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Business Intelligence in different decision settings
Master’s Thesis
Copenhagen Business School 2015
MSc in Business Administration and Information Systems (IM)
Supervisor: Ioanna Constantiou
Number of norm pages: 63
Number of characters: 144211
Date of submission: 22nd of January 2015
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Hin-Hey Karl Chung
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Abstract While Business Intelligence (BI) technologies advance and in practice more emphasis is placed
on using data to improve decision-making (Gartner, 2013b; LaValle, Hopkins, Lesser, Shockley, &
Kruschwitz, 2010), only few researchers have investigated BI from a decision-centric view
(Kowalczyk, Buxmann, & Besier, 2013). In fact, although BI claims to enhance decision-making, the
literature review reveals a major gap between the two research streams. On the one hand, decisions are
treated differently depending on the decision setting. On the other, BI focuses mainly on technological
and process-related aspects ignoring the decision characteristics. This paper attempts to close this gap
by examining the BI output usage in various decision settings. Established decision-making
frameworks with recent BI findings were examined in order to better understand the BI support. In
particular, the frameworks by Thompson (1967) and Gorry and Scott Morton (1971) form the basis for
classifying a decision properly. From a BI perspective the different BI tactics and devices identified by
Shollo (2013) build the foundation for the study. The study, conducted at an online advertising
company, finds different behavior in different settings and highlights the functional interaction
between rational and socio-political processes making a technological determinism approach, as
favored by the BI technological view, questionable. Instead, the insights imply a more setting
dependent approach and show the different roles of BI. In a structured analysis setting the rational BI
output inherently determines the ultimate decision while in other settings the BI information is
neglected and other socio-political devices, such as the expert and stakeholder, gain more importance.
Several recommendations are given based on the study’s findings and they call for more research on
BI from a decision-centric view to keep the promise of improving decision-making by using BI.
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Table of Content Abstract .................................................................................................................................................. II Table of Content ................................................................................................................................... III List of Abbreviations ........................................................................................................................... IV List of Figures ....................................................................................................................................... IV List of Tables ......................................................................................................................................... V
1 INTRODUCTION ........................................................................................................................... 1 1.1 The quest for making informed decisions .................................................................................. 1 1.2 Research Question ...................................................................................................................... 2 1.3 Method and Structure ................................................................................................................. 2
2 THEORETICAL FOUNDATION ................................................................................................. 4 2.1 Research on Organizational Decision-Making .......................................................................... 4
2.1.1 Rational view ...................................................................................................................... 4 2.1.2 Socio-political view ............................................................................................................ 8 2.1.3 Dynamic decision settings .................................................................................................. 9
2.2 Research on Business Intelligence ........................................................................................... 12 2.2.1 Technology view of BI ..................................................................................................... 12 2.2.2 Process view of BI ............................................................................................................ 15 2.2.3 BI from a decision-centric view ....................................................................................... 16
2.3 Theory reflection and synthesis ............................................................................................... 19 3 RESEARCH STUDY .................................................................................................................... 23
3.1 Study Design ............................................................................................................................ 24 3.2 Study Environment - plista ...................................................................................................... 25 3.3 Data Collection and Preparation .............................................................................................. 29 3.4 Study Analysis ......................................................................................................................... 31
3.4.1 Thematic Analysis ............................................................................................................ 31 3.4.2 Applying the Analysis to the Study .................................................................................. 33 3.4.3 Categorizing decisions ...................................................................................................... 35
4 FINDINGS ...................................................................................................................................... 37 4.1 Findings per Quadrant .............................................................................................................. 38
4.1.1 Analysis ............................................................................................................................ 39 4.1.2 Judgment ........................................................................................................................... 43 4.1.3 Bargaining ......................................................................................................................... 47 4.1.4 Inspiration ......................................................................................................................... 50
4.2 Findings across different settings ............................................................................................. 50
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5 DISCUSSION ................................................................................................................................. 52 5.1 Implications from the organizational decision making perspective ......................................... 53 5.2 Implications from the BI perspective ....................................................................................... 55 5.3 Implications and recommendations for the study context ........................................................ 56 5.4 Limitations ............................................................................................................................... 59
6 CONCLUSION .............................................................................................................................. 60
References ............................................................................................................................................. 61 Appendix ............................................................................................................................................... 66
List of Abbreviations BI Business Intelligence
BI&A Business Intelligence & Analytics
CDN Content Delivery Network
CIO Chief Information Officer
DE Data Engineering
DSS Decision Support System
DWH Data Warehouse
EIS Enterprise Information System
ETL Extract Transform Load
JF Jour Fixe
MIS Management Information System
PM Product Manager
OLAP On-line Analytical Processing
List of Figures Figure 1: Overview of the thesis structure ........................................................................................... 3 Figure 2: Information Systems – A Framework (Gorry & Scott Morton, 1971, p. 16) ....................... 6 Figure 3: Decision approaches matched to decision tasks (Nutt, 2002, p. 69) ..................................... 7 Figure 4: Evolution of contingencies over time through socio‐political processes and procedural
rationality (Royer & Langley, 2008, p. 257) ................................................................................. 10 Figure 5: A timeline of the evolution of systems supporting decision-making (Shollo, 2013, p. 31) 14 Figure 6: BI&A evolution: Key Characteristics and Capabilities (Chen et al., 2012, p. 1169) ......... 15
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Figure 7: Combination of Thompson’s (1967) contingency framework and Gorry and Scott
Morton’s (1971) MIS framework (structuredness dimension) ...................................................... 21 Figure 8: plista’s organizational structure .......................................................................................... 26 Figure 9: Example of an epic in Jira ................................................................................................... 28 Figure 10: Planned tickets for a two weeks sprint .............................................................................. 29 Figure 11: Overview of study design and analysis ............................................................................. 32 Figure 12: Extract of discussion and assigned codes and themes ...................................................... 33 Figure 13: Usage of BI output with BI tactics and devices in the decision making process .............. 34 Figure 14: Decision about a CDN in Thompson’s (1967) contingency framework .......................... 36 Figure 15: Data analysis – decision setting and BI tactics and devices for each decision ................. 37 Figure 16: Ticket describing a critical bug ......................................................................................... 40 Figure 17: Decision about resizing images for publisher in Thompson’s (1967) contingency
framework ...................................................................................................................................... 42 Figure 18: Decision about the Widget Creator in Thompson’s (1967) contingency framework ....... 46 Figure 19: Implications of the findings from the decision-making view ........................................... 55 Figure 20: Recommendations for splitting the backlog to the case company .................................... 58
List of Tables Table 1: Decision-making characteristics in an analysis setting ........................................................ 22 Table 2: Decision-making characteristics in a judgment setting ........................................................ 22 Table 3: Decision-making characteristics in a bargaining setting ...................................................... 23 Table 4: Decision-making characteristics in an inspirational setting ................................................. 23 Table 5: Planning level classification for the study ............................................................................ 36 Table 6: Overview of the distribution of studied decisions in the different decision settings ........... 39 Table 7: BI output tactics and devices in an analysis setting ............................................................. 43 Table 8: BI output tactics and devices in a judgment setting ............................................................. 47 Table 9: BI output tactics and devices in a bargaining setting ........................................................... 50 Table 10: Overview of the BI tactics and devices across Thompson’s (1967) contingency framework
51 Table 11: Overview of the BI tactics and devices across planning level ........................................... 52
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1 INTRODUCTION
“So can we do better? Do we have an alternative to relying on human intuition, especially in
complicated situations where there are a lot of factors at play? Sure. We have a large toolkit of
statistical techniques designed to find patterns in masses of data (even big masses of messy data),
and to deliver best guesses about cause-and-effect relationships. No responsible statistician would
say that these techniques are perfect or guaranteed to work, but they’re pretty good” (McAfee, 2010,
para. 7).
1.1 The quest for making informed decisions
For a few decades there has been a trend both in research and practice to make decisions based on
information and facts instead of trusting the human intuition (Baba & HakemZadeh, 2012). Similarly, it has
been reported that more and more data are captured to improve decision-making. For instance, IBM (2014)
states that two billion gigabytes of data are created every day leading to the fact that 90% of the world’s data
has been created in the last two years. This massive explosion of data enables enterprises to analyze the
information improving their performance. According to a study, top performing organizations are applying
analytics techniques five times more than lower performers due to exploitation of the data resource (LaValle
et al., 2010). Although providing decision-makers with the needed information has been investigated for
many decades under the label business intelligence (BI) or analytics, current advancement in technology
allows exploring further opportunities. Therefore, BI was the top priority for Chief Information Officers
(CIO) in 2009, 2012 and 2013 (Gartner, 2013a) and remains a top focus through 2017 (Gartner, 2013b).
Furthermore, special skills are required to gain insight from the data making a “data scientist the sexiest job
of the 21st century” (Davenport & Patil, 2012). Accordingly, extensive research was published in the last few
years centering the BI technology (Chen, Chiang, & Storey, 2012).
Nevertheless looking critically and holistically, it appears that along the way the actual purpose was
forgotten, namely decision-making. Often BI has been driven from a technological perspective but
researchers are calling for a decision-centric view highlighting decision-making rather than technological
possibilities (Kowalczyk et al., 2013). When examining the decision-making research one can find several
indications implying that a full data driven approach is inappropriate (Royer & Langley, 2008). In fact,
different studies show that different factors affect the decision and the decision-making process in which it is
situated (Nutt, 2002). However, from a BI perspective there is no such differentiation. Instead, due to the
mainly technical view, it has been described as a “one size fits all” solution leaving a gap between these two
research streams. Therefore, this paper explores the gap and highlights areas of connection.
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1.2 Research Question
In fact, several studies show that information has a symbolic value leading to the neglect of the actual
use of it (Feldman & March, 1981) despite a favoured fact based approach (Baba & HakemZadeh, 2012).
Furthermore, one can see the increased focus on BI (Chen et al., 2012). In order to better understand and
unfold these various views, this paper takes a decision-centric view of BI using decision-making as the
leading aspect. Following the indication that decisions are made differently depending on the given setting, it
is assumed that BI is also used differently. To validate this, the following research question guides the
research:
How does the BI output support organizational decision-making in different settings?
Two sub-questions have to be addressed in order to answer the research question:
• How are decisions made dependent on the setting?
• How does BI support decision-making?
The first sub-question investigates the school of decision-making. The different settings and the
decision-making process are identified. The second sub-question investigates the BI support for decision-
making processes. The two sub-questions complement the comprehension for the quest of the research
question. It should be noted that the study does not attempt to evaluate if and how BI improves decision-
making. Instead, before improvement areas can be investigated, it is necessary to understand how BI is used.
1.3 Method and Structure
After this introduction, the literature review about decision-making and BI is presented. First, diverse
aspects of different views from decision-making theories are explained. In addition to the decision-making
process the different decision settings are investigated. Mainly Thompson's (1967) framework is highlighted.
Second, the BI literature review shows the evolution of BI from a technical as well as process perspective.
This section further includes findings of the connection between decision-making and BI and addresses the
research question from a theoretical view. It is shown that currently BI is not linked to setting dependent
decision-making. Hence, a theoretical foundation is synthesized upon the research streams and used in a
qualitative study at an online advertising company. Their internal and external product development is the
focus of the research. After the description of the study design, the study analysis is explained. The findings
of the research study are based on a thematic analysis, which was performed in a prior-research approach.
The presentation of the findings are structured in the different decision settings as well as across all settings.
Finally, the paper concludes with a reflection and gives recommendations for future research. Figure 1 gives
an overview of the structure.
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Figure 1: Overview of the thesis structure
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2 THEORETICAL FOUNDATION
2.1 Research on Organizational Decision-Making
This section presents the literature review of the decision-making research body. Several frameworks
and theories are discussed. Decisions are the selection of a course of action, which derives from a problem or
opportunity (Choo, 2005). Although there are many studies concerning decision-making on individual and
group level, for this paper it is relevant what the literature refers to as ‘organizational decision-making’
(Mintzberg, Raisinghani, & Théorêt, 1976). One major difference between individual or group and
organizational decision-making is the unrealistic environment of controlled laboratory settings (Mintzberg et
al., 1976). “A criticism of such an approach has been its failure to capture how people make decisions in
complex real-world settings” (Selvaraj & Fields, 2012, p. 8). Accordingly, Shapira (2002) identifies
characteristics of the difference: ambiguity in terms of preferences and information, on-going processes of
decision making, which are interrelated and sequential, incentive systems and rules, and conflict among
different preferences.
Different models are investigated, which gained popularity over many decades. This section is divided
into the rational view and the socio-political view, a distinction of decision-making aspects as proposed by
Royer and Langley (2008). The dynamic interplay between these two views concludes this section. The
findings of this literature review address the first research sub-question on how decisions are being made
depending on the setting.
2.1.1 Rational view
“Pfeffer and Sutton (2006) argue that companies which base their decisions on evidence have a
competitive advantage. This is mainly because management by intuition, the alternative approach to
basing decisions on evidence (Gaynard, 2010), is hardly defensible” (Baba & HakemZadeh, 2012, p.
837).
Studies highlight that making decision rationally using evidence such as information and facts is
favourable (Baba & HakemZadeh, 2012). Rationality refers to the “process of information search and
analysis associated with organizational issues”(Royer & Langley, 2008, p. 2). Over many decades,
normative frameworks and typologies have been developed. The classification of decisions into those models
shall direct decision-making. These concepts aim to address the main problem of decisions, which is
uncertainty. The usage of the concepts claims to reduce the uncertainty and consequently lead to better
decisions.
Simon (1957) introduces the distinction between programmed and non-programmed decisions or
cited and re-labeled as structured and unstructured decisions (Gorry & Scott Morton, 1971). Structured
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decisions are characterized by their repetitiveness and routines so that there is a determined procedure
(program) of handling re-occurring decision and problem patterns. Highly structured decisions can be at least
partially automated (Gorry & Scott Morton, 1971). In contrast, unstructured decisions require a “custom-
tailored treatment” (Simon, 1977, p. 8) in which human judgment and evaluation are necessary (Gorry &
Scott Morton, 1971).
Another common categorization of decisions is to distinguish into planning levels: operational
(control), tactical (or management control), and strategic (planning) decisions (Anthony, 1965). Main
differences between these planning levels are the resource commitment, the impact on the organization’s
future direction and the time frame. Operational decisions concern short-term tasks, which require less
judgment and focuses more on effectiveness and efficiency. Since strategic decisions predict an
organization’s long-term future environment, more judgment by few top-level managers is required. The
strategic issues are open-ended and highly complex due to the conflict of interest and the interplay of soft or
non-quantitative factors (Mintzberg et al., 1976). Tactical decisions are the link between operational and
strategic decisions insuring that resources are available to perform the operational tasks, which are situated in
the strategic context. Whereas operational decisions are potentially structured, strategic decisions tend to be
unstructured (Mintzberg et al., 1976; Simon, 1977). Hence, tactical decisions tend to be in between, which
Gorry and Scott Morton (1971) define as semi-structured. The classifications into structured and
unstructured as well as operational, tactical, and strategic decision types have been used in other researchers’
work (Gorry & Scott Morton, 1971; Mintzberg et al., 1976). For instance, Gorry and Scott Morton (1971)
combine those two dimensions for their 3x3 matrix and give some examples of decisions for each cell as
shown below.
