Pointers on the Covelent Case and Affordance

Theory
Table of content
I. INTRODUCTION...................................................................................................................................1
II. LEARNING OBJECTIVES OF THE CASE STUDY ..................................................................................................1
III. CASE SUMMARY ...............................................................................................................................1
1. Background on Covelent ............................................................................................................ 1
2. Transformative Promise of GenAI ................................................................................................ 2
3. Cautious Adoption..................................................................................................................... 2
4. Ethical and Practical Concerns .................................................................................................... 2
5. GenAI Opportunities within Covelent ........................................................................................... 2
6. Some questions for consideration while reading the case (these are not the assessment questions) ..... 2
IV. KEY CONCEPTS AND FRAMEWORKS THAT MIGHT STRENGTHEN STUDENTS’ LEARNING ................................................2
1. Starting with a Framework or Theory........................................................................................... 3
2. Functional Review of the Organisation (Covelent).......................................................................... 7
V. RECOMMENDED READINGS (FOR THE CASE STUDY OVERALL) ..............................................................................8
VI. CONCLUSION ...................................................................................................................................8

Pointers on the Covelent Case and Affordance Theory

I. Introduction

This ‘supporting document’ accompanies the Covelent case study, focusing on consulting firms'
strategies to deliver value to clients while maximising benefits from Generative AI (GenAI). The aim
of the ‘supporting document’ is to provide students with some pointers. The document was written
idependent of the assessment brief with students’ learning from reading the actual case in mind.
These pointers are therefore not part of the formal brief. Students are reminded to respond to the
actual brief rather than this document.

II. Learning Objectives of the case study

• Understand the opportunities and challenges presented by GenAI in the consulting industry.
• Analyse Covelent’s strategic approach to leveraging GenAI for client value and internal benefits.
• Evaluate the ethical and practical considerations of implementing GenAI in business practices.

III. Case Summary

1. Background on Covelent
Founded in early 2023 by Nik Nicholas, Covelent is a boutique consulting firm focused on growth
strategies. Despite its small size, Covelent has a global network of consultants and serves Fortune
Global 500 companies across various industries. The firm employs a hypothesis-based approach to

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consulting, developing decision trees based on their understanding of the client's industry and
trends.

2. Transformative Promise of GenAI

GenAI has evolved significantly, particularly with the launch of ChatGPT 3 in 2022. GenAI's
capabilities extend beyond text generation to include image, video, and code generation. Research
by global firms indicates that GenAI is expected to transform business operations, contributing to
value creation and the development of new business models.

3. Cautious Adoption
Despite its potential, the adoption of GenAI is cautious due to concerns over data security, ethical
implications, and the accuracy of AI-generated content. Gartner’s hype cycle indicates that GenAI is
currently at the peak of inflated expectations, with real-world implementation being limited and
focused on low-risk areas.

4. Ethical and Practical Concerns

Key ethical concerns include bias, transparency, human agency, and environmental impact. Practical
issues involve the reliability of AI outputs, data privacy, and the integration of GenAI into existing
business models. Covelent’s workforce survey highlights these concerns, showing a mix of optimism
and apprehension among professionals.

5. GenAI Opportunities within Covelent

Nik Nicholas envisions GenAI as a tool to enhance productivity and knowledge management within
Covelent. By integrating GenAI into their consulting processes, Covelent aims to streamline project
timelines and improve decision-making. However, Nicholas emphasises that GenAI should be viewed
as an enabler of business strategy rather than a standalone strategy.

6. Some questions for consideration while reading the case (these are not the assessment questions)
• What are the primary opportunities that GenAI presents for consulting firms like Covelent?
• How can Covelent ensure that the advice they provide on GenAI delivers true value to their
clients?
• What are the potential risks and ethical concerns associated with GenAI, and how can
consulting firms address these issues?
• How can Covelent differentiate itself from larger consulting firms in the GenAI space?
• In what ways can GenAI be integrated into Covelent’s internal processes to enhance
efficiency and knowledge management?

IV. Key Concepts and Frameworks that might strengthen students’ learning

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To teach students to approach the task of reviewing the implications of adopting GenAI, or any other
emerging technology, I recommend encouraging students to consider multiple perspectives and
methodologies. Students should be equipped to analyse the impact and feasibility of GenAI from
different angles, ensuring a critical multi-perspective evaluation.

