The rapid development of computational methods expands the horizon of opportunities in research methods. Scholars have acknowledged the potential of computationally intensive research approaches for theorizing IS phenomena. However, computationally intensive theory building is still at a nascent stage. This presentation focuses on how to leverage computational methods in the theorizing process, associated challenges, and respective strategies.

1. Leveraging Computational Methods
for Theorizing IS Phenomena
Malmi
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2. Digital Trace Data & Computational Methods
• Digitization of so many IS phenomena
• Digital traces -> new data sources
• Computational tools -> new methodological tools
• Digital trace data
• Boundless opportunities for theory building (Berente et al., 2019)
• Computational tools
• Capacity for richer understanding of socio-technical phenomena
(Latour 2010)
• Proximity to the object of the study
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3. Digital Trace Data
• “Records of activity (trace data) undertaken through an online information
system (thus, digital)” (Howison et al., 2011, p. 769)
• Pros
• Found data (Howison et al., 2011)
• Less time consuming to collect data (APIs, programming skills)
• Less biases in the data collection process
• Cons
• Not instantiations of predefined operationalized concepts (Lindberg, 2020)
• Ethical concerns (Freelon 2018)
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4. Theory Building Using Computational Methods
Theory
Computational
Analysis
Data
Human
Analysis
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Abductive Research Process
• Generate Patterns
• Visualize Patterns
• Construct Theory
Interpretation

5. Example 1: Organizing Visions in the Digital World
(Amadoru et al., 2021)
• Goal: Understand how social media discourse influences the diffusion
of emerging digital technologies
• Priori Theory: Organizing Vision Theory
Phase 1:
Collect
Tweets
Phase 2:
Identify
Peaks
Phase 3:
Develop
Theoretical
Framework
Phase 4:
Code
Tweets
Phase 5:
Identify
Patterns
Phase 6:
Develop
Theory
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Note: For simplicity this is presented as a sequential process. But this is an iterative process
going back and forth between theory, data, computational analysis, and human analysis.

