece of writing, the literature review must be defined by a guiding concept (e.g., your research objective, the problem or issue you are discussing, or your argumentative thesis). It is not just a descriptive list of the material available, or a set of summaries." Taylor, D.  The literature review: A few tips on conducting it . University of Toronto Health Sciences Writing Centre.

Goals of Literature Reviews

What are the goals of creating a Literature Review?  A literature could be written to accomplish different aims:

• To develop a theory or evaluate an existing theory
• To summarize the historical or existing state of a research topic
• Identify a problem in a field of research 

Baumeister, R. F., & Leary, M. R. (1997). Writing narrative literature reviews .  Review of General Psychology , 1 (3), 311-320.

What kinds of sources require a Literature Review?

• A research paper assigned in a course
• A thesis or dissertation
• A grant proposal
• An article intended for publication in a journal

All these instances require you to collect what has been written about your research topic so that you can demonstrate how your own research sheds new light on the topic.

Types of Literature Reviews

What kinds of literature reviews are written?

Narrative review: The purpose of this type of review is to describe the current state of the research on a specific topic/research and to offer a critical analysis of the literature reviewed. Studies are grouped by research/theoretical categories, and themes and trends, strengths and weakness, and gaps are identified. The review ends with a conclusion section which summarizes the findings regarding the state of the research of the specific study, the gaps identify and if applicable, explains how the author's research will address gaps identify in the review and expand the knowledge on the topic reviewed.

• Example : Predictors and Outcomes of U.S. Quality Maternity Leave: A Review and Conceptual Framework:  10.1177/08948453211037398  

Systematic review : "The authors of a systematic review use a specific procedure to search the research literature, select the studies to include in their review, and critically evaluate the studies they find." (p. 139). Nelson, L. K. (2013). Research in Communication Sciences and Disorders . Plural Publishing.

• Example : The effect of leave policies on increasing fertility: a systematic review:  10.1057/s41599-022-01270-w

Meta-analysis : "Meta-analysis is a method of reviewing research findings in a quantitative fashion by transforming the data from individual studies into what is called an effect size and then pooling and analyzing this information. The basic goal in meta-analysis is to explain why different outcomes have occurred in different studies." (p. 197). Roberts, M. C., & Ilardi, S. S. (2003). Handbook of Research Methods in Clinical Psychology . Blackwell Publishing.

• Example : Employment Instability and Fertility in Europe: A Meta-Analysis:  10.1215/00703370-9164737

Meta-synthesis : "Qualitative meta-synthesis is a type of qualitative study that uses as data the findings from other qualitative studies linked by the same or related topic." (p.312). Zimmer, L. (2006). Qualitative meta-synthesis: A question of dialoguing with texts .  Journal of Advanced Nursing , 53 (3), 311-318.

• Example : Women’s perspectives on career successes and barriers: A qualitative meta-synthesis:  10.1177/05390184221113735

Literature Reviews in the Health Sciences

• UConn Health subject guide on systematic reviews Explanation of the different review types used in health sciences literature as well as tools to help you find the right review type
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• Last Updated: Sep 21, 2022 2:16 PM
• URL: https://guides.lib.uconn.edu/literaturereview

Libraries | Research Guides

Literature reviews, what is a literature review, learning more about how to do a literature review.

• Planning the Review
• The Research Question
• Choosing Where to Search
• Organizing the Review
• Writing the Review

A literature review is a review and synthesis of existing research on a topic or research question. A literature review is meant to analyze the scholarly literature, make connections across writings and identify strengths, weaknesses, trends, and missing conversations. A literature review should address different aspects of a topic as it relates to your research question. A literature review goes beyond a description or summary of the literature you have read. 

• Sage Research Methods Core Collection This link opens in a new window SAGE Research Methods supports research at all levels by providing material to guide users through every step of the research process. SAGE Research Methods is the ultimate methods library with more than 1000 books, reference works, journal articles, and instructional videos by world-leading academics from across the social sciences, including the largest collection of qualitative methods books available online from any scholarly publisher. – Publisher

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• Last Updated: Jan 17, 2024 10:05 AM
• URL: https://libguides.northwestern.edu/literaturereviews

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Literature Reviews

• What is a literature review?
• Steps in the Literature Review Process
• Define your research question
• Determine inclusion and exclusion criteria
• Choose databases and search
• Review Results
• Synthesize Results
• Analyze Results
• Librarian Support

What is a Literature Review?

A literature or narrative review is a comprehensive review and analysis of the published literature on a specific topic or research question. The literature that is reviewed contains: books, articles, academic articles, conference proceedings, association papers, and dissertations. It contains the most pertinent studies and points to important past and current research and practices. It provides background and context, and shows how your research will contribute to the field. 

A literature review should: 

• Provide a comprehensive and updated review of the literature;
• Explain why this review has taken place;
• Articulate a position or hypothesis;
• Acknowledge and account for conflicting and corroborating points of view

From  S age Research Methods

Purpose of a Literature Review

A literature review can be written as an introduction to a study to:

• Demonstrate how a study fills a gap in research
• Compare a study with other research that's been done

Or it can be a separate work (a research article on its own) which:

• Organizes or describes a topic
• Describes variables within a particular issue/problem

Limitations of a Literature Review

Some of the limitations of a literature review are:

• It's a snapshot in time. Unlike other reviews, this one has beginning, a middle and an end. There may be future developments that could make your work less relevant.
• It may be too focused. Some niche studies may miss the bigger picture.
• It can be difficult to be comprehensive. There is no way to make sure all the literature on a topic was considered.
• It is easy to be biased if you stick to top tier journals. There may be other places where people are publishing exemplary research. Look to open access publications and conferences to reflect a more inclusive collection. Also, make sure to include opposing views (and not just supporting evidence).

Source: Grant, Maria J., and Andrew Booth. “A Typology of Reviews: An Analysis of 14 Review Types and Associated Methodologies.” Health Information & Libraries Journal, vol. 26, no. 2, June 2009, pp. 91–108. Wiley Online Library, doi:10.1111/j.1471-1842.2009.00848.x.

Meryl Brodsky : Communication and Information Studies

Hannah Chapman Tripp : Biology, Neuroscience

Carolyn Cunningham : Human Development & Family Sciences, Psychology, Sociology

Larayne Dallas : Engineering

Janelle Hedstrom : Special Education, Curriculum & Instruction, Ed Leadership & Policy ​

Susan Macicak : Linguistics

Imelda Vetter : Dell Medical School

For help in other subject areas, please see the guide to library specialists by subject .

Periodically, UT Libraries runs a workshop covering the basics and library support for literature reviews. While we try to offer these once per academic year, we find providing the recording to be helpful to community members who have missed the session. Following is the most recent recording of the workshop, Conducting a Literature Review. To view the recording, a UT login is required.

• October 26, 2022 recording
• Last Updated: Oct 26, 2022 2:49 PM
• URL: https://guides.lib.utexas.edu/literaturereviews

How To Write An A-Grade Literature Review

3 straightforward steps (with examples) + free template.

By: Derek Jansen (MBA) | Expert Reviewed By: Dr. Eunice Rautenbach | October 2019

Quality research is about building onto the existing work of others , “standing on the shoulders of giants”, as Newton put it. The literature review chapter of your dissertation, thesis or research project is where you synthesise this prior work and lay the theoretical foundation for your own research.

Long story short, this chapter is a pretty big deal, which is why you want to make sure you get it right . In this post, I’ll show you exactly how to write a literature review in three straightforward steps, so you can conquer this vital chapter (the smart way).

Overview: The Literature Review Process

• Understanding the “ why “
• Finding the relevant literature
• Cataloguing and synthesising the information
• Outlining & writing up your literature review
• Example of a literature review

But first, the “why”…

Before we unpack how to write the literature review chapter, we’ve got to look at the why . To put it bluntly, if you don’t understand the function and purpose of the literature review process, there’s no way you can pull it off well. So, what exactly is the purpose of the literature review?

Well, there are (at least) four core functions:

• For you to gain an understanding (and demonstrate this understanding) of where the research is at currently, what the key arguments and disagreements are.
• For you to identify the gap(s) in the literature and then use this as justification for your own research topic.
• To help you build a conceptual framework for empirical testing (if applicable to your research topic).
• To inform your methodological choices and help you source tried and tested questionnaires (for interviews ) and measurement instruments (for surveys ).

Most students understand the first point but don’t give any thought to the rest. To get the most from the literature review process, you must keep all four points front of mind as you review the literature (more on this shortly), or you’ll land up with a wonky foundation.

Okay – with the why out the way, let’s move on to the how . As mentioned above, writing your literature review is a process, which I’ll break down into three steps:

• Finding the most suitable literature
• Understanding , distilling and organising the literature
• Planning and writing up your literature review chapter

Importantly, you must complete steps one and two before you start writing up your chapter. I know it’s very tempting, but don’t try to kill two birds with one stone and write as you read. You’ll invariably end up wasting huge amounts of time re-writing and re-shaping, or you’ll just land up with a disjointed, hard-to-digest mess . Instead, you need to read first and distil the information, then plan and execute the writing.

Step 1: Find the relevant literature

Naturally, the first step in the literature review journey is to hunt down the existing research that’s relevant to your topic. While you probably already have a decent base of this from your research proposal , you need to expand on this substantially in the dissertation or thesis itself.

Essentially, you need to be looking for any existing literature that potentially helps you answer your research question (or develop it, if that’s not yet pinned down). There are numerous ways to find relevant literature, but I’ll cover my top four tactics here. I’d suggest combining all four methods to ensure that nothing slips past you:

Method 1 – Google Scholar Scrubbing

Google’s academic search engine, Google Scholar , is a great starting point as it provides a good high-level view of the relevant journal articles for whatever keyword you throw at it. Most valuably, it tells you how many times each article has been cited, which gives you an idea of how credible (or at least, popular) it is. Some articles will be free to access, while others will require an account, which brings us to the next method.

Method 2 – University Database Scrounging

Generally, universities provide students with access to an online library, which provides access to many (but not all) of the major journals.

So, if you find an article using Google Scholar that requires paid access (which is quite likely), search for that article in your university’s database – if it’s listed there, you’ll have access. Note that, generally, the search engine capabilities of these databases are poor, so make sure you search for the exact article name, or you might not find it.

Method 3 – Journal Article Snowballing

At the end of every academic journal article, you’ll find a list of references. As with any academic writing, these references are the building blocks of the article, so if the article is relevant to your topic, there’s a good chance a portion of the referenced works will be too. Do a quick scan of the titles and see what seems relevant, then search for the relevant ones in your university’s database.

Method 4 – Dissertation Scavenging

Similar to Method 3 above, you can leverage other students’ dissertations. All you have to do is skim through literature review chapters of existing dissertations related to your topic and you’ll find a gold mine of potential literature. Usually, your university will provide you with access to previous students’ dissertations, but you can also find a much larger selection in the following databases:

• Open Access Theses & Dissertations
• Stanford SearchWorks

Keep in mind that dissertations and theses are not as academically sound as published, peer-reviewed journal articles (because they’re written by students, not professionals), so be sure to check the credibility of any sources you find using this method. You can do this by assessing the citation count of any given article in Google Scholar. If you need help with assessing the credibility of any article, or with finding relevant research in general, you can chat with one of our Research Specialists .

Alright – with a good base of literature firmly under your belt, it’s time to move onto the next step.

Need a helping hand?

Step 2: Log, catalogue and synthesise

Once you’ve built a little treasure trove of articles, it’s time to get reading and start digesting the information – what does it all mean?

While I present steps one and two (hunting and digesting) as sequential, in reality, it’s more of a back-and-forth tango – you’ll read a little , then have an idea, spot a new citation, or a new potential variable, and then go back to searching for articles. This is perfectly natural – through the reading process, your thoughts will develop , new avenues might crop up, and directional adjustments might arise. This is, after all, one of the main purposes of the literature review process (i.e. to familiarise yourself with the current state of research in your field).

As you’re working through your treasure chest, it’s essential that you simultaneously start organising the information. There are three aspects to this:

• Logging reference information
• Building an organised catalogue
• Distilling and synthesising the information

I’ll discuss each of these below:

2.1 – Log the reference information

As you read each article, you should add it to your reference management software. I usually recommend Mendeley for this purpose (see the Mendeley 101 video below), but you can use whichever software you’re comfortable with. Most importantly, make sure you load EVERY article you read into your reference manager, even if it doesn’t seem very relevant at the time.

2.2 – Build an organised catalogue

In the beginning, you might feel confident that you can remember who said what, where, and what their main arguments were. Trust me, you won’t. If you do a thorough review of the relevant literature (as you must!), you’re going to read many, many articles, and it’s simply impossible to remember who said what, when, and in what context . Also, without the bird’s eye view that a catalogue provides, you’ll miss connections between various articles, and have no view of how the research developed over time. Simply put, it’s essential to build your own catalogue of the literature.

I would suggest using Excel to build your catalogue, as it allows you to run filters, colour code and sort – all very useful when your list grows large (which it will). How you lay your spreadsheet out is up to you, but I’d suggest you have the following columns (at minimum):

• Author, date, title – Start with three columns containing this core information. This will make it easy for you to search for titles with certain words, order research by date, or group by author.
• Categories or keywords – You can either create multiple columns, one for each category/theme and then tick the relevant categories, or you can have one column with keywords.
• Key arguments/points – Use this column to succinctly convey the essence of the article, the key arguments and implications thereof for your research.
• Context – Note the socioeconomic context in which the research was undertaken. For example, US-based, respondents aged 25-35, lower- income, etc. This will be useful for making an argument about gaps in the research.
• Methodology – Note which methodology was used and why. Also, note any issues you feel arise due to the methodology. Again, you can use this to make an argument about gaps in the research.
• Quotations – Note down any quoteworthy lines you feel might be useful later.
• Notes – Make notes about anything not already covered. For example, linkages to or disagreements with other theories, questions raised but unanswered, shortcomings or limitations, and so forth.

If you’d like, you can try out our free catalog template here (see screenshot below).

2.3 – Digest and synthesise

Most importantly, as you work through the literature and build your catalogue, you need to synthesise all the information in your own mind – how does it all fit together? Look for links between the various articles and try to develop a bigger picture view of the state of the research. Some important questions to ask yourself are:

• What answers does the existing research provide to my own research questions ?
• Which points do the researchers agree (and disagree) on?
• How has the research developed over time?
• Where do the gaps in the current research lie?

To help you develop a big-picture view and synthesise all the information, you might find mind mapping software such as Freemind useful. Alternatively, if you’re a fan of physical note-taking, investing in a large whiteboard might work for you.

Step 3: Outline and write it up!

