Introduction to Digital Management
Grégoire et al. (2024). Mobilizing new sources of data: opportunities and
recommendations.
- The authors argue that digital transformation has revolutionized how data
are generated and used, creating new opportunities, and responsibilities,
for management researchers and practitioners.
- New data sources enable deeper insights into organizational behavior,
decision-making, and innovation but also require careful methodological,
ethical, and theoretical handling
- The article serves as a call to action for researchers to embrace new data
while maintaining rigor, transparency, and theoretical grounding.
- Society is in a transformational era (Gruber, 2023):
o New technologies (wearables, neuroimaging, sensors).
o Cheaper experiments and cooperation between academia and
practice.
o Digital footprints from AI, blockchain, social media, and sensors →
“big data.”
- Challenge:
o Accessing, interpreting, and validating these data sources is difficult;
o “apparent advances may have feet of clay” if rigor is missing.
- Key opportunities from new data sources
A. New technologies = new data
o Quantitative opportunities:
Wearables (Apple Watch, Oura Ring) → objective, continuous
data.
Smartphones & GPS data → behavioural patterns, workplace
analytics.
Computer-assisted content analysis (CATA) / NLP / topic
modelling → massive text data (code and quantify data)
Video data → emotion, decision-making, and leadership
analysis.
o Qualitative opportunities:
Video diaries, immersive tech, remote data collection → new
methods to reach participants (e.g., crisis zones, war,
disaster).
Augmented and virtual reality (VR/AR) → simulate realistic
decision-making scenarios for experiments.
o Macro-level opportunities:
Blockchain → transparency, transaction verification, DAOs.
Satellite imaging & sensors → measure sustainability
practices.
B. Big data as input and output
, o Generative AI & machine learning → both use and create data for
management research.
o Examples:
AI in hiring (bias, fairness, transparency).
HR chatbots and AI assistants generating behavioural data.
Blockchain records → transactional logs or governance
research.
VR headsets → collect micro-level physical and behavioural
data.
- Recommendations:
1. Take data context seriously
o Researchers must understand where data come from and what they
represent.
o Explain how, when, where, and why data were collected.
o Clarify who produced data, under what constraints, and what might
be missing.
o Provide details about geographical, temporal, or other contextual
factors to situate the new data in a specific empirical setting.
o For machine learning: define training/testing context and historical
or institutional setting.
o In case questions: when applying digital data (e.g., from customer
analytics), discuss contextual bias, data representativeness, and
collection methods.
2. Be transparent and ethical
o Transparency is non-negotiable when using new digital data.
o Clearly describe:
Access and consent procedures.
Ethical review and anonymization.
Model transparency: inputs, algorithms, data subsets.
o Handle machine learning bias: models often reflect Western-centric
data
o In a company implementing AI hiring tools, managers must ensure
ethical transparency; explain data sources, audit bias, and disclose
algorithms.
3. Align theory and method (theory-method fit)
o Ensure that data and analytical methods actually measure the
theoretical constructs (conceptual validity).
o Avoid “data chasing” without a clear conceptual fit.
o In machine learning:
Justify feature selection, training dataset, and algorithm
choice.
Validate with alternative models or randomization tests.
o Clearly map how raw data → constructs → theoretical arguments.
, o If a case asks how a firm can use big data to improve leadership
decisions, mention that alignment between the type of data (emails,
sentiment, etc.) and the construct (leadership communication) is
crucial for valid conclusions.
- Implications for digital management
For research For practice
Datafication opens new Firms that harness new data ethically
epistemological frontiers for and analytically soundly will gain an
management; digital traces can edge in innovation, HR, and strategic
reveal new insights into decisions.
organizations, culture, and behavior.
Calls for interdisciplinary Managers need data literacy:
collaboration (data science + understanding limitations, biases, and
management theory). context of digital data.
Ethical and transparent data
management builds trust and
legitimacy with employees and society
Takeaways from lecture about Gregoire et al.:
- New means of collecting data (e.g. wearables, sensors, etc.).
- Decreasing costs of conducting field experiments and interventions.
- Big data and machine learning.
For scientific research and theory Practical take aways
Take data context seriously Identify untapped data sources
Take data transparency and ethical Access data through partnerships/
considerations seriously technology
Mind the alignment of theory and Analyze for actionable insights
method
, Act on data driven recommendations
Kraus et al. (2022). Digital transformation in business and management
research.
- Goal: map and analyze how digital transformation (DT) research has
evolved in business and management over the past decade (2010–2020).
(1) identify the main themes
(2) propose a synergistic framework linking the fragmented research into
unified picture.
- DT has become critical for firms, industries, and economies. The paper
shows that DT is not just about technology, but about strategic,
organizational, and societal transformation.
Term Meaning
Digitization Converting analogue information into digital form
(scanning).
Digitalization Using digital technologies to improve processes and
create value
Digital Deep, strategic integration of digital technologies into
Transformation all aspects of an organization, changing how it
(DT) operates and creates value.
DT ≠ just technology. It’s a strategic renewal process involving culture,
leadership, structure, and new business models.
- Major themes in DT research (5 dominant clusters)
1. Structural change & value creation
o DT alters organizational structures, industries, and business models.
o Firms must reconfigure operations, adopt open innovation, and shift
to platform-based or data-driven models.
o Example: transition from selling products to offering digital services.
2. Use of digital technologies
o DT success depends on integrating technology with strategy, not
just adoption.
o SMEs face challenges in resources and digital skills.
3. Dynamic capabilities
o Firms need sensing, seizing, and transforming capabilities to adapt
and innovate through DT.
o DT builds on dynamic capabilities to manage change and exploit
opportunities.
