Embedding Knowledge Management in R&D Capability
Transformation in Software Startups
Nabil Georges Badr
a
Independent Researcher and Engaged Scholar, Jacksonville, U.S.A.
Keywords: Knowledge Management, Organizational Development, Organizational Transformation, Action Research.
Abstract: Organizational development drives growth, especially for startups. This study presents a longitudinal action
research engagement exploring the strategic integration of Knowledge Management (KM) practices within
Organizational Development (OD) initiatives to catalyse scalable transformation in a software startup.
Grounded in dynamic capability theory and implemented through the continual improvement framework, the
intervention addressed operational inefficiencies, role ambiguity, and delivery challenges across R&D
functions. By layering KM methodologies, such as centralized repositories, stakeholder-driven assessments,
and iterative feedback loops into OD processes, the engagement reconstructed team structures, codified
decision-making routines, and fostered a culture of collaborative innovation. Through cross-functional
restructuring, strategic role definition, and embedded governance practices, KM was operationalized as both
an infrastructural asset and a dynamic capability enabler. The findings underscore KM’s pivotal role in
enhancing adaptability, aligning leadership vision with execution, and sustaining high-performance
trajectories under volatile growth conditions. This research contributes to startup literature by framing KM
not merely as a support function, but as a strategic lever for organizational resilience, learning, and value
creation.
1 INTRODUCTION
In today’s volatile and fast-paced startup ecosystems,
strategic organizational development (OD) has
emerged as a critical determinant of sustainable
growth and innovation (Cantamessa et al., 2018).
Startups, particularly those in software development,
are uniquely challenged by the need to rapidly scale
operations while navigating resource constraints,
fragmented workflows, and evolving market
expectations (da Silva et al., 2021). These pressures
are compounded by underdeveloped team structures
and ambiguous role definitions, often hindering
performance and resilience during crucial growth
phases.
Generally, Research and Development (R&D)
functions serve as the strategic nucleus for software
startups, positioning knowledge creation and
integration at the center of capability transformation.
Yet, to unlock the full potential of R&D
contributions, startups must engage in intentional OD
practices that harmonize human, procedural, and
a
https://orcid.org/0000-0001-7110-3718
technological assets. Knowledge management (KM)
thus becomes indispensable—not merely as a tool for
operational efficiency, but as a socio-technical
framework for cultivating dynamic capabilities
(Alavi & Leidner, 2001), institutionalizing learning
(Nonaka, 2009) (Nonaka & Takeuchi, 1995), and
driving organizational renewal (Ren & Argote, 2011).
Effective KM methodologies enable startups to
transform tacit insights into actionable routines,
thereby enhancing collaborative engagement and
innovation throughput. Practices such as centralized
repositories, iterative feedback loops, and cross-
functional learning rituals support adaptability while
mitigating knowledge fragmentation. When
embedded within an OD strategy, KM not only
elevates execution but serves as a strategic lever for
resilience and value co-creation (Bharadwaj et al.,
2015).
This paper presents a longitudinal action research
case study exploring the strategic deployment of KM
methodologies to catalyze organizational
transformation in a scaling software startup. Guided
Badr, N. G.
Embedding Knowledge Management in R&D Capability Transformation in Software Startups.
DOI: 10.5220/0013656800004000
Paper published under CC license (CC BY-NC-ND 4.0)
In Proceedings of the 17th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2025) - Volume 2: KEOD and KMIS, pages
323-331
ISBN: 978-989-758-769-6; ISSN: 2184-3228
Proceedings Copyright © 2025 by SCITEPRESS – Science and Technology Publications, Lda.
323

by continual improvement processes and grounded in
OD theory and capability lifecycle models (Adelman,
1993; Coghlan, 2019), the study demonstrates how
knowledge-led interventions, aligned with leadership
priorities and customer-centric design, can unlock
measurable improvements in delivery, innovation,
and operational alignment. Through an integrated
framework, the paper illuminates how KM functions
as the cornerstone of sustainable growth in emergent
organizational settings.