Once you’re satisfied that you have digested and distilled all the relevant literature in your mind, it’s time to put pen to paper (or rather, fingers to keyboard). There are two steps here – outlining and writing:

3.1 – Draw up your outline

Having spent so much time reading, it might be tempting to just start writing up without a clear structure in mind. However, it’s critically important to decide on your structure and develop a detailed outline before you write anything. Your literature review chapter needs to present a clear, logical and an easy to follow narrative – and that requires some planning. Don’t try to wing it!

Naturally, you won’t always follow the plan to the letter, but without a detailed outline, you’re more than likely going to end up with a disjointed pile of waffle , and then you’re going to spend a far greater amount of time re-writing, hacking and patching. The adage, “measure twice, cut once” is very suitable here.

In terms of structure, the first decision you’ll have to make is whether you’ll lay out your review thematically (into themes) or chronologically (by date/period). The right choice depends on your topic, research objectives and research questions, which we discuss in this article .

Once that’s decided, you need to draw up an outline of your entire chapter in bullet point format. Try to get as detailed as possible, so that you know exactly what you’ll cover where, how each section will connect to the next, and how your entire argument will develop throughout the chapter. Also, at this stage, it’s a good idea to allocate rough word count limits for each section, so that you can identify word count problems before you’ve spent weeks or months writing!

PS – check out our free literature review chapter template…

3.2 – Get writing

With a detailed outline at your side, it’s time to start writing up (finally!). At this stage, it’s common to feel a bit of writer’s block and find yourself procrastinating under the pressure of finally having to put something on paper. To help with this, remember that the objective of the first draft is not perfection – it’s simply to get your thoughts out of your head and onto paper, after which you can refine them. The structure might change a little, the word count allocations might shift and shuffle, and you might add or remove a section – that’s all okay. Don’t worry about all this on your first draft – just get your thoughts down on paper.

Once you’ve got a full first draft (however rough it may be), step away from it for a day or two (longer if you can) and then come back at it with fresh eyes. Pay particular attention to the flow and narrative – does it fall fit together and flow from one section to another smoothly? Now’s the time to try to improve the linkage from each section to the next, tighten up the writing to be more concise, trim down word count and sand it down into a more digestible read.

Once you’ve done that, give your writing to a friend or colleague who is not a subject matter expert and ask them if they understand the overall discussion. The best way to assess this is to ask them to explain the chapter back to you. This technique will give you a strong indication of which points were clearly communicated and which weren’t. If you’re working with Grad Coach, this is a good time to have your Research Specialist review your chapter.

Finally, tighten it up and send it off to your supervisor for comment. Some might argue that you should be sending your work to your supervisor sooner than this (indeed your university might formally require this), but in my experience, supervisors are extremely short on time (and often patience), so, the more refined your chapter is, the less time they’ll waste on addressing basic issues (which you know about already) and the more time they’ll spend on valuable feedback that will increase your mark-earning potential.

Literature Review Example

In the video below, we unpack an actual literature review so that you can see how all the core components come together in reality.

Let’s Recap

In this post, we’ve covered how to research and write up a high-quality literature review chapter. Let’s do a quick recap of the key takeaways:

• It is essential to understand the WHY of the literature review before you read or write anything. Make sure you understand the 4 core functions of the process.
• The first step is to hunt down the relevant literature . You can do this using Google Scholar, your university database, the snowballing technique and by reviewing other dissertations and theses.
• Next, you need to log all the articles in your reference manager , build your own catalogue of literature and synthesise all the research.
• Following that, you need to develop a detailed outline of your entire chapter – the more detail the better. Don’t start writing without a clear outline (on paper, not in your head!)
• Write up your first draft in rough form – don’t aim for perfection. Remember, done beats perfect.
• Refine your second draft and get a layman’s perspective on it . Then tighten it up and submit it to your supervisor.

Psst… there’s more!

This post is an extract from our bestselling short course, Literature Review Bootcamp . If you want to work smart, you don't want to miss this .

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38 Comments

Thank you very much. This page is an eye opener and easy to comprehend.

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You’re welcome, Yinka. Thank you for the kind words. All the best writing your literature review.

Thank you for a very useful literature review session. Although I am doing most of the steps…it being my first masters an Mphil is a self study and one not sure you are on the right track. I have an amazing supervisor but one also knows they are super busy. So not wanting to bother on the minutae. Thank you.

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if anyone is to get a paper done on time and in the best way possible, GRADCOACH is certainly the go to area!

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Thank you so much Derek for such useful information on writing up a good literature review. I am at a stage where I need to start writing my one. My proposal was accepted late last year but I honestly did not know where to start

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Very comprehensive and eye opener for me as beginner in postgraduate study. Well explained and easy to understand. Appreciate and good reference in guiding me in my research journey. Thank you

Thank you. I requested to download the free literature review template, however, your website wouldn’t allow me to complete the request or complete a download. May I request that you email me the free template? Thank you.

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BA 302 Business Communication - Research: Literature Reviews

• Research Process
• Literature Reviews
• Evaluating Resources
• Search Techniques

Steps to Creating a Literature Review

Step 1: Planning your search

Step 2: Selecting a database

Step 3: Conducting your search

Step 4: Evaluating your results

Step 5: Managing your references

What is a Literature Review?

A literature review is a systematic survey of the scholarly literature published on a given topic.  Rather than providing a new research insight, a literature review lays the groundwork for an in-depth research project analyzing previous research. Type of documents surveyed will vary depending on the field, but can include:

• journal articles,
• dissertations.

A thorough literature review will also require surveying what librarians call "gray literature," which includes difficult-to-locate documents such as:

• technical reports
• government publications
• working papers

Purpose of the Lit Review

What's the point, purposes of the literature review.

• Delimit the research problem
• Avoid fruitless approaches
• Identify avenues of future research
• Seek new lines of inquiry
• Gain methodological insight

Reasons for Conducting a Literature Review

• Distinguishing what has been done from what needs to be done
• Discovering important variables relevant to the topic
• Synthesizing and gaining new perspective
• Identifying relationships between ideas and practices
• Establishing the context of the topic
• Rationalizing the significance of the problem
• Enhancing and acquiring subject vocabulary
• Understanding the structure of the subject
• Relating ideas and theory to applications
• Identifying main methodologies and research techniques that have been used
• Placing research in a historical context to show familiarity with state-of-art development

Questions to consider

• What is the overarching question or problem your literature review seeks to address?
• How much familiarity do you already have with the field? Are you already familiar with common methodologies or professional vocabularies?
• What types of strategies or questions have others in your field pursued?
• How will you synthesize or summarize the information you gather?
• What do you or others perceive to be lacking in your field?
• Is your topic broad? How could it be narrowed?
• Can you articulate why your topic is important in your field?

Adapted from Hart, C. (1998).  Doing a literature review : Releasing the social science research imagination. London: Sage. As cited in Randolph, Justus. “A Guide to Writing the Dissertation Literature Review.” Practical Assessment, Research and Evaluation , 14(13), p. 2.

Acknowledgements

Merinda Hensley gave permision for content to be  borrowed by permission from Literature Review: Demystified LibGuide from the University of  Illnois  at Urbana-Champaign.

Getting Started

Once you've decided what you want to write about you will need to conduct a systematic review of journal literature to establish what has been written in your field.

Databases enable you to combine search terms and locate high quality journal articles, conference papers and proceedings from a wide range of sources. Have a look at the Accessing Databases tab to choose the right one for your subject area. There are links to brief online tutorials or pdf guides to help you with using each of the databases there too.

• Brilliant for conducting a thorough, systematic & exhaustive search of the literature
• You can cross concepts together and so be more precise about what you are searching for
• Some databases (BREI, PsycINFO) include a thesaurus so you can check terminology
• The results are valid, reliable and authoritative (academic articles)

What about Google?

G o o g l e and G o o g l e Scholar are not the most efficient or effective tools for searching the literature. Here are a few reasons why:

  • You can only narrow searches by date, not subject   • You cannot give words meaning e.g. primary/first   • Links are unstable and not verified and so you may not be able to access the results   • Pdfs look like they are freely available but often they are not

In addition to this, you also need to carefully evaluate all internet resources:

  1. Who authored the information?   2. What expertise does the writer have to comment?   3. What evidence is used? Are there citations in the piece?   4. What genre is the document: journalism, academic paper,blog, polemic?   5. Is the site/document/report funded by an institution?   6. What argument is being made?   7. When was the text produced?   8. Why did this information emerge at this point in history?   9. Who is the audience for this information?   10. What is not being discussed and what are the political consequences of that absence?   (Taken from Brabazon, T. (2006) 'The Google Effect: Googling, blogging, wikis and the flattening of expertise', Libri, v. 56, pp 157-167)

• You may find this guide for evaluating internet resources (compiled by UWE Library Services) useful too

 And finally.... • They retrieve a huge number of results – which wastes valuable time and leads to information overload and frustration!

• << Previous: Research Process
• Next: Evaluating Resources >>
• Last Updated: Feb 15, 2024 4:59 PM
• URL: https://libguides.oakwood.edu/c.php?g=395299

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Writing a Literature Review

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A literature review is a document or section of a document that collects key sources on a topic and discusses those sources in conversation with each other (also called synthesis ). The lit review is an important genre in many disciplines, not just literature (i.e., the study of works of literature such as novels and plays). When we say “literature review” or refer to “the literature,” we are talking about the research ( scholarship ) in a given field. You will often see the terms “the research,” “the scholarship,” and “the literature” used mostly interchangeably.

Where, when, and why would I write a lit review?

There are a number of different situations where you might write a literature review, each with slightly different expectations; different disciplines, too, have field-specific expectations for what a literature review is and does. For instance, in the humanities, authors might include more overt argumentation and interpretation of source material in their literature reviews, whereas in the sciences, authors are more likely to report study designs and results in their literature reviews; these differences reflect these disciplines’ purposes and conventions in scholarship. You should always look at examples from your own discipline and talk to professors or mentors in your field to be sure you understand your discipline’s conventions, for literature reviews as well as for any other genre.

A literature review can be a part of a research paper or scholarly article, usually falling after the introduction and before the research methods sections. In these cases, the lit review just needs to cover scholarship that is important to the issue you are writing about; sometimes it will also cover key sources that informed your research methodology.

Lit reviews can also be standalone pieces, either as assignments in a class or as publications. In a class, a lit review may be assigned to help students familiarize themselves with a topic and with scholarship in their field, get an idea of the other researchers working on the topic they’re interested in, find gaps in existing research in order to propose new projects, and/or develop a theoretical framework and methodology for later research. As a publication, a lit review usually is meant to help make other scholars’ lives easier by collecting and summarizing, synthesizing, and analyzing existing research on a topic. This can be especially helpful for students or scholars getting into a new research area, or for directing an entire community of scholars toward questions that have not yet been answered.

What are the parts of a lit review?

Most lit reviews use a basic introduction-body-conclusion structure; if your lit review is part of a larger paper, the introduction and conclusion pieces may be just a few sentences while you focus most of your attention on the body. If your lit review is a standalone piece, the introduction and conclusion take up more space and give you a place to discuss your goals, research methods, and conclusions separately from where you discuss the literature itself.

Introduction:

• An introductory paragraph that explains what your working topic and thesis is
• A forecast of key topics or texts that will appear in the review
• Potentially, a description of how you found sources and how you analyzed them for inclusion and discussion in the review (more often found in published, standalone literature reviews than in lit review sections in an article or research paper)
• Summarize and synthesize: Give an overview of the main points of each source and combine them into a coherent whole
• Analyze and interpret: Don’t just paraphrase other researchers – add your own interpretations where possible, discussing the significance of findings in relation to the literature as a whole
• Critically Evaluate: Mention the strengths and weaknesses of your sources
• Write in well-structured paragraphs: Use transition words and topic sentence to draw connections, comparisons, and contrasts.

Conclusion:

• Summarize the key findings you have taken from the literature and emphasize their significance
• Connect it back to your primary research question

How should I organize my lit review?

Lit reviews can take many different organizational patterns depending on what you are trying to accomplish with the review. Here are some examples:

• Chronological : The simplest approach is to trace the development of the topic over time, which helps familiarize the audience with the topic (for instance if you are introducing something that is not commonly known in your field). If you choose this strategy, be careful to avoid simply listing and summarizing sources in order. Try to analyze the patterns, turning points, and key debates that have shaped the direction of the field. Give your interpretation of how and why certain developments occurred (as mentioned previously, this may not be appropriate in your discipline — check with a teacher or mentor if you’re unsure).
• Thematic : If you have found some recurring central themes that you will continue working with throughout your piece, you can organize your literature review into subsections that address different aspects of the topic. For example, if you are reviewing literature about women and religion, key themes can include the role of women in churches and the religious attitude towards women.
• Qualitative versus quantitative research
• Empirical versus theoretical scholarship
• Divide the research by sociological, historical, or cultural sources
• Theoretical : In many humanities articles, the literature review is the foundation for the theoretical framework. You can use it to discuss various theories, models, and definitions of key concepts. You can argue for the relevance of a specific theoretical approach or combine various theorical concepts to create a framework for your research.

What are some strategies or tips I can use while writing my lit review?

Any lit review is only as good as the research it discusses; make sure your sources are well-chosen and your research is thorough. Don’t be afraid to do more research if you discover a new thread as you’re writing. More info on the research process is available in our "Conducting Research" resources .

As you’re doing your research, create an annotated bibliography ( see our page on the this type of document ). Much of the information used in an annotated bibliography can be used also in a literature review, so you’ll be not only partially drafting your lit review as you research, but also developing your sense of the larger conversation going on among scholars, professionals, and any other stakeholders in your topic.

Usually you will need to synthesize research rather than just summarizing it. This means drawing connections between sources to create a picture of the scholarly conversation on a topic over time. Many student writers struggle to synthesize because they feel they don’t have anything to add to the scholars they are citing; here are some strategies to help you:

• It often helps to remember that the point of these kinds of syntheses is to show your readers how you understand your research, to help them read the rest of your paper.
• Writing teachers often say synthesis is like hosting a dinner party: imagine all your sources are together in a room, discussing your topic. What are they saying to each other?
• Look at the in-text citations in each paragraph. Are you citing just one source for each paragraph? This usually indicates summary only. When you have multiple sources cited in a paragraph, you are more likely to be synthesizing them (not always, but often
• Read more about synthesis here.

The most interesting literature reviews are often written as arguments (again, as mentioned at the beginning of the page, this is discipline-specific and doesn’t work for all situations). Often, the literature review is where you can establish your research as filling a particular gap or as relevant in a particular way. You have some chance to do this in your introduction in an article, but the literature review section gives a more extended opportunity to establish the conversation in the way you would like your readers to see it. You can choose the intellectual lineage you would like to be part of and whose definitions matter most to your thinking (mostly humanities-specific, but this goes for sciences as well). In addressing these points, you argue for your place in the conversation, which tends to make the lit review more compelling than a simple reporting of other sources.