In this ‘supporting document’ I discuss the following approaches to analyse GenAI adoption in and
for consulting firms

1. Integrating Frameworks and Theory

a. Generic frameworks
b. Technology (adoption) models
c. Example: Affordance Theory
d. Ethics

2. Functional Review of Consulting Tasks

1. Starting with a Framework or Theory

Up front question: Which frameworks do you remember from our short ‘key framework revision’
and why?

Grounding the review in relevant academic theories, for example ‘Innovation Diffusion’, allows to
evaluate how GenAI might spread through the consulting industry, Technology Acceptance Model
(TAM) allows to assess the perceived usefulness and ease of use of GenAI, and ‘Organisational
Change Management’ allows to consider factors such as resistance to change and the ethical
implications of AI. Affordance theory is covered in more depth and includes relevant literature. I
chose affordance theory due it its simplicity, its many applications, its link to the dynamic
interactions between people, organisations and the technologies, as well as its link to ‘unexpected’
affordances.

Below some brief pointers for students. Please note that the list should be seen as indicative, not
exhaustive. Students should be reminded that there is no expectation to use all frameworks and it is
recommended to focus on a small number of frameworks and critically review these in depth.

a) Ten generic frameworks that could be considered for the analysis

1. The VRIO Framework: assesses resources and capabilities based on Value, Rarity, Imitability,
and Organisation.
2. SWOT Analysis: identifies internal strengths and weaknesses, as well as external
opportunities and threats.
3. PESTLE / PESTEL Analysis: analyses the macro-environmental factors: Political, Economic,
Social, Technological, Legal, and Environmental.

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 4. Scenario Planning: develops multiple scenarios to anticipate future uncertainties and
develop flexible strategic plans.
5. Stakeholder Engagement: develops strategies for engaging and communicating with key
stakeholders.
6. Porter’s Generic Strategies: identifies three strategies to achieve competitive advantage
(Cost Leadership, Differentiation, and Focus).
7. Value Chain Analysis: examines the primary and support activities within an organisation to
identify areas for value creation.
8. Three Horizons Framework: manages innovation and growth by categorising initiatives into
three time-based horizons and encourages ‘futures thinking’.
9. ADKAR Model: manages change through Awareness, Desire, Knowledge, Ability, and
Reinforcement.
10. Kotter’s 8-Step Change Model: guides organizations through the process of change
management.

b) Five Technology (Adoption) Frameworks

1. Gartner Hype Cycle (mentioned in the case study):

• Innovation Trigger: A potential technology breakthrough kicks things off.
• Peak of Inflated Expectations: Early publicity produces several success stories—often
accompanied by failures.
• Trough of Disillusionment: Interest wanes as experiments and implementations fail to
deliver.
• Slope of Enlightenment: More instances of how the technology can benefit the enterprise
start to crystallise and become more widely understood.
• Plateau of Productivity: Mainstream adoption starts to take off. Criteria for assessing
provider viability are more clearly defined.

2a. Roger’s Diffusion of Innovation (DOI) Theory:

• Focuses on the spread of innovations through a social system over time.
• Innovation: An idea, practice, or object perceived as new.
• Communication Channels: The means by which information about the innovation is
transmitted.
• Time: The period over which adoption occurs.
• Social System: The group of individuals adopting the innovation.

2b. Rogers’ Innovation Adoption Curve based on Diffusion of Innovations Theory:

• Innovators: The first individuals to develop or adopt an innovation.
• Early Adopters: Respected opinion leaders who adopt new ideas early but carefully.
• Early Majority: Deliberate before adopting new ideas but adopt sooner than the average.
• Late Majority: Sceptical and adopt innovations after the average member of society.
• Laggards: Last to adopt an innovation, typically due to aversion to change or financial
constraints.

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3. Moore’s Crossing the Chasm:
• Focuses on the challenge of transitioning from early adopters to the early majority.
• Early Market: Innovators and Early Adopters.
• Chasm: The gap that must be crossed to reach the Early Majority.
• Mainstream Market: Early Majority, Late Majority, and Laggards.