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OPERATIONAL
CONTROL
MANAGEMENT
CONTROL
STRATEGIC
PLANNING
STRUCTURED
Accounts Receivable
Order Entry
Inventory Control
Budget Analysis –
Engineered Costs
Forecasting – Short
term
Tanker Fleet Mix
Warehouse and Factory
Location
[SEMI-STRUCTURED] Production Scheduling Variance Analysis –
Overall Budget
UNSTRUCTURED Cash Management
PERT/COST systems
Budget Preparation
Sales and Production
Planning
Mergers and
Acquisitions
New Product Planning
Figure 2: Information Systems – A Framework (Gorry & Scott Morton, 1971, p. 16)
Furthermore, Mintzberg (1976) developed a framework in his book “The Structure of "Unstructured"
Decision Processes”. Reducing uncertainty is again the main target for this approach. This framework
includes three decision-making phases: identification, development, and selection (of a choice). During the
identification phase a problem, opportunity or crisis is determined and cause-effect relationships are
diagnosed. In the development phase ready-made solutions are considered or custom-made solutions are
developed. Finally, a selection among the options is based on evaluated criteria. Alongside these phases there
are routines which specify who does what, how and when, similar to a business process definition. A
problem is fully structured if all three phases are structured. It is worth noting that the phases can be
performed in iteration and do not always follow a linear procedure.
Another popular framework is Thompson’s (1967) contingency model, which addresses the
uncertainty issue by evaluating “the clarity about the objectives and the means of producing results that are
inherent in a decision task“ (Nutt, 2002, p. 68). Objectives define what to do and the means define how to do
it. While the major challenge in the objective clarity is the preference alignment among different parties, for
the means it is the knowledge about cause-effect relationships. The clarity of those two dimensions is either
known or unknown, which leads to the two by two matrix as shown in Fehler! Verweisquelle konnte nicht
gefunden werden..
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Figure 3: Decision approaches matched to decision tasks (Nutt, 2002, p. 69)
The analysis approach is recommended when means and objectives are clear. If this setting is given,
criteria are well defined and alternatives to choose from are unambiguous. This situation is highly desired
since the uncertainty is very low or not given. Hence, using a calculation or computational method can
evaluate the best option with the maximum outcome according to the agreed criteria. An example is the
optimization of logistics routes against pre-defined variables such as travel time, fuel usage, toll
minimization etc.
Judgment is recommended when objectives are clear but the means are not. In general, the collective
judgment is based on an expert’s advice for identification of the best option to reach the goals. This situation
may arise when working on unfamiliar problems or innovative solutions.
Conversely, bargaining is recommended for decisions when the means are clear but the objectives
are not. Different stakeholders negotiate about the goals to determine the direction. The decision is often
reached by an agreement to satisfy all involved stakeholders. This situation may prevail due to shifting
expectations in an existing procedure.
Inspiration is recommended when both means and objectives lack clarity. If this setting is given, it is
suggested that decision-makers network with stakeholders to explore preferences towards the two
dimensions. This situation is characterized with high uncertainty. An example is a public organization such
as a university or a hospital, which has to comply with many external parties.
Analysis Judgment
Bargaining Inspiration
Means
Goals
unknownknown
unknown
known
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Nutt (2002) conducted an empirical study, comprising more than 300 strategic decisions, to
investigate “the value of the prescription in this model” (p. 67). This study reveals that decisions, which
follow the prescriptions, are more successful than those decisions that deviate.
Despite the past and on-going research efforts on the rational decision-making models, one has to
acknowledge its limitations as well. One major pitfall is that in theory it is assumed that humans act totally
rationally. However, “in reality what exists is “bounded rationality”: humans can only be rational to a
certain extent. This is because it is not always possible to define goals, and it is impossible to consider all
possible alternatives and to evaluate all possible consequences” (Shollo, 2013, p. 49). People use heuristics
that simplify and shortcut the decision-making process (Royer & Langley, 2008) leading to sufficient
outcome rather than optimal decisions (Choo, 2005).
2.1.2 Socio-political view
The ongoing process of decision-making, incentive systems and omni-present conflict potential of
organizational actors due to different objectives need to be reflected in a real world scenario (Shapira, 2002).
In addition to the bounded rationality and the intuition, socio-political processes hinder the application of
pure rational approaches. Following Royer and Langley (2008), socio-political processes “are social
interactions between people around organizational issues” (p. 2). Balogun, Pye, and Hodgkinson (2008)
highlight that more attention should be paid on the social environment of the decision-maker. The socio-
political view is more descriptive than normative. For instance, a study conducted by Tetlock (1985) reveals
that people behave differently in the information search and analysis for decision-making due to the
accountability to others. Depending on whether the analyzer knows the goals of his counterpart, he tends to
position his analysis to acceptable outcomes or increases analysis efforts to avoid disagreement.
A decision-maker needs to use information in order to be considered competent, even if the
information is actually not relevant for the ultimate decision. Feldman & March (1981) claim that much of
the information is used after a decision has been made in order to justify it. Although much of the
information gathered has little decision relevance, more information is requested since “it is better from the
decision maker’s point of view to have information that is not needed than not to have information that might
be needed” (p. 174). Hence, the decision-maker tends to request more information. This is also referred as
information incentive. Information is perceived as highly symbolic in order to follow an accepted rational
approach (Feldman & March, 1981; Shollo, 2013). Thus, the rational view and the socio-political view are
interdependent (Tetlock, 1985). Langley (1989) further argues that those views are symbiotic:
“Formal analysis would be less necessary if everybody could execute their decisions themselves, and
nobody had to convince anybody of anything. In fact, one could hypothesize that the more decision
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making power is shared between people who do not quite trust one another, the more formal analysis
will be important.” (p. 609)
A rational approach is perceived as positive and useful whereas socio-political aspects are regarded
as counter-productive (Shollo, 2013). This can be explained by Nutt’s (2002) empirical study of Thompson’s
(1967) contingency framework. Royer and Langley (2008) note that no socio-political processes are
prevalent in the analysis quadrant, “while the use of majority judgment implies social interaction based on
collegiality, bargaining often implies power differentials and conflict, while inspiration implies the effective
use of persuasion by talented leaders” (p. 3). When other approaches are applied although analysis is
recommended, Nutt (2002) observed that the success rate (in terms of adoption, decision value, and
implementation time) decreases. Specifically, when judgment or inspiration is used instead of analysis the
adoption rate dropped since not all options were evaluated equally and sufficiently from a quantitative
perspective. Bargaining increased the implementation time significantly due to cooptation, a socio-political
process to create agreement and commitment among the decision-makers (Nutt, 2002). Hence, when socio-
political processes are applied although a rational approach is recommended, the outcome is worse leading to
the perception of counter-productive effects. However, it can also be noticed that the success rate declines if
analysis is used instead of other recommended socio-political approaches. Using an overly quantitative
approach when lacking “clarity about means and/or objectives would offer few insights into the choice to be
made, and may create misleading ones” (Nutt, 2002, p. 91).
In fact, socio-political processes are necessary even if they are perceived as counter-productive.
Drawing on Grint's (2005) typology of problems, power and authority we can see that when problems are
characterized by a high level of uncertainty (i.e. wicked problems), increased collaboration is needed (i.e.
normative power) and a leadership style by asking questions is required. This implies that collaboration is
needed to reduce uncertainty. Similar to rational approaches based on information, socio-political process
based on collaboration can decrease ambiguity of a decision. Therefore, the next section elaborates the
dynamic interplay between rational and socio-political processes for reducing uncertainty and in particular,
how the decision setting changes due to these two views.
2.1.3 Dynamic decision settings
Since both the rational and socio-political views co-exist, a neutral position should be taken rather than
preferring one over the other (Royer & Langley, 2008). As seen in Nutt’s (2002) study, there were
mismatches in applying the recommended approach correctly. It is difficult to classify decisions into the
contingency model for utilizing rational or socio-political processes as prescribed. Grint (2005) uses three
different problems to showcase that the decision settings vary with the perspectives. He argues that decision-
makers render their “environment suitable for their intended strategies” (p. 1492) and, thus, they are more
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active in shaping their context than conventional contingency theories have planned. The decision is
“situated” rather than situational.
Looking over a time-horizon, the decision setting can also change. Respectively, the decision can be
situated differently (Gorry & Scott Morton, 1971). The degree of uncertainty evolves and may be reduced
by rational and socio-political processes (Stinchcombe, 1990). Since the understanding of a problem
improves over time, the separating line between unstructured and structured decisions moves down towards a
higher structuredness. The decision becomes more structured and can be routinized and automated. For
instance, inventory control used to be an unstructured problem, which requires high involvement of middle
managers. Today with advancement of information technology and experience in the matter, the decision is
structured and automated. Moreover, unstructured problems can be broken down into sub-problems, which
might have a structured nature (Gorry & Scott Morton, 1971).
Similarly, the same effect can be observed in Thompson’s contingency model. According to Royer
and Langley (2008) procedural rationality reduces the ambiguity about means while socio-political processes
reduce the ambiguity about objectives. Illustratively, the change of situated decisions shall be presented here.
Starting from the inspirational quadrant (high uncertainty in both dimensions) different paths are possible to
approximate the analysis quadrant (low uncertainty in both dimensions).
Figure 4: Evolution of contingencies over time through socio‐political processes and procedural
rationality (Royer & Langley, 2008, p. 257)
Analysis Judgment
Bargaining Inspiration
Means
Goals
unknownknown
unknown
known
rationality
socio-political
processes A
B
C
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Rationality requires certainty about objectives to be efficient (Simon, 1957). Consequently, it is
logical to start decreasing the uncertainty about goals through socio-political processes. Path A enables
complementary application of rationality and socio-political processes. In an incremental reinforcement
process the decision set is broken down and the uncertainty in the two dimensions decreases “where
reduction of uncertainty about goals helps reduce uncertainties about means that in turn favors additional
reduction of uncertainties about goals” (Royer & Langley, 2008, p. 5). Commitment to an agreed objective
favours the application of certain approaches (Nutt, 2002). However, people do not always follow the
prescriptive path. Time critical decision may lead to solutions, which are quick and dirty rather than well
validated (Blount, Waller, Leroy, Starbuck, & Farjoun, 2005). Path B (escalation of commitment) focuses on
the uncertainty of goals. Early agreement and commitment to an objective neglects the effective rational
analysis of the means. The possible approaches are not reflected and conflicting information is ignored. On
the other hand, path C (paralysis) focuses on the uncertainty about means. Highly logical approaches can
lead to conflicting analysis and competing proposals since the ultimate goal is unclear (Royer & Langley,
2008). Routines can help to follow a more efficient pattern (path A). For instance, the framework by
Mintzberg (1976) gives a proposal for the routines. However, routines may not be performed identically
every single time when used (Royer & Langley, 2008). Similar to using rational models, the given setting is
socially constructed and, hence, the routines might not be executed as designed.
In summary, this section elaborates different organizational decision-making models from a rational
and a socio-political view as well as their dynamic interplay. It reveals on how decisions are being made in
different settings as asked in the first sub-question. Foremost, the distinction between the dimension of
structuredness by Simon (1977), the dimension of planning level by Anthony (1965), respectively the
combination of these two in the framework by Gorry and Scott Morton (1971), and Thompson’s (1967)
contingency framework have been investigated. Rational approaches using these models are favourable as
they reduce uncertainty in a legitimate way. Information analysis and use are key activities. However, there
are limits due to bounded rationality and the symbolic character of information. Socio-political processes are
necessary to reduce ambiguity about goals although often perceived as counter-productive since there might
be conflicts and time-consuming activities involved. The right application of rationality and socio-political
processes is difficult since decision-making is situated. The dynamic interplay of those processes highlights
that decision-making evolves over time and routines can help to adopt the decision-maker’s behaviour to
follow more effective and efficient paths. Still, entire success cannot be granted due to socially constructed
environments.
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2.2 Research on Business Intelligence
Davenport (2010) acknowledges that “organizations must have a strong focus on decisions and their
linkage to information. Businesses need to address how decisions are made and executed, how they can be
improved, and how information is used to support them” (p. 2).
For effective decision-making information is needed. Particularly, in the rational view of decision-
making information use is required to prove a legitimate way of working. Over many years Business
Intelligence (BI) has been investigated by practitioners as well as researchers. It is claimed that BI improves
decision making by providing the right information to the right person at the time. Surveys by Gartner, an IT
market research company, highlight that in 2009, 2012 and 2013, BI was the top priority for CIOs (Gartner,
2013a) and remains a top focus through 2017 (Gartner, 2013b). The market for BI software and services has
increased and it is forecasted that it continues to grow over the next years (Vesset, McDonough, Schubmehl,
& Wardley, 2013). Its newest development into big data is the new battlefield for competitive advantage
(Manyika et al., 2011). Similarly, a trend of more BI related publications can be noticed in the last decade
(Chen et al., 2012). However, there are many different perceptions of what BI means. In order to clarify this
blurry perception, this chapter elaborates on BI from a technical, process, and a decision-centric view. The
distinction between technology and process view follows the literature review by Shollo (2013). Despite the
manifold publications in BI several literature reviews call for more research focusing on BI for its main
purpose, namely supporting decision-making (Kowalczyk et al., 2013; Shollo & Kautz, 2010). Hence, the
decision-centric view on BI unveils a gap between the BI and the decision-making process. Therefore, this
chapter focuses on the decision-centric view and, specifically, it elaborates how the BI output is used in
different decision settings. This section addresses the second research sub-question on how BI supports
decision-making.
2.2.1 Technology view of BI
BI technologies are comprised of various parts such as tools, packages, platforms, systems and
applications (Petrini & Pozzebon, 2009), which (1) gather and store, (2) process and analyze, and (3) present
data (Shollo, 2013). BI technologies have their roots in the management information systems (MIS) from the
early 60s and have matured into BI. In fact, they still continue to emerge within a new theme called big data.
All technologies strive to provide insights on data for a better decision-making.
In the mid-1960s MIS was the first attempt to support managers in their decision-making using IT
(Scott Morton, 1983). While MIS is limited mainly to structured data, its development into decision support
systems (DSS) and subsequently executive information systems (EIS) started to handle unstructured data as
well. Structured data can be gathered and stored easily in relational or flat files since their structure is pre-
defined, whereas unstructured data such as emails or video files is less structured. Although these systems
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became more and more user-friendly due to their more interactive nature and search functionality, a major
constraint was the manual effort and the specific knowledge to transform and load data (Petrini & Pozzebon,
2009). The processed data is mainly presented to a few top-level managers. With the rise of the data
warehousing (DWH) technology, extraction, transformation, and loading (ETL) tools and on-line analytical
processing (OLAP), the use of DSS and EIS became more popular (Shollo, 2013). A DWH gathers and
stores data (mainly structured) into a single repository systematically. ETL tools processes data by
integrating and transforming it. OLAP analyzes data through various operations such as aggregations.