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What is a Literature Review? How to Write It (with Examples)

A literature review is a critical analysis and synthesis of existing research on a particular topic. It provides an overview of the current state of knowledge, identifies gaps, and highlights key findings in the literature. 1 The purpose of a literature review is to situate your own research within the context of existing scholarship, demonstrating your understanding of the topic and showing how your work contributes to the ongoing conversation in the field. Learning how to write a literature review is a critical tool for successful research. Your ability to summarize and synthesize prior research pertaining to a certain topic demonstrates your grasp on the topic of study, and assists in the learning process. 

Table of Contents

• What is the purpose of literature review? 
• a. Habitat Loss and Species Extinction: 
• b. Range Shifts and Phenological Changes: 
• c. Ocean Acidification and Coral Reefs: 
• d. Adaptive Strategies and Conservation Efforts: 
• How to write a good literature review 
• Choose a Topic and Define the Research Question: 
• Decide on the Scope of Your Review: 
• Select Databases for Searches: 
• Conduct Searches and Keep Track: 
• Review the Literature: 
• Organize and Write Your Literature Review: 
• Frequently asked questions 

What is a literature review?

A well-conducted literature review demonstrates the researcher’s familiarity with the existing literature, establishes the context for their own research, and contributes to scholarly conversations on the topic. One of the purposes of a literature review is also to help researchers avoid duplicating previous work and ensure that their research is informed by and builds upon the existing body of knowledge.

What is the purpose of literature review?

A literature review serves several important purposes within academic and research contexts. Here are some key objectives and functions of a literature review: 2  

• Contextualizing the Research Problem: The literature review provides a background and context for the research problem under investigation. It helps to situate the study within the existing body of knowledge. 
• Identifying Gaps in Knowledge: By identifying gaps, contradictions, or areas requiring further research, the researcher can shape the research question and justify the significance of the study. This is crucial for ensuring that the new research contributes something novel to the field. 
• Understanding Theoretical and Conceptual Frameworks: Literature reviews help researchers gain an understanding of the theoretical and conceptual frameworks used in previous studies. This aids in the development of a theoretical framework for the current research. 
• Providing Methodological Insights: Another purpose of literature reviews is that it allows researchers to learn about the methodologies employed in previous studies. This can help in choosing appropriate research methods for the current study and avoiding pitfalls that others may have encountered. 
• Establishing Credibility: A well-conducted literature review demonstrates the researcher’s familiarity with existing scholarship, establishing their credibility and expertise in the field. It also helps in building a solid foundation for the new research. 
• Informing Hypotheses or Research Questions: The literature review guides the formulation of hypotheses or research questions by highlighting relevant findings and areas of uncertainty in existing literature. 

Literature review example

Let’s delve deeper with a literature review example: Let’s say your literature review is about the impact of climate change on biodiversity. You might format your literature review into sections such as the effects of climate change on habitat loss and species extinction, phenological changes, and marine biodiversity. Each section would then summarize and analyze relevant studies in those areas, highlighting key findings and identifying gaps in the research. The review would conclude by emphasizing the need for further research on specific aspects of the relationship between climate change and biodiversity. The following literature review template provides a glimpse into the recommended literature review structure and content, demonstrating how research findings are organized around specific themes within a broader topic. 

Literature Review on Climate Change Impacts on Biodiversity:

Climate change is a global phenomenon with far-reaching consequences, including significant impacts on biodiversity. This literature review synthesizes key findings from various studies: 

a. Habitat Loss and Species Extinction:

Climate change-induced alterations in temperature and precipitation patterns contribute to habitat loss, affecting numerous species (Thomas et al., 2004). The review discusses how these changes increase the risk of extinction, particularly for species with specific habitat requirements. 

b. Range Shifts and Phenological Changes:

Observations of range shifts and changes in the timing of biological events (phenology) are documented in response to changing climatic conditions (Parmesan & Yohe, 2003). These shifts affect ecosystems and may lead to mismatches between species and their resources. 

c. Ocean Acidification and Coral Reefs:

The review explores the impact of climate change on marine biodiversity, emphasizing ocean acidification’s threat to coral reefs (Hoegh-Guldberg et al., 2007). Changes in pH levels negatively affect coral calcification, disrupting the delicate balance of marine ecosystems. 

d. Adaptive Strategies and Conservation Efforts:

Recognizing the urgency of the situation, the literature review discusses various adaptive strategies adopted by species and conservation efforts aimed at mitigating the impacts of climate change on biodiversity (Hannah et al., 2007). It emphasizes the importance of interdisciplinary approaches for effective conservation planning. 

How to write a good literature review

Writing a literature review involves summarizing and synthesizing existing research on a particular topic. A good literature review format should include the following elements. 

Introduction: The introduction sets the stage for your literature review, providing context and introducing the main focus of your review. 

• Opening Statement: Begin with a general statement about the broader topic and its significance in the field. 
• Scope and Purpose: Clearly define the scope of your literature review. Explain the specific research question or objective you aim to address. 
• Organizational Framework: Briefly outline the structure of your literature review, indicating how you will categorize and discuss the existing research. 
• Significance of the Study: Highlight why your literature review is important and how it contributes to the understanding of the chosen topic. 
• Thesis Statement: Conclude the introduction with a concise thesis statement that outlines the main argument or perspective you will develop in the body of the literature review. 

Body: The body of the literature review is where you provide a comprehensive analysis of existing literature, grouping studies based on themes, methodologies, or other relevant criteria. 

• Organize by Theme or Concept: Group studies that share common themes, concepts, or methodologies. Discuss each theme or concept in detail, summarizing key findings and identifying gaps or areas of disagreement. 
• Critical Analysis: Evaluate the strengths and weaknesses of each study. Discuss the methodologies used, the quality of evidence, and the overall contribution of each work to the understanding of the topic. 
• Synthesis of Findings: Synthesize the information from different studies to highlight trends, patterns, or areas of consensus in the literature. 
• Identification of Gaps: Discuss any gaps or limitations in the existing research and explain how your review contributes to filling these gaps. 
• Transition between Sections: Provide smooth transitions between different themes or concepts to maintain the flow of your literature review. 

Conclusion: The conclusion of your literature review should summarize the main findings, highlight the contributions of the review, and suggest avenues for future research. 

• Summary of Key Findings: Recap the main findings from the literature and restate how they contribute to your research question or objective. 
• Contributions to the Field: Discuss the overall contribution of your literature review to the existing knowledge in the field. 
• Implications and Applications: Explore the practical implications of the findings and suggest how they might impact future research or practice. 
• Recommendations for Future Research: Identify areas that require further investigation and propose potential directions for future research in the field. 
• Final Thoughts: Conclude with a final reflection on the importance of your literature review and its relevance to the broader academic community. 

Conducting a literature review

Conducting a literature review is an essential step in research that involves reviewing and analyzing existing literature on a specific topic. It’s important to know how to do a literature review effectively, so here are the steps to follow: 1  

Choose a Topic and Define the Research Question:

• Select a topic that is relevant to your field of study. 
• Clearly define your research question or objective. Determine what specific aspect of the topic do you want to explore? 

Decide on the Scope of Your Review:

• Determine the timeframe for your literature review. Are you focusing on recent developments, or do you want a historical overview? 
• Consider the geographical scope. Is your review global, or are you focusing on a specific region? 
• Define the inclusion and exclusion criteria. What types of sources will you include? Are there specific types of studies or publications you will exclude? 

Select Databases for Searches:

• Identify relevant databases for your field. Examples include PubMed, IEEE Xplore, Scopus, Web of Science, and Google Scholar. 
• Consider searching in library catalogs, institutional repositories, and specialized databases related to your topic. 

Conduct Searches and Keep Track:

• Develop a systematic search strategy using keywords, Boolean operators (AND, OR, NOT), and other search techniques. 
• Record and document your search strategy for transparency and replicability. 
• Keep track of the articles, including publication details, abstracts, and links. Use citation management tools like EndNote, Zotero, or Mendeley to organize your references. 

Review the Literature:

• Evaluate the relevance and quality of each source. Consider the methodology, sample size, and results of studies. 
• Organize the literature by themes or key concepts. Identify patterns, trends, and gaps in the existing research. 
• Summarize key findings and arguments from each source. Compare and contrast different perspectives. 
• Identify areas where there is a consensus in the literature and where there are conflicting opinions. 
• Provide critical analysis and synthesis of the literature. What are the strengths and weaknesses of existing research? 

Organize and Write Your Literature Review:

• Literature review outline should be based on themes, chronological order, or methodological approaches. 
• Write a clear and coherent narrative that synthesizes the information gathered. 
• Use proper citations for each source and ensure consistency in your citation style (APA, MLA, Chicago, etc.). 
• Conclude your literature review by summarizing key findings, identifying gaps, and suggesting areas for future research. 

The literature review sample and detailed advice on writing and conducting a review will help you produce a well-structured report. But remember that a literature review is an ongoing process, and it may be necessary to revisit and update it as your research progresses. 

Frequently asked questions

A literature review is a critical and comprehensive analysis of existing literature (published and unpublished works) on a specific topic or research question and provides a synthesis of the current state of knowledge in a particular field. A well-conducted literature review is crucial for researchers to build upon existing knowledge, avoid duplication of efforts, and contribute to the advancement of their field. It also helps researchers situate their work within a broader context and facilitates the development of a sound theoretical and conceptual framework for their studies.

Literature review is a crucial component of research writing, providing a solid background for a research paper’s investigation. The aim is to keep professionals up to date by providing an understanding of ongoing developments within a specific field, including research methods, and experimental techniques used in that field, and present that knowledge in the form of a written report. Also, the depth and breadth of the literature review emphasizes the credibility of the scholar in his or her field.  

Before writing a literature review, it’s essential to undertake several preparatory steps to ensure that your review is well-researched, organized, and focused. This includes choosing a topic of general interest to you and doing exploratory research on that topic, writing an annotated bibliography, and noting major points, especially those that relate to the position you have taken on the topic. 

Literature reviews and academic research papers are essential components of scholarly work but serve different purposes within the academic realm. 3 A literature review aims to provide a foundation for understanding the current state of research on a particular topic, identify gaps or controversies, and lay the groundwork for future research. Therefore, it draws heavily from existing academic sources, including books, journal articles, and other scholarly publications. In contrast, an academic research paper aims to present new knowledge, contribute to the academic discourse, and advance the understanding of a specific research question. Therefore, it involves a mix of existing literature (in the introduction and literature review sections) and original data or findings obtained through research methods. 

Literature reviews are essential components of academic and research papers, and various strategies can be employed to conduct them effectively. If you want to know how to write a literature review for a research paper, here are four common approaches that are often used by researchers.  Chronological Review: This strategy involves organizing the literature based on the chronological order of publication. It helps to trace the development of a topic over time, showing how ideas, theories, and research have evolved.  Thematic Review: Thematic reviews focus on identifying and analyzing themes or topics that cut across different studies. Instead of organizing the literature chronologically, it is grouped by key themes or concepts, allowing for a comprehensive exploration of various aspects of the topic.  Methodological Review: This strategy involves organizing the literature based on the research methods employed in different studies. It helps to highlight the strengths and weaknesses of various methodologies and allows the reader to evaluate the reliability and validity of the research findings.  Theoretical Review: A theoretical review examines the literature based on the theoretical frameworks used in different studies. This approach helps to identify the key theories that have been applied to the topic and assess their contributions to the understanding of the subject.  It’s important to note that these strategies are not mutually exclusive, and a literature review may combine elements of more than one approach. The choice of strategy depends on the research question, the nature of the literature available, and the goals of the review. Additionally, other strategies, such as integrative reviews or systematic reviews, may be employed depending on the specific requirements of the research.

The literature review format can vary depending on the specific publication guidelines. However, there are some common elements and structures that are often followed. Here is a general guideline for the format of a literature review:  Introduction:   Provide an overview of the topic.  Define the scope and purpose of the literature review.  State the research question or objective.  Body:   Organize the literature by themes, concepts, or chronology.  Critically analyze and evaluate each source.  Discuss the strengths and weaknesses of the studies.  Highlight any methodological limitations or biases.  Identify patterns, connections, or contradictions in the existing research.  Conclusion:   Summarize the key points discussed in the literature review.  Highlight the research gap.  Address the research question or objective stated in the introduction.  Highlight the contributions of the review and suggest directions for future research.

Both annotated bibliographies and literature reviews involve the examination of scholarly sources. While annotated bibliographies focus on individual sources with brief annotations, literature reviews provide a more in-depth, integrated, and comprehensive analysis of existing literature on a specific topic. The key differences are as follows: 

References 

• Denney, A. S., & Tewksbury, R. (2013). How to write a literature review.  Journal of criminal justice education ,  24 (2), 218-234. 
• Pan, M. L. (2016).  Preparing literature reviews: Qualitative and quantitative approaches . Taylor & Francis. 
• Cantero, C. (2019). How to write a literature review.  San José State University Writing Center . 

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Artificial Intelligence and Business Value: a Literature Review

• Open access
• Published: 25 August 2021
• Volume 24 , pages 1709–1734, ( 2022 )

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• Ida Merete Enholm 1 ,
• Emmanouil Papagiannidis 1 ,
• Patrick Mikalef 1 &
• John Krogstie 1  

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Artificial Intelligence (AI) are a wide-ranging set of technologies that promise several advantages for organizations in terms off added business value. Over the past few years, organizations are increasingly turning to AI in order to gain business value following a deluge of data and a strong increase in computational capacity. Nevertheless, organizations are still struggling to adopt and leverage AI in their operations. The lack of a coherent understanding of how AI technologies create business value, and what type of business value is expected, therefore necessitates a holistic understanding. This study provides a systematic literature review that attempts to explain how organizations can leverage AI technologies in their operations and elucidate the value-generating mechanisms. Our analysis synthesizes the current literature and highlights: (1) the key enablers and inhibitors of AI adoption and use; (2) the typologies of AI use in the organizational setting; and (3) the first- and second-order effects of AI. The paper concludes with an identification of the gaps in the literature and develops a research agenda that identifies areas that need to be addressed by future studies.

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1 Introduction

While Artificial Intelligence (AI) is not something new, it has gained much attention in recent years (Ransbotham et al., 2018 ). AI has been argued to be a force of disruption for businesses worldwide and in a wide range of sectors (Davenport & Ronanki, 2018 ). Organizations implementing AI applications are expected to attain gains in terms of added business value, such as increased revenue, cost reduction, and improved business efficiency (AlSheibani et al., 2020 ). A recent study by MIT Sloan Management Review found that more than 80% of organizations see AI as a strategic opportunity, and almost 85% see AI as a way to achieve competitive advantage (Ransbotham et al., 2017 ). In the search for competitive advantage, many organizations are thus investing in AI technologies. However, despite the growing interest in AI, many companies struggle to realize value from AI (Fountaine et al., 2019 ). The expected benefits of AI may be absent even though companies invest time, effort, and resources into the adoption process (Makarius et al., 2020 ).

