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Syllabi

Esade Business School Barcelona

The summary or overview of a course includes the course content and structure, the prerequirements, a brief description of the learning objectives, mandatory and/or voluntary course literature as well as important examination information.

Prerequisites (knowledge of topic)
Participants should have basic familiarity with quantitative research methods and statistical methods. Prior experience with R or Python is helpful but not mandatory – we will provide introductory materials during the course setup. An interest in human-computer interaction, consumer behavior, or behavioral science is recommended. No prior knowledge of voice analytics or conversational AI is required.
 
Hardware: Participants must bring a laptop with a working microphone and speakers/headphones for audio testing. 

Software: R (latest version) and RStudio. Local Python installation or Google Colab.  Detailed installation instructions will be provided before the course.

Learning objectives
After completing this course, participants will be able to: 

  1. Understand the theoretical foundations of voice analytics and conversational AI in behavioral research,
  2. Collect, process, and analyze unstructured voice data using R and Python,
  3. Extract acoustic features from speech signals and interpret their behavioral significance,
  4. Design and implement voice-based conversational agents for research applications,
  5. Integrate voice analytics into experimental designs as independent variables, dependent variables, or mediators,
  6. Apply voice technology to test substantive hypotheses about human behavior,
  7. Develop research ideas that leverage voice data and conversational AI tools, and
  8. Follow open science practices for reproducible voice analytics research.


Course content
The human voice captures what surveys and clicks cannot: Emotion in real-time, cognitive load under pressure, personality through prosody, and authentic reactions before a more conscious reflection. Yet, most behavioral researchers lack the tools to leverage this rich data source. In this 5-day workshop, you’ll master the complete pipeline from audio processing and feature extraction to developing deployable conversational agents for primary data collection. You’ll learn to extract acoustic features that reveal distinct psychological states, transcribe and analyze natural language data, and design voice-based AI systems that can conduct interviews or to develop experimental interventions at scale.

What makes this course unique:

  • Theory meets practice: We cover the fundamentals of speech science and behavioral research foundations while building production-ready interfaces and reproducible analytics pipelines.
  • Full-stack skills: You will lean the foundations from audio preprocessing to feature extraction to LLM-powered conversational agents all in one course.
  • Hands-on from day one: We analyze real voice data, build working prototypes, and you will leave with a mini-project tailored to your own research.
  • Open and responsible: We also cover ethical data practices and how to share your methods transparently with others.

This course is ideal for PhD students and researchers in marketing, psychology, economics, communication, HCI, and related fields who want to expand their methodological toolkit and apply voice analytics methods and develop conversational AI agents in their research.

 

Structure

Day 1 – Foundations + setup + project scoping

  • Morning: Why voice data matters; conversational AI & voice technology trends; speech production basics; voice as a behavioral data layer; examples from the behavioral sciences.

  • Afternoon: Software setup (R/Python); first audio workflows (read/plot/spectrograms); ‘idea generation’ break-out; select capstone question/application.


Day 2 – Data collection & preprocessing

  • Morning: Audio collection do’s/don’ts; devices & validity; consent/privacy; segmentation, VAD, basic denoising; open-science workflow.
  • Afternoon: Lab Session—batch processing audio; trimming/normalizing; building a reproducible pipeline; creating a feature-ready dataset.


Day 3 – Feature extraction & construct building

  • Morning: Feature families (prosody, spectral, voice quality); theory-to-measurement mapping; feature extraction pipelines; reliability and aggregation decisions.

  • Afternoon: Lab Session—extract features; build composite measures; modeling / prediction; validation checks; interim project checkpoint.


Day 4 – Speech-to-text + conversational toolbuilding

  • Morning: ASR (Whisper API); transcription quality and bias; linguistic features; typical conversation metrics.
  • Afternoon: Lab Session—building voice agents (STT→LLM→TTS) with logging; connect to a simple tool (e.g., database/query); define an evaluation plan.


Day 5 – Evaluation, applications, and synthesis

  • Morning: Evaluating voice models and agents (validity, robustness, fairness, latency); experimental designs for voice agents; cases and current research directions.
  • Afternoon: Mini-project ‘idea blitz’ presentations; feedback; next steps and resources.

