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Thirty-first European Conference on Information Systems (ECIS 2023), Kristiansand, Norway 1
STUDYING DYNAMICS AND CHANGE WITH DIGITAL
TRACE DATA: A SYSTEMATIC LITERATURE REVIEW
1
Research Paper
Sandro Franzoi, University of Liechtenstein, Vaduz, Liechtenstein, sandro.franzoi@uni.li
Thomas Grisold, University of Liechtenstein, Vaduz, Liechtenstein, thomas.grisold@uni.li
Jan vom Brocke, University of Liechtenstein, Vaduz, Liechtenstein, jan.vom.brocke@uni.li
Abstract
Digital trace data offer promising opportunities to study dynamics and change of various socio-
technical phenomena over time. While we see a surge of empirical and conceptual articles, we lack a
systematic understanding of why, how, and when digital trace data are or can be used to study dynamics
and change. In this article, we present the findings of a systematic literature review to uncover common
approaches, motivations, findings, and general themes in the existing literature. We systematically
reviewed 40 studies that were published in premium outlets in the information systems field. Our review
sheds light on (1) underlying purposes of such studies, (2) utilized data sources, (3) research contexts,
(4) socio-technical phenomena of interest, (5) applied analytical methods, and (6) measures that are
being used. Building on our findings, we point to several implications for research and shed light on
avenues to advance this field in the future.
Keywords: digital trace data, dynamics, change, computational, process, temporal, literature review
process theorizing, process research
1 Introduction
Digital trace data research is an emerging genre in the information systems field (Berente et al., 2019;
Lindberg, 2020; Miranda et al., 2022). Broadly speaking, digital traces are residuals that are left behind
when actors perform activities with digital technologies (Howison et al., 2011). Digital traces offer novel
opportunities for research that studies temporal dynamics and change to explain how socio-technical
phenomena take shape, persist, and dissolve over time (Burton-Jones et al., 2015; Langley & Tsoukas,
2017; Pentland et al., 2021). This is because digital traces are typically equipped with temporal
information, indicating when a given activity has been performed (Pentland et al., 2021). Furthermore,
they enable a fine-granular view of how actions are carried out in both private and work-related contexts
(Pentland, 2015). Despite the increasing interest, and a growing number of conceptual and empirical
articles (e.g., Berente et al., 2019; Miranda et al., 2022; Pentland et al., 2020; vom Brocke et al., 2021),
we lack a systematic understanding of how, why, and when this form of research is conducted.
In this paper, we review the literature on research using digital trace data to study dynamics and change
in socio-technical phenomena. Drawing from research published in renowned outlets in the information
systems field, we analyze the motivations, approaches, and findings that are associated with this stream
1
Author version. Please cite as: Franzoi, S., Grisold, T., vom Brocke, J. (2023). Studying Dynamics and Change with Digital
Trace Data: A Systematic Literature Review. European Conference on Information Systems (ECIS) 2023 Proceedings.
Dynamics and Change with Digital Trace Data
Thirty-first European Conference on Information Systems (ECIS 2023), Kristiansand, Norway 2
of research. By systematically reviewing 40 articles, we shed light on and discuss the following
questions:
1. For what purposes are digital trace data used in studies that examine dynamics and change of
socio-technical phenomena?
2. What type of data are being used, which sources do they come from and what research contexts
are they embedded in?
3. How are digital traces analyzed to obtain insights into dynamics and change?
4. What are trends, themes, and patterns in research using digital trace data for studying dynamics
and change?
We provide systematic answers to these questions. At the core of our research are the following
observations. First, digital trace data are used to support theory building and theory testing, and they
provide the grounds for developing or advancing computational techniques. Furthermore, these data are
collected from different data sources, such as workflow systems, social media, or online communities.
In consequence, they can be used to study socio-technical phenomena in all kinds of research contexts,
including healthcare, innovation, and finance, among others. Various techniques are used to analyze
these data, such as sequence analysis, econometric modeling, or process mining. Finally, digital trace
data are analyzed with regard to different measures, such as complexity, frequency, and user behavior,
among others.
Our review points to several implications for digital trace data research. Overall, we call for more
research that appreciates and leverages how digital trace data can provide novel explanations for
dynamics and change in the digital age. Along these lines, we see promising opportunities for combining
different measures and methods to study various socio-technical phenomena. To embrace these
opportunities, the information systems field can build up and intensify synergies with adjacent research
domains—such as computer science or data science—in order to integrate new types of data sources as
well as computational techniques to analyze them.
2 Research Background
2.1 Digital Trace Data Research in Information Systems Research
Digital trace data-based research is attracting increasing attention in information systems research
(Miranda et al., 2022) and beyond (Lazer et al., 2020; Simsek et al., 2019). Digital trace data are broadly
defined as residuals of events or activities that are left behind when actors interact with digital
technologies (Freelon, 2014; Howison et al., 2011). They typically appear in large quantities and can be
collected in various contexts. Since almost everything we do is mediated by or enabled through digital
technologies, digital trace data are thought to provide far-reaching insights into various phenomena
(Lazer et al., 2020). Furthermore, digital trace data are typically unbiased with regard to existing data
collection strategies and research designs (Golder & Macy, 2014). The interest in digital trace data––
both in private as well as work-related contexts––has been further fueled by considerable advancements
of computational techniques to analyze them. To this end, an ever-growing frontier of pattern
recognition techniques offers a variety of perspectives to analyze, explain, and predict the development
of various phenomena based on digital trace data sources (Oliver et al., 2020).