4. Technology Acceptance Model (TAM):

• Perceived Usefulness: The degree to which a person believes that using a particular system
would enhance their job performance.
• Perceived Ease of Use: The degree to which a person believes that using a particular system
would be free from effort.
• Behavioural Intention to Use: The intention to use the technology based on perceived
usefulness and ease of use.
• Actual System Use: The real-world utilization of the technology.

5. Unified Theory of Acceptance and Use of Technology (UTAUT):

• Performance Expectancy: The degree to which using the technology will provide benefits in
job performance.
• Effort Expectancy: The degree of ease associated with the use of the technology.
• Social Influence: The degree to which individuals perceive that important others believe they
should use the new technology.
• Facilitating Conditions: The degree to which an individual believes that an organisational and
technical infrastructure exists to support the use of the technology.

c) Example: Affordance Theory

One theoretical angle I brought into the case study analysis, in addition to Gartner’s Hype curve,
which is mentioned in the actual case study, is Affordance theory:
• Affordance originally refers to what is offered, provided or furnished to someone or
something by an object (Gibson, 1986).
• In the IS field, Markus and Silver (2008) firstly used the term of functional affordance to
define the relationship between an IT artefact and a specified user or user group.
• Majchrzak and Markus (2012) defined ‘affordances’ as the ‘action potential’ of technology,
denoting what goal-orientated actors / users can do through using a digital artefact, based
on their capabilities and goals.
• Functional affordance is defined as ‘possibilities for goal-oriented action afforded to
specified user groups by technical objects’ (Markus and Silver, 2008, p.622).
• Affordances and Affordance Theory is frequently used in user design, e.g., for developing
user interfaces (UI) or user experiences (UX), however, it can be used also in other contexts.
• For example, there are Positive & Negative affordances (e.g., my Smartwatch can measure
my heartbeat – but I can also be tracked by others without me knowing that I am
‘observed’).
• Questions students could ask themselves:
o How do digital tools and infrastructure enable and constrain the consultants’ work?

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 o When there is a tension between positive and negative affordances, how could
managerial practices take place to balance such tension?
o What kind of managerial capabilities could potentially be built?

Related Literature (on Affordance Theory)

• Gibson, J. J. 1986. The ecological approach to visual perception, Hillsdale, NJ: Lawrence
Erlbaum Associates.
• Majchrzak, A. and Markus, M.L. 2012. Technology Affordances and Constraints in
Management Information Systems (MIS). Encyclopedia of Management Theory, (Ed: E.
Kessler), Sage Publications, Available at SSRN: https://ssrn.com/abstract=2192196
• Markus, M.L. and Silver, M.S., 2008. A foundation for the study of IT effects: A new look at
DeSanctis and Poole's concepts of structural features and spirit. Journal of the Association
for Information systems, 9(10), p.5.
• Pozzi, G., Pigni, F. and Vitari, C., 2014. Affordance theory in the IS discipline: A review and
synthesis of the literature. In AMCIS 2014 Proceedings.

An article with good visualisations (diagrams) on affordance theory can be found here:
Guo, Yanli & Zhu, Yi & Chen, Jianbin. (2021). Business Model Innovation of IT-Enabled Customer
Participating in Value Co-Creation Based on the Affordance Theory: A Case Study. Sustainability. 13.
5753. 10.3390/su13105753. PDF accessible here:
https://www.researchgate.net/publication/351764568_Business_Model_Innovation_of_IT-
Enabled_Customer_Participating_in_Value_Co-
Creation_Based_on_the_Affordance_Theory_A_Case_Study

Videos (explaining affordances more generally rather than solely affordances in IS research)

General videos explaining the terminology of affordances:

https://www.youtube.com/watch?v=POuSiTrlSuw
https://www.youtube.com/watch?v=iiVRgmfunLA&t=14s
https://www.youtube.com/watch?v=UYlASVcirqQ

Video geared at a more academic audience (Jenny L Davis):

https://www.youtube.com/watch?v=5QN8WokJQ_Q

d) Frameworks on Ethics

For a deeper exploration of AI’s ethical implications (bias, fairness, transparency), see these articles
in the Journal of Information Technology Teaching Cases:

• Fischer, I. 2023, Evaluating the ethics of machines assessing humans The case of AQA: An
assessment organisation and exam board in England. Journal of Information Technology
Teaching Cases, https://doi.org/10.1177/20438869231178844

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• Fischer, I., Beswick, C. and Newell, S. 2021, Rho AI – Leveraging artificial intelligence to address
climate change : financing, implementation and ethics, Journal of Information Technology
Teaching Cases, https://journals.sagepub.com/doi/full/10.1177/2043886920961782

2. Functional Review of the Organisation (Covelent)

A complementary analysis to the framework approach outlined above could be a hands-on analysis
on how GenAI could be integrated within a consulting firm. When reviewing the following examples,
students might want to consider the concept of the firm’s own LLM (Large Language Model), as
outlined in the case.