Moreover, user-friendlier displays of the data have been developed such as charts and tables in online
dashboards and spreadsheets. The technological advances have led to the term BI in the last decade.
The table below from Shollo (2013) highlights the purpose and illustrative references for each
development era.
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Figure 5: A timeline of the evolution of systems supporting decision-making (Shollo, 2013, p. 31)
Due to the extensive BI research in the last few years, Cheng et. al (2012) noticed different evolution
steps in BI and analytics (BI&A) during their extensive literature review. One major advancement is the
improved ability to handle unstructured data. BI&A 1.0 technology used to mainly handle structured data as
outlined above. Due to the rise of the Internet and user-generated content though web 2.0 applications and
social networks more advanced analytics and data mining techniques are needed for handling the
unstructured data in the BI&A 2.0 wave. Further in the BI&A 3.0 wave, Cheng et. al (2012) address the
emergence of ubiquitous sensor data such as from mobile devices. The table below summarizes the key
characteristics of the BI&A evolution according to Cheng et. al (2012).
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Figure 6: BI&A evolution: Key Characteristics and Capabilities (Chen et al., 2012, p. 1169)
In line with the BI&A 3.0 wave, a new theme is emerging: big data. Its main characteristics are the three
Vs of data: volume, velocity and variety (Brynjolfsson & McAfee, 2012; Laney, 2001). It is claimed that
traditional technology is no longer capable of handling the high volume of data, its velocity (speed of change
of the data) and its variety (structured and unstructured data). The integration of more different data shall
give more insights on patterns and predict future behaviour next to understanding the past. To avoid
confusion ongoing in this paper, the term BI is used representatively for all the outlined technologies and
development milestones.
Despite the evolution of different sets of technology, it is relevant to note that the technology view
supports decision-making mainly by gathering, storing, processing, analyzing, and presenting the data with
IT tools. However, it is still left unclear how decision-making is ultimately affected by using these tools. The
usage of the data has not been investigated by this view yet. Rather, it is assumed that by deploying these
technologies, the decision-making process is improved (Shollo, 2013). The term “technological determinism”
captures that perspective (Kling, 2000).
2.2.2 Process view of BI
The process view of BI focuses on decision-making with its precedent information-related activities.
Thus, this view is also referred as the managerial approach of BI (Petrini & Pozzebon, 2009). The BI process
can be divided into the phases of 1) gathering and storing of information, 2) information processing and
analysis, and ultimately 3) the use of information for decision-making (Shollo, 2013). This is similar to the
Chen et al./Introduction: Business Intelligence Research
Table 1. BI&A Evolution: Key Characteristics and Capabilities
Key CharacteristicsGartner BI Platforms Core
Capabilities Gartner Hype CycleBI&A 1.0 DBMS-based, structured content
• RDBMS & data warehousing• ETL & OLAP• Dashboards & scorecards• Data mining & statistical analysis
• Ad hoc query & search-based BI• Reporting, dashboards & scorecards• OLAP• Interactive visualization• Predictive modeling & data mining
• Column-based DBMS• In-memory DBMS• Real-time decision• Data mining workbenches
BI&A 2.0 Web-based, unstructured content• Information retrieval and extraction• Opinion mining• Question answering• Web analytics and web
intelligence• Social media analytics• Social network analysis• Spatial-temporal analysis
• Information semanticservices
• Natural language questionanswering
• Content & text analytics
BI&A 3.0 Mobile and sensor-based content• Location-aware analysis• Person-centered analysis• Context-relevant analysis• Mobile visualization & HCI
• Mobile BI
well-being, and (5) security and public safety. By carefullyanalyzing the application and data characteristics, researchersand practitioners can then adopt or develop the appropriateanalytical techniques to derive the intended impact. In addi-tion to technical system implementation, significant businessor domain knowledge as well as effective communicationskills are needed for the successful completion of such BI&Aprojects. IS departments thus face unique opportunities andchallenges in developing integrated BI&A research andeducation programs for the new generation of data/analytics-savvy and business-relevant students and professionals (Chen2011a).
E-Commerce and Market Intelligence
The excitement surrounding BI&A and Big Data has arguablybeen generated primarily from the web and e-commercecommunities. Significant market transformation has beenaccomplished by leading e-commerce vendors such Amazonand eBay through their innovative and highly scalable e-commerce platforms and product recommender systems.Major Internet firms such as Google, Amazon, and Facebookcontinue to lead the development of web analytics, cloudcomputing, and social media platforms. The emergence ofcustomer-generated Web 2.0 content on various forums,newsgroups, social media platforms, and crowd-sourcingsystems offers another opportunity for researchers and prac-
titioners to “listen” to the voice of the market from a vastnumber of business constituents that includes customers, em-ployees, investors, and the media (Doan et al. 2011; O’Rielly2005). Unlike traditional transaction records collected fromvarious legacy systems of the 1980s, the data that e-commercesystems collect from the web are less structured and oftencontain rich customer opinion and behavioral information.
For social media analytics of customer opinions, text analysisand sentiment analysis techniques are frequently adopted(Pang and Lee 2008). Various analytical techniques have alsobeen developed for product recommender systems, such asassociation rule mining, database segmentation and clustering,anomaly detection, and graph mining (Adomavicius andTuzhilin 2005). Long-tail marketing accomplished byreaching the millions of niche markets at the shallow end ofthe product bitstream has become possible via highly targetedsearches and personalized recommendations (Anderson2004).
The Netfix Prize competition2 for the best collaborativefiltering algorithm to predict user movie ratings helped gener-ate significant academic and industry interest in recommendersystems development and resulted in awarding the grand prizeof $1 million to the Bellkor’s Pragmatic Chaos team, which
2Netflix Prize (http://www.netflixprize.com//community/viewtopic.php?id=1537; accessed July 9, 2012).
MIS Quarterly Vol. 36 No. 4/December 2012 1169
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phases like Mintzberg et al. (1976) defines for decision-making (identification, development, and selection).
These phases also resemble the activities, which the technology view supports. Therefore, gathering and
storing of data involves obtaining the required data from various sources. Internal data produced by the
organization and external data about customers, competitors, markets, products in the market, environment,
technologies, acquisitions, alliances, and suppliers (Negash 2004) might be relevant for decision-making.
This data can be structured and unstructured. Subsequently, the acquired information is processed and
analyzed. A lot of research focuses on goal-oriented and metric-driven methods such as Balanced Scorecard
and Corporate Performance Management (Petrini & Pozzebon, 2009; Shollo, 2013). Finally, the information
is used for making the decision. Due to the technological advances, the scope of BI reaches more people than
the top-level manager. The difference between the presentation phase in the technology view and the use of
information in the process view is the human interpretation of the presented information, which requires
individual knowledge (Choudhury & Sampler, 1997). Feldman & March (1981) ascertain that information
gathering and use process are split and performed by different people leading to the information incentive.
Although organizations possess more information than they use, they look for more information. However,
most studies about BI neglects the actual information use and rather focus on the previous process steps
(Arnott & Pervan, 2008; Tu & Chang, 2007). Since this is the case and this paper aims to look deeper into
the BI support for acting on the supplied information, specifically within different settings, the next section
elaborates on the available research.
2.2.3 BI from a decision-centric view
Neither the technology nor the process view gives clear guidance on how information is being used and
how it affects decision-making. Rather, the two views show support for the precedent activities before the
decision is actually made. From the rational view of decision-making it can be noticed that these activities
are necessary for depicting a sound approach. However, investigating BI from a decision-centric view can
reveal the final and relevant effect of BI information for taking action. The decision-centric view puts the
decision in focus and all activities related to the decision are placed around it. In contrast to the technology
view, this view does not assume that deploying more advanced technology reduces the uncertainty. Rational
and socio-political processes as well as their dynamic interplay, as outlined in the previous chapter, dominate
and determine the decision-making process.
Gorry and Scott Morton (1971) combined the two dimensions by Simon (1957) and Anthony (1965)
into their own MIS framework for classifying decision problems as shown in Figure 2:. The implication by
classifying decisions into those dimensions is to identify information characteristics as well as skill
requirements for BI systems (DSS in their paper) for different decision settings. Specifically, they highlight
different information characteristics depending on the dimension of the planning type (operational control to
strategic planning). One reason why many BI efforts fail is that they pursue a “total systems approach”
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which tries to handle all areas. Instead, it is favourable to have a fundamental shift in approach between
operational and strategic situations (Gorry & Scott Morton, 1971).
Looking more closely at the dimension of structuredness of problems in their framework, a less
normative approach can be identified. For structured problems, it is possible to create a set of programmed
solutions. Unstructured problems are less a technical issue. Critical for this problem type is the development
of a model of the organization and its environment in order to understand and predict the future environment
(Gorry & Scott Morton, 1971). The supplied information is sufficient but the models for interpreting and
acting on these are simple and limited. BI can help in an educative manner by “assisting the evolution of the
manager’s decision making ability through increasing his understanding of the environment” (Gorry & Scott
Morton, 1971, p. 31). Further, they prefer descriptive models to normative models in order to ensure an
objective analysis of the situation before designing an information system support. Hence, for different
decision areas different approaches and systems to gather, analyze, and use information are required.
Similarly, Clark (2010) investigates BI in three complementary practices. He calls the first practice
decision development and it is at the operational level. Decision development happens when decisions are
well-structured and it is possible to implement (BI) “tools and methods for gathering information, evaluating
alternatives, and making the decision” (Clark, 2010, p. 12). The capability crafting practice focuses on
interaction in less structured decisions and is at the tactical level. In a more descriptive way, it explores
capabilities as well as how humans and systems interact. Finally, the practice “issue illumination can be seen
as IT support for the unstructured decision-making and uncertainty management that often characterizes the
strategy-making process” (Clark, 2010, p. 26). Here, organizational systems are so complex and dynamic
that goals among different stakeholders are unclear and need to be aligned (socio-political process). IT can
only facilitate the enlightening process. Clark’s (2010) research indicates the role of BI and considers socio-
political processes in addition to rationality. However, it still leaves unanswered how the information or BI
output is actually used in the different settings.
Recent research by Shollo (2013) investigates the use of the BI output in an interpretive study of the
strategic process of IT project prioritization in an international financial institution. In this context the BI
output is the outcome of the cost-benefit analysis of each project. However, the process “is characterized by
high complexity, uncertainty, ambiguity and it is inherently political” (p. 118). Therefore, her findings show
that the BI output is used in multiple ways and does not always inform the decision-maker. Instead, different
tactics were applied according to the context due to political and symbolic use of the information. The tactics
are:
• Interpreting
The BI output is interpreted by the personal knowledge and expertise of the user in order to understand BI
output (liaison forces). It is possible that other devices are used to interpret ambiguous BI output.
• Reframing
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The BI output is reframed in order to make it more persuasive.
• Supplementing
The BI output is supplemented with another device(s) in order to legitimize and complement a weak BI
output as well as to influence other decision-maker for a collective judgment (complementary force).
• Substituting
The BI output is substituted by another device(s) in order to delegitimize the BI output and to shift attention
to the substitute (competing force).
Karpik (2010) acknowledges that an “incommensurability problem” cannot be valued only quantitatively.
He argues that singularities (goods that cannot be measured by standard methods) such as music or movies
require ‘judgmental devices’ to provide consumers additional or alternative knowledge to make a choice.
The market competition happens via qualities such as labels, brands and guides rather than via the price.
Drawing on these findings, Shollo (2013) similarly identified devices, which are “deployed by decision-
makers in order to achieve something, such as to reduce uncertainty or to convince others” (p. 209). Thus,
devices do not only act as source of knowledge but also as an approach to shape the judgment. The identified
devices are:
• Networks
A network consists of social contacts. As a device it is used to obtain additional information in order to better
understand the situation and to shape collective judgment. It is based on informal communication.
• Sponsors
A sponsor is an authority who acts as a guarantee and can exert influence due to his formal position. As a
device he is used to support a specific position.
• Expertise
An expert is an authority who can exert influence due to his specific knowledge. As a device he or she is
used for supporting a position by providing a trustful evaluation and understanding.
• Labels
A label is a name or category to specify attributes, e.g. the project status as ongoing. As a device it is used to
quickly describe the given issue.
• Presentation Activity
A presentation of the issue is held to inform other decision-makers. As a device it is used to better
understand the issue and shape the collective judgment.
In contrast to the technical view, these findings reveal that BI has a facilitating role (Shollo &
Galliers, 2013) rather than improving decisions only by using information rationally. Additional activities are
necessary shaping the collective judgment with different tactics and devices (Shollo & Constantiou, 2013).
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As Gänswein (2011) proposes, “impersonal sources are not capable of providing qualitative information
while on the other hand this kind of information is particularly important for diagnosing strategic decision
problems” (p. 221). This is qualitative information about preferences and objectives, which may not be
objectively assessable using technologies.
Representatively, this issue of incommensurability can be encountered in a project prioritization
within an organization, which is also the investigated decision-making process in this paper’s research study.
The project prioritization was studied in the context of a company’s product development process. Product
development is regarded as a strategic direction within marketing research (Marks & Yardley, 2004). One
major issue in product development is resource scarcity, which leads to allocation decisions on how to
prioritize projects (Kavadias & Loch, 2003). Many studies focus on financial models as a solution such as
the net present value or other payback methods in a sense of a return of investment (Cook & Green, 2000;
Cooper & Edgett, 2003). By applying a pure analytical and rational method a prioritization is rather simple
due to the numerical ranking - the project with the highest score is implemented first. Although this very
number driven methodology is preferred, in reality there are qualitative aspects to be considered. Particularly,
the product development has to follow the overall business strategy (Cooper & Edgett, 1997). Similar to
Karpik’s (2001) findings, other qualities, such as brands and labels for singularities, are effective and
judgment devices are necessary to gain a full picture.
In summary, BI experienced many different development steps. Yet from a technical perspective, it
can hold the general claim to support decision-making only partially. As seen in Shollo’s (2013) study of
strategic decisions, BI is not the only input for making decisions. The decision-making process is more
complex to be computationally fulfilled. The technological view presented the different technologies and
development steps of BI to support decision-making but mainly during the information preparation. From a
process view it can be acknowledged that only a few studies deal with the actual decision-making using BI
information. Finally, the decision-centric view highlights the few theoretical contributions how BI output as
a product supports decision-making in the second sub-question. In particular, it raises the concern of the
incommensurability of problems and the use of the BI output leading to different tactics and devices.
2.3 Theory reflection and synthesis
The research about organizational decision-making reveals various settings in which decisions are
situated. As Nutt (2002) identified, organizations are more successful when applying different approaches,
which are suitable for their respective setting. Rationality as well as socio-political processes are in place.