4. Consumer behaviour
Grégoire et al. (2024). Mobilizing new sources of data: opportunities and
recommendations.
- The authors argue that digital transformation has revolutionized how data
are generated and used, creating new opportunities, and responsibilities,
for management researchers and practitioners.
- New data sources enable deeper insights into organizational behavior,
decision-making, and innovation but also require careful methodological,
ethical, and theoretical handling
- The article serves as a call to action for researchers to embrace new data
while maintaining rigor, transparency, and theoretical grounding.
- Society is in a transformational era (Gruber, 2023):
o New technologies (wearables, neuroimaging, sensors).
o Cheaper experiments and cooperation between academia and
practice.
o Digital footprints from AI, blockchain, social media, and sensors →
“big data.”
- Challenge:
o Accessing, interpreting, and validating these data sources is difficult;
o “apparent advances may have feet of clay” if rigor is missing.
- Key opportunities from new data sources
A. New technologies = new data
o Quantitative opportunities:
Wearables (Apple Watch, Oura Ring) → objective, continuous
data.
Smartphones & GPS data → behavioural patterns, workplace
analytics.
Computer-assisted content analysis (CATA) / NLP / topic
modelling → massive text data (code and quantify data)
Video data → emotion, decision-making, and leadership
analysis.
o Qualitative opportunities:
Video diaries, immersive tech, remote data collection → new
methods to reach participants (e.g., crisis zones, war,
disaster).
Augmented and virtual reality (VR/AR) → simulate realistic
decision-making scenarios for experiments.
o Macro-level opportunities:
Blockchain → transparency, transaction verification, DAOs.
Satellite imaging & sensors → measure sustainability
practices.
B. Big data as input and output
, o Generative AI & machine learning → both use and create data for
management research.
o Examples:
AI in hiring (bias, fairness, transparency).
HR chatbots and AI assistants generating behavioural data.
Blockchain records → transactional logs or governance
research.
VR headsets → collect micro-level physical and behavioural
data.
- Recommendations:
1. Take data context seriously
o Researchers must understand where data come from and what they
represent.
o Explain how, when, where, and why data were collected.
o Clarify who produced data, under what constraints, and what might
be missing.
o Provide details about geographical, temporal, or other contextual
factors to situate the new data in a specific empirical setting.
o For machine learning: define training/testing context and historical
or institutional setting.
o In case questions: when applying digital data (e.g., from customer
analytics), discuss contextual bias, data representativeness, and
collection methods.
2. Be transparent and ethical
o Transparency is non-negotiable when using new digital data.
o Clearly describe:
Access and consent procedures.
Ethical review and anonymization.
Model transparency: inputs, algorithms, data subsets.
o Handle machine learning bias: models often reflect Western-centric
data
o In a company implementing AI hiring tools, managers must ensure
ethical transparency; explain data sources, audit bias, and disclose
algorithms.
3. Align theory and method (theory-method fit)
o Ensure that data and analytical methods actually measure the
theoretical constructs (conceptual validity).
o Avoid “data chasing” without a clear conceptual fit.
o In machine learning:
Justify feature selection, training dataset, and algorithm
choice.
Validate with alternative models or randomization tests.
o Clearly map how raw data → constructs → theoretical arguments.
, o If a case asks how a firm can use big data to improve leadership
decisions, mention that alignment between the type of data (emails,
sentiment, etc.) and the construct (leadership communication) is
crucial for valid conclusions.
- Implications for digital management
For research For practice
Datafication opens new Firms that harness new data ethically
epistemological frontiers for and analytically soundly will gain an
management; digital traces can edge in innovation, HR, and strategic
reveal new insights into decisions.
organizations, culture, and behavior.
Calls for interdisciplinary Managers need data literacy:
collaboration (data science + understanding limitations, biases, and
management theory). context of digital data.
Ethical and transparent data
management builds trust and
legitimacy with employees and society
Takeaways from lecture about Gregoire et al.:
- New means of collecting data (e.g. wearables, sensors, etc.).
- Decreasing costs of conducting field experiments and interventions.
- Big data and machine learning.
For scientific research and theory Practical take aways
Take data context seriously Identify untapped data sources
Take data transparency and ethical Access data through partnerships/
considerations seriously technology
Mind the alignment of theory and Analyze for actionable insights
method
, Act on data driven recommendations
Kraus et al. (2022). Digital transformation in business and management
research.
- Goal: map and analyze how digital transformation (DT) research has
evolved in business and management over the past decade (2010–2020).
(1) identify the main themes
(2) propose a synergistic framework linking the fragmented research into
unified picture.
- DT has become critical for firms, industries, and economies. The paper
shows that DT is not just about technology, but about strategic,
organizational, and societal transformation.
Term Meaning
Digitization Converting analogue information into digital form
(scanning).
Digitalization Using digital technologies to improve processes and
create value
Digital Deep, strategic integration of digital technologies into
Transformation all aspects of an organization, changing how it
(DT) operates and creates value.
DT ≠ just technology. It’s a strategic renewal process involving culture,
leadership, structure, and new business models.
- Major themes in DT research (5 dominant clusters)
1. Structural change & value creation
o DT alters organizational structures, industries, and business models.
o Firms must reconfigure operations, adopt open innovation, and shift
to platform-based or data-driven models.
o Example: transition from selling products to offering digital services.
2. Use of digital technologies
o DT success depends on integrating technology with strategy, not
just adoption.
o SMEs face challenges in resources and digital skills.
3. Dynamic capabilities
o Firms need sensing, seizing, and transforming capabilities to adapt
and innovate through DT.
o DT builds on dynamic capabilities to manage change and exploit
opportunities.
4. Consumer behaviour
