PhD in Management

Vast amounts of high-frequency and high-dimensional business and financial data pose major challenges for analysis, storage, and interpretation. Such data are characterized by complex dependencies across time, entities, or space, making standard modeling approaches difficult to scale and interpret. This PhD project focuses on exploiting latent structure in multidimensional data to compress information efficiently without significant loss. A central objective is to reduce dimensionality across meaningful structural dimensions while preserving interpretability and statistical relevance of the data. By doing so, we aim to address both methodological efficiency and practical usability in data-rich environments.

The research builds on tensor-based representations of multivariate time series to model high-dimensional data in a structured and parsimonious way. Such representations allow multidimensional dependencies to be captured explicitly while avoiding the proliferation in the number of parameters. By combining low-rank tensor decompositions with statistical and optimization-based techniques, the project develops scalable estimation and inference methods suitable for dynamic, correlated business and financial data. Focus is given to computational efficiency and interpretability in high-dimensional settings.

The expected outcomes of the project are twofold: (i) the development of methods that leverage structural information in high-dimensional data to achieve efficient compression without loss of relevant information; and (ii) the application of these methodologies to financial and business data to enhance economic interpretability. Together, these outcomes enable clearer insights along a smaller set of economically relevant dimensions.

Area of Excellence (AoE): AI, Data Science and Business

Supervisor

Title: Associate Professor
Name: Dr. Maria Grith
Affiliation: Department of Finance, NEOMA Business School
Contact: maria.grith@neoma-bs.fr

The integration of explainable AI (XAI) and generative AI (GAI) presents transformative potential – and critical challenges – for organizational knowledge management. This project investigates how to calibrate accuracy–explainability trade-offs, customize transparency for diverse user roles, and embed fairness- and privacy-aware governance. It addresses the tension between model fidelity and cognitive load, the impact of explanation granularity on trust and data disclosure, and the risks of over-reliance on GAI for knowledge creation and transfer. By examining these dynamics, the research seeks to develop frameworks that enhance both the effectiveness and ethical robustness of AI-augmented knowledge systems.

The study employs a mixed-methods approach, combining scoping reviews, laboratory and field experiments, fairness audits, privacy-calculus experiment, interviews and surveys, and case studies. These methods will assess domain-specific thresholds, user trust mechanisms, bias mitigation, and the organizational impact of GAI on knowledge workflows.

Outcomes include actionable guidelines for role-adaptive explainability, bias detection and mitigation protocols, and workflow redesigns that optimize GAI’s contribution to knowledge transfer. The research will advance both theory and practice in AI-driven knowledge management, offering organizations evidence-based strategies to deploy AI responsibly and effectively.

Supervisor:

Professor Aleš Popovič, SISCAD, ales.popovic@neoma-bs.fr

Project linked to the Area of Excellence: AI, Data Science & Business

Objective: This project seeks to examine how digital technology adoption influences firm resilience and to explicate the mechanisms through which digital transformation helps firms to anticipate, adapt to and recover from environmental disruptions. The project will draw on the resilience literature on the one hand and the digitalization literature on the other hand. The study aims to develop an integrated model that links the adoption of digital technologies to multidimensional resilience outcomes: preparedness, response and recovery. The potential research questions are the following:

1. How does the adoption of digital technologies influence firm resilience in the face of external shocks and environmental turbulence?

2. Through which organisational capabilities or mechanisms does digital adoption enhance adaptive capacity and recovery performance?

3. To what extent do industry characteristics, innovation orientation, international orientation or firm size moderate the relationship between digital adoption and resilience?

Method: The project will adopt a mixed-methods research design. Quantitative data will be collected through a survey. A survey instrument comprising questions and established scales will be designed and set up on an online platform. Data will be collected in France and potentially in other countries. Semi-structured interviews with senior managers will be conducted either before or after the data collection through a survey.

Expected outcomes: The project aims to advance knowledge on how technology impacts strategic outcomes such as resilience. Practically, it will offer insights to managers on how to leverage digital technologies to enhance resilience outcomes.

Supervisor:

Professor Ana Colovic

Area of Excellence: The Complexity Advantage

Contact email: ana.colovic@neoma-bs.fr

Entrepreneurship research has dominantly followed a “narrow perspective” in defining what constitutes the phenomenon of entrepreneurship. The focus has also been largely on the European and/or US American contexts. Recently, scholars have called for a broader conceptualization of entrepreneurship to understand what represents most entrepreneurship in the world - “everyday entrepreneurship”. This is because research findings from developed countries' contexts might not be equally applicable in developing countries due to distinct market dynamic that are influenced by institutional, historical, and/or cultural factors. Given the importance to improve entrepreneurial activities in developing countries to enhance economic and social development, as well as to unveil the diversity of entrepreneurial activities, investigating this matter is of high practical and theoretical relevance. 

The list of possible research questions includes, but not limited to:  

• How does technology (e.g. mobile, internet, digitalization) affect everyday entrepreneurship in developing countries? 

• How do adversity and extreme conditions (e.g. war, crisis) influence the development of entrepreneurial activities? How could entrepreneurship help to overcome adverse and extreme conditions?

• How does understanding the phenomenon of entrepreneurial activities in developing countries help us to revisit entrepreneurship theories? 

• How do informal and formal institutions impact entrepreneurial activities in developing countries?   

• What is the relationship between informal and formal entrepreneurial activities? 

• How do entrepreneurial activities in developing countries improve the well-being of underprivileged people and their communities?

Candidates are welcome to use either qualitative or quantitative method as long as it suits the proposed research question. Candidates could also use either primary or secondary data (existing data bases, archival data, etc.). 

Area of Excellence: The World We Want

Supervisor:

Bisrat Misganaw, Associate Professor, Strategy and Entrepreneurship
Email: bisrat.misganaw@neoma-bs.fr 

As society places increasing emphasis on sustainability, both in everyday decision-making and in expectations placed on firms, remanufactured goods (i.e., returned products that are inspected, restored, and re-certified) are central to closed-looped supply chains offering an important opportunity for positive environmental impact. They carry a significantly lower carbon footprint and require far fewer resources to produce than new products. Yet consumer adoption often lags because prior use can trigger quality uncertainty, perceived risk, and low trust in the remanufacturing process. This PhD work will investigate mechanisms and moderating factors that influence consumers’ acceptance of remanufactured products.

Vignette-based experiments (Prolific/CloudResearch) and laboratory studies will be used to measure consumer acceptance through realistic decision scenarios and hands-on product inspection. To supplement consumer experiments, machine learning techniques will be used to scrape and perform computational analysis of public social-media discourse to capture broader sentiment trends and shifts in public perceptions of remanufactured goods. 

By combining perspectives from supply chain management and consumer marketing, the project aims to generate a more integrated understanding of how consumers evaluate the quality, risk, and credibility of remanufactured goods. From a business perspective, results will provide evidence-based guidance for firms to design remanufacturing programs and communications to generate revenue from previous manufacture products while having a positive impact on the environment. Improving acceptance of remanufactured goods supports waste reduction and sustainability transitions.

Interest in interdisciplinary research at the intersection of supply chain management and marketing; familiarity with experiments and quantitative analysis; basic programming (Python/R) is an advantage.

Area of Excellence: The World We Want

Supervisor(s):

Dina Ribbink, Ph.D.

HoD SISCAD, Full Professor

Dina.ribbink@neoma-bs.fr

Ekatarina Salnikova, Ph.D.

Assistant Professor, Marketing

Ekaterina.salnikova@neoma-bs.fr