In the information systems field, we see an emerging body of knowledge that addresses different aspects
of digital trace data research (Berente et al., 2019; Lindberg, 2020; Miranda et al., 2022). A growing
number of empirical studies has been using digital trace data to examine various socio-technical
phenomena (Hukal et al., 2019; Müller et al., 2016; Pentland et al., 2021; Weinmann et al., 2022).
Furthermore, methodological guidance has been provided to frame and analyze these data from different
perspectives (Berente et al., 2019). Also, there have been works that examined and discussed the quality
and features of digital trace data sets (Howison et al., 2011; Pentland et al., 2020; Vial, 2019). Moreover,
Dynamics and Change with Digital Trace Data
Thirty-first European Conference on Information Systems (ECIS 2023), Kristiansand, Norway 3
there have been discussions about the opportunities and threats of digital trace data (Grover et al., 2020;
Østerlund et al., 2020).
2.2 Studying Dynamics and Change with Digital Trace Data
Recent claims have stressed that information systems research should embrace the dynamics and change
of the digital age (Benbya et al., 2020; Mousavi Baygi et al., 2021). These arguments emphasize how
socio-technical phenomena dynamically evolve and change over time – often in unexpected ways. To
get at these dynamics, there have been calls for adjusting established research approaches and theorizing
practices (Baiyere et al., 2023; Grover & Lyytinen, 2022).
Research that foregrounds dynamics and change is typically conducted in the spirit of ‘process research‘
(Langley et al., 2013). Generally speaking, process research refers to (empirical) studies that emphasize
dynamics and change in phenomena as they take shape, persist, and dissolve over time (Langley et al.,
2013; Poole & van de Ven, 2004). This stream of research is inherently concerned with temporality
(Cloutier & Langley, 2020) and studies are traditionally carried out employing qualitative methods, such
as interviews and/or ethnographies (Langley, 1999).
In the information systems field, process research emerged as one distinctive lens to understand socio-
technical phenomena (Burton-Jones et al., 2015). Digital trace data provide new and promising
opportunities for process research and, in particular, for studying dynamics and change of socio-
technical phenomena (Grisold et al., 2020; Pentland et al., 2021; vom Brocke et al., 2021). Since these
data tend to be equipped with temporal information, they provide fine-granular insights into which
action was taken at which point in time (Pentland et al., 2020). They are, thus, thought to enable
researchers to discover promising insights into the rapid change and emergent dynamics that are
associated with the digital age (Benbya et al., 2020; Grisold, Kremser et al., 2023). Recent works have
sought to advance process research by employing appropriate computational techniques, for example,
by integrating methodological knowledge from adjacent fields, such as business process management
(Kallio et al., 2022; Mendling et al., 2020; Pentland et al., 2021).
However, while we see a surging interest in the use of digital trace data to study dynamics and change
of socio-technical phenomena, we lack systematic knowledge of this stream of research. Understanding
how researchers in the information systems field study dynamics and change with digital trace data,
what kinds of phenomena they study, and to what extent the use of digital trace data advances theoretical
knowledge is important to further guide the development of the field.
3 Research Method
We conducted a systematic literature review to analyze and categorize prior work that uses digital trace
data to study dynamics and change in socio-technical phenomena. In our literature review, we drew on
the established approaches of vom Brocke et al. (2009) and Webster and Watson (2002).
The framework of vom Brocke et al. (2009) outlines five distinct phases of a systematic literature review.
First, the underlying scope of coverage of the review should be defined, which can either be exhaustive,
exhaustive and selective, representative, or central (Cooper, 1988; vom Brocke et al., 2009). We apply
a representative and central review that typifies large groups of articles by using a sample. In the second
phase, key terms and concepts have to be defined (vom Brocke et al., 2009), which we included in our
research background (see section 2). The third step pertains to the literature search process itself, where
we defined search parameters and queried databases (vom Brocke et al., 2009). We conducted a keyword
search as well as forward and backward searches. In the keyword search, we used the search string
“trace data” OR “temporal data” OR “sequence data” OR “time-stamped” to comb the full text of
articles.
We placed particular emphasis on the notion of ‘trace data’, because this term is tightly associated with
a new research genre in the information systems field (Berente et al., 2019; Miranda et al., 2022). An
emerging body of knowledge has been using this term to denote a specific form of data that are recorded
and collected on the grounds of interactions between digital technologies and their users (Howison et
Dynamics and Change with Digital Trace Data
Thirty-first European Conference on Information Systems (ECIS 2023), Kristiansand, Norway 4
al., 2011). Research in this field includes empirical studies (e.g., Pentland et al., 2021; Weinmann et al.,
2022) as well as methodological works that provide guidance on how to theorize with such data (e.g.,
Berente et al., 2019; Grisold et al., 2020; Pentland et al., 2020). Hence, by emphasizing the term ‘trace
data’, and by further searching for time and sequence-related features, we expected to be able to
represent this discourse.
