on the role of complexity for guiding enterprise ... · – “architectural thinking” not...

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

© Jun-15, IWI-HSG Slide 2

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


Agenda

Problem factors and complexity measurement system design principles 3

Methodology 2

Discussion and outlook 4

Complexity measurement as guidance for enterprise transformations 1


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


Complexity results from a series of (incremental) changes

Complexity Measurement

System

External Requirements Internal Changes

Decision makers (not necessarily management)

Operationalized measures

Complexity assessment


Complexity measurement as guidance (1)


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.


Complexity measurement as guidance (2)

Long-term goal: Complexity measurement as an integral part of complexity manage-ment and organizational/structural decision processes.


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)


Agenda


Methodology 2




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


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


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


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.


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


Agenda


Methodology 2




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.


Seven common hindering factors Factors that hinder current complexity management systems from informing enterprise transformations :


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


Hindering Factors

Design Principles


Construction and validation of design principles


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.


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


Hindering Factors

Design Principles


Principle B: Consistent ontology


process

Consistent ontology

Visualization


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


Hindering Factors

Design Principles


Principle C: Vizualization


process

Consistent ontology

Visualization


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


Hindering Factors

Design Principles


Principle D: Awareness and support


process

Consistent ontology

Visualization


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


Hindering Factors

Design Principles


Agenda


Methodology 2




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


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)


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)


Q & A

Robert Winter [email protected] www.iwi.unisg.ch +41 71 224 21 90

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