Using design thinking to uncover the social side of AI

Abstract

Can AI help secure the future of food? In this PhD project, together with several esteemed researchers, you have the opportunity to explore the social side of AI systems in the context of greenhouse agriculture. Through a human-centered design thinking approach, we want to discover what AI systems need to do for their users and how these innovative technologies complement or compete with users’ professional identity. The project aims to provide insights into how AI systems can help users to perform their job in a better way and provide recommendations about the forces that facilitate or hamper the adoption of these innovative technologies in practice. Together, we set the stage for a new era in sustainable greenhouse agriculture.

This PhD project will be supervised by Prof. Dirk Deichmann and other members of the Technology and Operations Management Department.

Keywords

Design thinking, innovation, human-AI interaction, creativity, AI, co-creation, design

Topic

In this PhD project, we aim to better understand the social side of AI systems and specifically shed light on human-AI interactions to explore the forces that facilitate or hamper the adoption of these innovative technologies by different users in the horticulture industry.

As part of the larger LEAP-AI grant, we formulated three challenges that are important.

Challenge 1: Understanding the AI model's job-to-be-done for users. Current AI systems are frequently criticized for their lack of flexibility. To address this concern, we need to better understand what users (i.e., growers) are trying to achieve and how an AI system can help them in performing their job in a better way. Also, we need to shed light on how AI systems complement or compete with users’ professional identity. We do this by investigating the latent needs of users in terms of AI systems. This provides insights into the forces that facilitate or hamper the adoption of AI systems.

Challenge 2: Discovering how users can better understand and validate the outcomes of AI systems. We specifically focus here on better understanding the usability and interpretability of AI systems. The complexity of the models and a lacking understanding of how the different factors influence predictions likely plays a big role in how AI systems are used and why their implementation may be resisted by users. Shedding light on this question will therefore provide additional insights to facilitate the adoption of such systems by users.

Challenge 3: Exploring how the sharing of data among different stakeholders can be improved. There is a risk of increased dependency between stakeholders owing to the sharing of sensitive data. Therefore, we also aim to study the risks involved in adopting AI systems and explore how data sharing with different stakeholders, including competitors, in the ecosystem could be facilitated. On the one hand, increased data sharing in the ecosystem will improve the quality of the AI system recommendations, thereby facilitating the quality of decision making of every participating user. On the other hand, the insights from our inquiry will also contribute to the adoption of AI systems because it sheds light on the risks that users perceive and can recommend strategies to tackle those risks.

Approach

To address the challenges laid out above, together with you, we aim to conduct a multi-method study following principles of design thinking. Data can be collected through, for instance, observatory approaches, interviews, field experiments, co-creation workshops, surveys, or by leveraging archival information.  

Required profile

We seek candidates who have:

• A (nearly) completed Masters’ degree (including a research thesis) in business or management, organizational psychology, social sciences, design studies, or a related area
• Demonstrated experience of conducting research, including either quantitative (e.g., surveys, experiments) or qualitative (e.g., interviews, observation) methods
• A strong interest in studying design thinking, innovation, and human-AI interactions (experience in horticulture/agriculture is not required but a bonus)
• A high willingness to learn as well as a flexible and collaborative mindset
• Excellent oral and written communication skills in English (proficiency in Dutch is not required but a bonus)

Required by ERIM

All application documents required by ERIM can be found here.

The PhD candidate is expected to start 1 July 2025.

Expected output

Your journey at RSM involves continuous learning and knowledge development. The final output will be a PhD thesis which typically consist of a number of high-quality papers that have either been published or that aim for publication in the field’s leading journals.

Cooperation

This project is part of an NWO grant called LEAP-AI. We work closely together with other researchers from Delft University of Technology and Wageningen University & Research. The project is co-financed by Blue Radix, Ridder, and HarvestAi and in partnership with Glastuinbouw Nederland and Vertify, this project represents a powerful collaboration between academia and industry leaders of which you will be part of. Together, we work to set the stage for a new era in sustainable greenhouse agriculture.

At RSM, you will be part of the Innovation Management group in the Technology and Operations Management department, consisting of diverse, ambitious, and collaborative (international) faculty. We publish in the very top management journals, in collaboration with several renowned international scholars and industry partners. In its teaching, the group links state-of-the-art management theories with business practice.

Societal relevance

Insights of this PhD project will contribute to the project consortium’s goal to develop flexible, human-friendly AI systems for greenhouse agriculture that increase sustainability of greenhouses, decrease costs for greenhouse products, and improve food security.

Scientific relevance

Findings of this PhD project will significantly enhance our understanding of design thinking, innovation, and human-AI interactions by developing and extending theoretical developments in these emerging scientific areas.

Literature references & data sources

Some more information about the larger NWO grant called LEAP-AI can be found here:

• https://www.nwo.nl/en/news/two-consortia-to-start-research-on-the-use-of-ai-in-horticulture-and-livestock-farming
• https://www.rsm.nl/news/detail/15726-rsm-researcher-wins-eur13m-nwo-grant-to-research-food-security/

Employment conditions

ERIM offers fully-funded and salaried PhD positions, which means that accepted PhD candidates become employees (promovendi) of Erasmus University Rotterdam. Salary and benefits are in accordance with the Collective Labour Agreement for Dutch Universities (CAO).

Erasmus University Rotterdam aspires to be an equitable and inclusive community. We nurture an open culture, where everyone is supported to fulfil their full potential. We see inclusivity of talent as the basis of our successes, and the diversity of perspectives and people as a highly valued outcome. EUR provides equal opportunities to all employees and applicants regardless of gender identity or expression, sexual orientation, religion, ethnicity, age, neurodiversity, functional impairment, citizenship, or any other aspect which makes them unique. We look forward to welcoming you to our community.

Contact information

For questions regarding the PhD application and selection procedure, please check the Admissions or send us an e-mail via phdadmissions@erim.eur.nl.