Literature
Core Readings:

  • Hildebrand, C., Efthymiou, F., & Busquet, F. (2020). Voice Analytics in Business Research: Conceptual Foundations, Acoustic Feature Extraction, and Applications. Journal of Business Research, 121, 364-374.
  • Busquet, F., Efthymiou, F., & Hildebrand, C. (2023). Voice Analytics in the Wild – Validity and Predictive Accuracy of Common Audio Capturing Devices. Behavior Research Methods.
  • Busquet, F., & Hildebrand, C. (2023). voiceR: Voice Analytics for Social Scientists. R Package Documentation.
  • Jurafsky, D., & Martin, J. H. (2023). Speech and Language Processing (3rd ed.). Relevant chapters on speech processing and dialog systems.

Methodological Resources:

  • Wickham, H., & Grolemund, G. (2017). R for Data Science: Import, Tidy, Transform, Visualize, and Model Data. O’Reilly Media.
  • Sueur, J. (2018). Sound Analysis and Synthesis with R. Springer.
  • VanderPlas, J. (2016). Python Data Science Handbook: Essential Tools for Working with Data. O’Reilly Media.


Additional Background Readings:

  • Scherer, K. R., Johnstone, T., & Klasmeyer, G. (2003). Vocal expression of emotion. In R. J. Davidson, K. R. Scherer, & H. H. Goldsmith (Eds.), Handbook of affective sciences (pp. 433-456). Oxford University Press.
  • McAleer, P., Todorov, A., & Belin, P. (2014). How do you say ‘Hello’? Personality impressions from brief novel voices. PloS one, 9(3), e90779.

 

Examination part

  1. Mini-Project Presentation (30% of grade): Participants will develop and present a research idea that applies voice analytics or conversational AI methods to address a research question. The presentation should include: (a) research question and theoretical motivation, (b) proposed methodology incorporating voice/AI tools, (c) expected contributions, and (d) preliminary code or analysis plan, (e) ethical considerations. Presentations will be 10 minutes with 5 minutes for questions.
  2. Capstone Project Assignment (70% of grade): Participants need to hand in a paper two weeks after the class using the same outline as in the mini-project presentation (see #1 examination). The report should not be longer than five pages and should illustrate the either already conducted or envisioned voice-analytics component or voice-agent prototype. 

Grading for both assignments will be based on the theoretical understanding of core concepts covered in class, methodological rigor, creativity of the application, quality of the implementation, and clarity of the contribution of the project.

 

Supplementary aids
Extended Open Book: Participants are free to use all course materials, readings, software documentation, online resources, and their own notes during the mini-project development and presentation. Access to R, Python, and relevant APIs is permitted and encouraged for demonstrating technical implementation.

Examination relevant literature & content

All course readings listed above are examination-relevant, with particular emphasis on:

Core Methodological Papers:

  • Hildebrand, C., Efthymiou, F., & Busquet, F. (2020). Voice Analytics in Business Research. Journal of Business Research, 121, 364-374.
  • Busquet, F., Efthymiou, F., & Hildebrand, C. (2023). Voice Analytics in the Wild. Behavior Research Methods.

Technical Documentation:

  • voiceR package documentation and vignettes
  • OpenAI API documentation for speech and language models
  • R for Data Science (Wickham & Grolemund, 2017) – selected chapters on data manipulation and visualization

 

Participants should be familiar with the conceptual foundations, methodological approaches, and practical implementation strategies covered in these materials. The mini-project will require synthesis across these sources.

Prerequisites 

Doctoral-level training in at least one of:
•    Marketing
•    Psychology
•    Behavioral science
•    Economics
•    Information systems

Prior exposure to:
•    Experimental design
•    Regression analysis / basic statistics
•    Reading and interpreting peer-reviewed journal articles

Hardware
Laptop required (Mac, Windows, or Linux)

Software
Students will use:
•    ChatGPT (Plus or Team strongly recommended)
•    Google Scholar
•    Qualtrics or Google Forms (survey deployment)
•    R or Python (for analysis; templates provided)
•    Excel / Google Sheets
•    PowerPoint / Slides for Presentations
•    Optional: Prolific, CloudResearch, Zotero or Mendeley (reference management)

Learning objectives

By the end of the course, students will be able to:
1.    Use generative AI strategically across the research pipeline
2.    Generate high-quality, theory-driven research questions using AI-augmented ideation.
3.    Rapidly map and synthesize literature using GenAI-supported workflows.
4.    Design and execute a pilot study that can support a publishable research program.
5.    Collect, clean, and analyze data from surveys and/or public sources.
6.    Write a full pilot-study paper including hypotheses, methods, results, and future studies.
7.    Build a credible publication roadmap (journal targeting, contribution framing, next-study logic).