We queried the database EBSCOhost to search through premium information systems journals. To this
end, we drew from the AIS Senior Scholars’ Basket (AIS, 2011). The associated journals are listed in
Table 1. Our search covers articles published until the 10th of October 2022. The fourth step of the
framework comprises the analysis and synthesis of the collected literature (vom Brocke et al. 2009),
which we describe in our findings section (see section 4). The fifth and last phase revolves around the
development of a research agenda (vom Brocke et al., 2009) and directions for future research, which
we pick up in our discussion section (see section 5).
As a result of this systematic literature search process, we identified a total number of 120 articles (‘hits’
cf. Table 1), which were obtained through the keyword search and additional forward and backward
searches. In an initial analysis, the first author scrutinized the respective titles, abstracts, and keywords
to remove articles that did not concern research on dynamics and change using digital trace data. When
a case was not clear, it was discussed among the co-authors to determine the article’s relevance.
Specifically, we included a study in our analysis if it fulfilled two criteria: (1) the study uses digital trace
data (not necessarily termed this way) that were collected from people’s interactions with digital
technologies (Grover et al., 2020) and (2) the study has a temporal component (i.e., dynamics and change
over time). After eliminating articles that were deemed irrelevant, we were left with a final sample of
31 relevant studies (‘relevant’ cf. Table 1). Additionally, we included 9 articles from other outlets that
we deemed relevant through forward and backward searches. All relevant studies included in our
literature review are marked with an asterisk (*) in the reference list.
Table 1 summarizes the respective number of retrieved articles (‘hits’) as well as the final sample of
relevant studies that we reviewed in detail (‘relevant’). After finalizing our sample, we coded all relevant
articles in our review. We coded the studies according to the categories purpose, data type, data source,
research context, method of analysis, and measures to find an answer to our four research questions (see
section 1). The results of our analysis are outlined in the next section.
Journal
Hits
Relevant
European Journal of Information Systems
1
0
Information Systems Journal
10
1
Information Systems Research
14
6
Journal of the Association of Information Systems
21
7
Journal of Information Technology
0
0
Journal of Management Information Systems
27
6
Journal of Strategic Information Systems
0
0
Management Information Systems Quarterly
38
11
Total in keyword search
111
31
Added through forward and backward search
9
9
Total
120
40
Table 1. Number of articles identified and screened in the literature review
4 Findings
We departed from four questions that are central to our research endeavor. These questions revolve
around (1) the purpose of digital trace data for research on dynamics and change, (2) the data types, data
sources, research contexts, and phenomena under scrutiny (3) the analytical methods and measures, and
Dynamics and Change with Digital Trace Data
Thirty-first European Conference on Information Systems (ECIS 2023), Kristiansand, Norway 5
(4) general themes and patterns that emerged throughout our analysis and synthesis. To address these
questions, we conducted an in-depth analysis of all articles that we deemed relevant from our systematic
literature review. Table 2 showcases three diverse studies from our review and their respective coding
along the dimensions of interest. These studies are intended to illustrate the broad range of research
using digital trace data to study dynamics and change and to shed light on the categories that we coded
for all relevant articles. We structure our findings along the four broad areas of interest. We discuss them
in the following.
Study
Purpose
Data
Research Context
Analysis
Measures
Nan & Lu
(2014)
Theory
building
Posts from a
university
online forum
Self-organization in
online communities
during a crisis
Content analysis
and temporal
progression of
posts
Coding
categories; time;
frequency of
posts
Weinmann
et al. (2022)
Theory
testing
Mouse-cursor
movement data
Online fraud
decisions
Regression
analysis
Mouse movement
speed and
deviation;
Fraudulent
responses
Zhang
et al. (2022)
Method
advancement
Data collected
from Wikipedia
Repetitive
collaboration
patterns (routines)
Sequential
pattern mining
analysis and
clustering
analysis
Frequency of
sub-sequences
Table 2. Coding of exemplary studies
4.1 Purpose
The first dimension that we scrutinized for all studies during our analysis and synthesis phase is their
respective purpose. Our goal with this analysis was to find an answer to the question: For what purposes
are digital trace data used in studies that examine dynamics and change of socio-technical phenomena?
Our analysis revealed three central purposes that studies pursue: theory building, theory testing, and
methodological advancement.