2 BACKGROUND
2.1 Organizational Development in
Startups
In highly dynamic startup ecosystems, organizational
development (OD) emerges as a deliberate, theory‐
driven intervention to foster capability building,
improve process maturity, and align culture with
strategic objectives. OD originated as a planned,
long‐term effort to enhance an organization’s renewal
processes through applied behavioral science and
change‐agent facilitation (Beckhard, 1969). Over
time, OD theories have evolved to address both
stability and change, integrating concepts of learning,
resilience, and adaptability, qualities vital for young
ventures navigating uncertainty (Garengo &
Bernardi, 2007).
Startups differ from established firms in that they
operate under extreme resource constraints,
accelerated growth expectations, and evolving
business models (da Silva et al., 2021). These
conditions frequently give rise to fragmented
workflows, role ambiguity, and ad hoc decision
making, which can derail performance and inhibit
scaling (Giardino et al., 2015). Early OD efforts in
startups must therefore emphasize structural clarity,
process standardization, and team capability
development to mitigate the high failure rate observed
in the first five years of operation (Cantamessa et al.,
2018).
2.2 Essential KM Practices for OD
Within OD, continuous improvement models offer a
structured lens for integrating KM into
transformation efforts. Organizational development
practices prescribe learning cycle, for example,
prescribes iterative phases of assessment,
intervention, and monitoring, enabling startups to
validate knowledge‐led changes and calibrate
interventions in real time (Beckhard, 1969).
Similarly, action research methodology, rooted in
practitioner inquiry and collaborative problem
solving, has been successfully applied to guide
iterative OD interventions where KM artifacts, such
as dashboards and knowledge maps, serve as both
diagnostic and change‐management tools (Coghlan,
2019).
Knowledge management (KM) practices
complement OD by offering systematic practices to
capture, codify, and disseminate both tacit and
explicit knowledge, thereby institutionalizing
learning and driving dynamic capabilities (Nonaka,
2009). In nascent ventures, where knowledge resides
disproportionately with founding teams or key
technical experts, KM practices such as centralized
repositories, collaborative rituals, and feedback loops
become foundational to preserving critical insights
and preventing knowledge loss as teams expand
(Badr, 2018). KM frameworks rest on socio‐technical
foundations, recognizing that people, processes, and
technology must coalesce to enable effective
knowledge flows (Ren & Argote, 2011). For startups,
embedding KM as a socio‐technical system within
OD interventions ensures that knowledge‐centric
routines—such as design reviews, post‐mortems, and
best‐practice coding standards—are not peripheral
activities but core organizational processes that
reinforce innovation and operational consistency
(Bharadwaj et al., 2015).
2.3 KM as Dynamic Capability
A stream of research highlights the linkage between
KM capabilities and organizational performance in
technology‐driven contexts. For instance, startups that
invest in knowledge repositories and collaborative
platforms report faster product iterations and improved
cross‐functional coordination, leading to shortened
time‐to‐market and enhanced customer responsiveness
(da Silva et al., 2021). These findings underscore the
dual role of KM in driving both efficiencies through
process codification, and innovation via knowledge
recombination and serendipitous learning.
Dynamic capabilities theory further articulates
how firms sense opportunities, seize resources, and
transform operations to maintain competitive
advantage. In startup settings, the integration of KM
within OD reconstructs routines that underpin
dynamic capabilities, such as rapid prototyping,
customer‐centered iteration, and ambidextrous
exploration, thus enabling ventures to pivot
effectively and sustain value creation under volatile
market conditions (Badr, 2018).
Empirical studies of startup transformations
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reveal that leadership commitment and cultural
alignment are critical precursors to KM‐driven OD
success. Founders and early executives must model
knowledge‐sharing behaviors, allocate resources for
knowledge infrastructure, and incentivize cross‐team
collaboration to overcome the inertia of informal
practices (Bharadwaj et al., 2015). Without this
sponsorship, KM initiatives risk devolving into
disconnected artifacts rather than becoming integral
elements of organizational capability. As startups
scale, the interplay between KM and OD influences
talent management and organizational architecture.