The introduction of AI in organizational operations signals a new set of barriers and challenges (Duan et al., 2019 ). Some of these include bridging cross-domain knowledge to develop models that are accurate and meaningful (Duan et al., 2019 ), identifying, integrating and cleansing diverse sources of data (Mikalef & Gupta, 2021 ), and integrating AI applications with existing processes and systems (Davenport & Ronanki, 2018 ). To capture the potential value from AI, organizations need to understand how to overcome these challenges as well as the value-adding potential of these technologies. Yet, recent research on AI is more focused on a technological understanding of AI adoption than identifying the organizational challenges associated with its implementation (Alsheibani et al., 2020 ). While some studies have identified research gaps (Dwivedi et al., 2019), and looked at important aspects in being able to leverage AI technologies (Mikalef & Gupta, 2021 ), there is still a lack of a holistic understanding of how AI is adopted and used in organizations, and what are the main value-generating mechanisms.

In this paper we attempt to address this gap by providing a synthesis of the current body of knowledge and developing an agenda that can help advance our knowledge. We therefore perform a systematic collection of the extant literature, and put forward a narrative review by summarizing the existing body of literature and providing a comprehensive report which guides future studies (Templier & Paré, 2015 ). The objective of this paper is to identify in which ways organizations can deploy AI, and what value-generating mechanisms AI can enable. The first step in our study is collecting studies that examine organizational adoption and use of AI from 2010 onwards. After assessing the papers' relevance and quality, the remaining studies are analyzed and synthesized which lead to a framework form understanding AI business value. Based on the synthesis, a research agenda is created, identifying areas that need to be addressed by future research.

2 Research Methodology

The review was conducted in six distinct stages, following the established method of a systematic literature review in order to ensure that all relevant literature to date was included in our analysis (Kitchenham, 2004 ). First, the review protocol was developed which outlined the choice and structure of keywords and phrases. Second, the inclusion and exclusion criteria for relevant publications were identified in order to filter those publications that were of interest towards our review. Third, the search for papers was performed based on the pre-defined phrases as combinations of the keywords. The articles found in the search were critically assessed before performing data extraction and synthesizing the findings. The previously mentioned stages (Fig.  1 ) are described in further detail in the next subsections.

Stages of the study selection process.

2.1 Protocol Development

The systematic literature review started by developing a review protocol following the method of the Cochrane Handbook for Systematic Reviews of Intervention (Higgins, 2008 ). In this protocol, the main research questions were established together with the search strategy, inclusion, exclusion, and quality criteria. The method of synthesis was also established in the protocol. The following research questions motivated the review process: What aspects enable or inhibit AI use in the organization? What are the types of AI uses in organizations? Through what mechanisms is AI value realized? These research questions formed the basis for deciding how to proceed in the next steps a what sets of keywords and data sources to utilize.

2.2 Inclusion and Exclusion Criteria

A number of inclusion and exclusion criteria were applied to set boundaries for the systematic literature review. Studies were included if they were focused on how AI can provide business value or how AI is adopted and used in an organizational context. This meant that studies that focused on solely technical aspects of AI, such as technical infrastructure or benchmarking of difference models were not in the scope of papers that were selected. Only publications from 2010 onwards were selected since the majority of organizational uses of AI, with novel methods, have been in the last decade. Studies not written in English were excluded from this review. In addition, the systematic literature review included journal articles and conference proceedings. Book series, dissertations, reports, and webpages were excluded, as were also other publications that were not peer-reviewed.

2.3 Data Sources and Search Strategy

The first step in the search strategy was to form search strings. Two sets of keywords were created (Appendix Table 8 ): the first set containing keywords related to AI and associated technologies, and the second set regarding the organizational perspective. Keywords from the two sets were combined to form the search string using wildcard symbols in order to reduce the number of search strings. The search terms were then applied in the search engine Google Scholar, as well as several other electronic databases such as Scopus, Business Source Complete, Emerald, Taylor & Francis, Springer, Web of Knowledge, ABI/inform Complete, IEEE Xplore, and the Association of Information Systems (AIS) library. This was done to ensure that all relevant articles had been indexed. The collection procedure started on September 14, 2020 and was concluded on September 30, 2020. To further ensure that the most important articles had been identified, we performed a separate search in the AIS basket of eight journals using the same sets of strings.

2.4 Quality Assessment

Two of the co-authors went through the papers independently after the eligibility check and assessed their quality in terms of several criteria. Studies were examined in terms of scientific rigor , credibility , and relevance . Scientific rigor meaning that the appropriate research method has been applied. Credibility refers to if the research is believable and the findings are well presented. Relevance refers to if the findings are relevant to the academic community and organizations engaging in AI projects. Together these quality criteria ensure that the papers remaining after this stage are likely to make a valuable contribution to the review. After this stage, 43 papers were left for data extraction and synthesis.

2.5 Data Extraction and Synthesis of Findings

A concept matrix was created in order to categorize the studies and synthesize findings. This was done by analyzing the papers and organizing information from the studies in a spreadsheet. Organizing the studies in this way makes it easier to make comparisons across studies and translate the findings into higher-order interpretations. The studies were analyzed based on the following areas of focus: organizational performance outcomes of AI, adoption, and use of AI in an organizational context, and organizational change caused by the adoption of AI. The information recorded included the research methodology, important definitions, level of analysis, key findings, theories used, context of investigation, and other important concepts from the paper. Two of the co-authors performed the data extraction based on the developed matrix, and then through an iterative process all co-authors reached a consensus about the context included in each category, and about adding additional dimensions to capture all relevant data. The remaining 43 papers were all analyzed and added to the concept matrix before the findings were synthesized.

3 Definitions

While AI has gained much attention in the last years due to the recent advancements in computer hardware, computer network speeds, the vast amount of available data, and processing algorithms (Alsheibani et al., 2020 ), there is considerable ambiguity about what the notion means and what it entails. The development of AI consists of several sub-disciplines based on fundamentally different approaches (Schmidt et al., 2020 ), and their meaning is often used interchangeably to encompass a broad set of technologies and applications (Dwivedi et al., 2019). Therefore, it is essential to draw a clear distinction between these core concepts and provide comprehensive definitions. We draw a distinction between three key areas of focuses: AI as a scientific discipline , technologies used to realize AI , and AI capabilities . These three levels provide a distinction between the discipline and its objective, the tools and technologies used to attain the goal, and the organizational capacity to use a set of diverse tools and technologies that support AI. In the sub-sections below, we present the definitions used in past research and provide a synthesis of the current body of knowledge.

3.1 Artificial Intelligence

Several definitions of AI have been published in an attempt to distinguish it from other conventional information technologies (Table 1 ). To understand the concept of AI, it is necessary to first understand the notions of " artificial " and " intelligence " separately. " Intelligence " can be described as involving mental activities, such as learning, reasoning, and understanding (Lichtenthaler, 2019 ). " Artificial ", on the other hand, refers to something that is made by humans, rather than occurring naturally (Mikalef & Gupta, 2021 ). By combining these two together, Artificial Intelligence can be understood as making machines capable of simulating intelligence (Wamba-Taguimdje et al., 2020 ).

From the definitions in Table 1 , it is evident that there is a consensus that AI refers to giving the computer human-like capabilities, meaning that computers are able to perform tasks that normally require human intelligence. This includes activities such as understanding, reasoning, and problem-solving (Mikalef & Gupta, 2021 ). AI emulates human performance by acting as an intelligent agent, which performs actions based on a specific understanding of input from the environment (Eriksson et al., 2020 ). In other words, the aim of AI is to try to reproduce human cognition by emulating how humans learn and process information. Cognitive technology is a term often used when referring to this capability. Cognitive technologies resemble the action of the human mind (Bytniewski et al., 2020 ),meaning that it provides the computer the function to think and act like a human.

In their definition, some scholars focus on the idea that AI should not need to be explicitly programmed to perform an intelligent task (Demlehner & Laumer, 2020 ). It should be able to sense, interpret, learn, plan, comprehend, and act on its own (Demlehner & Laumer, 2020 ; Kolbjørnsrud et al., 2017 ; Wang et al., 2019 ), meaning that AI should be able to correctly interpret external data, learn from this data, and use this learning to achieve specific goals and tasks through flexible adaption (Makarius et al., 2020 ). Doing so should be achieved without following predetermined rules or action sequences throughout the whole process (Demlehner & Laumer, 2020 ).

It is also identifiable that there are two main ways of defining AI. The first of these defines AI as a tool that solves a specific task that could be impossible or very time-consuming for a human to complete (Demlehner & Laumer, 2020 ; Makarius et al., 2020 ). The second group of definitions regards AI as a system that mimics human intelligence and cognitive processes, such as, interpreting, making inferences, and learning (Mikalef & Gupta, 2021 ). Both categories of definitions share some similarities but also present some important differences. A common notion in both categories is that AI does not necessarily replace humans, but instead, AI operates as an augmentation agent for performing difficult and time-consuming tasks (Mikalef & Gupta, 2021 ). Yet, both categories of definitions have some diverging points.

While one category of definitions assumes that AI is perfectly capable of imitating human behavior (Kolbjørnsrud et al., 2017 ; Wang et al., 2019 ), the second category of definitions regards AI as a tool, assuming it cannot exactly replicate human capabilities (Wamba-Taguimdje et al., 2020 ). Another noticeable difference is that some definitions refer to AI as a discipline of scientific inquiry (Schmidt et al., 2020 ), while others perceive the notion as an applied capacity of a system or machine (Afiouni, 2019 ; Lee et al., 2019 ). These definitions show that there are noticeable underlying assumptions, and some important differences about what AI is and what it encompasses. For the purpose of this article, we adopt the stance that AI is an applied discipline that aims to enable systems to identify, interpret, make inferences, and learn from data to achieve predetermined organizational and societal goals.

3.2 AI Technologies

Moving from the broad definition of what AI encompasses, the next level of definitions attempts to capture the techniques used to realize the objectives set in the previous definitions. Our analysis of the extant literature points out to the fact that this can be achieved through several different ways, with the largest proportion of studies focusing on cases where machine learning, and deep learning were being used. This section provides an overview of how some of the main types of AI technologies are defined in the literature, highlighting some key aspects of them, and outlining some important differences in terms of their application areas.

3.2.1 Machine Learning and Deep Learning

Machine learning is a subset of AI techniques, and one of the most widely used methods over the last few years. Machine learning has gained a lot of interest over the past few years, particularly due to the increase in data availability coupled with advances in computational power (Afiouni, 2019 ). Several definitions of machine learning exist in the literature, some of them shown in Table 2 as identified in our sample of papers. The objective of machine learning is to train a machine to be able to learn from data and make inferences, predictions, and identify associations, which can guide decisions (Afiouni, 2019 ; Wang et al., 2019 ). Machine learning techniques accomplish this by parsing data, learning for data, and making informed decisions based on what has been learned (Wang et al., 2019 ). This is an inductive approach in which decision rules are identified based on the collected data using statistical methods (Schmidt et al., 2020 ).

Machine learning algorithms can be further sub-divided into four categories: supervised , semi-supervised , unsupervised , and reinforcement learning (Wang et al., 2019 ). In supervised learning, the training data include the target value (Schmidt et al., 2020 ). The system then identifies patterns from the training data and infer its own rules from the labeled data (Afiouni, 2019 ). For unsupervised learning approaches, however, the target value is not included in the training set. The system has to analyze the structure of the training data and its statistical properties to solve the problem (Afiouni, 2019 ). Unsupervised learning is often used to discover hidden patterns in the data set with prominent applications being automatic clustering, anomaly detection, and association mining (Schmidt et al., 2020 ). In semi-supervised learning, both labeled and unlabeled data are used (Quinio et al., 2017 ). In contrast, reinforcement learning does not learn from past data (Afiouni, 2019 ). Rather, it enables learning from feedback received through interactions with an external environment (Quinio et al., 2017 ). The core idea is that the system has an objective set by a human agent and receives rewards based on how well the objective is met, which involves finding the best strategy or combination of actions (Afiouni, 2019 ).

Machine learning can be either shallow or deep . All four training categories apply to both shallow and deep machine learning. Shallow-structured learning architectures are the most traditional, where it learns from data described by pre-defined features (LeCun et al., 2015 ). In contrast, deep machine learning, usually referred to as deep learning, can derive structure from data in a multi-layered manner (Wang et al., 2019 ). What differentiates deep learning from the more traditional machine learning is the use of an artificial neural network architecture (Afiouni, 2019 ; Wamba-Taguimdje et al., 2020 ) Neural network solutions refer to the human brain’s functionality (Jelonek et al., 2019 ) by imitating human neurons (Schmidt et al., 2020 ). Deep learning is based on creating deep neural networks with several hidden layers, where the layer closest to the data vectors learns simple features, while the higher layers learn higher-level features (Quinio et al., 2017 ). It represents the world through a hierarchy of concepts, in which each concept can be divided into more straightforward concepts (Borges et al., 2020 ). In recent years, deep learning has become an area with considerable attention due to its many use cases and its ability to produce remarkably accurate results in various domains (Wang et al., 2019 ).

3.2.2 Other AI Technologies

While machine learning applications appear to be dominating the research interest in the Information Systems (IS) domain, there are also several other key AI technologies that have been examined in empirical studies and are presented in Table 3 . Today, most of these technologies are used in combination with machine learning or deep learning, to provide solutions that to evolve and learn. For instance, in the case of chatbots both natural language processing (NLP) and machine learning are applied (Baby et al., 2017 ). The functionality enabled through NLP allows chatbots to understand and communicate using the human language. On the other hand, the machine learning algorithms facilitate chatbots to learn and evolve as they get access to more data (Castillo et al., 2020 ). Other notable types of AI technologies studies in IS empirical works are presented in Table 3 .

3.3 AI Capabilities

While the previous definitions concern the broader quest of what AI aims to achieve, as well as the methods and technologies used to actualize these objectives, the notion of an AI capability is revolved around the organizational capacity to deploy such applications in support of operations (Mikalef & Gupta, 2021 ). With AI becoming an increasingly important asset for organizations, there is a growing body of research examining how such technologies and techniques can be leveraged towards the attainment of organizational goals (Bytniewski et al., 2020 ; Schmidt et al., 2020 ; Wang et al., 2019 ). The notion of an AI capability has thus been introduced to explain how this value is achieved, and how organizations should be organized in order to realize value from AI investments.