6. Phase 1: Data Collection
• Salesforce Social Studio
• Keywords: ‘blockchain’, ‘block chain’ & ‘chain of blocks’
• Time Frame: 2008 – 2017
• 308,429 tweets
Digital Trace
Data: Tweets
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7. Phase 2: Identify Peaks
• Identify Twitter peaks
• Notion of peaks adopted in prior organizing vision studies (Gorgeon and Swanson,
2011)
• Mechanism to extract points of interest in a relatively large dataset
• Lehmann peak detection algorithm (Lehmann et al. 2012)
• Tools: No readily available implementation, implemented in R
• Extracted 135 peaks and associated tweets
Computational Analysis
Generate Patterns via Peak
Detection Algorithm
Visualize Patterns: Peaks
Theory
Organizing Vision Theory
(Past Empirical Studies)
Informed
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8. Phase 2: Identify Peaks Cont…
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9. Phase 3: Develop Theoretical Framework
• Review literature of organizing vision functions
• Seminal organizing vision paper (Swanson and Ramiller 1997)
• IT legitimation taxonomy (Kaganer et al. 2010)
• Other related literature
• Develop theoretical framework
• Iterate between literature & sample coding of Twitter data
Theory
Organizing Vision
Functions
Interpretation
Legitimation
Mobilization
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10. Phase 3: Develop Theoretical Framework Cont…
Organizng Vision
Function
Category Subcategory Theoretical Grounding References
INTERPRETATION
(What is it?)
System Functionality, Configuration, & Characteristics (Kaganer et al., 2010)
Technology Basics, Functionality, Configuration, & Characteristics (Swanson & Ramiller, 1997)
Domain - (Swanson & Ramiller, 1997)
LEGITIMATION
(Why do it & who’s
doing it)
Diffusion Product, Process, Business Model, & EndUser (Nambisan et al., 2017; Yoo et al., 2010)
Value Rationale & Success Story (Kaganer et al., 2010; Swanson &
Ramiller, 1997)
Alliance - (Kaganer et al., 2010)
Reputation - (Kaganer et al., 2010; Swanson &
Ramiller, 1997)
Normative Moral & Transformation (Kaganer et al., 2010)
Regulative - (Kaganer et al., 2010)
MOBILIZATION
(How to do it?)
Implementation Challenges, Strategies, & Successes (Kaganer et al., 2010; Swanson &
Ramiller, 1997)
Market Venues, Roles, & Opportunities (Swanson & Ramiller, 1997)
Resources Knowledge, Technological, Human, & Financial (Carlile, 2002; Fry, Stoner, & Hattwick,
2004; Swanson & Ramiller, 1997)
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11. Phase 4: Code Tweets
• Develop coding protocol
• Theoretical framework & coding protocol developed in parallel
• Coding protocol was informed by the framework
• Code tweets
• Final inter-coder reliability: functional level average 0.86 & individual level
average 0.735
Human Analysis
Generate Patterns:
Qualitative Codes
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12. Phase 5: Identify Patterns of Organizing Vision Functions
• Frequency analysis
• Organizing vision functions and subcategories of the
functions
• Visualize organizing functions over time
• Qualitative inquiry
• Manual reading into tweets
Computational Analysis
Generate Patterns:
Frequencies
Visualize Patterns via
Graphs
Human Analysis
Qualitative Inquiry
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13. Phase 5: Identify Patterns of Organizing Vision Functions Cont…
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14. Phase 6: Develop Theory
• Three conjectures on how organizing vision functions operate through social
media discourse
• Example Conjecture: The legitimation function can operate through end user
acceptance at the very early stage of discourse on a digital innovation.
• End user acceptance appears in later stages as per existing organizing vision literature
• Seek related theory to enquire ‘why’ and to reason possible explanation
• Social Media is a similar example: outside in type of diffusion (Leonardi and Vaast 2017)
• Refer to the paper for more information
• Amadoru, M., Fielt, E., & Kowalkiewicz, M. (2021). Organizing Visions in the Digital World:
The Case of the Blockchain Discourse on Twitter. In Proceedings of the 42nd International
Conference on Information Systems (ICIS 2021).
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15. Challenges & Strategies
• How to start?
• Example 1 works well for a phenomenon where there is already some theory
• Zoomed out view of the phenomenon
• Understand the phenomenon sufficiently to apply computational methods
• Keep human in the loop
• Zoom into subsections, small samples of data
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16. Challenges & Strategies Cont…
• Validity & Robustness
• Be transparent in every step of the research process
• Computational algorithms
• Assess the stability of the algorithm (e.g., parameters)
• Validate algorithmic output in multiple ways
• Focus on replicability of the process not the output
• Use multiple data sources (Lindberg 2020)
• Moving from patterns to theory
• Be patient 
• Experiment several different theories or theoretical concepts
• Identify constructs, relationships, and mechanisms (Lindberg 2020)
• Have an agile mindset
• Be flexible to start over, it is an agile process 16

17. Seminal Papers
• Lindberg, A. (2020). Developing theory through integrating human
and machine pattern recognition. Journal of the Association for
Information Systems, 21(1), 90-116.
• Berente, N., Seidel, S., & Safadi, H. (2019). Research commentary—
data-driven computationally intensive theory
development. Information Systems Research, 30(1), 50-64.
• Johnson, S. L., Gray, P., & Sarker, S. (2019). Revisiting IS research
practice in the era of big data. Information and organization, 29(1),
41-56.
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18. Exemplars
• Miranda, S. M., Kim, I., & Summers, J. D. (2015). Jamming with Social
Media. Mis Quarterly, 39(3), 591-614.
• Lindberg, A., Berente, N., Gaskin, J., & Lyytinen, K. (2016).
Coordinating interdependencies in online communities: A study of an
open source software project. Information Systems Research, 27(4),
751-772.
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Thank you!
Malmi