Information analysis and use are regarded as necessary for making decisions. However, when looking at BI,
which supports on these information activities, it is obvious that there is a gap between the findings in
decision-making and the BI research stream. In particular, there is a lack of research about BI information
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usage in different decision settings. In order to answer the research question, this paper assumes that looking
at BI from a decision-centric view uncovers new findings, which can be used for a better support of BI in
decision-making depending on the decision setting. Other researchers also raise this very issue and call for
more studies of BI focusing on decision-making (Clark, 2010; Kowalczyk et al., 2013). Since the separate
exploration of the two research streams of decision-making and BI was not capable of answering this paper’s
conundrum, a synthesis to find further indications on how BI is used in different settings is required. The
synthesis of the two research streams is achieved through systematic review. “At its simplest … the process
of systematic review involves the juxtaposition of findings from multiple studies with some analysis of
similarities and/or differences of findings across studies” (Popay & Mallinson, 2010, p. 291). However, a
simple juxtaposition can lead to challenges such as bias and thus lead to wrong conclusions (Petticrew &
Roberts, 2006). Therefore, it is crucial to be as transparent and consistent as possible. According to Sharland
(2012), the systematic review should be led by a specific research question to overcome these challenges. In
order to do so and to contribute to both research streams, this section elaborates possible synthesis, which is
used as the theoretical foundation for the subsequent research study. The following synthesis is based on the
aforementioned literature reviews. First, it is necessary to comprehend what the specific decision setting
characteristics are and how they are related in order to analyze decision-making appropriately. Second, for
each setting it is investigated how and which BI tactic and devices are applied.
Since the decision and the various settings are in focus, this paper draws on the findings from
Thompson (1967) and Gorry and Scott Morton (1971). These frameworks are chosen not only because of
their popularity but also because of their clear structure to categorize decisions, thus making them highly
applicable. Moreover, several parallels can be drawn to make a conjunction for elaboration. Thompson’s
(1967) contingency framework tackles the major issue of uncertainty by breaking it down into two
dimensions: clarity of objectives and clear means for producing the result. In an analysis setting the approach
recommends to apply (rational) analysis of the problem. The decision phases (identification, development,
selection) are structured and the selection follows strict pre-defined criteria. Therefore, when both objectives
and means are clear it can be assumed that the decision tends to be structured and, hence, is driven by
rationality rather than socio-political process. On the other hand, when both objectives and means lack clarity
it can be assumed that the decision tends to be unstructured. In an inspirational approach the decision phases
are unstructured and socio-political processes are necessary to reduce the uncertainty in each decision
making phase. Accordingly, in a judgmental or bargaining setting the problem tends to be semi-structured.
The following illustration shows the combination of the two frameworks:
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Figure 7: Combination of Thompson’s (1967) contingency framework and Gorry and Scott Morton’s
(1971) MIS framework (structuredness dimension)
In addition the structuredness, the planning level dimension of Gorry and Scott Morton’s (1971) MIS
framework can be compared with Thompson’s (1967) contingency framework. Accordingly, since
operational decisions tend to be structured, it can be concluded that operational decisions tend to be situated
in the analysis quadrant. Correspondingly, strategic decisions tend to be situated in the inspirational quadrant
since they tend to be unstructured. Although the combination of the two frameworks appears to be possible,
it should be treated with caution. In fact, it is important to note that decisions on the planning level
dimension can be situated in all quadrants as shown in Nutt’s (2002) study of strategic decisions. The
combination is rather guidance for further classification of decisions, which allows finding more relevant
characteristics for the BI usage. This conjoint framework functions as the theoretical foundation focusing on
decision-making. The next sub-sections give an overview of the additional decision setting characteristics for
each quadrant in Thompson’s (1967) contingency framework.
Analysis
In the analysis setting the objective and the means are clear. Hence, rationality is high as criteria are
well defined against which the achievement is measured and the approach is determined. In addition, socio-
political processes take place on a low level as less or no negotiation and alignment is needed. This leads to
Analysis Judgment
Bargaining Inspiration
Means
Goals
unknownknown
unknown
known
structured
unstructured
semi-structured
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the fact that most of the decisions situated in this quadrant are structured. Furthermore, operational decisions
tend to be analysis decisions due to the structured problem.
Rationality high
Socio-political processes low
Structuredness structured
Planning operational (tendency)
Table 1: Decision-making characteristics in an analysis setting
Judgment
In a judgment setting the objective is clear while the means lack clarity. Hence, rationality is middle as
ambiguity, on how to achieve the defined objective, requires judgment. In addition, socio-political processes
take place on a middle level as more collaboration is required and an expert judgment is consulted. This
leads to the fact that most of the decisions, which are situated in this quadrant, are semi-structured.
Furthermore, tactical decisions tend to be judgment decision due to the semi-structured problem.
Rationality middle
Socio-political processes middle
Structuredness semi-structured
Planning tactical (tendency)
Table 2: Decision-making characteristics in a judgment setting
Bargaining
In the bargaining setting the means are clear while the objective lacks clarity. Hence, rationality is
middle as the success measurement of the different possible approaches is ambiguous leading to assumptions
and guesses. In addition, socio-political processes take place on a middle to high level, as more negotiation
and alignment are required. This leads to the fact that most of the decisions, which are situated in this
quadrant, are semi-structured. Furthermore, tactical decisions tend to be bargaining decisions due to the
semi-structured problem.
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Rationality middle
Socio-political processes middle - high
Structuredness semi-structured
Planning tactical (tendency)
Table 3: Decision-making characteristics in a bargaining setting
Inspiration
In the inspirational setting both the objective and the means are ambiguous. Hence, rationality is limited
as it is unclear what and how to do it. In addition, socio-political processes take place on a high level as more
collaboration is required and various preferences from the stakeholders have to be identified. This leads to
the fact that most of the decisions, which are situated in this quadrant, are unstructured. Furthermore,
strategic decisions tend to be inspirational decisions due to the unstructured problem.
Rationality low - middle
Socio-political processes high
Structuredness unstructured
Planning strategic (tendency)
Table 4: Decision-making characteristics in an inspirational setting
The outlined systematic review specifies the different decision settings. It is a crucial step for the
research study to have guidance on how to classify the decisions properly by looking at various
characteristics. Building upon this foundation, BI information is used. Shollo (2013) findings can be used as
an indication of the various usage of the BI output. Her study concentrates on strategic decisions that can be
placed as mainly unstructured and less analytical. The various tactics and devices show the connection of
socio-political processes with a rational approach using information from BI systems. Particularly, the
devices network, expert and sponsors refer to socio-political processes. However, it must be noted that
Shollo’s (2013) findings are based on a single organization and thus might not be entirely transferable or
generalizable. Furthermore, there is no study at hand indicating the actual BI usage for operational or tactical
decisions. In order to understand how BI can support in the different decision settings and, hence, answer
this paper’s research question, a research study is conducted.
3 RESEARCH STUDY
As introduced above, the systematic review faces several challenges such as bias and limited
transferability of literature findings. In order to validate and expand the synthesis of the BI usage in the
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different decision setting a research study was conducted. The research study investigates decisions made
within an online advertising company. With the possibility to compare findings from Shollo (2013) a similar
organizational activity was observed, namely the company’s product development process. As Gorry and
Scott Morton (1971) advise, a descriptive approach is recommended rather than a normative one when
dealing with unstructured problems. Thus, in addition to the prior-research findings the study contains an
exploratory part. The following sub-sections describe the design of the study as well as the research setting
in detail. Particularly, the reader understands the several influences impacting the decisions and the use of BI
information during the decision-making process. The decisions are explored and classified in the different
dimensions of the decision settings. The subsequent chapter reveals the findings of the study.
3.1 Study Design
In order to design a research study Crotty (1998) suggests following a four elements process, in which
the different processes inform one another: (1) epistemology, (2) theoretical perspective, (3) methodology,
and (4) methods.
(1) The epistemology refers to the knowledge, the understanding on “how we know what we know”
(Crotty, 1998, p. 8). This study takes the epistemology from a constructivism point of view. This perspective
can be placed in between the objectivism and subjectivism views. While objectivism assumes that there is an
objective truth, subjectivism claims an individual, temporary and non-objective meaning. In between, the
constructivism view assumes that all social phenomena are constructed individually but their meanings can
be shared and are inter-subjective (Crotty 1998). This view is adopted here because there is already some
widespread knowledge available on which this paper draws on such as Thompson’s (1967) contingency
model. However, the new concept of looking at BI in various decision settings is scarcely explored, which
requires further theory building research. For instance, leaning on Shollo’s (2013) findings, first insights are
given. Therefore, instead of favoring one of the radical ends, this paper places itself in the middle and argues
that the research study may reveal new findings, which enjoys the shared opinion among others due to its
reliance on existing concepts. Furthermore, the generation and validation of the findings take place in
collaboration with the study participants and is not solely dependent on the author’s observation.
(2) The theoretical perspective, placed within the epistemology, provides the reasoning for the logic and
criteria for the methodology (Crotty, 1998). Here, the interpretive approach adopts the same point of view as
constructivism and assumes that the meaning is constructed. While the other end, a positivist approach, aims
for general propositions, an interpretive approach identifies propositions, which are specific to one case (Lin,
1998). Orlikowski and Baroudi (1991) argue that “social processes can be usefully studied with an
interpretive perspective, which is explicitly designed to capture complex, dynamic, social phenomena that
are both context and time dependent” (p. 18). Further, Given (2008) states that the “interpretivist
approaches focus on the meanings attributed to events, places, behaviors and interactions, people, and
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artifacts” (Given, 2008, p. 517). This approach suits our purpose of understanding the use of BI in different
decision settings since making sense of the interaction between people and technology is the body of
research here. Additionally, only one company is studied and, hence, the findings are only transferrable to a
limited extent.
(3) The next element in the research design is the methodology, which describes the plan of action and
guides the methods to be applied (Crotty, 1998). Foremost, the object to be studied is the usage of BI in
different settings for decision-making while putting decision-making in the foreground. As outlined in the
research on decision-making, due to the symbolic value of information and the socio-political processes it is
crucial to design the study focusing on the social aspects of the interplay among different organizational
actors and between these actors and BI systems. Driven by the research question, it is investigated how
people use BI in different decision-settings. Hence, a qualitative research is appropriate because the way of
information usage needs to be obtained through narrative instances rather than numerical values (Elliott &
Timulak, 2005). This approach is common in social sciences (Flick, 2007). The goal of a qualitative study is
to identify underlying concepts (Hyde, 2000), interpret and make sense of a phenomenon (Flick, 2007). In
this sense, Elliott and Timulak (2005) place an “emphasis on understanding phenomena in their own right;
open, exploratory research questions” (p. 147) are distinctive features of qualitative studies. Due to the
novelty in this research space it is rather advisable to aim for comprehension of the phenomenon, rather than
to quantify the assumptions statistically. Hence, a qualitative study is appropriate for this paper’s purpose.
The theory synthesis from the systematic review derives assumptions in a deductive manner but it contains
some gaps in the theoretical foundation. Especially in the operational and tactical area, there is little evidence
of BI usage. Moreover, the transferability of Shollo’s (2013) findings in the strategic decision setting has to
be validated since this was not the focus in her studies. Therefore, a qualitative study can fill these gaps and
“go beyond” the current available knowledge.
According to Elliott and Timulak (2005), “bias is an unavoidable part of the process of coming to know
something” (p. 148). They argue that this is acceptable as the research is guided rather than leading to
uninformed assumptions. Hence, the following research activities of collecting the data, analyzing and
discussing it are directed to a certain extent by the acquired knowledge from the literature review. However,
at the same time, the research study leaves space for unexpected meanings and findings during that process.
In order to better understand the research study, the empirical setting is explained in the next section
followed by (4) the methods for collecting and analyzing the data of the case at hand.
3.2 Study Environment - plista
plista is an online advertising company founded in 2008. It is headquartered in Berlin, Germany and
currently employs around 120 people. Its business model is based on connecting online publishers with
advertisers through a recommendation platform using advanced data mining technologies. A publisher is a
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website owner - often of newspapers, blogs, and journals - who offers advertisement space on which the
advertiser can place a promotion. plista’s product range includes several online advertisement formats such
as a image-text combination and video ads, which are displayed in a recommendation box below a
publisher’s article. plista is the leader in the German speaking market and is further active in Spain, Croatia,
Slovenia and the Netherlands with plans to expand into more countries. In 2014 plista became a part of the
WPP Group, a worldwide marketing and advertising service provider with around 179,000 employees.
plista is organized in divisions with a connecting specialist team named “Product Management” (PM)
as shown in the dotted box below.
Figure 8: plista’s organizational structure
Sales refer to the business with the advertisers and cooperations refer to the business with the publishers.
The sales and IT department comprise the majority of the employees. The key to their success is the
recommendation platform, which provides personalized adverts to the user. Therefore, the product
development – internal tools as well as external advertising formats - was selected for the research study.
Several types of decisions could be observed during that process. Foremost, the prioritization of the
development engagements was investigated but adjacent relevant areas such as the IT infrastructure were
CEO
Sales
Campaign Management
Mobile
Sales Advertiser
Account Management
Cooperations Marketing IT
Data Engineering
Platforms
Services
Operations
Legal
HR
Finance
Product management
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also explored. In order to give the reader a better understanding, the product development and project
prioritization process at plista is explained in the following.
Product development and prioritization process at plista
Various parties participate in the product development process. Since plista’s business model is entirely
digital, products refer to all kind of digital artefacts, which are sold externally (e.g. ad formats) and which are
used internally for daily business (e.g. self programmed applications). It is the IT department, which
implements internal as well as external products. However, new requirements for these products are
requested by the business functions such as sales and cooperations, which are also referred to as product
owners since they are responsible for specific topics in their area of expertise. In order to coordinate the
requirements the PM team, as a staff function, gathers and consolidates the input from several product
owners. Having the overview and the constant exchange with all departments they undertake the
prioritization of the projects and tasks in collaboration with their counterparts. In weekly meetings (“jour
fixe”, JF) with each department they keep track of current status and pain points. All requirements or bugs
are captured in a system called Jira.
Jira is used within the entire organization. Every employee can create new tickets and assign or get
assigned to tickets. A ticket includes information on the issue such as its type (bug, new feature, task), a
description, estimated time, logged time, remaining time, criticality, current status, reporter, assignee, history
and comments. The tickets can be cross-referenced to other tickets, e.g. when a ticket blocks another ticket
due to the task dependency. Furthermore, tickets belong to a project space, e.g. a bug within a system is
described in a ticket, which belongs to the project space of the affected system. In addition to the ticketing
function, it is possible to perform analysis of the tickets. The results of the analysis are presented in reports
such as an overview of all tickets within a project by assignee and, hence, it shows the current and future
resource availability as well as the past utilization (logged time). Therefore, Jira can be regarded as a BI
system as it is capable of assisting with the phases of data gathering, analysis and presentation as outlined in
chapter 2.2.1.
