APPENDIX A – METHODS

In their work on the use of grounded theory techniques to perform literature

reviews, Wolfswinkel et al. (2013) proposed five main steps which they further break

down into eleven tasks that guide the review process from its inception until the actual

writing of the review article (see Table A.1). In the sections below we outline the

application of those tasks to the present study.

Table A.1. Using Grounded Theory for Literature Reviews (adapted from Wolfswinkel et al. 2013)Step Task(s)Define: determine the scope of the review Define inclusion/exclusion criteria

Identify fields of research Identify sources Establish search terms

Search: perform the actual search SearchSelect: apply inclusion/exclusion criteria and identify additional sources to produce the final sample

Refine the sample

Analyze: apply coding techniques to analyze each source

Open coding Axial coding Selective coding

Present: write the review Define structures to represent findings (e.g., tables, graphs)

Define the structure of the review itselfNote: Grounded theory techniques are applied in the Analyze step while the other steps are found in most works on literature reviews.

Step 1 – Define: Setting the Boundaries of the Review

The first step consists in drawing the boundaries of the review and is in line with

recommendations from extant literature on the topic (Paré et al. 2015). This entails the

definition of a number of criteria for inclusion and exclusion of sources, applicable fields

of research, relevant sources, as well as criteria to identify and retrieve those sources.

To initiate this first step, we searched online databases (AIS Library, Business Source

Complete, ScienceDirect, Web of Science) for peer-reviewed journal articles and

conference proceedings against the keyword “digital transformation”.

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This initial search enabled us to gain a general understanding of the coverage of

this topic in a number of disciplines including management, IS, health informatics, as

well as engineering. Information about those sources indicated that digital transformation

is a topic of interest in research and practitioner-oriented outlets alike. We decided to

focus our efforts on sources from IS literature to maintain a certain degree of

consistency with regards to the specific topics of interest while ensuring that our sample

size would remain manageable. We chose the AIS Library, Business Source Complete

and ScienceDirect as our main source databases. AIS Library provides access to

leading IS journals as well as IS conference proceedings, some of which are not

available in other databases (e.g., Web of Science) while Business Source Complete

provides access to other IS journals. We also included ScienceDirect to have the ability

to search for specific IS journals not included in other databases (e.g., the Journal of

Strategic Information Systems is inventoried in ScienceDirect).

Criteria for exclusion included: works in progress (conference proceedings);

panel introductions; sources not available in English; unavailable full papers; research

publications in journals not indexed in the Journal Citation Reports (JCR); teaching

cases; and pedagogical research papers. A cursory look at our initial search query

results, prior knowledge on the topic and discussions with other IS scholars led us to

define the following search query, which we adapted to each database but summarize in

SQL-like syntax for the purpose of genericity: “(abstract LIKE ‘%digital%’) AND

(abstract LIKE ‘%transform%’ OR ‘%disrupt%’)”. Separating the two terms ‘digital’

and ‘transformation’ enabled us to retrieve works highlighting the transformational power

of digital technologies without necessarily referring to the phenomenon as such. The

search term “disrupt” was selected because several of the sources we had identified in

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our initial search referred to “disruption” or “disruptive innovation” (Karimi and Walter

2015) in their abstracts while referring to “transformation” in the contents of the articles.

Step 2 – Search: Retrieving Results

For each selected database, we performed our search query (our last search

iteration took place on June 11, 2018). Unexpectedly, we had to adjust some of search

criteria, which led to this step being largely iterative. For instance, we originally used the

advanced search feature of the AIS Library to restrict results to peer-reviewed results

only. However, upon investigation of the returned results, we discovered that not all AIS-

inventoried publications are marked as peer-reviewed, even if they actually are (e.g.,

ICIS 2016 proceedings were not considered peer-reviewed in the database as of March

2018). This led us to perform a number of checks and cross-references against

variations of our initial search query to ensure that we were indeed retrieving search

results as per our expectations. Another instance occurred with AIS publications such as

Business Information and Systems Engineering (BISE) which do not always contain

abstracts. In those instances, we went back and performed our search query against full

articles for publications which we identified as missing from our search results.