While there are still few studies adopting an analysis of AI from the focus point of an organizational capability, there is growing body of research building on this concept as presented in Table 4 . The definitions differ slightly but all encompass what an organization should be able to do with AI investments, while some also include the desired outcomes of deploying an AI capability. The definition of Schmidt et al. ( 2020 ) for instance belongs to the latter category, as they define AI capabilities as " the ability of organizations to use data, methods, processes and people in a way that creates new possibilities for automation, decision making, collaboration, etc. that would not be possible by conventional means ". This definition includes not only data and methods, but also the people and processes required to orchestrate and leverage AI into action. Similarly, other definitions include complementary resources that are required to reap the benefits provided by AI technologies (Wamba-Taguimdje et al., 2020 ). All definitions through converge in that they have an underlying notion that an AI capability is about how an organization uses its AI-specific resources in order to enable value creation (Schmidt et al., 2020 ; Wamba-Taguimdje et al., 2020 ). These AI-specific resources can be both technological, e.g. training data and AI-algorithms (Schmidt et al., 2020 ), and non-technical, e.g. employee skills (Wamba-Taguimdje et al., 2020 ). Hence, the notion of AI capability extends the view of AI to not only focus on the technical resources, but also include all related organizational resources that are important in order to exploit the full strategic potential of AI.

4 Synthesis of Literature Review

This section presents the findings from the systematic literature review, structured according to the thematic codes that emerged during the analysis of past studies. The findings were obtained through an analysis process following the research methodology. To be able to assess the body of knowledge on AI and business value, we differentiated between three inter-dependent levels, which are depicted in Fig.  2 . In this organizational framework we show that there are several important factors relating to the technological readiness, organizational aspects, and environmental factors that have an important impact on the ability of organizations to deploy and utilize AI. In turn, we develop two broad categories of AI use in organizations and summarize the current knowledge regarding the applications within these categories. Next, we differentiate the impacts of AI into first-order effects and second-order effects. These represent impacts that materialize at the process and firm levels respectively. We therefore argue that second-order effects need to be examine first through the first-order effects they stem from. The section is structured in accordance with the organizing framework, concluding with an overview of theories that have been used in the study of AI and business value.

Organizational framework of AI and business value

4.1 Enablers and Inhibitors of AI Use

Based on the clustering of the context of papers, we find that enablers and inhibitors can be subdivided into three main categories: technological , organizational , and environmental . Based on this categorization we discuss what the current body of research and what we know so far about aspects that either accelerate AI deployments or generate obstacles for use. The findings are summarized in Table 5 and discussed below.

4.1.1 Technological

At the core of AI is data. Large data sets are used to train the AI (Pumplun et al., 2019 ; Schmidt et al., 2020 ). AI learns to make decisions based on these data sets, rather than based on an explicitly defined set of rules defined by expert knowledge (Pumplun et al., 2019 ; Schmidt et al., 2020 ). Therefore, an essential enabler of AI adoption in organizations is the data they produce, e.g., sensor data (Demlehner & Laumer, 2020 ), or have access to (Mikalef & Gupta, 2021 ). The term big data is often used to refer to these large data sets. According to Beyer and Laney ( 2012 ), big data is " high-volume, high-velocity, and/or high-variety information assets that require new forms of processing to enable enhanced decision making, insight discovery, and process optimization " (Mikalef et al., 2018 ). This definition captures big data’s main characteristics, namely the "three Vs": volume , velocity , and variety . To develop high-quality AI applications, large volumes of training data have to be available (Afiouni, 2019 ; Keding, 2020 ; Pumplun et al., 2019 ; Schmidt et al., 2020 ). A common challenge when using AI is the lack of enough training data (Baier et al., 2019 ). Velocity, or timeliness, refers to the speed at which the data are collected and updated (Gregory et al., 2020 ; Mikalef et al., 2018 ). Timeliness affects AI systems that heavily rely on the freshness of data, e.g., time-series forecasting. In addition, having a wider range of variety in the training data broadens the model’s ability to make predictions, thus increasing its accuracy (Wang et al., 2019 ).

Another critical aspect of the data used to train the AI is the quality of the data (Alsheiabni et al., 2018 ; Baier et al., 2019 ; Demlehner & Laumer, 2020 ; Lee et al., 2019 ). Data quality is crucial for providing reliable predictions (Alsheiabni et al., 2018 ). "Garbage-in, garbage-out" is a fundamental principle for AI (Lee et al., 2019 ), meaning that if training data has low quality, the insights generated by the AI will also be of low quality and not useful in the organizational context. Common problems regarding the data’s quality include incomplete data, incorrect entries, and noisy features (Baier et al., 2019 ). Recognizing these quality problems can be quite challenging. Thus, data scientists and domain experts need to collaborate closely to identify data quality problems (Baier et al., 2019 ). An important aspect of quality also relates to using data that are free from bias and follow responsible and trustworthy principles. Bias can be introduced in the used data at different points, such as during the generation or collection, or even during the processing. Ntoutsi et al. ( 2020 ) propose a number of methods in their work in order to understand, mitigate, and account for bias in order to reduce negative consequences. Such bias stems not only during the generation or collection, but is also a result of annotation, when data is assigned semantic meaning (Geva et al., 2019 ). Hence, we see that from the body of empirical studies that data characteristics are multifaceted, and are a core requirements in order to be able to actualize AI applications (Afiouni, 2019 ; Mikalef & Gupta, 2021 ).

Technology infrastructure

A complementary and equally important aspects for organizations is having the right technology infrastructure for adopting AI AlSheibani et al., 2020 ). To successfully deploy AI in an organization, three things are needed: computing power infrastructure , algorithms , and rich data sets (Wamba-Taguimdje et al., 2020 ). AI algorithms build models based on the data. These algorithms can be complex, and the data sets can be enormous. Therefore, the infrastructure could require massive amounts of computing power (Baier et al., 2019 ; Wamba-Taguimdje et al., 2020 ). In other words, having high speed and being ‘infinitely’ scalable (Wamba-Taguimdje et al., 2020 ). It is not feasible for many companies to have these resources on-site (Schmidt et al., 2020 ). Large companies, such as Google, Amazon, and Microsoft, have thus started to provide infrastructure for machine learning in the cloud (Borges et al., 2020 ), e.g., Google Cloud AI. These solutions give other organizations online access to the infrastructure necessary for adopting AI (Borges et al., 2020 ; Schmidt et al., 2020 ; Wang et al., 2019 ). Therefore, to adopt AI, companies either need access to a cloud-based solution or have the right computational hardware to facilitate the use of AI on their own.

4.1.2 Organizational

Organizational enablers and inhibitors of AI are concerned with how the organizational context, such as strategic orientation and organizational structure, affects the organization’s ability to adopt AI successfully.

The culture in the company is argued in research to be a strong force in the decision to adopt AI (Mikalef & Gupta, 2021 ; Pumplun et al., 2019 ). AI can be seen as an innovative technology, possibly changing the company’s business model and systems (Lee et al., 2019 ). Thus, the organization must be able to respond to this change. This includes having employees willing to use the new technology in the long run (Pumplun et al., 2019 ). Innovative cultures have a passion for and willingness to exploit new, opportunistic ideas, and are therefore more likely to embrace AI technologies (Mikalef & Gupta, 2021 ). Having employees who are continuously willing to learn and innovate will support the deployment and use of AI applications (Lee et al., 2019 ). This is because employees with an innovative mindset are more open to using a new technology, as well as being able to identify and seize new opportunities for applications of AI. Therefore, organizations with an innovative culture are posited to be better positioned to integrate AI in their work line (Mikalef & Gupta, 2021 ).

Top Management Support

One of the strongest determinants of AI adoption, and a recurrently noted aspect is top management support (Alsheiabni et al., 2018 ; AlSheibani et al., 2020 ; Alsheibani et al., 2020 ; Demlehner & Laumer, 2020 ). Adopting AI is a complicated process where many challenges must be faced, organizational as well as technological. Top managers and business owners should thus take part in exploring AI technologies and not leave this solely to the technologists (Alsheibani et al., 2020 ). For example, a company’s culture has shown to influence AI adoption, as discussed above, and top managers play a crucial role in establishing this culture (Lee et al., 2019 ). Also, top-level management can support the adoption of AI by allocating resources and providing capital funds (AlSheibani et al., 2020 ). The dedication and engagement of top-level management is thus suggested to be a strong contributor towards organizational AI deployment

Organizational Readiness

Organizational readiness refers to the availability of the complementary organizational resources needed for AI adoption (Alsheiabni et al., 2018 ; AlSheibani et al., 2020 ). As with other innovations, the adoption of AI requires financial resources through a dedicated budget (Pumplun et al., 2019 ). A high budget, with no obligations to meet specific performance targets, is suggested to enable the adoption of AI, as employees have the ability to learn while working with the development of AI solutions (Pumplun et al., 2019 ). Additionally, the implementation of AI is heavily dependent upon the skills of the organization’s human resources. Adopting new technology may lead to new skill requirements. Organizations adopting AI need employees with technical skills to create and deploy AI systems, e.g., they should be able to utilize technical AI libraries such as TensorFlow, PyTorch, or Keras (Pumplun et al., 2019 ). They also need domain experts who understand the tasks, workflows, and logic of the existing business processes and have the ability to consider how AI systems can improve them (Alsheibani et al., 2020 ; Pumplun et al., 2019 ). An evaluation of the internal availability of expertise is thus required in order to ensure that technical employees, as well as managerial staff know not only how to utilize such novel tools and technologies, but also for what business functions they should be targeted towards (Mikalef & Gupta, 2021 ).

Employee-AI Trust

AI systems have been shown to be able to perform tasks that replicate human cognition or automate previously manual tasks (Zheng et al., 2017 ). In many of these cases, humans were the ones responsible for carrying out such tasks, and the implementation of AI can consequently change the roles of the organization’s employees. Roles may need to be redesigned, and new roles can emerge. Thus, the employees need to understand the purpose of AI, what role it will play, and how it will change the employee’s role and responsibilities within the organization (Makarius et al., 2020 ). Employees possibly have to co-work or base their decisions based on AI systems. This means that they have to trust the AI system, and have an understanding about how they operate and reach conclusions (Makarius et al., 2020 ). The interaction between humans and AI is a complex process and building trust between humans and machines can be difficult. AI does not experience emotions the same way as a human does, and neither does it have the same empathy capabilities (Makarius et al., 2020 ). Employee-AI trust can thus be an inhibitor of AI use, with employees causing strong inertial forces to change. The problem of trust however, also applies to managers since they need to know that AI will operate according to the design directives. A manager’s willingness to trust an AI system is related to the degree to which there is an understanding of the technology (Keding, 2020 ).

AI Strategy

To reap the benefits of AI, organizations should develop an AI strategy (Finch et al., 2017a; Keding, 2020 ). The strategy should describe how the organization will adopt and implement AI in order to utilize its benefits. The actions described should align with the company’s existing goals (Keding, 2020 ). AI strategies are not merely stating what the organization would like to achieve with the implementation of AI, but also provide specific processes, plans, and timeframes for actualizing these objectives. In addition, an AI strategy is likely to require considerable modifications to how the organization is structured, the level of collaboration between departments, as well as how data is governed throughout the organization (Mikalef & Gupta, 2021 ). Thus, it is essential first to define the relative advantage and compatibility of the AI solution to organizational goals and strategy.

Compatibility

Compatibility refers to the fit between the desired application and technology (Pumplun et al., 2019 ). A stronger fit between the technology and the task will lead to higher levels of adoption and use (Mishra & Pani, 2020 ). The compatibility concept can be divided into two subcategories: business processes and business case (Pumplun et al., 2019 ). A concrete, solid business case has to be formulated and aligned with existing strategies (Alsheiabni et al., 2018 ; AlSheibani et al., 2020 ; Pumplun et al., 2019 ). This means defining an exact problem that the adoption of AI is intended to solve (Pumplun et al., 2019 ). A solid business case should describe what the AI technology will do and demonstrate how its algorithms will enhance business processes’ execution and outcomes (Alsheiabni et al., 2018 ). When adopting AI, new requirements will arise. The company’s business processes must be adapted to these new requirements for the adaption to be successful (Pumplun et al., 2019 ).

4.1.3 Environmental

Organizations operate in dynamic and constantly changing environments, consisting of actors such as competitors and government, that have an influence on how the organization can and should conduct business. This, in turn, exerts different types of pressure on the organization’s ability and propensity to adopt AI. This section presents environmental enablers and inhibitors of AI use.

Ethical and Moral Aspects

Ethical and moral aspects are essential aspects when adopting AI. AI systems have human-like abilities, which means that the boundaries between humans and machines become less transparent. Thus, the organization must ensure that AI applications have been developed based on ethical principles and do not embed within them unknown biases (S. A. Alsheibani et al., 2020 ; Baier et al., 2019 ; Coombs et al., 2020 ). AI ethics have been defined as "... a set of values, principles, and techniques that employ widely accepted standards of right and wrong to guide moral conduct in the development and use of AI technologies " (Alsheibani et al., 2020 ). AI ethics can help organizations make sure that their use of technology aligns with their values. Transparency, bias, and discrimination are some of the challenges that emerge when developing AI systems (Alsheibani et al., 2020 ; Baier et al., 2019 ). AI is data-driven, thus it can lead to potentially biased and discriminatory outcomes if the underlying data set is imbalanced or discriminatory (Baier et al., 2019 ). It can also replicate the biases and preconceptions of the system designer. In fact, there have been several reports on prominent companies such as Apple and Amazon, on misuse of AI which resulted in discrimination and bias (Dastin, 2018 ; Vigdor, 2019 ).

In taking a more holistic perspective on ethical and moral aspects surrounding AI, several public and private bodies have initiated working groups with the aim of defining key principles that should underlie AI use (European Commission, 2019a ). A recent report published by the European Commission, highlights seven key dimensions that organizations should consider when deploying AI applications (European Commission, 2019b ). These go beyond aspects related to bias, and include dimensions such as transparency of AI applications, accountability, safety and security, societal and environmental well-being, design for universal access, and human agency and oversight. The purpose of reports such as the above and other empirical works is to minimize the potential risks faced by organizations (Arrieta et al., 2020 ), and to ensure that AI applications enact behaviors that are more ethically correct than humans (Coombs et al., 2020 ). Building on such principles is also argued to help organizations balance between black-box and white-box AI applications, or in other words, finding the right equilibrium between accuracy and interpretability (Loyola-Gonzalez, 2019 ; Wanner et al., 2020 ).

Regulations

Government policies and regulations manifest the social attitudes on ethical and moral issues, and provide directives that shape how AI applications are developed. In May 2018, the General Data Protection Regulation (GDPR) was enforced in the European Union (EU) and the European Economic Area (EEA). GDPR regulates activities such as the processing of personal data. This new law has caused some issues for organizations employing AI solutions as they struggle to provide personal data to use in the training of their intelligent machines (Pumplun et al., 2019 ). Many data sets need to be anonymized to handle these new legal requirements, which makes the use of intelligent, self-learning algorithms more difficult or even impossible (Pumplun et al., 2019 ). GDPR increases the complexity of the deployment of AI (Baier et al., 2019 ; Pumplun et al., 2019 ) and can thus can lead to inhibited AI adoption. Other legal aspects that can prove to hurdles in the adoption of AI concern the intellectual property entailed in AI algorithms and the data sets used by it (Baier et al., 2019 ; Demlehner & Laumer, 2020 ). In addition to the governmental regulations, each industry has its own set of requirements that affect AI adoption (Coombs et al., 2020 ; Pumplun et al., 2019 ). This can be laws or other external circumstances that affect the company and its interaction with the environment (Pumplun et al., 2019 ). Highly regulated sectors, such as healthcare, may encounter additional challenges in deploying AI compared to less regulated sectors (Coombs et al., 2020 ).