Theory testing-related studies typically derive and test hypotheses from extant theory. Studies often
follow a quantitative approach and consist of, for instance, experimental studies, various statistical
analyses, or econometrics studies that examine socio-technical phenomena over time. For example,
Spohrer et al. (2021) utilize digital trace data from an eHealth application to evaluate an experiment on
combining behavior change techniques to foster stress alleviation. Other studies use digital trace data in
an econometric research approach to investigate causal relationships of content sampling for video-on-
demand services (Hoang & Kauffman, 2018) or employees’ blog posts and blog readership (Aggarwal
et al., 2012). Furthermore, such studies utilize digital traces from online communities (e.g., Faraj et al.,
2015; Kim et al., 2018). For example, digital trace data from Wikipedia was used to investigate gender
bias among contributors (Young et al., 2020), or data from Twitter to analyze information diffusion
patterns (Cheng et al., 2011). Furthermore, Weinmann et al. (2022) use mouse-cursor movement data to
scrutinize online fraud decisions as they are happening over time. Taken together, these studies use
digital trace data to evaluate quantitative research approaches, investigate causal relationships, or collect
the data as a primary source for theory testing.
The second main purpose we identified in the literature is theory building. In this category, we subsume
studies that instantiate, modify or extend theory (Grover & Lyytinen, 2015). For example, research relies
on data from dermatology clinics to theorize about the dynamics of organizational routines (Pentland et
al., 2021). Vaast et al. (2017) use digital trace data collected from a microblog after an oil spill; they
extend affordance theory by introducing the concept of connective affordances. Nan and Lu (2014)
Dynamics and Change with Digital Trace Data
Thirty-first European Conference on Information Systems (ECIS 2023), Kristiansand, Norway 6
instantiate complex adaptive systems theory in the context of self-organization during a crisis, where
they utilized trace data from an online community forum to explore the temporal dynamics of self-
organization after an earthquake. Østerlund et al. (2020) discuss different approaches to the analysis of
digital trace data and showcase them through data that depicts the development of a crowdsourced
citizen science project over time.
The third main purpose that we identified is methodological advancement. Here, we can distinguish
between two broad streams. In one stream, works shed light on the process of theorizing. For example,
Howison et al. (2011) discuss validity issues for temporal digital trace data and provide
recommendations to resolve them. Berente et al. (2019) draw on grounded theory as well as
computational theory discovery to derive a general method to use digital trace data for what they refer
to as ‘computationally intensive theory development’. Their approach consists of four iterative
processes, namely sampling, synchronic analysis, lexical framing, and diachronic analysis (Berente et
al., 2019). Building on this work, Miranda et al. (2022) discuss what forms of theoretical contributions
can arise from digital trace data research, stressing that outcomes can include, for example, patterns that
were identified in the data set. Lindberg (2020) suggests how digital trace data can be used for theory
development through an iterative process of combining human and machine pattern recognition. Finally,
Hartl et al. (2023) propose how change can be explained by applying temporal bracketing to the analysis
of digital trace data. Another stream is concerned with the development of computational methods for
the analysis of digital trace data. For example, Yeshchenko et al. (2022) develop a method to detect
changes in business processes by identifying process drifts. Kallio et al. (2022) propose a method that
combines process mining with variance modeling to uncover research questions and opportunities for
IS researchers.
4.2 Data, Research Contexts, and Phenomena
The second dimension in our literature review is data, research contexts, and phenomena. Here, we aim
to answer the following question: What type of data are being used, which sources do they come from
and what research contexts are they embedded in? We refer to the data types and sources, the overall
research context (i.e., research setting) in which digital trace data have been collected and the
phenomena being scrutinized.
Data types constitute the first central aspect. In general, digital trace data consist of large quantities of
fine-granular information about activities executed through an information system (Howison et al.,
2011; Lazer et al., 2020). Research that studies dynamics and change with digital trace data typically
capitalizes on event logs to examine the temporal dynamics of a given phenomenon over time (Pentland
et al., 2014; Pentland et al., 2020; van der Aalst, 2016). An event log includes time-stamped data about
process activities and it can entail additional information such as identifiers for cases or resources that
were used (Pentland et al., 2020). As a general observation, we found that data is collected either through
organizational information systems (e.g., workflow systems) or open-source platforms (e.g., GitHub
data). On a closer look, we find a large variety of digital trace data types that are utilized to examine
dynamics and change of socio-technical phenomena. First, several studies in our review leverage data
from workflow systems to theorize, test, or investigate organizational phenomena, such as
organizational routines (e.g., Gaskin et al., 2014; Hartl et al., 2023; Pentland et al., 2021; Wurm et al.,
2021). Second, digital trace data from online communities are used to conduct research on temporal
relationships between posts and/or users (e.g., Kim et al., 2018; Vaast & Pinsonneault, 2021). Such data
comprise, for example, posts and messages recorded in online communities such as GitHub (Lindberg
et al., 2016) or online communities that are dedicated to specific topics, such as health (Chen et al.,
2019) or cybersecurity (Benjamin et al., 2016). One popular source is Wikipedia, where digital trace
data are collected to study the temporal dynamics of emergent roles (Arazy et al., 2016), gender bias
(Young et al., 2020), or repetitive collaboration patterns (Zhang et al., 2022). Third, studies work with
digital trace data from social networking platforms such as Twitter (Vaast et al., 2017) or other types of
blogs (e.g., Aggarwal et al., 2012). These studies rely on posts and tweets to analyze the dynamics of
various phenomena such as the dissemination of swine flu news (Cheng et al., 2011). Fourth, digital
Dynamics and Change with Digital Trace Data
Thirty-first European Conference on Information Systems (ECIS 2023), Kristiansand, Norway 7
trace data research capitalizes on sensor data such as geospatial and GPS data from taxis (Zhang et al.,
2020) or data from people’s mouse movements (Weinmann et al., 2022) to infer users’ decisions,
intentions, and motives over time. Studies also utilize digital trace data from web applications, such as
eHealth apps (Spohrer et al., 2021) or video-on-demand platforms (Hoang & Kauffman, 2018).