Some examples where GenAI, and in particular a firm’s own GenAI LLM (for data privacy reasons)
might assist:

1. Market Research, Client Engagement & Proposal Development:

• Automated Market Research Reports: Train GenAI on industry data, competitor analysis, and
prospects goals to generate pitches as part of tender processes. This could free up
consultants to focus on strategic interpretation and bespoke pitch presentations.
• Real-time Trend Identification: Utilise GenAI to continuously scan news articles, social
media, and industry publications. Identify emerging trends and customer concerns, allowing
consultants to tailor prospective clients’ strategies proactively.
• Automated Contract Generation: Train GenAI on standard consulting agreements and client-
specific requirements. Generate customised contracts.

2. Project Management & Innovation Generation:

• AI-powered Scenario Planning: Train GenAI on historical data and industry trends. Use it to
generate multiple "what-if" scenarios for client projects.
• Brainstorming & Innovation Support: Utilise GenAI to analyse vast datasets and generate
creative solutions to client problems. This might spark new ideas and creative innovations
that consultants might not have considered without the support of GenAI.

3. Data Analysis & Reporting:

• Automated Data Cleaning & Summarisation: GenAI could perhaps handle data cleaning tasks
and generate clear summaries of complex datasets. This could allow consultants to focus on
analysing insights and formulating recommendations.
• Customisable Client Reports: Train GenAI on client-specific data and reporting preferences
to generate tailored reports.

4. Brand Management & Content Creation:

• GenAI could perhaps generate white papers, draft blog posts, and draft industry reports
which could then be ‘augmented’, re-written and improved, by humans.

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 • AI-powered Data Visualisation: Generate charts, graphs, and other visual representations of
client data with AI. This might allow consultants to focus on clearer communication of
complex insights and trends.

V. Recommended readings (for the case study overall)

• Bant, A., Poitevin, H., Greene, N. & Brethenoux, E. (2023), ‘Fiver Forces that Will Drive the
Adoption of GenAI, Harvard Business Review, 14 December, available at:
https://hbr.org/2023/12/5-forces-that-will-drive-the-adoption-of-genai (accessed 11 May 2024).

• Covelent, (2024), ‘2024 AI in the Workforce Survey: Insights on Generative Artificial Intelligence
and Perceptions and Expectations Among Professionals’, available at: 2024 AI In The Workforce
Survey: Insights on Generative Artificial Intelligence and Perceptions and Expectations Among
Professionals | Covelent Insights (accessed 11 May 2024).

• Dell'Acqua, F., McFowland III, E., and Mollick, E., Lifshitz-Assaf, H., Kellogg, K., Rajendran, S.,
Krayer, L., Candelon, F. & Lakhani, K. (2023), ‘Navigating the Jagged Technological Frontier: Field
Experimental Evidence of the Effects of AI on Knowledge Worker Productivity and Quality’,
Harvard Business School Technology & Operations Mgt. Unit Working Paper No. 24-013,
available at SSRN: https://ssrn.com/abstract=4573321, accessed 11 May 2023.

• Dutt, D., Beena, A., Perricos, C. & Sniderman, B. (2024), ‘Now decides next: Insights from the
leading edge of generative AI adoption. Deloitte’s State of Generative AI in the Enterprise
Quarter one report’, available at: us-state-of-gen-ai-report.pdf (deloitte.com) (accessed 11 May
2024).

VI. Conclusion

This ‘supporting document’ aimed to provide potential ideas of how to structure case discussions or
assessments. By examining Covelent’s case, students can explore the balance between leveraging
GenAI and addressing the associated risks and ethical concerns. The discussions should lead to a
deeper understanding of how consulting firms operate and how they might navigate the rapidly
changing landscape of GenAI to deliver value to their clients.