Environmental Pressure

An important driver of AI adoption is competitive pressure (AlSheibani et al., 2020 ; Demlehner & Laumer, 2020 ; Pumplun et al., 2019 ). Competitive pressure refers to how an organization is affected by its competitors and the action taken in response to these. Attainting a competitive advantage over rivals, means that organizations have to take action in order to reconfigure and adapt based on continuous and rapid change. The threat of losing a competitive advantage therefore acts as a force in motivating organizations to adopt IT innovations (AlSheibani et al., 2020 ). Competitive pressure can thus make organizations more prone to adopt AI in order to gain or maintain a competitive advantage. On the other hand, there is also a strong pull for the demand side. Customers are the ones who purchase goods and services from a company, which required that organizations need to meet and exceed the needs of its customers. When a company decides to adopt AI, it is also essential to consider its customer base’s knowledge and acceptance (Pumplun et al., 2019 ). Customers are increasingly expecting individualized services and products, such as the recommendation engine of Netflix. This will push the companies to adopt AI in order to design individualized, intelligent products (Pumplun et al., 2019 ).

The applications of AI span several diverse areas, such as marketing, production management, enterprise management, and customer service (Alsheiabni et al., 2018 ; Jelonek et al., 2019 ). AI applications can be deployed across the entire value chain of an organization, and it has the potential to revolutionize many key aspects of our daily lives (Wamba-Taguimdje et al., 2020 ). AI applications depending on their use can be divided very broadly into two categories: AI for automation and AI for augmentation. Automation refers to AI systems that are tasked in replacing human work, while augmentation enhances human intelligence by providing insight that can aid decision-making. Both automation and augmentation have applications in many organizational processes, or affect the organization’s customers through new or improved products and services that implement AI.

4.2.1 Automation

The notion of automation is not something new, it is an established concept relating to machines replacing humans, such as robots performing tasks on an assembly line. This description is true also for the automation enabled by AI, but it does not describe the radical changes that AI causes. Recent advances in AI have enabled machines to learn, improve, and adapt, thus increasing performance over time (Coombs et al., 2020 ). Therefore, AI technologies are able to automate more complex tasks involving cognition, such as learning and problem-solving (Lee et al., 2019 ). This automation is often called Intelligent Automation (Welling, 2019 ). Intelligent Automation enables the automatization of tasks that were previously considered too difficult to automate, such as knowledge and service work (Coombs et al., 2020 ). An example is the use of virtual robots to automatically process emails (Wamba-Taguimdje et al., 2020 ).

In the manufacturing and construction industries, AI is used to automate budgeting and planning, as well as inventory and replenishment (Wamba-Taguimdje et al., 2020 ). In the service context, AI can provide customers with digital and robot services to influence their customer experience (Prentice et al., 2020 ). An example of this is chatbots, which are conversational software systems that emulate humans’ communication capabilities (Nuruzzaman & Hussain, 2018 ). Chatbots can assist customers through a voice or text interface (Castillo et al., 2020 ). In the credit card insurance industry, chatbots are used to answer basic questions, resolve insurance claims, sell products, and ensure that the customers are adequately covered by their insurance (Nuruzzaman & Hussain, 2018 ). Chatbots are thus doing a job that was previously occupied by a human employee.

In addition to using AI for automating tasks within an organization, it can also create new or enhanced products and services to automate tasks for the customers. An example of this is conversational intelligent agents, such as Apple’s Siri and Amazon’s Alexa (Castillo et al., 2020 ; Prentice et al., 2020 ), which can automate tasks such as writing texts, making calls, and starting a playlist through voice commands. These agents can also be coupled with devices, such as Arduino and Raspberry Pi, to provide smart home automation through voice interaction (Matei & Iftene, 2019 ). This type of systems can automate simple day-to-day tasks at home, e.g., interactions with TV and lights. Another example is the introduction of facial recognition in smart phones, which automates the process of user authentication. These examples show the multitude of potential applications of AI, and the diversity of areas in which they can be used to automate tasks.

4.2.2 Augmentation

In recent years, AI has exceeded humans in performing certain complex tasks (Jarrahi, 2018 ). AI can process large amounts of information at high speed beyond humans’ cognitive capabilities (Jarrahi, 2018 ). Hence AI can be used to overcome the cognitive limitations of humans. Augmentation refers to integrating AI with human expertise to enhance decisions and optimize actions (Schmidt et al., 2020 ). The focus is on AI’s assistive role, indicating that it supports humans rather than replacing them.

Organizations often produce or have access to vast amounts of data. By considering this data, managers can make better-informed decisions. However, the data are often too complex to be analyzed by a human. Thus, managers can use AI to gain insights through data for better decision-making (Borges et al., 2020 ). Predictive analytics can learn from data and make accurate predictions and transaction-level decisions (Makarius et al., 2020 ). Possible use cases include interpreting previously unknown management control indicators and proposing corrective actions when sales decrease and the competition introduces new products (Bytniewski et al., 2020 ). AI can also be used in the analysis of opinions, attitudes, and emotions related to a particular product or a service (Jelonek et al., 2019 ), which is becoming more and more critical for organizations as they can get detailed insight to how their customers perceive their offerings (Bytniewski et al., 2020 ; Davenport & Ronanki, 2018 ).

In healthcare, computer vision vision can be used to process MRI images of the brain to mark tiny hemorrhages in the images for doctors (Jarrahi, 2018 ). AI can also detect cancer patterns (Jarrahi, 2018 ) or create surgical robots that can assist physicians during complicated surgeries (Makarius et al., 2020 ). In public relations, AI can be used to monitor social media and predicting media trends (Galloway & Swiatek, 2018 ). In marketing, AI can be applied to customer segmentation to classify customers based on preferences and lifestyle (Mishra & Pani, 2020 ). In fashion industries, AI is used to anticipate customer habits, predict future trends, and optimize recommendation systems (Wamba-Taguimdje et al., 2020 ).

AI can also be applied to products and services that organizations offer to enhance their customer’s intelligence. An example is Netflix’s recommendation engine, which uses various parameters based on the customer data, such as location, content watched, and the data searched by the user, to give personalized recommendations ( Netflix ( 2020 ) . Machine Learning , 2020-12-03). These personalized recommendations increase the likelihood of customers choosing to watch something they genuinely will like.

4.3 Impacts of AI

The question of how AI can lead to competitive performance is of interest to every business executive. To answer this question, the impacts of AI at both the process- (first-order) and firm-levels (second-order) should be studied. How does AI change business processes, and how does this lead to competitive performance? The next subsections address the first- and second-order impacts of AI.

4.3.1 First-Order Impacts

The first order effects of AI use are related to the changes it causes at the process level of an organization. Key performance indicators (KPIs) concerned with efficiency, effectiveness, capacity, productivity, quality, profitability, competitiveness and value are common measures of the performance improvements at the process level, and are used to monitor the output of an organization (Wamba-Taguimdje et al., 2020 ). To assess the impacts of AI on the process level, three different effects are discussed: process efficiency, insight generation and business process transformation.

Process Efficiency

Using AI to automate tasks or augment human intelligence in organizations can improve business process performance by increasing efficiency indicators (Coombs et al., 2020 ; Kirchmer & Franz, 2019 ). Automation of tasks through AI involves replacing human work with a machine. By automating tasks, organizations may relieve some employees of repetitive routine tasks, which enables them to focus on other knowledge-intensive activities that add more value to the organization (Makarius et al., 2020 ), thus increasing their productivity (Balasundaram & Venkatagiri, 2020 ; Bauer & Vocke, 2019 ; Bytniewski et al., 2020 ; Finch et al., 2017a). Moreover, machines can perform tasks quicker and with greater precision than humans, increasing organizations’ throughput, particularly in manufacturing industries and supply chain operations (Balasundaram & Venkatagiri, 2020 ; Finch et al., 2017a). Furthermore, AI use can reduce the time required to complete some key business processes (Coombs et al., 2020 ), and improve the error-rate and lag times by automatizing a series of tasks (Wamba-Taguimdje et al., 2020 ). For example, using AI in car manufacturing to automate visual recognition of barcodes and license plates improves efficiency compared to when performed by a human employee (Demlehner & Laumer, 2020 ). The replacement of human work by machines also includes reducing or eliminating errors made by human employees, and increasing transparency. Consequently, the quality of the results is suggested to be improved (Finch et al., 2017b ).

Insight Generation

One of the most prominent first-order effects of AI is that it can unlock insight and patterns hidden in large volumes of data (Mikalef & Gupta, 2021 ). By collecting, processing, and disseminating data within and between organizations, AI can present previously unknown information and help make insight-driven decision (Jelonek et al., 2019 ). According to Lichtenthaler ( 2019 ), " Even if two firms have access to the same internal and external knowledge, they may achieve different competitive positions if one firm has superior intelligence that enables specific insights as a basis for targeted competitive moves that the other firms lacks ". This suggests that organizations should foster ways by which they can leverage AI in order to gain insight that their competitors lack (Lichtenthaler, 2019 ).

The hidden value unlocked by AI can be used to make better-informed decisions, or even to partially automate tasks. AI can assist managers overcome their cognitive limitations by providing an efficient way to handle the large volumes of data available (Finch et al., 2017a; Keding, 2020 ). When decision-makers have access to more detailed knowledge, the quality and speed at which decisions are taken will increase (Keding, 2020 ). AI, therefore, enables faster and better decision-making (Wang et al., 2019 ). Organizations that can exploit AI’s informational effects are better positioned to quickly sense and respond to market dynamics (Wamba-Taguimdje et al., 2020 ). This capability of responsiveness is also known as organizational agility, and it consists of sensing, informed decision-making, and responding (Wang et al., 2019 ). AI, and deep learning, in particular, can play an active role in each of these activities. Specifically, AI applications can be steered towards systematically and effectively identifying patterns and underlying signals that humans may miss (Eriksson et al., 2020 ), and be trained to respond to these signals fast and accurately (Wang et al., 2019 ).

Business Process Transformation

As an innovative and (often) disruptive technology, AI enables organizations to innovate and transform business processes (Wamba-Taguimdje et al., 2020 ). The goal of all business processes is to convert inputs into valuable outputs, and new technology is expected to improve these processes through radical transformation (Mishra & Pani, 2020 ). AI is no exception, as it can enable the redesign of business processes with the intention of radically changing how current operations are executed (Mishra & Pani, 2020 ). Through this process, AI is also a driver for re-engineering and redesigning the existing organizational structure (Wamba-Taguimdje et al., 2020 ). It influences how human resources are being used, facilitating change in business processes and the organizational structure. The implementation of AI brings a new set of skills and capabilities for managers, employees, and AI to work together (Makarius et al., 2020 ). As a consequence, jobs may need to be redesigned, and new jobs can emerge. By using AI, organizations can reallocate resources, which, in the long term, have the potential to redraw the organizations’ organizational chart (Eriksson et al., 2020 ). In other words, the transformational effects of AI on business processes can be either direct, or indirect.

4.3.2 Second-Order Impacts

The second-order impacts of AI are related to the firm-level effects of AI use in operations. These effects can be divided into four categories: operational performance, financial or accounting performance, market-based performance, and sustainability performance.

Operational Performance

AI can have an impact on the operational performance in several ways, such as through the introduction of new products and services and enhancing the quality of existing products and services.

Introduction of new products and services One way of reaping the benefits of AI is for companies to identify opportunities to enter the market with a new offering (Mishra & Pani, 2020 ). AI can search through massive amounts of data to find patterns that can show opportunities for introducing new products and services. For example, by discovering shifts in customer preferences, organizations can find opportunities for entering markets with untapped profitable segments. Besides, as an innovative technology, AI facilitates the design of new products and services (Wamba-Taguimdje et al., 2020 ). In this regard, there are many possibilities for creating products and services that embed AI-based functionality. For example, organizations can use AI to introduce new services around conventional products in order to enhance customer service with applications such as recommender systems, chatbots, or intelligent agents (Alsheibani et al., 2020 ). In sequence, the introduction of new products and services can prompt business model innovation. Furthermore, studies have shown that AI-based recommendations can aid product developers in designing new products, particularly when it comes with design aid which can enhance creativity (Mikalef & Gupta, 2021 ). Business model innovation can, in turn, help companies preserve their market position (Lee et al., 2019 ).

Enhance the quality of products and services AI can also enhance the quality of already existing products and services. Davenport and Ronanki ( 2018 ) found in a survey that more than half of the executives said that their primary goal of adopting AI was to make existing products better. There are numerous ways AI can enhance the quality of products and services. For example, Netflix uses AI to enhance the video quality of their streaming services. Spotify uses AI to enhance their product in several ways, such as providing personalized song recommendations. Personalization of products and services are becoming more and more popular these days. By using AI to analyze customer data, organizations can provide a personalized experience to each customer, possibly causing the customers to perceive the product or service to have enhanced quality. Spotify, Netflix and Amazon are some of the many companies using AI to personalize the experience for customers.

Financial Performance

Over the last few years, AI has been gradually embedded in key organizational activities, prompting business growth is various sectors (Eriksson et al., 2020 ). Organizations that have implemented AI solutions have realized financial and accounting performance gains, such as increased revenue and cost reduction (Alsheiabni et al., 2018 ; Davenport & Ronanki, 2018 ). In a recent empirical study, Mikalef and Gupta (2021a) find that companies that have developed a structured approach to AI adoption and use, and developed an organizational capability around the novel technologies have realized performance gains. Their analysis points out to the fact that an AI capability has a positive effect on important financial and accounting performance indicators such as growth in overall financial performance. Nevertheless, to date there are few studies examining other measures of financial performance, such as return-on-investments, profitability, and gross profit margin after the introduction of AI.

Market-Based Performance

Marketing effectiveness Organizations using AI for marketing purposes are suggested to experience several benefits. A typical way in which AI can lead to marketing performance is to segment customers based on their needs to target the segments with different and customized marketing strategies. AI can enhance customer segmentation by processing and learning from existing customer data, enabling organizations to learn about their customers’ preferences and lifestyle in a whole new way. This capability enables a more precise segmentation because organizations can classify customers on a finer level (Mishra & Pani, 2020 ). Consequently, organizations can target their marketing better (Afiouni, 2019 ), and it opens for the possibility of delivering one-to-one marketing by personalizing the experience (Mishra & Pani, 2020 ). Thus, AI enhances the marketing effectiveness and accuracy by targeting the right customers with the right marketing strategy. Also, as customer behavior changes, segmentation suggestions from the AI system are regenerated so that organizations can effectively adapt their marketing strategy (Afiouni, 2019 ).