The second central aspect is the research context in which digital trace data were collected. In our
review, the most commonly studied research context is healthcare, where digital traces are leveraged to
investigate process event logs (Pentland et al., 2020) or the use of eHealth apps (Spohrer et al., 2021),
among others. Furthermore, the research context of innovation is of focal interest in studies on online
communities (e.g., Kyriakou et al., 2022) as well as studies that examine collaborative challenges across
organizations (Majchrzak & Malhotra, 2016). Another research context of digital trace data research is
crisis management. While Vaast et al. (2017) study microblogging use during an oil spill in the Gulf of
Mexico, Cheng et al. (2011) build a recommendation framework for swine flu news on Twitter. Adjacent
research contexts include contact tracing with trace data from mobile phones during the coronavirus
pandemic (Oliver et al., 2020) and mouse movement-based fraud detection (Weinmann et al., 2022).
Moreover, Hartl et al. (2023) study temporal changes in an onboarding process in the research context
of a financial institution. The idiosyncratic features of digital trace data such as timestamps and large
quantities also lend themselves to the research context of repetitive and routinized work, such as in
manufacturing (Wurm et al., 2021). Lastly, digital trace data research is commonly embedded in the
research context of corporate blogging (e.g., Aggarwal et al., 2012; Lu et al., 2015) and microblogging
(Vaast et al., 2017).
These research contexts enable the study of a variety of socio-technical phenomena. On the
organizational level, studies typically foreground organizational change from different perspectives
(e.g., Grisold et al., 2020; Hartl et al., 2023). Among others, Wurm et al. (2021), Pentland et al. (2021),
and Gaskin et al. (2014) use digital trace data to investigate the dynamics of organizational routines.
Other organizational-level phenomena are organizational learning (Avgar et al., 2018), behavior change
techniques (Spohrer et al., 2021) or collaboration patterns (Zhang et al., 2022), knowledge-sharing for
innovative outcomes (Majchrzak & Malhotra, 2016), or the formation of connective actions (Vaast et
al., 2017). A dominant theme is communication, where studies examine communicative genres in online
communities (Moser et al., 2013), information diffusion on a microblogging platform (Cheng et al.,
2011), or negative communication on blogs and its outcomes on a firm (Aggarwal et al., 2012). In
addition, phenomena such as leadership in online communities (Faraj et al., 2015) or job performance
(Lu et al., 2015) are also scrutinized with digital trace data. On the individual level, works focus on
individual behavior and decision-making, for example, in fraud decisions (Weinmann et al., 2022), in a
simulated business game (Kallio et al., 2022), or in the individual-level decisions of taxi drivers (Zhang
et al., 2020).
4.3 Analytical Methods and Measures
The third dimension that we emphasized in our review focuses on the analytical methods and measures
applied in digital trace data research around dynamics and change. By exploring this dimension, we aim
to answer the question: How are digital traces analyzed to obtain insights into dynamics and change?
In answering this question, we paid specific attention to the analytical methods that are applied as well
as the measures and variables of interest (i.e., dependent and independent variables).
Our review showed that scholars study a variety of measures. By measures, we mean variables and
constructs that are being used in order to operationalize observations regarding dynamics and change.
We found that four types of measures are most prominent in research with digital trace data; temporal
features, process composition, frequency, and user behavior measures. First, temporal features such as
throughput time (Hartl et al., 2023) leverage temporal information (i.e., timestamps) of digital trace data
to understand, explain, influence, or theorize about processual phenomena. Second, measuring and
analyzing process composition, or utilizing the building blocks of the process (i.e., nodes, edges, etc.),
is a common way of conducting research with digital trace data. For example, studies analyze process
complexity to study the dynamics of organizational routines or infer the structure of a process by
Dynamics and Change with Digital Trace Data
Thirty-first European Conference on Information Systems (ECIS 2023), Kristiansand, Norway 8
analyzing the number of ways through which a routine or a process can be performed (Pentland et al.,
2021; Wurm et al., 2021). Others also used process rules to investigate drift (change over time) of
processes (Yeshchenko et al., 2022). Third, studies measure the frequency of certain elements and how
the frequency changes over time; examples include the frequency of forum posts (Nan & Lu, 2014),
patterns (Zhang et al., 2022), process iterations (Hartl et al., 2023), or systems use (Spohrer et al., 2021).
Fourth, user behavior measures such as purchases on video-on-demand platforms (Hoang & Kauffman,
2018), disclosure behavior (Rhue & Sundararajan, 2019), or fraudulent decisions (Weinmann et al.,
2022) are used as measures for digital trace data research. Among others, additional measures such as
job performance or level of innovation were utilized. All measures are dependent on the respective
research context and phenomenon of the study.