There is a formal process for prioritizing projects captured as “epics” in Jira. Every epic contains a
priority score, which is computed as the division of the business value by the resource efforts. The business
value is the monthly added monetary value when deploying the complete epic and is determined by the
business function. The denominator is the effort spent in days by the IT for developing the requirements. The
priority score functions as the ultimate (rational) value for prioritizing the epics. As an example the following
graphic illustrates an epic description with the priority score and other information.
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Figure 9: Example of an epic in Jira
An epic is broken down into many tickets, which specify each requirement. From an IT perspective the
major constraints to perform the tasks is the resource availability and the resource skills. Embedded in the
agile software development method “Scrum”, every two weeks there is a “sprint”, which is the duration of a
development unit. For each sprint the short-term resource availability is known and can be planned as well as
allocated. Within a sprint one full iteration of analysis, design, implementation, test, deployment, and review
is performed. The purpose is to release new requirements and to fix bugs. Therefore, it is relevant for the
case study to investigate the prioritization made for the tickets in each sprint including the preceding and
following activities. This allows comprehending every type of decision in each decision phase in the context
of the product development. The following illustration shows an example of prioritized tickets within a sprint.
- 29 -
Figure 10: Planned tickets for a two weeks sprint
The planning of a sprint happens in two steps: First, the product managers (PM) prioritize a backlog of
all outstanding tickets. Second, the IT Heads, both Platforms and Data Engineering (DE), transfer the tickets
from the backlog into the sprint depending on the resource availability for this sprint’s time frame. Usually
the resources are allocated to half of their capacity leaving space for short-term flexibility. Finally, at the end
of each sprint the development team discusses their findings from the sprint in retrospect and plan the
upcoming sprint. In case some tickets are not finished from the last sprints, then they are transferred to the
next sprint automatically except if the product managers decide to de-prioritize the ticket (Interview with
Product Management, 2014).
3.3 Data Collection and Preparation
(4) There are various methods to obtain qualitative data for the research study. For this case two options
are followed. First, semi-structured interviews are common, in which the participants are asked open-ended
questions without predetermining the answer (Elliott & Timulak, 2005). In contrast to unstructured
interviews, semi-structured interviews follow some pre-determined topics to guide the conversation. The
main advantage of this method is the possibility to validate the researcher’s understanding and assumption as
well as to reveal unseen findings (Given, 2008). This often takes place in a face-to-face meeting between the
interviewer and interviewee. Second, a more natural environment is the participant observation. The
researcher as an observer collects data during the participant’s daily work. The main goal is to understand the
research topic through the everyday activities and performed meaning by the individuals (Given, 2008).
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While structured observation focuses on specific predefined rules, unstructured observation is holistic and
unfocused so that as much as possible is captured (Given, 2008).
Both methods do not only generate the qualitative data but also reveal individual perceptions of the
study participants during the interviews as well as shared meaning and action during the collaboration in
meetings. Therefore, these two methods are appropriate for understanding the social interaction as demanded
in this research study.
Another major aspect to consider is the sample of data to be collected. In a population a purposeful
sampling is broad and deep enough to represent all important aspects of the studied phenomenon (Elliott &
Timulak, 2005). Therefore for this study, the observed decisions should cover all aforementioned decision
dimensions. Although this research is partially comparable to Shollo’s (2013) study, plista as a company is
rather different to the financial institute as in her case in terms of industry, growth stage, size, culture etc.
This was chosen on purpose in order to investigate the transferability of her findings. Furthermore, only one
single company was chosen because it allows the in-depth study of various decisions without changing the
environment’s setting, e.g. industry, company size, corporate culture. It enables the observation of the
decision-making process over time within the limited study period.
Here, the selected participants cover all phases of decision-making of product development and, thus,
are representative for the study. Mainly the PM team and their corresponding counter part perform the search
for the options, evaluating and finally choosing the options. In each topic the IT teams are primarily in
charge of the implementation and, hence, the IT heads of the teams were included. Further, these team
managers also give input to the various options from an IT perspective and are, therefore, necessary in
addition to the PM team.
Semi-structured interview
All active participants in the prioritization process were interviewed on a one-to-one basis. This
enables the participants to provide a description of their perception of the decision-making process. The
interview comprised three basic themes: (1) background questions about the interviewee and his position, (2)
questions about the prioritization process, and (3) questions about information use during the process. The
background questions were used in the introductory phase of the interview for lowering any kind of
nervousness and to give the researcher a better understanding about the participant’s working context such as
education and his team’s environment. The second theme covered the individual’s point of view about the
prioritization process including any subjective evaluation. Finally, the respective information usage was
explored.
All interviews lasted around 45 to 60 minutes. It was a semi-structured interview with a guideline of
open-ended questions. Every interview was audio recorded in English. The main statements for the analysis
- 31 -
were transcribed and stored in a spreadsheet. In total, four interviews with four different people were
conducted, namely the CIO, two PMs and one developer / admin.
Field observation
The field observation happened over a timeframe of about two and a half months with the researcher
as silent observer. In total, 36 meetings about the product development or related topics were followed.
Mainly the weekly Product Management JF was the studied object since many prioritization decisions were
made during this event. This meeting lasted on average one hour and covered the discussions among the PMs
about the order of the tickets. Similar to the interviews, the field observations were audio recorded, the main
statements were transcribed and stored in a spreadsheet. However, these meetings were held in German and
the researcher did not demand to switch the language in order to preserve a natural environment, in which the
participants behave habitually. This choice was made since all participants’ mother tongue is German.
Therefore, the transcribed parts are translated for the ease for the reader. Another source for understanding
the interactions in the participant’s daily work life were meeting minutes and Jira tickets including comments.
For each ticket the digitally exchanged words were accessible and allowed to track the decision-making
process.
In total, 39 decisions were observed. During the data collection phase it was noticed that the field
observation was more appropriate to understand the BI output usage. In the retrospective interviews the
study participants were highly focused to showcase mainly examples of rational BI output usage. This can be
explained with the symbolic characteristics of the information, which legitimize a proper work. Therefore,
the interactions in meetings were more insightful and used as the main source for the data collection. At the
end of the study, a workshop was conducted with the study participants to reflect on the findings but also to
validate them.
3.4 Study Analysis
The following sections explain the analysis of the collected data. First, the thematic analysis is
presented, which gives the reader a comprehension from a theoretical perspective how the analysis is
conceptualized. Second, the analysis is applied to the study. Here, the iterative process of the performed
analysis is described. Third, it is explained how the decisions are categorized for the analysis.
3.4.1 Thematic Analysis
The data analysis of the qualitative data is performed using the thematic analysis. The thematic analysis
is a very common method for analyzing qualitative data (Guest, MacQueen, & Namey, 2011). However, this
is not the main reason for pursuing this analysis method. A thematic analysis is a structured method to
- 32 -
interpret meaning in qualitative information and group it into various emerging themes (Boyatzis, 1998). A
theme captures important patterns or meaning for the research question (Braun & Clarke, 2006, p. 82). There
are two contradicting ways of generating themes, a deductive and an inductive approach (Boyatzis, 1998).
On the one hand, the deductive approach is theory-driven identifying relationship and concepts in the data.
On the other hand, the inductive approach is data-driven, developing themes purely from the data. Although
these two options exist, it does not mean that one has to decide for either one. Rather Joffe (2011)
recommends a hybrid approach. She argues that the researcher shall analyze the data “with certain
preconceived categories derived from theories, yet one also remains open to new concepts that emerge” (p.
210). This is important in order to not only avoid ‘re-inventing the wheel’ but also to deal with findings that
do not fit in current knowledge. This hybrid or prior-research approach typically derives from a literature
review (Boyatzis, 1998) as it is the case in this research. Therefore, the thematic analysis provides the
appropriate tool for this paper’s purpose to analyze the data by the hybrid approach.
Figure 11: Overview of study design and analysis
Objectivism Subjectivism
constructionism
Interpretivism
Theory-driven approach
Data-driven approach
Prior-research approach
Positivism
Epistomology
Theoretical Perspective
Methodology
Methods
Data Collection
Data Analysis – Thematic Analysis
QualitativeQuantitative
Semi-structuredinterviews
Field observations
- 33 -
The above illustration gives an overview of the applied research design starting from the top view of
constructionism with its shared meaning as the middle of subjective and objective epistemology, continuing
with the interpretivism perspective for studying social interaction and finally applying the hybrid, prior-
research approach of the thematic analysis on the collected qualitative data from the interviews and field
observations.
3.4.2 Applying the Analysis to the Study
For the identification of the themes the transcribed meetings and interviews were first coded. A code
captured the basic meaning for each relevant quote. The following illustration shows an extract of the
analysis of a discussion during a meeting.
Figure 12: Extract of discussion and assigned codes and themes
In the first iteration the codes leaned closely on the original phrases in order to not loose any information.
Over multiples iterations and looking at similarities, these first order codes were validated. When the first
order codes were perceived as stable, second order codes were created. For the creation of the second order
codes the prior-research approach themes, as identified by Shollo (2013), are used and reflected. According
to her findings, the BI output usage can be interpreted, reframed, supplemented and substituted using several
devices. The application of the themes is as follows:
(1) the BI output is interpreted by applying the user’s knowledge and might be used for
making the decision, e.g. the number is perceived as high
(2) over time or from another perspective the first interpretation might be reframed, e.g. the
number is perceived as low when looking at other factors additionally
(3) if the BI output is not sufficient for making a decision and only used partially, it is
supplemented
(4) if the BI output is ignored or not available, it is substituted
- 34 -
After each tactic a decision could be made. The following graphic illustrates the decision-making
scenarios with the BI tactics.
Figure 13: Usage of BI output with BI tactics and devices in the decision making process
While interpretation and reframing may indicate the exclusive usage of the BI output, the supplementing
tactic uses the BI output only partially. The substitute tactic neglects the BI output completely. With these
different scenarios, the usage of BI is exhaustively covered (exclusive, partially, non-usage). Devices can be
applied in the different tactics. Therefore, if a tactic is used with a device, for that decision the devices are
extracted in addition to the BI tactic. Furthermore, it is important to note that a device incorporates rational
or socio-political processes. Network, expert and sponsors are socio-political devices, whereas a label is an
information providing rationale. Therefore, it is important to analyze the devices for this study in order to
understand the interplay between rational and socio-political processes. During the analysis the device
“stakeholder” replaced the device “sponsor”. The device “stakeholder” is defined broader by not limiting it
to guarantees only but also including all persons having any interest in the decision. For instance, the IT
heads do not act as a sponsor for new features from the business side but they have an interest as their teams
and resources are affected with each new requirement and its complexity. Further, a stakeholder must not be
in favour of a solution and can have an opposing position. Therefore, instead of having a new device, which
overlaps with the current “sponsor”, the device was extended. Furthermore, looking from a decision-making
perspective a stakeholder is a more common term.
The tactics and devices from Shollo (2013) were used as themes from a theoretically driven approach.
For opening the possibility that new themes may emerge, uncategorized codes were investigated separately.
This data-driven approach required multiple iterations to ensure that they do not fall into given themes. After
all second order codes were assigned and created, another iteration was performed to seek after combination
BI output
interpretation
Decision making
interpretation
Decision makingreframe
supplement
substitute
Devices:!label, sponsor, network, expert, presentation activity
Devices:!label, sponsor, network, expert, presentation activity
Decision making
Decision making
pure usage
partial usage
no usage
pure usageDevices:!label, sponsor, network, expert, presentation activity
interpretation
interpretation
- 35 -
of the codes for a possible consolidation. During this iteration an adjustment of the first order code or a re-
categorizing was made if necessary. In a final iteration, all codes were checked to see whether they were an
answer to one of the following questions:
(1) What is the setting or characteristic of that decision?
(2) How is the BI output perceived and used for the prioritization?
(3) What other information are needed and how are they acquired?
All remaining uncoded quotes were ignored but kept in the protocol.
3.4.3 Categorizing decisions
During the data analysis and theme generation it was investigated in which setting the decision could
be categorized. Several decision-making models as outlined in the systematic review are important. As the
leading model Thompson’s (1967) contingency framework was chosen. In order to classify the decisions, it
was first looked whether the prescribed approach for a quadrant was performed, e.g. consulting an expert for
a judgment quadrant. If that could not be identified, it was looked for hints whether the goals and means
were clear to all parties. As soon as one party indicated any doubts to one of the dimension, that dimension
was considered as unclear. For instance, the IT Head DE once raised: “I think the goal is still relatively
unclear. You should set up a workshop and make a brainstorm meeting”. Additionally, one challenge was the
timeframe of a decision and to define a single decision as finally made. As outlined in section 2.1.3 a
decision is “situated” and may change over time. For the analysis, however, only the last setting was taken
because the final performed approach shows how the BI output was ultimately affecting the selection of the
decision. This can be shown for the following case. For entering in the Australian market providers were
evaluated if a content delivery network (CDN) was appropriate to deliver static content to acceptable
response time for an acceptable price. The CIO acknowledged after introducing the Thompson’s (1967)
matrix to him:
“In the beginning we knew what we want to achieve but did not really know how, which provider
would be the best and if it is feasible.
After talking to provider #1 at the dmexco (a fair for digital marketing topics) and afterwards, we
knew we will go for a CDN but we were not sure whether we should direct all traffic or only the
Australian via them.”
(CIO Interview on November 12th 2014)
- 36 -
Figure 14: Decision about a CDN in Thompson’s (1967) contingency framework
The decision started from a judgment quadrant and moved to a bargaining section. Ultimately, in the
bargaining setting a decision was made. For this and all other decisions only the selection phase of the
decision was considered for the study analysis since it captures the actual (non) use of the BI output.
As for the structuredness it was looked for determined procedure of handling problem patterns. In case
there were routines in place, the decision was categorized as structured. Accordingly, with decreasing
routines and familiarity of the problem the structuredness decreased as well. With regards to the planning
level characteristics the following threshold were taken and aligned with the study participants based on the
resource effort for one full time employee.
Planning level Indicators
operational resource effort < 5 days
tactical 5 days < resource effort < 60 days
strategic resource effort > 60 days
Table 5: Planning level classification for the study
The setting for each decision is captured in a spreadsheet. For each decision the BI tactics and devices
are analyzed and assigned accordingly. The following screenshot illustrates the data analysis.
Analysis Judgment
Bargaining Inspiration
Means
Goals
unknownknown
unknown
known
All traffic
Talk to different providers
- 37 -
Figure 15: Data analysis – decision setting and BI tactics and devices for each decision
After the analysis the findings were presented to the study participants in a workshop. Besides making
the findings public, the workshop aimed to validate the themes and the categorizations of the decisions. The
study participants gave their individual perception of the various decision setting and it was looked for
deviations, which were then aligned during that meeting.
4 FINDINGS
As indicated in the study environment, allocating resources were the main challenge in the priority
decision. For example, the IT Head Platforms reported his own and his team’s availability in a JF meeting.