Each search result was assigned with a unique identifier that would serve as a

means to ensure the integrity of the data we would subsequently collect on the work. We

downloaded, for each search result, the paper in PDF format (using the unique identifier

as its name), along with any metadata that was available with the search result (e.g.,

year of publication) and placed this metadata into an Excel spreadsheet (using each

work’s unique identifier). The reference for each search result was also downloaded into

EndNote, but was not used for data analysis purposes.

Step 3: Select – Finalizing the Review Sample

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Our initial sample contained 381 works. Removing duplicate search results and

applying our criteria for inclusion and exclusion yielded a sample of 248 works. We then

proceeded to extract common citation information within each paper to perform a

preliminary backward and forward search. This process led us to include an additional

44 sources, 4 of which were located in practitioner outlets, including Westerman et al.’s

(2011) report on digital transformation which is considered the first on the topic. Our final

sample therefore includes a total of 282 works.

Step 4: Analyze – Gaining Insight from Sources

During this fourth step, techniques borrowed from grounded theory are effectively

applied to help the research build a thorough understanding of the corpus of literature

under review. This is performed through the use of three main techniques: open coding,

axial coding, and selective coding.

Following recommendations from Wolfswinkel et al. (2013), we randomly

selected each source within our sample and proceeded to code its contents. Consistent

with the nature of grounded theory as a process of gradual discovery, our coding was

largely iterative. We based our coding process on the collection of two main types of

data. First we collected a number of pre-defined, descriptive elements on each paper

which are summarized in Table A.2. Descriptive statistics pertinent to these elements

are available in Appendix C.

Table A.2. Initial Coding – Pre-defined Descriptive ElementsTitle of the paperAuthor(s)Publication outletType of publication outlet (research journal, practitioner journal, conference proceedings)Type of paper (empirical, conceptual)Context of application (e.g., healthcare, financial services)Theoretical perspective(s)MethodsSample information (for empirical papers)Definition if digital transformation, if any, or any related concept (e.g., digital transformation strategy)Indication as to whether DT is described as an exogenous phenomenon (e.g., a “threat”) that must be

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coped with or an endogenous phenomenon (e.g., an “opportunity” that firms use as a strategic tool generate a competitive advantage)Explanation for removal if the paper was subsequently removed from the sample

Second, we performed open coding and proceeded to annotate each source

while taking detailed notes with regards to findings, discussion points and other

elements we deemed relevant, and crafting a brief summary of the source within our

Excel spreadsheet. We also applied open coding techniques to record relationships

across elements, variables, concepts and constructs contained within our sources,

depending on the type of source under review. For instance, the relationships within a

quantitative, empirical paper were coded based on the results of the testing of

hypotheses (e.g., Karimi and Walter 2015). For a qualitative piece, we coded those

relationships based on propositions when available, or based on our reading of the

findings section of the paper (Ramasubbu et al. 2014). This process enabled us to

maintain a detailed account of the relationships that each source was either studying (for

an empirical piece) or proposing for future research (for a conceptual piece) to develop

an initial understanding of the concepts relevant to our phenomenon of interest as well

as the relationships that exist between them (each relationship contains 3 separate

codes: one for each element involved in the relationship and another for the direction or

the type of relationship). Our first iteration of open coding led to the identification of

2,894 coding instances and 404 unique first-order codes.

We then applied axial coding to refine our coding scheme and categorize codes

based on their meanings. For instance, first-order categories such as “experience with

digital mergers and acquisition” and “new ecosystem relationships” were grouped under

a higher-level category labelled “strategic partnerships”. Throughout this process we

kept going back and forth between our raw data, our open codes and we kept a separate

log to document the evolution of our categories for traceability purposes. We also used

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version control for our Excel spreadsheet to maintain an accessible history of our coding

process that could be consulted, contrasted and compared with our working version at

all times. In some instances, this led us to revisit our open codes to create new codes or

to apply different codes to our sources. Due to the number of coding instances, we

performed this step in two rounds. After the second round of axial coding, we retained

170 categories.