Course content

This course trains doctoral students to conduct rigorous, creative, and publishable research in a world where generative AI is an integral part of scientific discovery. Rather than treating GenAI as a writing assistant, students learn to use it as a theoretical and methodological partner across the full research pipeline. The course shows how GenAI fundamentally reshapes theory building, hypothesis generation, literature discovery, measurement design, and data exploration—enabling faster, broader, and more imaginative scientific search while also introducing new risks of conformity, reproducibility, and hallucination. Students are trained in the “New Tools, New Rules” framework (Blanchard et al. 2025), which specifies what parts of the research process should be automated, what must remain human-driven, and how scholars can preserve originality, insight, and contribution when working with AI.

Through intensive hands-on research project, students will work in groups to learn how to develop a scalable, journal-ready research program. Students will use GenAI to map literatures, generate and refine theory, design pilot studies, collect and analyze real data, and craft a full research narrative with a forward-looking publication strategy. The emphasis throughout is on producing research that meets the standards of top-tier journals in marketing, psychology, and the behavioral sciences: work that is theoretically grounded, empirically disciplined, and intellectually distinctive—even in an era where AI makes generating ideas and text trivially easy.


Structure

Day 1 – GenAI as a Scientific Partner

Morning – Learn
•    How GenAI changes research (based on Blanchard et al. 2025)
•    What AI is good and bad at in scientific discovery
•    Prompting frameworks for:
o    Idea generation
o    Theory mapping
o    Literature search
o    Construct development


Afternoon – Apply
•    Hands-on: AI-driven idea generation
•    Teams form (2–3 students)
•    Each team produces:
o    3–5 candidate research ideas
o    Initial theory sketches
•    Daily Seminar-style presentations


Day 2 – Literature & Research Ideation

Morning – Learn
•    AI-assisted literature discovery
•    Phenomenon-driven ideation
o    “The Seven Sins of Consumer Psychology” (Pham 2013)
•    Feedback Session

Afternoon – Apply
•    Teams build:
o    Theoretical propositions
o    Hypotheses
o    Key citations/References
•    Daily seminar-style presentations


Day 3 – Study Design & Data Strategy

Morning – Learn
•    Designing pilot studies with GenAI
•    Measurement creation and validation
•    Stimuli Creation
•    Utilizing GenAI to scrape data

Afternoon – Apply
•    Design
o    Survey instruments
      Measurements
      Stimuli
o    Experimental Manipulations
o    Scraping plans
•    Daily Seminar-style presentations
•    Instructor feedback

Day 4 – Data Collection & Analysis

Morning – Learn
•    Exploratory vs. Confirmatory Data Analysis
•    Qualitative vs. Quantitative analyses
o    Learning new analytical methods through GenAI
o    Mediation & Moderation (recap, if necessary)
•    Potential for synthetic participants


Afternoon – Apply
•    Collect data
•    Analyses with R / python
•    Data visualization with GenAI
•    Interpreting results with GenAI
•    Daily Seminar-style presentations
•    Instructor Feedback


Day 5 – Research Story & Publication Path

Morning – Learn
•    Writing a compelling paper – journal targeting
•    Writing session / feedback
•    Preparation for presentations

Afternoon – Apply
•    Group Presentations
•    Future research paths
•    Group feedback session


Literature 

Mandatory Article – Read Prior to Course Start: Will discuss on Day 1

  • Blanchard, Simon J., Nofar Duani, Aaron M. Garvey, Oded Netzer, and Travis Tae Oh (2025). “New Tools, New Rules: A Practical Guide to Effective and Responsible Generative AI Use for Surveys and Experiments in Research.” https://journals.sagepub.com/doi/abs/10.1177/00222429251349882
  • Ming-Hui Huang, Roland T Rust (2025). “The GenAI Future of Consumer Research,” Journal of Consumer Research, 52(1), 4–17, https://doi.org/10.1093/jcr/ucaf013
  • Julian De Freitas, Gideon Nave, Stefano Puntoni (2025). “Ideation with Generative AI—in Consumer Research and Beyond,” Journal of Consumer Research, 52(1),18–31, https://doi.org/10.1093/jcr/ucaf012

Recommended Overviews:

  • Goli, Ali and Singh, Amandeep (2024). “Can Large Language Models Capture Human Preferences?” Marketing Science, 43(4), 697-923, https://doi.org/10.1287/mksc.2023.0306
  • Pham, Michel Tuan (2013). “The Seven Sins of Consumer Psychology,” Journal of Consumer Psychology, 23(4), 411-423.
  • Craig J. Thompson, William B. Locander, Howard R. Pollio (1989). “Putting Consumer Experience Back into Consumer Research: The Philosophy and Method of Existential-Phenomenology,” Journal of Consumer Research, 16(20, 133–146, https://doi.org/10.1086/209203

Additional Background Readings:

  • Goli, Ali and Singh, Amandeep (2024). “Can Large Language Models Capture Human Preferences?” Marketing Science, 43(4) 697
  • Whetten, Davia A. (1989). “What Constitutes a Theoretical Contribution?” The Academy of Management Review, 14(4), 490-495.

Examination part

Given that this is a doctoral (or high graduate-level) seminar, the key output of the course is to develop research projects that will eventually lead to publications. Students will be evaluated based on the deliverables of each stage of the research process and their presentations:

1    Research Ideation & Theory Development (25%)
Assessed from Days 1–2 deliverables
•    Quality and originality of research ideas
•    Strength and clarity of theoretical framing
•    Quality of hypotheses and construct definitions
•    Use of literature to motivate the research question

2    Study Design & Data Strategy (20%)
Assessed from Day 3
•    Soundness of experimental / survey / scraping design
•    Appropriateness and validity of measures and stimuli
•    Feasibility and rigor of the data collection plan
•    Alignment between theory and design

3    Data Collection, Analysis & Interpretation (25%)
Assessed from Day 4
•    Quality and cleanliness of collected data
•    Correct use of statistical or qualitative methods
•    Appropriateness of models (e.g., regressions, ANOVA, mediation)
•    Depth of interpretation beyond mere significance

4    Final Research Paper & Publication Plan (20%)
Assessed from Day 5
•    Clarity and coherence of the research narrative
•    Theoretical contribution
•    Transparency of methods and results
•    Credibility of the future research and journal strategy

5    Seminar Participation & Presentations (10%)
Assessed across all 5 days
•    Quality of daily presentations
•    Constructive engagement in Q&A and peer feedback
•    Ability to articulate and defend ideas


Supplementary aids

Students may use:
•    GenAI tools (e.g., ChatGPT, Gemini, Grok, Claude, etc.)
•    Articles
•    Lecture slides
•    Code
•    Data
•    Statistical software

They may not outsource writing or analysis to humans outside their team.

 

Examination content

Given the nature of this course, I will not be giving examinations. Please see grading rubric above.

 


Examination relevant literature 

Given the nature of this course, I will not be giving examinations. Please see grading rubric above and required/supplementary readings.

 

Prerequisites (knowledge of topic)
Some knowledge of Experimental Design and Statistic Analysis (ANOVA, Planned contrasts, Regression, Mediation analysis)

Hardware: Laptop

Software: R or SPSS or other similar statistical packages


Learning objectives
In this seminar, students learn how to design and execute research that produces experimental data for analysis. The seminar introduces the students to methodological choices when designing experiments, field studies, and surveys, along with their possibilities and limitations. The selection of methods is presented in the larger context of the overall research process, which includes conception, design, and execution. In this context, students learn how to progress from theoretical research questions to scientifically rigorous research designs and how to interpret the results of their studies.
Thematically speaking, this seminar will examine a wide range of topics connected with the CB literature dealing with Digital and Technology-Consumer Interactions.


Course content
In this seminar, students learn how to design and execute research that produces experimental data for analysis. The seminar introduces the students to methodological choices when designing experiments, field studies, and surveys, along with their possibilities and limitations. The selection of methods is presented in the larger context of the overall research process, which includes conception, design, and execution. In this context, students learn how to progress from theoretical research questions to scientifically rigorous research designs and how to interpret the results of their studies. Thematically speaking, this seminar will examine a wide range of topics connected with the CB literature dealing with Digital and Technology-Consumer Interactions. 