JF PM & IT Head Platforms, 30th September 2014
“I won't be here in the office on Thursday. IT Head DE won't be here on Thursday either
TJ is also gone. This is why I am not on vacation but on stand by. I am at home and can be contacted
by email. I am only available for short bug fixes and releases but not for conceptual questions to
tickets.” (IT Head Platforms)
Here, the availability but also the skills were often reflected during the meetings. As the team is structured by
topics, systems, and expertise, some issues were highly dependent on these resource skills and their
availability. Additionally, during the study period issue dependencies was also observed as a major challenge.
The issues were interdependent leading to a lack of transparency and a higher complexity in making the
priority decision. The following dialogue highlights the challenge of the issue dependency.
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“So we can slowly start in December. The question is also the dependency with the interface
definition from the algo team.” (PM2)
“The interface definition can already start parallel to that and the two core programmer can sit
together with them. The frontend team are welcome to give their input but not actively working on it.
They have to know their responsibility area and interface definition is not part of frontend issues. I
see the risk that we run into the bottleneck CB (Frontend) and this is not great.”(IT Head Platforms)
As only specific developers could handle certain issues, the above example showcases the challenges
imposed by the issue and resource dependency. For overcoming the challenges and ultimately reaching the
priority decisions for the different settings the BI output was prescribed as the leading measure by the PM in
their interviews (Interview with PM1 and PM2). Following a pure rational approach would mean to rank the
projects by the priority score only. When conducting the final workshop, the PM was asked to name the top
projects from the epics in progress. Comparing their answers with the priority score it was shown that the
perceived top three projects did not have any priority score and thus a rational descending order by the score
would not reflect the reality. The prioritization activity was observed as not as rational and objective as
desired. Socio-political processes took place and, hence, the prioritization was more complex than described.
In the following sections the findings are presented for each quadrant respectively. This chapter
concludes with a summary across all quadrants.
4.1 Findings per Quadrant
During the study analysis it emerges that not only the quadrants in Thompson’s (1967) contingency
framework but also the planning levels are significant characteristics to the several BI output usage. For
instance, in the analysis quadrant the decisions tend to be operational problems but are not limited to it. In
fact, also tactical decisions were present in this quadrant but showing different decision-making behaviour
than on operational level. The below table illustrates the distribution of the decisions over the quadrants and
planning level dimensions. It is important to note that the study does not target a quantitative approach.
Therefore, the numbers are only indicators and not sufficiently significant enough. In terms of structuredness,
the decisions mainly followed the planning level dimension. Hence, they are not emphasized here.
- 39 -
quadrant operational tactical strategic Total
analysis 19 3 0 22
judgment 0 3 4 7
bargaining 0 9 1 10
inspirational 0 0 0 0
Total 19 15 5 39
Table 6: Overview of the distribution of studied decisions in the different decision settings
The next sections reveal the study findings per quadrant. Several examples are used to give the reader a
better insight into the observed decision-making process.
4.1.1 Analysis
In the analysis quadrant the goal preferences are aligned and the means clear. Given these conditions the
decisions were found as generally structured. Mainly operational decisions but also tactical decisions were
present in the study set. Further, it was observed that the usage of information was rational and generally
determining for the decision in this setting. For example, the IT Head Platforms read a ticket and thought
loudly during a meeting:
“5 hours estimate, minor priority and a business value of 500 Euro. There is no priority, no chance.”
(IT Head Platforms)
This behaviour, a rational interpretation of information, was observed for operational as well as tactical
decision. As the numbers could be compared against each other, not much space for different interpretation
possibilities were left. Hence, a higher priority score inherently gained a higher priority. Furthermore, the
tactic of reframing was not observed since it was a one-off decision based on the first given information, thus
no change in the understanding could occur. Despite this common rational behaviour, there were differences
in the information usage between operational and tactical decisions. Therefore in the following, the findings
are presented separately for these two planning levels starting with the operational decisions.
Although a rational interpretation took place, it is important to note that particularly for operational
decisions often the BI information was not present or the data quality was insufficient. The effort estimation
and / or the business value were missing frequently. Remarkably for these, when the BI output was missing,
a label often substituted the missing information. “Bugs are first” (PM 1) describes the special status and
consideration a ticket type was given if it describes a system issue found by a user. Heuristics were applied
and a recent bug gained a high priority automatically. For fixing the bugs the flexible time, which was not
- 40 -
planned for a sprint, was used. Hence, loosely coupled from the product development there was another
stream of support and bug fixing, which has a higher urgency than working on new features. Both streams
are captured in the same backlog and it was observed that “bug as highest item in backlog” (PM 1) was the
common practice. This was further stressed when the priority in the ticket was set to “critical”. While the
priority label was set to “major” per default, it gained or lost consideration when it deviated to “blocker”,
“critical” or “minor”. The following ticket illustrates how a critical bug was fixed within a couple of hours
due to its type and priority label.
Figure 16: Ticket describing a critical bug
In general, these labels were not challenged and it relied to the creator of the issue to properly classify it.
However, there were some exceptions to this implicit rule when other labels came into play.
“I asked Marta (Campaign Management). It is a very old ticket and when I found it I pinged her.”
“Some are also duplicates.” (PM 2)
In some cases the bugs were already outdated or duplicated (data quality issues), hence they lost their
legitimacy and were not forwarded to the IT department. The PMs made the decision to neglect that issue in
agreement with the creator. Since a bug was analyzed immediately right after the issue creation, the PMs
could assume that the bug was not critical or relevant anymore. This data quality issue may happen when the
bug did not impact the daily business and, thus, it was not followed further. Once the PMs reflected:
“Lately, I ‘cleaned’ all CRM tickets and contacted all stakeholders if it is an old ticket.” (PM1)
“Actually, once a month we should do ('cleaning') that. Or a student could also do that.” (PM2)
- 41 -
This ‘cleaning’ was done manually although in each case the issue was closed and classified as not relevant.
Beside the bugs and data quality issues another label “epic” was used when a bigger project was close to be
finished.
“Many small things for AdSpecs... Just to get that roll out started and to finally get the epic done.”
(PM1, PM Meeting Prio Backlog, 14th October 2014)
“I put the adspecs things up top.”
(PM1, PM Meeting Prio Backlog, 18th November 2014)
All related operational tasks for finalizing the project were prioritized higher than other projects’
activities as showcased above for the epic “Adspecs”. While the label “epic” was project related, the labels
“bugs” and “data quality” were important for the daily business. These labels were only applied for
operational decisions.
For the minority of tactical decisions within the analysis setting the applied tactics were more
complex since the decisions were semi-structured. A new device, which is referred as prototype, emerged
during the study of tactical decisions in this analysis quadrant. A prototype is defined as any kind of pilot or
sample test for the purpose of gaining more information. As such, a prototype functions similar to an expert
who informs about technical matters and indications for the outcome. In contrast to an expert it is an abstract
mechanism and not a socio-political device. Since semi-structured decisions were not entirely familiar to the
decision-makers, the prototype supported them to gain confidence for the means and goals by supplementing
or substituting the BI output. This can be seen in the example of a tactical decision to provide the publisher
the functionality to adjust images in their recommendation widget so that it looks better. However, this
feature would increase technical and maintenance costs. In the beginning it was clear how this feature would
be implemented technically. Nevertheless, there were uncertainties among the decision makers whether this
feature is worth the effort since there are multiple costs associated such as traffic and processing costs. The
usage of the feature and the added revenue remain unclear, i.e. the goal was not entirely agreed on. Hence,
starting from the bargaining quadrant the decision setting transformed slowly when the following factors
changed:
IT Head DE on October 13th (in the ticket comments PM-149):
“Situation changed, we will stick to a CDN, so we have fixed prices. I think its only about X
EUR per GB + some media servers (for cpu intensive image resizing)
We could slowly activate some more publishers, because its really hard to estimate the traffic of
all publisher images.”
- 42 -
As one of the major goal metric (traffic costs) changed, the decision moved to the analysis setting.
The prototype was applied as a substitute for the BI output and intensified the direction to the analysis. In
this case, a pilot test in the Australian market should be performed to gain more information if a general
switch to this function were desirable.
JF PM and IT Head DE, October 21st 2014
“I suggest to wait for results of the A/B Test. Let´s go on after it - fine?” (PM 2)
“If you do want to make an A/B test, we have to put it in the widget space.
Your A/B test will be original picture vs. resized and cropped.” (IT Head DE)
Figure 17: Decision about resizing images for publisher in Thompson’s (1967) contingency framework
The prototype provided more information in terms of goal clarification. After the insights were
gained, this decision was fully situated in the analysis setting and a final decision about this feature was
made. Similarly, for the other tactical decisions a prototype supplemented the BI output and provided
additional knowledge to the decision makers for the analysis. As the problems were semi-structured, more
information was needed for gaining confidence than it was provided by the BI output. In fact, the
Analysis Judgment
Bargaining Inspiration
Means
Goals
unknownknown
unknown
known
CDN
Prototype
- 43 -
interpretation of the information, both from the BI output and the results of the prototype, became more
complex but remained rational.
In summary within the analysis setting, the BI output was highly used for applying a rational
approach if it was available. The decision maker follows the BI information suggestion and less socio-
political processes were in use on the operational level. The table below summarizes the tactics for the BI
output use on the different planning level for the analysis quadrant. On operational level there was no
supplementation, only substitution with labels in case the BI output was missing. A new device “prototype”
can be found for supplying additional information increasing confidence in goals and means, thus leading
tactical decisions closer to this quadrant.
BI output tactics use in an analysis setting
Planning Level Interpreting Reframing Supplementing Substituting
Operational rational no
no with label
Tactical with prototype no
Table 7: BI output tactics and devices in an analysis setting
4.1.2 Judgment
An expert plays a significant role in defining the means for achieving a clear goal in the judgment
quadrant. Since socio-political processes are important and necessary to gain clarity, the usage of
information was observed as less rational here compared to the analysis setting. The interpretation became
more complex since both rational as well as socio-political information were considered. Further, also
reframing during the decision making process took place. Given that information is not the only source for
decision-making, the different applied tactics on the BI usage highlight the supplementation as well as
substitution. In the study this quadrant was composed of tactical and strategic decisions, which were mainly
semi-structured. In the following the decision making for tactical decisions are presented first. Afterwards,
the findings of strategic decisions in the judgment quadrant are explained.
For tactical decisions the consulted experts were the IT Heads who provided insights to the issue
dependencies as well as to the resource availability and dependency. In this role, the experts actively shaped
the judgment of the decision makers by giving the estimate and the complexity of the options. While doing
so, not only the resource availability but also technical issues were used to substitute or supplement the BI
output. It is important to note that the IT Heads were also affected by the decision and, hence, gained the role
of the stakeholder in addition to the expert role. For instance, in one case, the cooperation team required a
new feature for improving the reach. (1) The expert provided the current assessment of the problem and its
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impact of a change in terms of costs and resources based on the BI output. (2) Afterwards he supplemented
this statement with technical details. (3) Finally, the whole discussion about costs was reframed as he
indicated that another major release of an internal system would automatically fix the current limitation.
Hence, as part of a new project this feature appeared to be for free for the stakeholders since the costs for the
development of the internal system had already been agreed.
IT Head DE on October 13th 2014 (in the ticket comments - SALES-3314):
(1) “Change would lead to higher computational costs + serial development efforts”.
IT Head DE on October 27th 2014 (in the ticket comments - SALES-3314):
(2) “This is because of the Limitation in StatsJob. There one publisher only has a single channel.
Change would lead cause sum of all campaign impressions being wrong, if 1 physical impression is
linked to several channels”
(3) “But these limitations will be gone in Livecube2. There is no go live date for livecube2
including user frontend scheduled yet. You can find the current progress [LINK]=> Pub Insights”
Although the goal was aligned between the various parties, the means lacked clarity. As shown with
this example the means were highly influential on the priority decision and actively shaped by the expert.
Since the IT Heads had a double role as expert as well as stakeholder, they gained more negotiation power
and, thus, could exert their agenda. During the study it was observed that in this tactical judgment setting the
IT Heads used this special position to better manage their team’s workload and prioritized the activities
according to the learning rate, resource skills as well as the technical dependencies.
The same behaviour was applied for strategic decisions but the main difference was that,
additionally, stakeholders were involved and thus, decision-making became more complex. For instance, the
Widget Creator was a strategic product for publishers and the cooperation management team promoted it.
However, it was not clear what the requirements were and there were no skills available for this topic. Hence,
starting from an inspirational level several meetings were set up in order to understand the goal. During that
process, however, there were no priority score computed for this strategic decision. Therefore, this lack of
information was substituted by referring to the stakeholder’s preference.
“Had a chat today with Director Cooperations - again mentioned that it is a big topic with high
priority.” (PM2, JF PM & IT Head DE, November 11th 2014)
“Director Cooperations: Higher Priority than Publisher Insights” (PM-158 Ticket description)
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When the goals became clearer and the priority remained undecided, there was still the problem of
missing skills and insight how to implement it as exemplified here:
JF PM & IT Head Platforms, October 21st 2014
“We know widgets only from narratives. In general to get into this topic it is very difficult for us. It
is completely new territory. We don't know the tool but widgets are really new to us.” (IT Heads
Platforms)
Despite the expressed urgency by another stakeholder the expert could lower the priority due to his
special role in this quadrant. The discussion was not based on a rational metric such as the priority score, but
happened due to socio-political processes. The following discussion between the IT Head Platforms and the
PMs highlights the particular position held by the expert.
JF PM and IT Head Platforms, November 18th 2014
“The initial things always have to come from CB because of the interface. Currently MR also checks
if he can learn and then take some things from him. Same applies to AE. Therefore I would say to do
www first.” (IT Head Platforms)
“Our goal is to be time effective.” (PM2)
“I don't see the Widget Creator mature enough that we are about to take action there soon. There is
no final concept and also the API from the other team is not implemented yet. Therefore we should
already start with www and it should be finished before we can really start with the Widget Creator
in www.” (IT Head Platforms)
The following graphic shows the evolution of the decision setting.
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Figure 18: Decision about the Widget Creator in Thompson’s (1967) contingency framework
Similar to the argumentation above, the expert used the resource skills and issue dependencies to set
his agenda what led to a deprioritization of this project. There were rarely cases observed, in which a priority
score was available and used for legitimizing a decision in this quadrant. This also highlights that in some
cases there were no effort taken to compute that score. In fact, the rational information usage was ignored or
neglected and substituted by the expert and/or stakeholder as seen by the following quote:
“BV / PS - Not needed here, since it´s agreed by the management” (Ticket description PM-133)
In summary within the judgment setup, the BI output was often not available or neglected. Hence, the
interpretation was more complex and did not happen solely on a rational basis. Rather the expert or
stakeholders were considered additionally. Other information and opinions were used more often compared
to the analysis setting due to the socio-political processes. The table below summarizes the tactics for the BI
output use on the different planning level for the judgement quadrant. One can see that both tactical and
strategic decisions in this setting applied the same tactics but on strategic level the stakeholder’s device was
applied additionally.