Customer satisfaction Customer satisfaction is related to how satisfied a customer is with a company’s offerings, and it directly affects the loyalty and retention of customers. By using AI, companies can learn more about their customers’ behaviors and, in turn, use this enhanced understanding to proactively prevent any negative experiences (Riikkinen et al., 2018 ). In doing so, companies can provide offerings that reduce customer attrition, such as providing personalized services or offers. For example, by using AI in the interaction with customers, customer satisfaction can increase because customers get better informed and find better-customized solutions guided by AI (Schmidt et al., 2020 ). However, the use of AI can also lead to customer dissatisfaction. For example, customers interacting with AI-powered chatbots can find the experience frustrating and ineffective (Castillo et al., 2020 ). Hence, it is important in the design of AI systems that have a direct interaction with customers, that their experiences and perceptions are considered.

Sustainability Performance

AI’s disruptive potential can drive business model innovation toward sustainability (Toniolo et al., 2020 ). Sustainable business models describe how organizations create, deliver, and capture value in a way that contributes to the sustainable development of the company and society (Toniolo et al., 2020 ). In other words, companies should conduct their business while at the same time focusing on environmental and social matters. AI has the potential to impact individuals and society in a disruptive and long-term manner (Alsheibani et al., 2020 ).

Environmental AI can affect environmental sustainability, such as by minimizing energy costs, reducing energy consumption and, in turn, reducing negative environmental impacts (Borges et al., 2020 ; Toniolo et al., 2020 ). Also, the use of AI tools can help organizations to reduce pollution and waste (Toniolo et al., 2020 ). A growing body of research is also examining the impact that AI applications have in supporting circular economy strategies, by enabling organizations to pursue strategies that promote recycling, reduction of emissions, and re-use of materials (Rajput & Singh, 2019 ).

Social By considering social responsibility, organizations can improve their reputation and increase their market share, which in turn can affect their competitive advantage (Toniolo et al., 2020 ). The adoption of AI opens up many new challenges for organizations in fulfilling their social responsibilities. Examples are challenges regarding privacy and discrimination. Recall that the fundamental enabler of AI systems is data. Organizations need to ensure the privacy of data on their customers and employees (Lee et al., 2019 ). Also, they must ensure that the the use of AI does not result in discriminatory actions or results. As AI is based on data, the results can be biased or discriminatory if the underlying data is. AI systems understand neither the inputs they process nor their outputs (Keding, 2020 ). They learn by interpreting patterns in previous data to predict the future. Thus, the results may reflect suspicious patterns, such as sexism and racism, found in the underlying data (Keding, 2020 ). For example, in recruitment processes: if the AI system explores the existing recruitment process, and this process lacks diversity (e.g. gender and ethnicity), then the results of the system will continue to embrace this underlying discrimination (Afiouni, 2019 ). On the other side, as AI systems are objective, they can reduce human bias in processes, such as recruitment and customer segmentation (Afiouni, 2019 ; Toniolo et al., 2020 ). Also, employees’ safety and working conditions can be enhanced with the introduction of AI. Using AI robots in manufacturing where hazards may be present, the safety conditions for employees can increase (Toniolo et al., 2020 ). Besides, automating repetitive routine tasks causes employees to use their capabilities and competencies elsewhere, possibly leading to more meaningful and creative jobs (Toniolo et al., 2020 ). This change can affect how employees perceive their working environment.

Unintended Consequences and Negative Impacts

While research predominantly focuses on positive effects of AI deployment and use, several recent examples showcase that in the absence of appropriate AI governance practices, negative and unintended consequences can occur. One of the most prominent examples is the failure of organizations to identify and eliminate bias in the data or AI algorithms, which can result in discrimination or unfavorable outcomes to particular ethnic groups, genders, or population clusters. For instance, there have been several news reports on biased AI outcomes concerning gender (Dastin, 2018 ; Vigdor, 2019 ) and racial discrimination (Zuiderveen Borgesius, 2020 ). Such outcomes have negative effects on the image of the companies they involve, and in some cases have resulted to financial losses and significant fines (Engler, 2021 ). Such outcomes increase the pressure towards organizations that use AI to apply practices to reduce bias in data and algorithms throughout all stages of deployment. In fact, due to the surge of several noteworthy cases of bias and discrimination as a result of AI outcomes, governmental agencies such as the European Commission, are now proposing concrete regulations that will dictate how AI applications are developed and used.

Negative impacts due to AI use, however, are not restricted to biased outcomes, but include a number of other aspects such as black-box algorithms, lack of transparency and accountability, security concerns, as well as harm to society and the environment (Yudkowsky, 2008 ). An example of the effects such unintended consequences have had includes the growing requirement for organizations to introduce explainability in how AI algorithms reach certain outcomes (Arrieta et al., 2020 ). In addition, this move has sparked a general need to provide more transparency of the entire process from data collection to outcome generation (Loyola-Gonzalez, 2019 ). A lack of explainability practices and low transparency hampers individuals trust in AI systems and leads to non-use (Samek & Müller, 2019 ). In addition, cases of AI use for customer and citizen interaction (e.g. chatbots) that have not taken into account human-centric principles have resulted in frustration and complaints from users, hampering the corporate image (Marcondes et al., 2019 ).

4.4 Theories and Frameworks in Empirical Studies

In this section we examine the theoretical perspectives that were used in the sample of articles we analyzed. While not all articles built their investigations on a theoretical grounding, a surprisingly high number of papers did. In the table presented below (Table 6 ), we document those that have been employed, describing how they have been applied in the study of AI, and their overall scope of application. Despite still being at a nascent stage, the papers looking at different facets of AI in organizations demonstrate considerable variety in the use of theoretical perspectives. Specifically, we see that many articles use firm-level theories in studying aspects that contribute to the effective adoption and deployment of AI applications in the organizational setting, such as the TOE framework and the Resource-Based View (RBV) of the firm. As research in this domain is still at an early stage, it expected that the majority of work will be revolved around understanding how to deploy these novel technologies in operations, and complementary organizational resources need to be deployed to support these.

However, we find that several articles also examine the processes of AI development, and the knowledge-intensive practices that surround AI maturation. As AI applications involve a lengthy process of development and refinement, by tweaking algorithms, data, and analysis methods, they create an opportunity for organizations to learn by doing. Several studies have applied relative theoretical perspectives, such as organizational learning theory and theory of artificial knowledge creation to elucidate this process. In addition, as AI applications are heavily data-dependent, other articles such as that of Gregory et al. ( 2020 ) have worked on developing new theoretical perspectives such as the network effect, in an attempt to understand how AI platforms become more useful and of value as users and data increase. Finally, some studies have focused on the individual as the unit of analysis, with theoretical perspectives such as dual process theory looking into the interactions of human and AI for optimizing decision-making, and value co-destruction building on a dark-side angle of how negative interactions reduce use of AI systems.

5 Research Agenda

From the synthesis in Section  4 , several research gaps are identified in relation to the study of AI use in organizations. Through challenging assumptions and identifying areas where there is a significant lack of knowledge, this section aims to provide a framework for guiding future research. The goal is not to present an exhaustive list of potential research directions, but rather, to highlight some important gaps in our understanding of how AI is shaping the way organizations are conducting business and competing. We therefore define five research themes, with each presenting a number of research directions (D) that can help expand our knowledge. The research framework is presented in Fig.  3 , with the themes being represented in the enumerated circles.

AI and business value research framework

5.1 Theme 1: AI Adoption and Diffusion

D1.1 difficulties in the process of adopting and deploying ai.

Although the proposed business value that organizations can derive from AI is argued to be significant for all kind of business operations, there is still a very small percentage of companies that to date have adopted and deployed AI applications beyond pilot projects (Anon, 2020 ). Companies face a number of challenges when it comes to adopt and deploy AI (Alsheibani et al., 2018 ). According to Alsheibani et al. ( 2018 ) technological readiness, organizational readiness, and environmental readiness (environmental conditions such as government regulations) are important aspects that influence the adoption of AI. Other difficulties can include the costs in infrastructure, hiring capable employees and relying on external partners. Hence, the different dynamics that have a role in allowing organizations to adopt AI and in turn develop an AI capability require a deeper understanding. Due to the nature of AI that requires employees from different business units to work together to build AI applications, the socio-technical arrangements and the process through which AI applications are developed and deployed warrants further investigation (Holton & Boyd, 2019 ).

In addition, conflicts between shareholders and managers could have important consequences on the actual use of AI in operations. Specifically, the conflicting views where shareholders encourage automation for reducing costs (Dedrick et al., 2013 ), while managers promote augmentation may cause a paralysis in actual deployment (Shollo et al., 2020 ). Moreover, the use of AI might challenge cultural norms and act as a potential barrier for managers or even customers to accept AI technologies (Dwivedi et al., 2019 ). Hence, further enlightenment in these areas is needed as it is crucial identifying the difficulties and the cultural obstacles and knowing how to overcome them. Finally, the modes of human-AI symbiosis and the changes these induce in organizational structures require further investigation (Shrestha et al., 2019 ). AI is argued to lead to significant adjustments to how business and IT functions work, collaborate, and exchange knowledge, so finding optimal ways of doing so is critical for successful AI deployments.

D1.2 Responsible AI Governance

While investing in technological infrastructure for AI may be an important part, organizations hoping to use AI in core operations must be able to govern the necessary resources and have thorough practices and mechanisms for orchestrating and following up on projects from ideation to completion (Papagiannidis et al., 2021 ). In addition, AI applications require several phases of maturation, and are subject to continuous improvement and development. A core requirement for most types of AI applications (e.g., in public sector) is taking into account ethical aspects and principles of responsible design. Hence, the concept of AI governance is inextricably associated with responsible and ethical principles being embedded throughout the process of design, deployment, and evaluation. Therefore, being able to break down the concept of AI governance and outline which key activities underpin the notion is an important research quest.

Past studies on IT governance have shown that having established such practices not only helps optimize output, but also enables organizations to achieve business and IT fit (Tallon & Pinsonneault, 2011 ). Nevertheless, AI poses an additional concern since the effects towards, as well as the interactions with humans shifts fundamentally. This poses a requirement to examine not only how AI applications are developed so that they are aligned with responsible principles (European Commission, 2019a ), but also to anticipate and plan for their effects as the gradually become embedded in everyday activities. In their recent work, Amer-Yahia et al. ( 2020 ) outline what they refer to as “intellectual challenges”, which comprise of major themes organizations must consider when they plan to deploy AI applications that concern the changing nature of interaction between humans and technology. An important area of inquiry therefore concerns what responsible AI governance comprises of, as well as what are the effects of implementing such practices at different levels of analysis.

5.2 Theme 2. AI and Socio-organizational Change

D2.1 how does ai change organizational culture.

Organizational culture has been consistently noted as being an important part of AI adoption and use (Mikalef & Gupta, 2021 ). Innovative cultures are in a better position to adopt AI. But can an innovative technology like AI lead to alteration to the organizational culture itself? This question has yet to be examined, particularly in relation to the ripple effects the adoption and use of AI may have on different aspects of organizational culture, like learning, collaboration, and communication patterns. In addition, an interesting point to explore is if the adoption of innovative technologies like AI affects the organization’s ability to innovate further. Does the introduction of AI change the mindset of the employees to being more open to innovations? An interest field of inquiry therefore concerns if and through what mechanisms innovation outcomes are achieved as part of AI deployments. With the introduction of new and disruptive digital technologies, many prominent cases of organizations have documented an increase of innovation output (Nambisan et al., 2017 ). Future research therefore needs to examine through what arrangements organizations are able to harness the possibilities of AI technologies in order to drive innovation.

Taking a contrarian view, the dark side effects of AI also warrant further investigation in the context of organizational culture. The introduction of AI and displacement or shifting of several conventional job roles is likely to lead to increased tensions, conflict, and feelings of distrust towards the technology itself and the units that promote its deployment (Huang et al., 2019 ). Therefore, a major challenge for practitioners is how to be able to manage the human factor internally when planning their AI implementations. Negative perceptions can result in rigidity in digital transformation and lead to inertia, thus significantly impacting organizational performance. There is, as a result, a need for future research to examine how IT managers can plan for and deploy AI applications to minimize potential friction and facilitate trust and acceptance of newly deployed solutions.

D2.2 What is the Role of AI-driven Automation in Decision-making?

Automating processes through the use of AI is argued to reduce the workload of employees in certain activities and increase efficiency of process completion (Acemoglu & Restrepo, 2018 ). At the same time, AI is able to automate decision-making when provided with appropriate data and business rules (Duan et al., 2019 ). Delegating such authority to AI applications however raises the issue of how to prevent bias that AI models might have, and how to ensure that new decision-making structures are improved, rather than debased, with the introduction of AI (Cirillo et al., 2020 ). While a number of studies have opened up the discussion about what the optimal decision-making structures are and how organizations can ensure that the introduction of AI enhances them, there is still a lack of empirical studies examining the effects of such arrangements (Shrestha et al., 2019 ). Such studies require an understanding of the impacts from the individual level, up to the business and organizational level of analysis, in order to fully capture the nature and types of effects that blended human-AI arrangements have.

D2.3 How Does AI Change the Organizations Structure?

The connection between AI adoption and organizational structure is one of a reciprocal nature. Organizational structure may affect the ability to adopt AI, and AI adoption may affect the organizational structure. Pumplun et al. ( 2019 ) found that a company’s organizational structure may affect its ability to adopt AI and propose that " Departments who keep relevant data to themselves, an overreliance on status quo as well as slow and bureaucratically shaped corporate structures will have a negative effect on the adoption of AI in companies ". This proposition suggests that organizations structured in functional silos, will encounter more challenges when adopting AI. A reason for this can be that these structures do not facilitate a holistic approach to solve problems. On the contrary, agile organizational structures are more flexible and can respond quickly to change, thus supporting innovation. However, such arrangements have received little empirical attention to date. Therefore, future research needs to engage in the study of how organizational structures affect AI adoption. Nevertheless, such relationships are likely to have a dynamic and reciprocal nature. As identified during the systematic literature review, AI influences how human resources are used, possibly redesigning the organizational chart (Eriksson et al., 2020 ) (Wamba-Taguimdje et al., 2020 ). Previous roles and structures are likely to change, and new roles may emerge. Therefore a promising avenue for future research is to examine through longitudinal approaches how organizations transform in order to embrace AI technologies.

5.3 Theme 3. AI-driven Value Propositions

D3.1 how does the orientation of ai impact value propositions.