Our last step was selective coding, during which we strived to further refine and

integrate the categories that resulted from the axial coding process. It is during this step

that the framework presented in Figure 1 in the main body of the paper emerged as we

grouped together sets of relationships. Figure A.1 provides a graphical representation of

an excerpt of our coding data structure. Although useful for the purpose of integration,

this process also led us to reflect on the fact that, in the context of a literature review,

higher level categories inevitably become quite general and can hide the richness of the

underlying evidence they help to organize. As we applied these techniques using our

Excel spreadsheet, we ensured that the data structure supporting our coding process

enabled us to “drill up” from lower (e.g., “lower barriers to entry”) to higher level

categories (“environment – competitive landscape”) as well as “drill down” from higher to

lower level categories and relationships, effectively allowing us to consider our sources

from different vantage points. This enabled us to easily analyze the relative coverage of

relationships within our sample, highlighting those relationships that have received

extensive as well as those that have received little coverage in the literature. In doing so,

we strived to remain loyal to the richness of our data, which would have not been

possible had we adopted a quantitative approach (e.g., topic modelling) to study this

corpus without studying the relationships amongst the elements that compose its

sources.

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Figure A.1. Excerpt from Coding Data Structure

Note: The actual data structure was later imported into a table in a relational database containing an attribute for each coding level.

To assist in the analysis of those data at different levels of granularity, we

imported the contents of our Excel spreadsheet into a relational database. The choice of

a relational database was primarily based on the authors’ degree of comfort with the

writing of SQL queries. This approach helped us to validate emergent findings against

our codes by using simple SQL queries to perform counts, comparisons, and other

searches that are not readily available in Excel without the use of advanced

functionalities. This step proved useful to rapidly conduct thought experiments as well as

to help us make sense of our data. To help us make sense of the relationships between

our emergent codes, we also borrowed from visualization techniques used to study

networks (see Figure A.2) using the networkD3 library available in the R software

package. We treated our relationships as edges connected by pairs of codes (color-

coded based on their category) acting as vertices that we explored at varying levels of

detail (e.g., using codes built during the axial coding stage versus the selective coding

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stage), using the weight of edges as representations of the relative coverage of a given

relationship within our codes (i.e., based on the number of articles where a relationship

was present). Although this was not done to perform a quantitative analysis of the

relationships between our codes, it provided a convenient and interactive means to

explore those relationships in a graphical format.

Figure A.2. Visualizing Code Relationships as a Network

In Table A.3, we provide an overview of the high level categories that are

displayed in Figure 1 of the main body of the paper. For each category, we provide a

description based on the memos that we generated as part of our coding along with

some illustrative references. We also provide a list of sub-categories that are pertinent to

the high level category. Two points should be observed. First, two of our categories,

digital divide (a negative impact at the society level; 9 sources) and digital

entrepreneurship (a structural change that helps organizations create value using digital

technologies; 8 sources), are provided here but have been omitted from the paper

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because they were significantly under-represented compared to other categories.

Although this in itself is a finding of our review, we chose not to include them in the main

body of the paper to preserve space. Nevertheless, future research may add to this

comparatively smaller body of research by examining their role in the context of DT, e.g.,

by studying contradicting evidence with regards to the benefits and the challenges

between digital technologies and the digital divide. Second, although research in IS has

traditionally differentiated between agility and ambidexterity, we put them together

because in the context of DT, works have used both terms to refer to the overarching

notion that the use of digital technologies to create value is based on an ability to sense

and respond to change, which also involves an ability to simultaneously execute

exploration and exploitation. Future research may separate these notions to maintain

conceptual clarity; however, for this review, we chose to group them together because of

their relative interchangeability within our sample.