Analysis Judgment
Bargaining Inspiration
Means
Goals
unknownknown
unknown
known
Meetings
Expert
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BI output tactics use in a judgment setting
Planning level Interpreting Reframing Supplementing Substituting
Tactical partially rational,
socio-political factors
considered
yes with expert
Strategic with expert, stakeholder
Table 8: BI output tactics and devices in a judgment setting
4.1.3 Bargaining
In the bargaining quadrant, where the means are clear but the goals lack clarity, the studied decisions
were all semi-structured mainly on tactical planning level. One bargaining decision was strategic. However,
in contrast to the other quadrants there was no difference on the decision making between the planning levels
in this bargaining quadrant. It was observed that for all planning levels the usage of information was less
rational compared to the analysis setting leading to a more complex interpretation of it. Hence, different
devices were applied in addition to the BI usage for the priority decision.
The following example illustrates the interplay of information usage with the supplement of it in
order to reach a decision. The scenario dealt with a tactical decision about the termination of the web
browser support for the Internet Explorer (IE) 8 and version 9. The IT team had the interest to stop the
support because of high manual effort for many fixes in the source code. On the other side, the marketing
and communication team were interested to provide a good presentation to all users on their website. Hence,
their rational was that whenever there were still users with those browser versions, it should be supported. As
they provided the numbers of usage by web browser they could see that the IE was still in use. However, the
IT Head Platforms reframed the number as very low and stated:
“It will take more than one week to get it all IE8 ready. Only 3% of all our visitors use this browser.”
(IT Head Platforms, JF PM & IT Platforms, 23rd September 2014)
Further, the expert supplemented his point of view with technical arguments and that other
companies such as Google stopped the support in 2012. Another supplement happened by a network
approach when he convinced the product managers.
JF PM & IT Platforms, 23rd September 2014
“By looking at the numbers we can see that not many people uses the IE8. If we can stop the support
for this, we can better maintain our code and save some resources from doing IE hacks. Only
marketing said they would like to have it because there are few users but it takes a lot of resources
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just for a few. Also Google stopped their IE 8 support in 2012. Static pages are fine but the
registration and subsequent activities should be stopped with that browser.” (IT Head Platforms)
“According to us, we would also say to stop IE 9 support by looking at the numbers.” (PM 2)
The expert’s reasoning combined with the network built with the PM convinced the marketing and
communication team. This decision gained priority and the follow up task of providing a notification to the
IE8 and 9 users were implemented two weeks later. It was observed that the BI output, one-week effort vs.
3% user base, facilitated the discussion, which was then supplemented with other information and devices.
These devices of network and expert were also applied in other instances for substituting the BI information,
e.g. when the BI output was not available.
Another popular tactic was the substitution of the BI information with the stakeholder device. In
these cases, the BI output was ignored and socio-political processes took over. Stakeholders were often the
top management who exerted their formal power to clarify the goal according to their view or to the view of
a party who sought for support. For instance, the IT Head DE found a possibility to increase the daily
revenue by 350 Euro. When he presented the idea to the PMs, they were concerned with the risks and
suggested to test the idea first on a few instances. Particularly, the advertisement metrics might not match
with the advertisers’ reporting and all employees should be aware before implementing it. While the PMs
were seeking for more information in order to reduce a potential flaw, the IT Head DE reached out to a
stakeholder. For the next JF meeting he obviated the need for a discussion with the following:
JF PM and IT Head DE, November 18th 2014
“I talked with the CIO about it and it's decided. We take those 350 Euro and it is released.
Also the CIO said we don't have to make a big issue out of it and just tell the people our decision.
I agreed with the CIO and it's ok from the management side.” (IT Head DE)
The CIO of plista, decided this issue using his formal authority. The IT Head DE used the stakeholder’s
device to cut down the discussion. There were many other issues in which the stakeholder, mainly the top
management but also a client, made a decision to set the goal. This approach had the effect that a long
running discussion could be avoided but also raised concerns among lower powered team members. Neither
a rational approach could be followed here nor a discussion and agreement based on shared opinion were
possible. These management decisions resulted in sudden change of planned activities, particularly when the
management initiated a new solution or idea. However, it also gives the management a tool to be flexible and
initiate or change a course of action. The stakeholder’s opinion gained priority among the decision makers
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and cooptation took place. The PMs already acknowledged these incidents and reflected during the
interviews:
“Sometimes, there is an authority such as the CEO, who says this has to be done first, then we just
do it. It is not well structured somehow. There is a process set up for usual cases but there are
special events happening decoupling the usual workflow.” (Interview PM 1)
“Then sometimes the management comes to you and says this product has a priority of ... Then you
have to prioritize other issues down so that you can meet the deadline, which was given by the
management. The management sometimes arrives with some numbers, which we can use. Then we
can argue with the other using the business value.
Our task in this case is to have an overview of what is happening. The management is not involved in
everything so we have many developers working on something and some issues have to wait.
Our clients have to be satisfied. If there is an issue like a widget on our customer's page looks
strangely, then it has a high priority of course. Especially if it is a big client, then it is really
important.” (Interview PM2)
Similarly, for a strategic decision, it was the CIOs personal opinion about a CDN provider what led
among other factors to the selection of a provider. He met several representatives during the dmexco fair.
During the interview he reflected that he got along better with the provider #1 representative than with the
provider #2 sales person. The following extract of the interview shows his decision-making process:
“We had a good talk and connection. Therefore, I realized that it is probably the most interesting
way to go with them.
….
The sales representative (of provider #2) was a German doctor who was a really pushy sales person.
I did not feel the personal connection what I had with provider #1.
When I asked him about provider #2, he said they were quite slow and it manifested my feeling that
provider #2 is old established player.
provider #1 is newer in the market and shows more willingness to provide a good service, more
flexiblity and is more innovative.” (CIO)
For this very decision there were also rational information such as response time and traffic costs
evaluated. However, this social factor also influenced the decision maker and, ultimately, provider #1 was
selected. This example shows again the stakeholder preference as an impacting factor on the decision.
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In summary within the bargaining setup, the BI output was only partially used for supporting a
preference. Further, it is used to facilitate a follow up discussion and negotiation. Thus, shaping the
collective judgment by applying other devices was usual. Supplementing and substituting of the BI
information took place. Beside the expert and the network, mainly the stakeholder device played a crucial
role in this setting for both tactical and strategic decisions. The following table summarizes the applied
tactics and devices in the bargaining quadrant.
BI output tactics use in an bargaining setting
Planning level Interpreting Reframing Supplementing Substituting
Tactical /
Strategic
partially rational,
socio-political factors
considered
yes with expert, network, stakeholder
Table 9: BI output tactics and devices in a bargaining setting
4.1.4 Inspiration
In the inspirational quadrant no decision was observed. However, during the writing of this paper there
was one decision situated in this setting but the ultimate decision was not finally made. Although this study
is not of quantitative nature, this very low amount leads to the difficulty of extracting meaningful findings.
4.2 Findings across different settings
The findings within the different decision settings show that the BI output is hardly used rationally for
making decisions. In theory the score computed by the BI system should predetermine the priority. Even in
the analysis setting, where the BI output is interpreted in a rational way if available, it is often replaced by
other devices. Despite the desired approach to follow an objective ordering, there were no cases, in which a
priority decision between two or more projects was based on the priority score. Often the competing
decisions were situated differently in the four quadrants so there was also no actual need to generate a
complete BI output for each decision. Rather other devices supplemented or substituted the BI output and
socio-political processes were promoted.
Looking across all quadrants one can see the different use of devices depending on the setting. The
application of the devices in the supplement and substituting tactics is solely dependent on the availability of
the BI output. Furthermore, the findings reveal that the interpretation of the BI output became more complex
when either the goal or the means were ambiguous. Accordingly, reframing took place when the decisions
provided different interpretation possibilities. The following table summarizes the findings for each quadrant.
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Quadrant Interpretation Reframe Supplement Substitute
Analysis
rational if
information
available
no with prototype with label
Judgment
partially rational,
socio-political
factors considered
yes with expert, stakeholder
Bargaining
partially rational,
socio-political
factors considered
yes with expert, network, stakeholder
Inspirational n/a n/a n/a n/a
Table 10: Overview of the BI tactics and devices across Thompson’s (1967) contingency framework
Another major finding in this study is that by applying Thompson’s (1967) contingency framework the
two dimensions of goal and means clarity had an impact on the priority decision. Several examples showed
that the goal clarification was highly dependent on the stakeholder’s preferences, which have to be aligned.
The business value was associated with the goals clarity leading to different outcome depending on the
agreed objectives among the stakeholders. Similarly, the expert device impacted the means clarification,
which in turn affected the effort estimate. Accordingly, the newly identified device prototype acted as goals
and means clarifier and was used to substitute the BI output in order to gain more information and certainty
approximating the analysis setting. It was also shown that a decision must not remain in one single quadrant
over its timespan. The socio-political processes “re-situated” the decisions.
Looking at the planning level of the studied decisions further findings were uncovered. The following
overview highlights the BI output tactics and the devices according to each planning level.
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Planning Level Interpretation Reframe Supplement Substitute
operational
rational if
information
available
no no with label
tactical
partially rational,
socio-political
factors considered
yes
with expert,
stakeholder,
network, prototype
with expert,
stakeholder,
network
strategic
partially rational,
socio-political
factors considered
yes
with expert,
stakeholder,
network
with expert,
stakeholder,
network
Table 11: Overview of the BI tactics and devices across planning level
As all operational decisions were well structured and resided within the analysis quadrant, the rationality
for using the BI output was high leading to application of labels only for substituting missing BI outputs or
the prototype for supplementing it. Heuristics were applied according to the labels and, thus, making a BI
output irrelevant, e.g. critical bugs were always the highest priority. When the structuredness decreased on
tactical and strategic level, the application of socio-political processes increased. This indicates the need to
converge to unambiguous goals and means by using more and different devices when a higher impact on the
organization is expected and/or the problem is (somewhat) unfamiliar. Accordingly, the interpretation of the
BI output became more complex with the increasing planning level. These findings were presented to and
validated by the study participants and, thus, accomplishing the requested inter-subjectivity. They indicated
that the findings reflected their personal impression of the decision-making processes and gave them a more
structured perspective.
5 DISCUSSION
The findings address this paper’s research question by showing different roles of the BI output according
to its decision setting. While it is used rationally in an operational and well-structured decision in the
analysis quadrant, it rather facilitates a follow-up discussion in other setups. This puts Shollo’s (2013)
findings in a more structured perspective, when she acknowledges, “… the BI output can facilitate
discussions about the assumptions underpinning its analysis, which might lead to adoption of other
perspectives. […] The role of the BI output in this dimension is not to relieve uncertainty, but rather, to
reduce equivocality by creating shared understandings of choice situations, making explicit different
preferences and values, which can then be thoroughly debated” (Shollo, 2013, p. 215). These findings
highlight socio-political processes, which occur in different settings and diminish a ‘one-size fits all’
approach of BI. However, it is possible to argue that the findings from the study may be misleading when
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other underlying assumptions and perspectives are taken. The following sections aim to give the reader a
more critical perspective. First, general concerns are raised from a decision-making view. This section
discusses classification issues and the general application of taxonomies. Second, BI related issues are
investigated. Here, the technological determinism as in the technological view of BI is reflected with the
study’s findings. Third, despite the critics the findings can be used for recommendations in the studied case
environment. Finally, this paper’s limitations are summarized.
5.1 Implications from the organizational decision making perspective
It was assumed that by looking at BI from a decision-centric view one could identify new findings on
how BI supports decision-making. Overall the analysis of the study was mainly driven by the prior literature
review. Foremost Thompson’s (1967) contingency theory and Gorry & Scott Morton’s (1971) matrix
(planning level vs. structuredness) were used to classify decisions. While their distinct dimensions for
decision classification were appropriate for addressing this paper’s research question, their popularity
permits to study these findings for other contexts in future. In a conjoint approach it was shown that a
decision could be analysed from various perspective giving it broader range of characteristics for analysis.
Therefore, this paper applied these frameworks from the organizational decision-making. During the study it
was investigated how the BI output was used in these frameworks. As outlined in chapter 2.1.3, due to the
dynamic nature and subjective perception of a decision it is questionable whether the classification of the
studied decision in these frameworks was correct. It depends highly on the perspective and timeframe of the
investigation. Since the priority decision was influenced by the objective and means clarification and the
decision-making process was stretched over several meetings, continuing research should aim to decouple
this effect. Provided that the BI output is only used for the goal or mean clarification and not for another
dependent variable, less complexity in the data collection and analysis can be encountered. Therefore,
following the situated decisions over time was a crucial part for identifying the devices properly. A solely
retrospective approach, e.g. conducting interviews, may not reveal these findings. Despite the study
challenges a fundamental question regarding the use of taxonomies should be discussed here.
One major issue of using taxonomies are their descriptive and, at the same time, prescriptive nature. On
the one hand, a decision is described and analyzed on its (external) characteristics in order to position it in
the matrix. On the other, there are approaches prescribed in each quadrant determining the consequent action.
For instance, a decision is located in the judgment quadrant if the means lack clarity but the goals are
unambiguous, thus an expert shall judge on the best option to achieve the objective. For cases, in which the
expert was consulted and, thus, the prescriptive approach was taken as a characteristic of the decision, it
raises the question if the decision was actually situated in the judgment quadrant correctly. As Nutt (2002)
acknowledges, the applied approach does not always follow the recommended approach leaving space for
optimization. Hence, the performed classification for this study implies that there is optimization potential
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already in decision-making as such, e.g. when an analysis approach was more appropriate than a judgment
approach.
In this paper’s study the applied approach, if observable, was considered as the determining evident for
placing the decision into a quadrant. By doing so it is ensured that the used BI output tactics and devices
were assigned to the practiced behaviour. This way, it ascertains a less subjective interpretation by the author
regarding the goal and means clarity, which was in turn validated by the study participants. Related to this
benefit of inter-subjectivity is the consequence that only the last ultimate decision setting was taken as the
practiced decision tactic when the decision moved across several quadrants. The downside of taking the last
decision setting might explain the non-existence of inspirational decisions in the study. As for the
inspirational quadrant, it is recommended to “network with the key stakeholders” (Nutt, 2002, p. 70).
However, by applying this recommended approach, the goals and / or the means became clear between the
different stakeholders and decision-makers leading to the move to another quadrant.
Moreover, it raises the concern whether the BI tactics and devices was affected by or even initiated the
practiced approach. Looking into the analysis quadrant highlights this issue. Nutt (2002) classifies decisions
as analytic “when the evaluations involved a pilot test” (p. 76). He sees a pilot test as the practiced approach
for the analysis quadrant. In this paper, it was assumed that the recommended approach imposes the various
BI tactics, i.e. the position of the decision in the analysis quadrant prescribes a pilot test, what in turn
determines the BI tactics. Therefore, it is possible to argue that the pilot test led necessarily to the new
prototype device as a supplement in the analysis setting. Accordingly, the expert approach in the judgment
and the stakeholders approach in the bargaining quadrant had to impact the BI tactics.