These measures are utilized to scrutinize digital trace data with a variety of analytical methods. In our
review, process mining emerged as a popular method for analyzing process-oriented digital trace data.
Process mining has been integrated from the field of business process management (Mendling et al.,
2021; van der Aalst, 2016). Scholars leverage various distinct process mining techniques to theorize
about change from different perspectives (Grisold et al., 2020; Hartl et al., 2023; Pentland et al., 2021;
Wurm et al., 2021). Digital trace data are also analyzed with clustering analysis (e.g., Zhang et al., 2022),
content analysis, and sequence analysis techniques (e.g., Lindberg et al., 2016). Apart from these
computationally intensive methods of analysis, studies also carry out analyses of digital trace data that
build on classic statistical procedures such as comparative statistics and regression analyses (e.g.,
Majchrzak & Malhotra, 2016) or they apply econometric modeling to determine unilateral causation
(e.g., Rhue & Sundararajan, 2019). Furthermore, we observed that studies often apply a mixed methods
approach that combines quantitative analyses with qualitative forms of inquiry such as interviews (e.g.,
Moser et al., 2013) or contextual insights from organizations (e.g., Hartl et al., 2023).
4.4 General Themes and Patterns
In our analysis, we classified and analyzed 40 relevant articles according to their main purpose, data,
research context, phenomenon, methods of analysis, and measures. To answer the fourth question of our
analysis What are trends, themes, and patterns in research using digital trace data for studying
dynamics and change?, we identify general themes and trends in our review. Our study maps existing
literature along several dimensions and themes, which are summarized in Figure 1.
Dynamics and Change with Digital Trace Data
Dimensions
Themes
Purpose
Theory Building
Theory Testing
Method Advancement
Data
Workflow
System
Online
Community
Social
Media
Sensor Data
Wikipedia
Research Context
Healthcare
Innovation
Blogging
Crisis
Finance
Manufacturing
Socio-technical
Phenomena
Process &
Routine
Dynamics
Organizational
Change
Decision
-making
Collaboration
Communication
Analytical Method
Content
Analysis
Sequence &
Cluster
Analysis
Process
Mining
Comparative
Statistics
Econometric
Modeling
Measures
Temporal
Features
Process
Composition
Frequency
User Behavior
Figure 1. Themes in digital trace data research on dynamics and change
Dynamics and Change with Digital Trace Data
Thirty-first European Conference on Information Systems (ECIS 2023), Kristiansand, Norway 9
With regard to the purpose of the studies in our analysis, studies engage in theory testing, instantiation,
modification, or extension of theory (theory building) and studies contribute with methodological
advancements. It is important to note that studies can contribute with methodological advancements
while also providing a theoretical contribution (i.e., theory building) (e.g., Pentland et al., 2021). Works
that promote methodological advancements and theory-building studies tend to apply a mixed methods
approach by combining computational techniques with qualitative insights. Studies concerned with
theory testing tend to use experiments and various statistical analyses (e.g., Weinmann et al., 2022).
Overall, the most utilized form of digital trace data (i.e., data type) are data from workflow systems (11
studies) and data from online communities (11 studies) followed by data from social media or blogs (4
studies), Wikipedia (3 studies) and sensor data from applications or devices (3 studies). Furthermore, as
discussed in section 4.2, digital trace data with temporal information are used to study various
phenomena in different research contexts. We found that the most prevalent research contexts are
healthcare (5 studies), crisis and fraud (5 studies), manufacturing (2 studies), innovation (2 studies),
blogging (2 studies), finance (1 study), and cybersecurity (1 study). In these settings, digital trace data
were primarily used to study the phenomena of collaboration, organizational change, process and routine
dynamics, communication, and decision-making. Moreover, we discovered that the most frequently
employed analytical methods to study dynamics and change with digital trace data are process mining
(6 studies), followed by sequence and cluster analyses (5 studies), econometric modeling (4 studies),
comparative statistics (4 studies) and content analyses (4 studies). Furthermore, it is important to
mention that studies often apply a mixed methods approach to complement digital trace data with other
forms of qualitative inquiry to contextualize the findings. We also found that digital trace data can be
used to analyze a broad variety of measures and variables ranging from process composition (e.g.,
process complexity or process rules) (e.g., Pentland et al., 2021; Yeshchenko et al., 2022), temporal
features (i.e., throughput time) and frequency to user behavior measures (e.g., purchase behavior or
fraud behavior) (e.g., Rhue & Sundararajan, 2019; Weinmann et al., 2022).
Taken together, we can synthesize the following general observations. First, mixed methods approaches
are widely used and typically rely on both qualitative and quantitative insights. Second, digital trace
data are collected and analyzed within a wide range of research contexts. Third, we find that dynamics
and change, and especially the temporal aspects and effects of change over time, are investigated from
different angles. Lastly, there is a stark increase in research on dynamics and change with digital trace
data in recent years, which might be based on the growing availability of such data as well as
increasingly sophisticated computational methods to analyze them. This trend is also visible in our
review, which shows that research on dynamics and change with digital trace data experienced an
upsurge from 2011 until today. All relevant studies we analyzed were published in this period of time.