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Table A.3. High-Level Categories Generated during Data AnalysisHigh Level Category and Description Lower Level Category and Sample ReferencesDigital technologies: combinations of information, computing, communication and connectivity technologies.

Social media (includes technologies that enable individuals to communicate with one another) – Miranda et al. (2016); Selander and Jarvenpaa (2016)

Mobile (includes hardware and software) – Hanelt et al. (2015); Sia et al. (2016) Analytics (includes software as well as processes that analyze [big] data to generate

actionable insight) – Günther et al. (2017); Saldanha et al. (2017) Internet of things (includes general discussions on IoT as well as specific forms of

hardware devices such as sensors) – Fitzgerald (2016); Newell and Marabelli (2014)

Platforms & ecosystems (includes various types of platforms and ecosystems, e.g., two-sided and multi-sided platforms) – Tan et al. (2015); Tiwana et al. (2010)

Environmental changes: High level trends fueled by the increased use of digital technologies by firms as well as individuals.

Consumer behavior & expectations (consumers increasingly rely on digital technologies) – Granados et al. (2008); Sia et al. (2016)

Competitive landscape (lower barriers to entry) – Mithas et al. (2013); Ramasubbu et al. (2014)

Increased availability of data (user-generated data, IoT generated data) – Newell and Marabelli (2015); Saldanha et al. (2017)

Strategic responses: Elements describing how an organization intends to use digital technologies.

Digital business strategy (fusion between organizational and IT strategies) – Bharadwaj et al. (2013); Pagani (2013)

Digital transformation strategy (separate strategy to execute transformation as an IT-driven initiative) – Hess et al. (2016); Matt et al. (2015)

Changes in value creation paths: How an organization generates value for stakeholders using digital technologies.

Value propositions (products and services offered) – Delmond et al. (2017); Sia et al. (2016)

Value networks (resources involved in the creation of products and services, e.g., suppliers, customers) – Hansen and Sia (2015); Riasanow et al. (2017)

Digital channels (channels that rely on digital technologies, e.g., social media) – Sia et al. (2016); Xie et al. (2014)

Agility and ambidexterity (ability to exploit existing resources while remaining aware of opportunities through exploration) – Dixon et al. (2017); Gray et al. (2013)

Structural changes: Changes that are required for an organization to realize value using digital technologies.

Organizational structure (e.g., creating spinoffs, cross-functional teams) – Dremel et al. (2017); Svahn et al. (2017)

Organizational culture (e.g., encouraging trial and error) – Karimi and Walter (2015); Li et al. (2017)

Leadership (e.g., appointing a CDO) – Benlian and Haffke (2016); Haffke et al. (2016)

Employee roles and skills (training current employees, determining the needs of the upcoming digital workforce) – Lyytinen and Rose (2003); Svahn et al. (2017)

Digital entrepreneurship (design and launch of digital business) – Standaert and Jarvenpaa (2017); Tumbas et al. (2015)

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Organizational barriers: Hinderers that prevent an organization from realizing value using digital technologies, unless otherwise addressed.

Inertia (e.g., path dependence, institutional barriers) – Remane et al. (2017); Woodard et al. (2012)

Resistance (e.g., employees unwilling to use new technology, innovation fatigue) – Selander and Jarvenpaa (2016); Yeow et al. (2017)

Negative impacts: Detrimental outcome of the DT process for a given actor (e.g., firm, industry).

Security & privacy (e.g., of individuals) – Günther et al. (2017); Newell and Marabelli (2015)

Digital divide – Chatfield et al. (2015); Newell and Marabelli (2015)Positive impacts: Beneficial outcome of the DT process for a given actor (e.g., firm, industry).

Operational efficiency (automation, costs savings) – Holotiuk and Beimborn (2017); Pagani (2013)

Organizational performance (innovation, profit, growth) – Nwankpa and Datta (2017); Svahn et al. (2017)

Industry & society improvement (well-being of individuals, bridging the digital divide) – Agarwal et al. (2010); Srivastava and Shainesh (2015)

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