Day 1
Morning
1. Research Foundations
Overview of the research process. Generation of research questions, design, and analysis of results.
2. Research Methods Landscape
Experimental vs. Non-experimental designs. Causation vs. Correlation.
3. Types of Validity
Internal vs. External Validity.
Rigor vs. Relevance in research


Afternoon
Consumer-Tech Research Topic: Devices
Discussion Papers:

  • Melumad, Shiri and Michel Tuan Pham (2020), “The Smartphone as a Pacifying Technology,” Journal of Consumer Research, 47(2), 237-255.
  • Song, C. E., & Sela, A. (2022). Phone and Self: How Smartphone Use Increases Preference for Uniqueness. Journal of Marketing Research, 60(3), 473-488

 


Day 2
Morning
1. Experimental Designs
Pre and Post measure designs; Factorial designs; Longitudinal studies
2. Measurement Issues
From constructs to variables; Scaling; Construct Validity; Reliability; Levels of Measurement

Afernoon
Consumer-Tech Research Topic: Interaction Modality
Discussion Papers:

  • Shen, Hao, Meng Zhang and Aradhna Krishna (2016), “Computer Interfaces and the “Direct-Touch” Effect: Can iPads Increase the Choice of Hedonic Food?”, Journal of Marketing Research, 53(5), 745- 758.
  • Rhonda Hadi and Ana Valenzuela (2020), “Good Vibrations: Consumer Responses to Technology-Mediated Haptic Feedback,” Journal of Consumer Research, 47(2), 256–27.

 


Day 3
Morning
Questionnaire Design
Techniques for designing effective questionnaires. Sources of bias.

Afternoon
Consumer-Tech Research Topic: Consumer Responses to AI
Discussion Papers:

  • Zehnle, M., Hildebrand, C., & Valenzuela, A. (2025). Not all AI is created equal: A meta-analysis revealing drivers of AI resistance across markets, methods, and time. International Journal of Research in Marketing.
  • Puntoni S, Reczek RW, Giesler M, Botti S. (2021) “Consumers and Artificial Intelligence: An Experiential Perspective.” Journal of Marketing. 85(1):131-151
    Consumer-Tech Research Topic: The Pychology of AI
    Discussion Papers:
    3
    — Internal —
  • Longoni, C., Bonezzi, A., Morewedge, C. (2019). “Resistance To Medical Artificial Intelligence”, Journal of Consumer Research, 46 (4), 629-650.
  • Aaron M. Garvey, TaeWoo Kim Adam Duhachek (2022) “Bad News? Send an AI. Good News? Send a Human,” Journal of Marketing.

 


Day 4
Morning
Data Collection and Analysis
Selection of appropriate statistical test;
Statistical conclusion validity; descriptive and inferential statistics

Afternoon
Consumer-Tech Research Topic: Chatbots
Discussion Papers:

  • Bergner, A. S., Hildebrand, C., & Häubl, G. (2023). Machine Talk: How Verbal Embodiment in Conversational AI Shapes Consumer-Brand Relationships. Journal of Consumer Research
  • Julian De Freitas, Zeliha Oğuz-Uğuralp, Ahmet Kaan Uğuralp, Stefano Puntoni, AI Companions Reduce Loneliness, Journal of Consumer Research.
    Consumer-Tech Research Topic: Robots
    Discussion Papers:
  • Mende, M., Scott, M. L., van Doorn, J., Grewal, D., & Shanks, I. (2019). Service robots rising: How humanoid robots influence service experiences and elicit compensatory consumer responses. Journal of Marketing Research, 56(4), 535-556.
  • Castelo, N., Boegershausen, J., Hildebrand, C., Henkel, A. (2023): Bots at the Frontline: How Consumers Perceive Firms that Employ Service Robots, Journal of Consumer Research

 


Day 5
Morning
Credibility of Research Findings. Ethics in Research
Threats to validity and reliability; Sources of bias in research
Human subject protections (IRB). Informed consent. Use of deception in research

Afternoon
Discussion Resarch Ideas


Literature
Recommended Overviews:
Trochim, W., Donnelly, J.P., Arora, K. (2016) Research Methods: The Essential Knowledge Base. Cengage. 2nd Ed.

Additional Background Readings:
See readings listed in each of the sessions

Examination part
Attendance and active participation in class: 10%
Paper critique and application exercises: 30%
Research Proposal: 20%
Research Final Paper: 40%
TOTAL: 100%

Supplementary aids
Trochim, W., Donnelly, J.P., Arora, K. (2016) Research Methods: The Essential Knowledge Base. Cengage. 2nd Ed.

Examination content
Research Final Paper (10-15 pages, typed, 1 in. margins, double spaced, 12 pt. type). You will be required to outline the theoretical framework supporting a particular research question and one or two experiments designed to test the unanswered question related to one of the class topics. In theory, this should be something that you’re really interested in doing; it will be most valuable to you if you can tie it to something you’re actually working on or would like to work on.


Examination relevant literature
It will depend on the research question chosen by each of the students.