On the Role of Complexity for Guiding Enterprise Transformations
Jannis Beese, Stephan Aier, Robert Winter Institute of Information Management University of St. Gallen [email protected]
Developing Impactful Complexity Measurement and Management Systems
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Very large organizations Ongoing enterprise transformation programs
– Up to several hundred projects at a time (decentral decision-making) – Focus on project deliverables
There is no alternative to incremental changes – Running system – “Architectural thinking” not developed & institutionalized
Need to control complexity – Incorporate complexity management into transformation management
Cumulative PhD Thesis of Jannis Beese – 1st step: identify design principles for complexity measurement systems – 2nd step: design of (situational) complexity measures – 3rd step: design of complexity management systems (based on situational
measurement system) – 4th step: integration of complexity management into transformation
management
Research & presentation context
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Agenda
Problem factors and complexity measurement system design principles 3
Methodology 2
Discussion and outlook 4
Complexity measurement as guidance for enterprise transformations 1
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How can complexity measurement systems guide enterprise transformations?
Research question Which principles should guide the design of complexity measurement systems in order to be useful for informing enterprise transformations?
Approach: Iterative process • Conceptual analysis of focus
group data • Combined with and
supported by literature review
Background: Complexity measurement • is not a goal by itself • supports and guides complexity management • is recognized to be important, but complexity measurement systems
often fail to achieve the desired impact during enterprise transformations.
Focus Group
Workshop
Review and preparation
Use results to guide literature review
Use insights to structure workshops
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Complexity results from a series of (incremental) changes
Complexity Measurement
System
External Requirements Internal Changes
Decision makers (not necessarily management)
Operationalized measures
Complexity assessment
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Complexity measurement as guidance (1)
Complexity Measurement
System
Key questions:
• How can an improved understanding of complexity be used to inform decision makers during enterprise transforma-tions?
• Can a series of appropriately informed incremental changes converge to a more efficient global state of the enterprise?
Complexity management methods rely on measurement systems in order to assess the current situation and in order to evaluate the success of current transformations.
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Complexity measurement as guidance (2)
Long-term goal: Complexity measurement as an integral part of complexity manage-ment and organizational/structural decision processes.
Complexity Measurement
System
Context dependency: • Objects
(e.g. IS complexity, product complexity) • Goals (e.g. agility, efficiency) • Targeted users (e.g. IT managers, steering
committees, project managers) • Environmental factors (e.g. technological
advances, market factors)
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Agenda
Problem factors and complexity measurement system design principles 3
Methodology 2
Discussion and outlook 4
Complexity measurement as guidance for enterprise transformations 1
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Methodological positioning Design science research according to Peffers et al. (2007). Abductive approach following Fischer et al. (2012), based on focus group data, literature and conceptual analysis. Reasoning for DSR + focus groups (Hevner and Chatterjee, 2010) • allows “the researcher to clarify any questions about the design
artifact as well as probing the respondents on certain key design issues”
• allows “deeper understandings, not only on the respondents’ reaction and use of the artifact but also on other issues that may be present in a business environment that would impact the design”
• allows “the emergence of ideas or opinions that are not usually uncovered in individual interviews”
Sources: (Peffers et al., 2011; Fischer et al., 2012; Hevner and Chatterjee, 2010)
Target artifact: Design principles for complexity measurement systems
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Design principle
Source: (Aier et al., 2011)
Rationale
Design Principle
Statement Implication Key Action Measure
Design Principle Metamodel (Aier et al., 2011)
• Address the identified problem factors • Used for guidance in the design of complexity measurement
systems • Link real world problems and solutions by a well-defined
structure
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Focus group description
Duration: Three two-day workshops between June 2014 and February 2015
“Complex” Industries:
Sources: (Sigel & Gale, Global Brand Simplicity Index 2014)
Extract from the Global Brand Simplicity Index 2014:
Participants: Between 13 and 16 enterprise architects & IT managers from ten large companies (insurance, banking, utilities)
• Large enough for the emergence of different ideas and viewpoints
• Small enough to discuss complex questions in-depth
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Research method (1) Abductive approach Starting observation: Costly complexity measurement systems and initiatives often fail to inform enterprise transformations.
Understand the problem space, i.e. complexity measurement systems and their relation to complexity management as a part of enterprise transformation management.
Find common hindering factors as potential causes for the starting observation.
Derive solutions for the problem causes.
Data Reports from experts (IT Managers/Architects/Senior Management) and case studies.
Consolidated notes from workshops 1&2 Common measures, goals, processes, etc.
Consolidated notes from all workshops (mostly WS 3)
Conceptualization of focus group data, then mapping well-known research results (e.g. TAM, building consistent ontologies, etc.) to problems.
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Research method (2): Focus groups, literature review and conceptual analysis
Workshop 1 – Goals and Scoping
Need for complexity management: • Why do we try to “manage”
complexity? • How is this currently approached?
Workshop 2 – Conceptual Development
Understanding complexity: • Definitions/Drivers/Measures (What?) • Methods/Processes (How?) • Specific goals (Why?)
Workshop 3 – Complexity Measurement System Design
• Complexity Measurement Frameworks
• Impact of complexity measurement • Inter-construct relations • Context analysis • Usage factors
We used the results (notes of discussions, workshop results, participant presentations) of the previous workshops to prepare guiding frameworks for the next workshop. The cumulative set of documents from all workshops builds the basis for the focus group data.