The potential use cases for AI technologies within the organizational sphere are manifold, and a plethora of value-adding applications have been suggested both for private and public organizations (Davenport & Ronanki, 2018 ). One broad categorization that can be made involves the distinction between the use of AI for internal- and external-oriented functions. Internal functions involve using AI for improving internal business processes, such as decision-making, or for streamlining internal business processes. On the other hand, external functions include using AI in products and services that are in direct contact with customers Some examples of the later include the use of AI to recommend songs of interest to listeners by Spotify. It is therefore expected that the value-adding possibilities of AI applications are very diverse in nature. To date, there are to the best of our knowledge no studies that differentiate on performance metrics depending on the use case of AI. Furthermore, such an area of inquiry also raises the question of what the appropriate metrics are in order to be able to capture effects of AI and how to benchmark different similar applications.

D3.2 What is the Role of Complexity in AI Application Inimitability and Value?

While high complexity in AI applications may lead to black-box systems with limited transparency, high complexity can also result in difficult to imitate projects, leading to a longer period where firms can sustain an edge over their rivals (Wamba-Taguimdje et al., 2020 ). Nevertheless, the notion of complexity is compound, and involves aspects such as how many features are included in the model (Monostori, 2003 ), the diversity of data sources used, the interactions with other systems and processes, as well as the breadth and depth of activities they span. There are instances where large cooperation’s, such as Alibaba’s fraud risk management system (Chen et al., 2015 ), initiated high complexity projects that yielded significant returns. Nevertheless, some of these projects had little success and the value creation for the business was little if none. Hence, the correlation between the complexity of an AI system and the value creation for the business requires further exploration. Understanding when value creation is adding based on the complexity of the AI system could allow organizations to identify what aspects of their developed AI projects lead to a competitive advantage. As a result, a deeper understanding of how complexity adds or retracts value in the case of AI applications presents an interesting field of study, as well as developing deeper theorizing on the phenomenon of digital complexity (Benbya et al., 2020 ).

5.4 Theme 4. Competitive Value of AI

D4.1 what are the effects of ai on financial performance.

One of the key expectations from practitioners before adopting AI applications is that they can help improve financial performance indicators, such as revenue, growth, and help reduce costs (Alsheiabni et al., 2018 ; Eriksson et al., 2020 ). Nevertheless, there is a long chain of causal associations, and to date it is still not clear if and how AI can help organizations achieve financial performance gains. From our sample of articles there were none that studied the long-term financial consequences of AI adoption. Instead, the focus was on identifying short-term operational trends. Thus, it is important, particularly for small and medium-sized enterprises to elucidate the financial impacts that AI applications have in the long-term. As there are large financial investments tied to AI adoption, it is critical for firms that do not have large slack resources to know exactly the timeline in which AI applications start generating positive financial returns, and through what means and mechanisms. Prior studies have documented that there are large associated costs incurred by some organizations due to technology adoption, and which have resulted in significant financial losses (Chakravorty et al., 2016 ). It is therefore important to understand where the equilibrium lies between investing in the necessary AI resources, and the expected financial return.

D4.2 What are Appropriate Key Performance Indicators (KPIs) to Measure AI Success?

Measuring the impact of an AI project is challenging as the results are often difficult to capture with purely quantitative measures. While businesses use KPIs to measure performance, AI applications are often gauged in their success in completely different measures. Some examples of AI success measures include calculating various metrics such as Mean Squared Error, Confusion Matrix and F1-score (Kawaguchi et al., 2017 ). These metrics are good for determining the overall performance of a model, but they say very little about the overall project success. More organizational-focused KPIs could prove more valuable, after AI applications have been deployed and used in practice (Ehret & Wirtz, 2017 ). Nevertheless, such measures are typically very context specific. In addition, the selected KPIs should be quantifiable and provide managers with insights about the impact of the AI project in the business (Glauner, 2020 ). There is as a result a large gap in understanding what appropriate measures are to identify AI outcomes, and help guide key stakeholders.

D4.3 How Can AI Drive Innovation?

New products and services have been developed building on the functionality and affordance enabled by AI (Plastino & Purdy, 2018 ). Some prominent examples include Netflix’s recommendation systems, Amazon’s chatbot Alexa, and Tesla self-driving cars. Although AI is the technological innovation behind these services and products, there is little understanding regarding the socio-technical dynamics that lead to innovation to be generated. While undoubtedly the novel technologies that support AI have an important impact on the creation of such innovation output, the role of managers and knowledge workers, as well as their interactions needs to be understood in more detail. As new digital solutions are now one of the main components of innovations, it is imperative to understand the nexus of associations that surrounds technology-driven innovation. To date, research on the business value of AI has not built sufficiently on the growing body of knowledge on digital innovation (Nambisan et al., 2020 ). Thus, there is a need to understand the phenomenon of AI and its role in driving innovation in a more structured and theory-driven manner, that can allow for more nuanced understanding of how such outcomes are achieved.

5.5 Theme 5. AI and the Extended Organization

D5.1 extended organizational boundaries and partnerships.

All businesses, despite their size and industry must interact with the external environment in order to remain competitive and evolve (Yang & Meyer, 2019 ). A sought-after option by many such organizations is engaging in different forms of organizational relationships, such as mergers, acquisitions, joint ventures and alliances. Yet, when it comes to AI applications literature largely sees the development of AI as an activity that happens in the focal organization. As organizations typically have complementary key datasets, or interlinked organizational processes, it is important to examine how these relationships dictate the types of AI applications that are developed, as well as how they prompt organizations to engage in different forms of organizational engagements. Large corporations have access to AI resources that are unavailable for the majority of the businesses, especially for small and medium-sized enterprises (Garbuio et al., 2011 ). Despite the managers' efforts for pioneering AI initiatives, it is not always possible to achieve goals due to limitations in key resources (Pellikka & Ali-Vehmas, 2016 ). A possible model to mitigate such limitations could be to engage in such strategic alliances. Doing so enables the organizations to have access to resources which they would not be able to acquire by themselves in other circumstance, while at the same time, both companies are able to increase their business value and benefit from each other's capabilities. Nevertheless, research regarding governance schemes for effectively cooperating under such AI-specific partnerships is still at an early stage in research. Building on this avenue helps understand that dynamics and conflicts of interest in such collaborative arrangements, as well as optimal ways of organizing. Furthermore, a prominent area of study is how organizations develop the necessary IT infrastructure to facilitate such inter-organizational collaboration around AI.

D5.2 What is the Role of AI in Shaping the Reputation of the Organization?

Maintaining D5.2 What is the role of AI in shaping the reputation of the organization?a good reputation with customers and partners is essential for organizations. It can affect several business areas, such as market value, ability to attract more skilled employees, and customers’ loyalty (Eccles et al., 2007 ). An organization’s reputation is highly linked with the ability of customers and stakeholders to trust the organization, and in turn has significant effects on overall financial performance. Yet, the introduction of AI technologies can influence the level of trust among critical external entities, such as customers and business partners. While AI technologies may have many of the same capabilities as humans, in cases where there is a lack of transparency on where and how AI is used, issues of distrust may arise. Some early studies have shown that in order for humans to garner feelings of trust towards AI outcomes, they need to understand how such technologies work, and have clearly defined indications of safety and reliability (Marcus & Davis, 2019 ). Thus, organizations adopting AI must be aware of the role of trust, how to build trust, and in turn, how trust influences their reputation and interaction with external stakeholders. Thus, a promising area for further research is to understand how the introduction of AI affects the trust people have in the organizations and, in turn, how it affects the organization’s reputation. Such research can examine the technical features of AI, how communications patters influence trust-formation, as well as if there are any cultural differences among individuals in how they perceive AI applications (Felzmann et al., 2019 ).

5.6 Cross-cutting Challenges

The themes presented above that form our proposed research agenda, and the corresponding directions described within these themes, also raise several important concerns regarding the extended information value chains of organizations and the related activities within these (Abbasi et al., 2016 ; Koutsoukis & Mitra, 2003 ). In Table 7 , we present some of the core challenges within the information value chain, and their relationship to our directions presented above. Specifically, we follow the distinction regarding the sequence of activities within the information value chain that differentiate between data, information, knowledge, decisions, and actions.

The table indicates that there are several cross-cutting challenges among the future directions which we defined. For example, when looking at the data artefact, issues regarding how data infrastructures are designed and deployed, as well as how they need to be adapted to the socio-technical context present a challenge that span several research directions within the first theme. Further challenges such as that of integrating data from a variety of sources, as well ensuring high quality input to AI algorithms, present serious obstacles for contemporary organizations (Ransbotham et al., 2018 ).

Similarly, defining the procedures that surround information access, processing, and representation constitute tough obstacles for private and public organizations, as they concern technical facets of AI, as well as organizational and procedural aspects that span the entire organization (Dwivedi et al., 2021 ; Schaefer et al., 2021 ). As AI applications span multiple units within organizations, being able to deal with the technical requirements, as well as the necessary organizational changes that are needed to generate business value, is a challenge that organizations of all size-classes with be required to face (Mikalef & Gupta, 2021 ). The same applies also concerning how knowledge that is derived from AI applications or infused into such applications, is managed within organizations. Being able to harness the knowledge that AI applications can deliver is critical in generating business value out of AI applications, so it is important that organizations are structured appropriately in order to leverage such technologies in ways that contribute to value generation (Collins et al., 2021 ).

A final consideration regarding the cross-cutting themes of AI in organizations has to do with how decision-structures are shifted, as well as what competitive actions such technologies enable. There has been an ongoing debate about the different configurations of decision-making structures that utilize the strengths of human and AI agents, as well as their potential to generate business value (Shrestha et al., 2019 ). Adding to this, to be able to evaluate the value of AI applications, it is also important to have appropriate indicators of the value they deliver, as well as associate their use with the ability to attain a competitive advantage (Dwivedi et al., 2021 ).

6 Conclusion

AI is increasingly becoming important for organizations to create business value and achieve a competitive advantage. However, many AI initiatives fail even though time, effort, and resources have been invested. There is a lack of a coherent understanding of how AI technologies can create business value and what type of business value can be expected.

In this paper, we present a narrative review to identify how organizations can deploy AI and what value-generating mechanisms such AI uses have. The result of this analysis consists of three parts. First, several enablers and inhibitors of AI use are identified. These antecedents of AI adoption consist of technological, organizational, and environmental resources and conditions. Second, different use cases for AI are distinguished. Organizations can use AI technologies to automate tasks or augment humans, either for internal or external purposes. Internal purposes mean using AI to improve internal business processes, where the customer is not in direct contact with the AI-solution. Furthermore, external purposes mean using AI in products and services that are in direct contact with the customers. Lastly, the impacts of AI are discussed, specifically how organizations change and how this leads to competitive performance. Several implications of AI at both the process- and firm-level are identified.

The findings in this article have several implications for how to manage AI in organizations. By considering the enablers and inhibitors found, organizations can better assess their ability to adopt AI successfully and know which changes to make. Moreover, by knowing how AI can be used, organizations can make better decisions about where in their value chain to implement AI solutions. Lastly, knowing the possible effects of AI adoption can better prepare organizations to introduce AI in their line of work. We conclude this study by presenting a research agenda that identifies areas that need to be addressed by future research to understand AI technologies’ value-generating mechanisms in the broader organizational environment. While this study may not follow an exhaustive approach in documenting and presenting the themes in the paper, we attempt to present themes through the IT-business value perspective. In addition, although a systematic approach was used in searching for and analyzing the paper contents, we did not follow a specific method for documenting and reporting results, such as PRISMA (Moher et al., 2015 ).

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Enholm, I.M., Papagiannidis, E., Mikalef, P. et al. Artificial Intelligence and Business Value: a Literature Review. Inf Syst Front 24 , 1709–1734 (2022). https://doi.org/10.1007/s10796-021-10186-w

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Please note you do not have access to teaching notes, crafting a healthier future: exploring the nexus of product design, digital innovations and dynamic marketing for obesity prevention. a literature review.

British Food Journal

ISSN : 0007-070X

Article publication date: 2 May 2024

The current work aims to present a review of academic literature that systematizes the body of knowledge related to marketing and consumer behavior in order to identify the most effective variables that encourage the consumer towards a proper and better lifestyle, accordingly the paradigm of management, marketing and technology efforts to promote a “better” society preventing obesity.

Design/methodology/approach

A literature review was carried out to examine the studies of marketing and consumer behavior published in international peer-reviewed journals over the last twenty-three years (2000–2023). Our review finally considered a total amount of 46 articles.

Findings elucidate three overarching themes and associated sub-hemes, encompassing: (1) Product design for obesity prevention, including aspects such as labeling, nomenclature, packaging and assortment; (2) Technology-supported preventive measures, involving mobile applications, self-monitoring, short message services and digital therapeutics; and (3) Marketing and communication strategies, incorporating social advertising, nudge, social influence and initiatives targeting childhood obesity prevention. Furthermore, a comprehensive research agenda is presented, delineating potential avenues for future investigations predicated on the utility of the results in fostering subsequent endeavors within the realms of: efficacy and effectiveness studies; personalization and tailoring; behavioral change techniques and gamification; user experience and acceptance; cost-effectiveness and implementation; as well as ethical and privacy concerns.

Research limitations/implications

Main limitations are related to the characteristics of the analyzed literature, resulting in only English journal articles, book chapter and so on. Thus, other relevant contributions in different languages discussing interesting insights might have been neglected.

Practical implications

This study offers several insights to managers, marketers and policymakers involved in the issue of the obesity prevention. Since obesity represents a crucial challenge for public health at a global level, with its incidence reaching epidemic proportions in recent decades, the results may be extremely useful and powerful because suggesting – by employing a robust resulting corpus of knowledge on this domain – several practical features, actions and tactics to face such an important challenge. Moreover, this paper offers for scholar and researcher a systematized knowledge around the issues of obesity prevention, together with a detailed research agenda emerging by the critical analysis of the emerging insights, and to practitioners systematized useful insights to project and develop their future business strategies.

Social implications

By providing several actions and tactics for obesity prevention (e.g. as for product labeling, naming, packaging, assortment; the exploitation of new technologies for mobile applications design, self-monitoring, short message service (SMS) alert systems, digital therapeutics; the role of social advertising, nudge, social influence) this work perfectly match the emerging societal orientation related to business, marketing and technology efforts to create a “better” society.

Originality/value

The study shed lights the need for a holistic approach to obesity prevention, involving interaction between individual main topics. Importantly this is the first study to analyze the issue of obesity prevention by considering a multidisciplinary corpus of literature, analyzed trough an individual-centric orientation.

• Product design
• Communication
• Chronic disease
• Digital therapeutics

Giannattasio, A. , Sestino, A. and Baima, G. (2024), "Crafting a healthier future: exploring the nexus of product design, digital innovations and dynamic marketing for obesity prevention. A literature review", British Food Journal , Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/BFJ-10-2023-0897

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