5 Discussion and Implications
In this article, we reviewed the existing literature that studies dynamics and change of socio-technical
phenomena with digital trace data (Miranda et al., 2022). Our analysis revealed several aspects and
themes that are emerging in this stream of research. In what follows, we discuss the implications of our
work and provide avenues for future research.
5.1 Novel Insights Enabled through Digital Trace Data
A number of conceptual arguments stress that digital trace data research provides means to generate
novel insights about socio-technical phenomena (e.g., Berente et al., 2019; Miranda et al., 2022; Oliver
et al., 2020; vom Brocke et al., 2021). So far, however, we lack systematic knowledge about how the
anticipated novelty is represented in actual empirical findings. Based on our analysis, we make several
observations.
First, digital trace data allow researchers to investigate change and temporal dynamics on fine-granular
levels. To this end, we found that studies draw a nuanced picture of how and when change takes shape
over time. Research on organizational routines is a case in point (Feldman et al., 2016). While research
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Thirty-first European Conference on Information Systems (ECIS 2023), Kristiansand, Norway 10
in this field has traditionally used manually collected qualitative data, such as interviews and
ethnographies (e.g., Dittrich et al., 2016), more recent studies build on digital trace data (Lindberg et al.,
2016; Pentland et al., 2021; Wurm et al., 2021). Thereby, these studies locate the dynamics of
organizational routines at the level of specific activities that might not be observable by means of
traditional research approaches. Certain dynamics of organizational routines, for example, can be
detected while those who perform the routines might even be unaware of them (Pentland et al., 2021).
Second, digital trace data offer means of measuring and operationalizing temporal dynamics. This is
because they are collected in large varieties, and event logs depict recurrent activities that are
consistently labeled in the whole data set. In GitHub, for example, it can be systematically examined
how the distribution of a fixed set of activities changes over time (Lindberg et al., 2016). Building on
this observation, it has been argued that digital trace data enable more systematic knowledge about
theories that were traditionally studied using qualitative approaches, such as interviews. For example,
Grisold, Gau et al. (2022) use digital trace data to measure structuration processes in collaboration
environments, thus developing in-depth explanations about when, how, and why individual actors
influence social structures, and vice versa (Essén & Värlander, 2019). This general observation is
supported by the fact that well-known phenomena are increasingly studied with new constructs –such
as the dynamics of organizational routines by various perspectives on process composition (Pentland et
al., 2021)– and that new constructs are developed to study new kinds of phenomena altogether (Grover
& Lyytinen, 2022).
In light of these observations, we encourage future research to identify phenomena that have been
studied from abstract angles (such as in structuration theory) and could be studied on a more fine-
granular level with digital trace data. Furthermore, new constructs can be developed that provide new
perspectives and angles to study socio-technical phenomena.
5.2 Embracing Opportunities for Studying Dynamics and Change
We found that studies leverage a wide range of data sources, analytical methods, and phenomena (see
Figure 1). This points to the numerous opportunities that can be embraced when studying dynamics and
change using digital trace data. When looking at GitHub, for example, we see that one and the same
data source can be leveraged to analyze various socio-technical phenomena, such as organizational
routines (Lindberg et al., 2016), or structuration processes (Grisold, Gau et al., 2022).
At the same time, however, there appear to be path-dependent trends and patterns. Studies that focus on
a specific phenomenon, for example, tend to use certain data sources and computational techniques.
Twitter studies tend to analyze the content of tweets (Vaast et al., 2017); instead of, for example, social
relations between users or other use patterns (such as likes or retweets) (Zöller et al., 2020). Similarly,
studies using digital trace data from GitHub typically analyze the distribution of activity types (Grisold,
Gau et al., 2022; Lindberg et al., 2016); instead of, for example, the evolving code base itself or
comments made within specific activity types (e.g., Issue Comment Events). In a similar vein, research
on organizational routines (e.g., Pentland et al., 2020; Pentland et al., 2021; Wurm et al., 2021) tends to
utilize event logs from workflow systems, such as enterprise resource planning systems. These studies
focus on time stamps, activities, and, if available, other resource-related information in the data.
Furthermore, across these studies, we find a tendency to use process mining as a computational
technique to analyze the data (e.g., Pentland et al., 2020; Pentland et al., 2021; Wurm et al., 2021).
We encourage future research to continue embracing the full spectrum of opportunities that emerge in
digital trace data research to focus on dynamics and change. This includes exploring all kinds of data
sources along with a variety of available features in data sets. To this end, it seems promising to combine
different information in event logs, such as geospatial information, or integrate insights from qualitative
data to provide more context to analyses (Oliver et al., 2020; Pentland et al., 2020; Whelan et al., 2016;
Zhang et al., 2020). Furthermore, one can leverage methods from other research fields (vom Brocke et
al., 2021), such as biology and medicine, and use digital traces for descriptive, explanatory, and
predictive research designs (e.g., Karahanna et al., 2018; Oliver et al., 2020). Finally, since digital trace
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Thirty-first European Conference on Information Systems (ECIS 2023), Kristiansand, Norway 11
data can be accrued in all kinds of contexts, future research may explore how digital trace data can be
used in other research contexts.