Exploratory Nature
Exploratory Nature
Confirmatory and Exploratory Nature
• Participant presentation of previous projects
• Open discussion on current complexity problems
• Researcher led workshop (world café method)
• Researcher led discussion • Participant case presentation
• Researcher presentation of consolidated results
• Researcher led workshop on framework development, processes and impact factors
• Open discussion of results
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Agenda
Problem factors and complexity measurement system design principles 3
Methodology 2
Discussion and outlook 4
Complexity measurement as guidance for enterprise transformations 1
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Identification of hindering factors
Measurement System Impact
Hindering Factors
Design Principles
Starting point: Set of all focus-group workshop data (written notes, workshop results [cards, orderings, sortings], participant presentations).
Grouping / Abstraction / Conceptualization: Done by two researchers, definitions and concepts taken from literature (mostly Weidong & Lee, 2005). Surprisingly straightforward, unclear points were discussed in the final workshop.
Completeness: Is merely suggested by the fact that further discussions in a fairly large focus-group as well as our literature analysis did not provide any new factors (saturation). Additional, less common hindering factors might exist for specific organizations/industries.
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Seven common hindering factors Factors that hinder current complexity management systems from informing enterprise transformations :
Complexity Measurement
System
Complexity of the measurement system
Unclear terminology
Measures inapt for goals
Ability to obtain measures
Inconsistent presentation
Lack of support and awareness
Inexplicable results
Measurement System Impact
Hindering Factors
Design Principles
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Construction and validation of design principles
Measurement System Impact
Hindering Factors
Design Principles
Construction starting from hindering factors:
Guided mostly by literature and supported by suggestions from the focus group.
We cannot prove that the four resulting principles are complete. However, they address all identified hindering factors and map nicely to the relevant aspects of Al-Gahtani & King’s TAM (1999). While usage is only a precursor of impact, this at least suggests completeness in that regard.
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Principle A: Context-aware design process
Context-aware design
process
Consistent ontology
Visualization
Awareness and support
Context-aware design process Statement: The design and adaptation of complexity measurement systems needs to be aware of the specific context. Rationale: Depending on the context, certain types of complexity drivers are relevant or irrelevant and not every measure is applicable to every object and system. Implications: Take into account • Specific goals • Targeted users • Types of analyzed objects • Available data
Measurement System Impact
Hindering Factors
Design Principles
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Principle B: Consistent ontology
Context-aware design
process
Consistent ontology
Visualization
Awareness and support
Consistent ontology Statement: Complexity measures should be based on a consistent ontology, which names and describes all relevant objects, properties and their relations. Rationale: Complexity is inherently hard to define precisely, but the relations that lead to a complexity assessment need to be explicable in more detail, in order to allow for actionable advice. Implications: • Give a clear definition of relevant objects • Describe relations between objects/measures • Relate measures to goals and the overall
assessment • Identify and resolve potential conflicts
Measurement System Impact
Hindering Factors
Design Principles
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Principle C: Vizualization
Context-aware design
process
Consistent ontology
Visualization
Awareness and support
Visualization Statement: Results and explanations of complexity assessments should be presented in a simple, unified and consistent fashion. Rationale: Complexity measurement systems must be accessible for users from different roles, organizations and with different knowledge. Results should be consistent across all aggregation levels. Implications: • Comply with corporate design • Use the same format for all reports • Present aggregated and detailed results similarly • Highlight important results in an easy graphical
way
Measurement System Impact
Hindering Factors
Design Principles
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Principle D: Awareness and support
Context-aware design
process
Consistent ontology
Visualization
Awareness and support
Awareness and support Statement: Measurement systems need to be supported by raising awareness and making people responsible for driving their usage and development. Rationale: In order to create impact, people need to be aware of the existence and potential applications of the measurement system. Additionally, the effort involved in the gathering measures needs to be justified by explaining the resulting benefits.
Implications: • Define clear responsibilities • Explain benefits and potential use cases • Train people in the usage of the measurement
system
Measurement System Impact
Hindering Factors
Design Principles
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Agenda
Problem factors and complexity measurement system design principles 3
Methodology 2
Discussion and outlook 4
Complexity measurement as guidance for enterprise transformations 1
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Discussion (1)
• There is considerable interest in structured complexity management, especially for insurance/banking due to increasing regulatory pressure.
• Complexity assessments are an important part of this process. • The suggested principles are a first step towards building better
complexity measurement systems, which might overcome current difficulties.
Relevance
- Abductive approach does not guarantee that these are the only hindering factors and valid design principles - Focus group is influenced by complex and rapidly changing regulatory requirements (banking/insurance)
+ All involved companies employ sophisticated complexity management and reduction programs + Banking and insurance are key industries for complexity management + All participants are very experienced
Generalizability
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Discussion (2)
• Complexity management is more than just measurement, involving e.g. strategic, cultural and leadership components.
• More abstract academic approaches based on complexity theory are not covered in this work – focus group participants found them to be interesting, but not easily applicable to solve their real-world problems.
(Note: At the time of submission, the four identified principles were not yet presented to the participants of the focus group. This has happened in May 2015 with a positive reaction and no change requests)
Limitations (other than generalizability)
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Outlook
• Principles provide first step towards impactful complexity assessments. Still a need to institutionalize these principles in a company and derive adherent processes.
• Strengthen generalizability and design: Quantitative analyses with a broader scope (industry, organizational size) useful to guarantee that the constructs are really complete and disjoint.
• Provide supporting processes and reference models: Use principles to describe concrete process of building a complexity measurement framework.
• Integrate insights from other areas, e.g. for goal identification, control mechanisms, communication principles.
• Integrate insights from complexity theory based research (as long as applicable and communicable to practitioners)