Switching the view may reveal other findings and also connects to the next point of consideration.
Another issue with the positioning into taxonomies is the fundamental question if it really matters where
decisions are located. Drawing the analogy from the resource-based view and the dynamic capabilities view
from the strategic management school, instead of positioning units into matrixes, internal resources and
capabilities are more important for being successful and capable to actively change the position (Teece,
2007). Similarly, one can argue that building and using BI capabilities can transform the decision-making
settings and approaches.
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Figure 19: Implications of the findings from the decision-making view
This figure shows the different views using the example of the judgment quadrant. Due to external
factors (goals and means clarification) prescribing a best approach, BI tactics and devices are affected by this
very approach, e.g. the prescribed approach requires asking an expert, the BI tactic is to supplement or
substitute the BI output using the expert device. The alternative view would be that BI capabilities are able to
change the setting for the decision and enable other approaches. Therefore, future research may investigate
the BI tactics and devices in all decision-making phases to understand the dynamics of the devices and how
they interact during the BI output generation. Before the selection phase of decision-making, the devices and
BI capabilities might support decision-making in the dimension of goal and means clarity. This behaviour
might influence the decision. This calls for more future investigation of the BI tactics and devices during the
identification and development phases of decision-making. Although the technological BI view goes in this
direction by claiming better decisions due to information supply, it comprises no evidences of socio-political
processes, which proved to be highly influencing. Thus, further research should investigate BI capabilities
from a decision-centric perspective having a more holistic approach.
5.2 Implications from the BI perspective
From a BI perspective, particularly the research from Shollo (2013) shaped the identification of the
themes. As Shollo (2013) states, “… the BI process and the BI technologies lead to better decisions only
when the decision-makers’ preferences are aligned, the decision is well structured, and the BI output
Analysis Judgment
Bargaining Inspiration
Means
Goals
unknownknown
unknown
known Expert
Describing external factors
Prescribed approach
BI tactics and devices enable
BI capabilities
impose
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captures all the variables and their causal relationships” (p. 213). Accordingly, the findings from the
analysis quadrant reflect this statement as in this setting the mentioned conditions are given. Similarly, the
bargaining quadrant highlighted what Shollo (2013) acknowledges, ”decision-makers also had to cope with
ambiguous organizational goals and conflicting interests since the environment never presented itself as an
unambiguous signal. Thus, they were more cautious in using the BI output in their decision practices
because of political and symbolic intentions embedded in the BI output during its development” (p. 215).
Although a new device “prototype” was identified, it might leave critics whether the analysis was rather
theory-driven and left no possibilities for new themes to emerge. However, as explained above the identified
BI output tactics by Shollo (2013) were generic and exhaustive making it valid for use. Furthermore, no
other research was available during writing of this paper indicating the different BI output usage. Therefore,
the focus was placed on the devices to better understand the interplay of rationality and socio-political
processes. Despite the general preference to work in a highly rational manner, it was revealed by using the
organizational decision-making research that socio-political process are highly in use and should be
respected as a necessary procedure to overcome organizational challenges such as the goal clarification.
Apart from that it was shown that the devices were used depending on the decision setting. Since the
judgmental and bargaining setting share common decision characteristics such as semi-structured problems
on tactical level, it is crucial to identify the ambiguity in the objective or means properly. The devices of
expert and stakeholder gained different importance in the respective quadrants.
Since the technological perspective is dominant in the BI research stream and regards BI from a
technological determinism view, this paper shows that both the BI output shaped the actor’s judgment but
also vice versa, the actors shaped the BI output. For operational decisions in the analysis quadrant the BI
output determined the user’s action. The technology enabled decision-making by providing credible
information if available. On the other hand, for tactical and strategic decisions in the judgment and
bargaining quadrants the decision-makers supplemented or substituted the BI output. The technology either
limited or was perceived as irrelevant by the decision-maker. Hence, a one-size fits all BI solution is not
feasible. In fact, BI should be regarded in various practices as also introduced by Clark (2010). Future
quantitative studies may investigate the distribution of the decisions in a specific context and identify the
most practiced settings. For the most common settings it can be focused on how BI improves decision-
making such as in term of effectiveness or efficiency. In another step the findings can be used to explore BI
design and system improvements.
5.3 Implications and recommendations for the study context
Since this study was conducted in a qualitative manner, the findings are highly context specific. A
quantitative study would further validate the findings for other contexts and make them generalizable.
However, one can build upon the findings here and give some options on how to deal with the implications
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for the given environment. Therefore, in the following several recommendations are provided to the case
company with regards to their prioritization activity for the product development.
The analysis quadrant was mainly comprised of operational decisions, which were well structured.
Nevertheless, labels such as “bugs” and according heuristics were applied. This behaviour raises the question
if theses heuristics should be institutionalized to explicit routines. Therefore, it is possible to split the backlog
respectively. As bugs, critical or blocker tasks automatically gained a higher urgency to be worked on, the
sprint planning process could be changed in having one backlog containing only bugs and critical tasks and
another backlog for developing new features. Per definition for the bugs and critical tasks no priority score
has to be determined, thus making this exercise explicitly irrelevant. This way, a more transparent separation
is accomplished and it diminishes the manual effort to prioritize bugs. However, it should be reflected if the
backlog containing the bugs has to be completely processed before it can be worked on new features. As
expressed by the IT Head Platforms, working only on bugs demotivates the staff. Therefore, a more balanced
approached should be pursued creating the need to evaluate appropriate measures for splitting the workload
on bug fixing and new feature development such as reserving a certain share of time per resources.
The data quality topic has been discussed in other studies and research work (Marshall & De la Harpe,
2009). In this study, it was found that data quality issues automatically de-prioritize issues leading to the
question whether they are worth to put manual effort in. Hence, data quality issues should be addressed
computationally to save human resources. Duplicates could be prevented via text matching during the
creation of issues, e.g. if two different people report the same issue in a certain timeframe. Moreover, the
system could identify outdated bugs using the creation date, notifies the creator and closes the issue after
several notifications. Data quality issues could be encountered in both backlogs. Therefore, it could be
investigated if the identified tasks and project should further be separated to a respective backlog containing
each outdated and duplicated issues.
As for new features, decisions on all planning levels would be located in the other backlog with an
increased need for socio-political processes. At this point one central question has to be reflected. Since the
findings indicate that socio-political processes are in place, it should be clarified if that state is actually
acceptable or if a more rational approach should be imposed. Since a rational approach would lead to a more
objective prioritization but also a less flexible due to the strict predetermined ranking, the decision-makers
should acknowledge the potential need for the socio-political processes to clarify the goals and means over a
changing timeframe. Thus, instead of tending to one of the extreme ends a more balanced level should be
sought. This can be achieved in handling the decisions for new features according to its settings as performed
in the study. A basic requirement would be to implement Thompson’s (1967) contingency framework into
the BI system so a classification of each decision into the different quadrants is possible. Assuming that for
each quadrant the prescriptive approaches were followed the consequent options could change the current
workflow. For operational decisions in the analysis quadrant it was shown that a rational approach based on
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the BI output was applied. As a result a ranking among theses decisions can be accomplished though the
ranking by the priority score, which could potentially be done automatically by the BI system. Since
currently only for bigger projects (epics) a priority score is given and the related sub-issues only refer to the
project, it would be more transparent showing the related project’s priority score for each associated sub-
issue. The benefit would be a more rational prioritization for these operational issues as determined.
Accordingly, tactical and strategic decisions in the judgment and bargaining quadrant can be made
following the prescriptive approaches. For both quadrants it remains unanswered how BI can improve the
decision making, whether in effectiveness or efficiency. However, for these instances the devices experts and
stakeholder are in the foreground respectively. In fact, this study revealed that for decisions in the judgment
quadrant the expert’s opinion on the means is not challenged. Therefore favouring a fast decision-making,
the expert’s advice in this setting should be followed. For these cases the expert’s judgment could be
highlighted particularly and the priority adjusted to the expert’s advice. Respectively, in a bargaining setup
the negotiation between the stakeholders is the determining factor for the prioritization. Hence, for this
highly socio-political process a BI output can facilitate the discussion by highlighting the effects of each
preference. The priority still would be made in agreement or by stakeholders’ authority.
Figure 20: Recommendations for splitting the backlog to the case company
The figure above illustrates the recommendations and was shown in the final workshop to the study
participants. Although a split into multiple backlogs leads to higher transparency, it should also be
! Bugs%and%cri,cal%/%blocker%issues%in%a%separate%backlog%to%be%worked%on%first%(opera,onal,%analysis)%"%no%priority%score%
%! Split%resources%for%this%separate%backlog%like%a%support%
team%or%,me?boxing?%
Cri$cal(fix(backlog(
! Comprises%all%decision%seCngs%%! Make%priority%score%visible%for%related%issues%to%project %! For%analy,cal%decisions%ranking%by%priority%score%%! For%other%seCngs:%highlight%different%roles%(expert,%
stakeholder)%and%their%judgment%/%preference%
! Balance%between%cri,cal%fix%and%new%feature%backlog?%
New(features(backlog(
! Con,nue%or%change%of%current%approach%–%impose%more%ra,onal%or%socio?poli,cal%processes?%! Applica,on%of%Thompson‘s%model%"Make%current%decision%seCng%explicitly%! Make%heuris,cs%to%explicit%rou,nes%leading%to%higher%transparency%
Implica$ons(
! Data%quality%issues%to%be%solved%via%system%filter%(outdated%/%duplicates)%
Data(quality(issues(
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acknowledged that the complexity increases, as multiple backlogs have to be managed. In addition to these
recommendations, the implications from the organizational decision-making and BI are also valid and
already imply potential improvement areas such as the correct classification of a decision.
5.4 Limitations
As the above discussion shows, this paper’s constraints highlight potential for future research.
Foremost, the choice of theories directed but also limited this research. Since this paper centered decision-
making, the research of the decision-making school was in focus. Although the selected models such as
Thompson’s (1967) contingency model were appropriate to address this paper’s research question, other
models might lead to other findings. Furthermore, this paper synthesized findings from the decision-making
with the BI stream, which is mainly regarded from a technological view. However, in a broader perspective,
BI could be abstracted to an information management view. Information management from an organizational
lens “deals with the management of all information processes involved in the information lifecycle with the
goal of helping an organization reach its competitive and strategic objectives” (Detlor, 2010, p. 103).
Several information management frameworks highlight not only technological but also processual and
organizational aspects (Detlor, 2010; Marchand, Kettinger, & Rollins, 2002). Continuing this thought, it is
also possible to investigate this research question from a broader social informatics perspective. Social
informatics regards IT in their mutual interaction and shaping with their “institutional and social contexts”
(Day, 2007, p. 576). Different theories and models are available and might identify new or explain the
findings from another angle. However, the applied models in this paper were appropriate to discuss the
paper’s research question as different settings could be identified in which the actual BI usage were
explained.
Despite the theoretical aspects another limiting factor was the conducted study. Mainly the investigation
of a single business process of a single organization and the limited study set constrained the identification of
possible further findings. As a qualitative study the findings are highly context dependent and might not even
be applicable to other business processes within the same organization. Studies in other contexts, both
business processes as well as companies (sector, size, culture etc.), might show other characteristics.
Furthermore, in this study 39 decisions were analyzed. This limited set only reflects a partial extract of the
reality. Therefore, the findings are not generalizable requiring a quantitative study over a longer time period.
Nevertheless, given the limited timeframe and scope of this thesis the conducted study was sufficiently to
highlight the different usage of BI in different settings.
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6 CONCLUSION
“So can we do better?”
The answer to this introductory question – whether decision-making can be improved by using
(objective) information - is not a simple yes or no. It depends on the various decision settings how
information is used. This paper gives the reader a more holistic view on BI from a decision-centric
perspective. Several models are introduced and used for the study. The findings highlight a full data-driven
approach as possible and feasible but only under certain conditions. Specifically, in a well-structured
analysis setting BI can leverage the information resource. For other settings it is shown that the information
are often neglected. In fact, socio-political processes are more important. Particularly, experts and
stakeholders are demanded to clarify goals and means. However, the BI information supports decision-
making by facilitating a discussion. It indicates that BI should not be regarded as a total systems approach
(Gorry & Scott Morton, 1971). Instead, it is suggested to have a more distinct approach for different settings.
Nevertheless, one should acknowledge the limitations of this study. Possible study options are highlighted
and discussed in this paper and reveals that future research is needed to better understand the actual BI
support in decision-making.
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Appendix
1. Interview questions to the Product Managers
Background Questions:
1. Identification: a. Name; b. Title c. Profession / education d. Nr of years working in the current position. e. position in the past
2. Description of work responsibilities. 3. How is your department organized, divided, in projects, groups, teams? 4. Description of the department or development area:
a. Purpose (Vision, Mission) b. Main customers, c. Systems under ownership, d. Specialization or functions of the department
Questions for the decisions making process (prioritization process):
5. What is the goal and on what does it depent on (metrics)? 6. What is the process of decision making currently as you see it? 7. Who participates in the decision making process? 8. What kind of decisions are they required to make? 9. Whose responsibility is to make the decisions? 10. Who is affected by the decisions? How are they involved in the decision-making process?
Information Use for Decision Support
11. What information do you collect and what information are you provided with? 12. What is the process for collecting generating and supplying the information? 13. How accurate does the information need to be? 14. In what timeframe does the information need to be supplied? 15. Can you describe an example of how do you use this information when making decisions? 16. How do you interact with this information, if you do?
2. Interview questions to the CIO and developer
Background Questions:
1. Identification: a. Name; b. Title c. Nr of years working in the current position. d. Profession / education e. Position in the past
2. Description of work responsibilities. 3. How is your department organized, divided, in projects, groups, teams?
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4. Description of the department or development area: a. Purpose (Vision, Mission) b. Main customers, c. Systems under ownership, d. Specialization or functions of the department
Questions for the decisions making process (CDN decision):
5. What was the trigger for initiating the whole discussion? (How does it usually happens?) 6. What is the goal and what does it depent on (metrics)? 7. How did process of decision making happen (as you see it)? 8. Who participates in the decision making process? And how? Your role? 9. What kind of decisions are they required to make? 10. Whose responsibility is it to make the decisions?
Information Use for Decision Support
11. What information do you collect and what information are you provided with for making the decision? (subject)
12. What are your sources for the needed information? 13. How accurate does the information need to be? 14. In what timeframe does the information need to be supplied? 15. How specific does the information need to be? 16. Can you describe an example of how do you use this information when making decisions?
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3. SALES-3314 ticket
4. PM-133 epic
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5. PM-149 epic