5.3 Advancing Research by Promoting Cross-Disciplinary Knowledge
Exchange
We found indications that digital trace data research promotes cross-disciplinary dialogues between
different research communities (vom Brocke et al., 2021). While the information systems field has a
strong tradition in socio-technical research (Sarker et al., 2019), it has been less concerned with
explicitly technical matters, such as knowledge that centers around computational methods to analyze
digital trace data. This, however, is needed to analyze digital trace data. For example, a recent call for
‘process science’ (vom Brocke et al., 2021) encourages researchers from information systems, computer
science, and organization science to jointly analyze, explain and promote change based on digital trace
data sets.
Along these lines, we see that recent moves have been incorporating new computational methods from
other fields. For example, we found that process mining (van der Aalst, 2016) is increasingly used as a
method to visualize and analyze temporal dynamics of organizational routines based on digital trace
data (Pentland et al., 2021). This method has been adopted from the field of business process
management. This field of research is concerned with developing methodological knowledge to
visualize and analyze the performance of business processes (Mendling et al., 2021). Adapted to
dynamics and change in the information systems field, it is used to reveal the temporal dynamics of
organizational routines, which in turn, informs the development of theoretical explanations (Berente et
al., 2019; Miranda et al., 2022).
In light of this example, we expect that researchers will find additional means to study digital trace data
when they intensify efforts to connect the information systems field with other scientific communities.
Computational methods that are used in public policy-making, for example, serve various purposes,
such as creating awareness of a societal problem or evaluating cause-and-effect relationships after an
intervention was implemented (Oliver et al., 2020). Such methods can support innovative theorizing
about novel socio-technical phenomena (Grover & Lyytinen, 2022) or evaluating design science
research projects (vom Brocke et al., 2020). In general, the information systems field might benefit from
initiatives that make such methods accessible to a wider audience (Grisold, Kremser et al., 2023).
5.4 Limitations and Outlook
Our literature review comes with certain limitations. In our literature search, we focused on research
that centers around ‘trace data’. Our motivation was that this term is associated with a new research
genre in the information systems field that advances empirical knowledge as well as methodological
knowledge (Berente et al., 2019; Lindberg, 2020; Miranda et al., 2022). To this end, our analysis
surfaced several trends and themes in this emerging stream of research. At the same time, other
keywords (e.g., ‘big data’ or ‘computationally intensive theorizing’) may have led to additional
complementary insights. While we counteracted any potential biases through forward and backward
searches, we will expand our systematic literature review with more keywords in the future.
Furthermore, we observe that digital trace data-based research is gaining prominence in other fields,
such as the social sciences in general terms (Lazer et al., 2020), and the management field more
specifically (Simsek et al., 2019). There were even calls to establish cross-disciplinary research agendas
(Oliver et al., 2020; vom Brocke et al., 2021). While other research fields (such as biology) may reveal
themes and topics that are relevant for information systems research, we did not systematically integrate
them in our analysis. Moreover, reviewing the development of digital trace data research in the
information systems field in general, rather than exclusively focusing on dynamics and change, seems
promising. We are planning to expand our study in future works by including different fields and
adopting a broader focus. Lastly, we see an opportunity in expanding our study with quantitative
analyses (e.g., scientometrics). Such approaches could corroborate our findings and allow for tracing
and unpacking how this evolving field has been unfolding over time. For example, it would be
Dynamics and Change with Digital Trace Data
Thirty-first European Conference on Information Systems (ECIS 2023), Kristiansand, Norway 12
interesting to investigate how different authors, studies, and/or ideas have been forming, and shaping
digital trace data research. We plan to integrate quantitative analyses to enhance the understanding of
research on dynamics and change with digital trace data in future work.
6 Conclusion
There is a surging interest in the information systems field around digital trace data research and
computationally intensive theorizing. Our study is the first to systematically analyze the state of this
field with regard to research that investigates dynamics and change of socio-technical phenomena over
time. Drawing on a total number of 40 articles published in high-quality information systems outlets,
we observe that this stream of research pursues different interests and themes. To this end, we found
that studies center around different (1) research purposes, (2) data types, (3) contexts, (4) phenomena,
(5) methods, and (6) measures. Our findings allow us to map current trends and patterns, and we provide
a number of directions for future research.
Acknowledgements
This research was carried out, among others, within the framework of the Schöller Senior Fellowship
on “Process Science – The Interdisciplinary Study of Continuous Change”. The research has also been
funded by the ERASMUS + program of the European Union (EU Funding 2021-1-LI01-KA220-HED-
000027575 “Developing Process Mining Capabilities at the Enterprise Level”) and the Liechtenstein
Research Fund (grant number 705076 “Process Science: Conceptual foundation for the interdisciplinary
study of continuous change”).
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