Role clarity exercises (e.g., RACI matrices) and
competency taxonomies help delineate
responsibilities, reduce overlaps, and foster
accountability (Garengo & Bernardi, 2007).
Concurrently, knowledge bases must evolve to
support branching capability lifecycles—retiring
obsolete practices, renewing critical skills, and
redeploying intellectual assets into new product
domains (Badr, 2018).
Notwithstanding these benefits, startups face
barriers in operationalizing KM within OD. Common
challenges include limited change‐management
expertise, competing short‐term priorities, and
technology adoption hurdles in resource‐constrained
environments (Okanović et al., 2020). Addressing
these obstacles requires a phased approach: initial
low‐overhead practices (e.g., peer reviews, learning
retrospectives), followed by incremental investments
in digital platforms, and culminating in governance
structures, such as change control boards and design
councils, that institutionalize knowledge flows
(Tunnicliffe et al., 2021).
3 APPROACH
Our study builds on this rich theoretical and empirical
foundation by examining how an 18-month, action‐
research–driven OD initiative leveraged integrated
KM methodologies to transform a software startup’s
R&D capability. Informed by OD principles, the
intervention prioritized leadership engagement,
customer-centric assessments, and the establishment
of a central knowledge base. By synthesizing
continuous improvement cycles with dynamic
capability theory, the case illustrates how KM can
function as both catalyst and enabler of startup
resilience, innovation, and scalable performance.
3.1 Case Study Setting
The General Manager of Company X commissioned
us to investigate and recommend measures to address
the dual demands of sales oversight and growth
acceleration at the three-year-old startup. At that
point, Company X, a software development firm
holding significant government contracts and guided
by a proactive leadership team, employed 48 full-time
staff. Its structure included a small sales force, an
operations unit responsible for logistics, project
management, and post-implementation support, and a
16-member R&D department charged with core
software development and solution delivery.
Although a human resources team handled
recruitment and compensation, organizational
development efforts were ad hoc, making sustainable
expansion feel out of reach. The GM had growing
concerns about R&D’s performance, chronic project
delays, uneven team output, blurred role definitions,
and uncertainty around deliverable statuses for key
clients. The engagement was thus designed to achieve
three strategic goals: enhance delivery capability and
precision, establish long-term revenue growth, and
strengthen the startup’s competitive position.
3.2 Engagement Summary
Following initial scoping sessions with the General
Manager and R&D Director, we structured the
engagement into three concurrent streams, each with
defined deliverables (see Figure 1).
Figure 1: Company transformation engagement of three
parallel streams of activities.
Stream 1 – Strategic and Operational Assessment:
This stream carried out a thorough review of the
company’s strategic initiatives and operational
practices. Activities included clarifying strategic
objectives, auditing active projects, and vetting future
opportunity pipelines. We also mapped technology
strategy gaps and adjusted pre-sales proposals to better
match market demands. From these insights, we
Embedding Knowledge Management in R&D Capability Transformation in Software Startups
325

formulated targeted improvement recommendations
and implemented changes to bolster strategic
coherence and operational efficiency.
Stream 2 – Technical Platform Architectural
Guidance: Here, we evaluated the deployment
readiness of core technical components and tackled
client challenges by introducing automation and agile
practices. We developed and refined product
architecture models to align with organizational
standards and support seamless implementation.
Additionally, we delivered a modular platform
blueprint for future product launches, balancing
architectural rigor with project oversight.
Stream 3 – Organizational Assessment: Focused on
optimizing how the company delivers products and
solutions, this stream reviewed team structures and
processes against delivery objectives, then issued
prioritized recommendations for improvement. Over
an 18-month period, we tracked and evaluated the
implementation of those changes to ensure ongoing
organizational refinement and sustained performance
gains.
We conducted our longitudinal case study
centered on the organizational assessment stream
(stream 3). This paper however uses insights from
streams 1 and 2 were instrumental in framing the
broader transformation effort in framing the broader
transformation effort.
3.3 Empirical Inquiry
Once leadership endorsement was secured, the
process began in three cycling stages (Figure 2).
Figure 2: Continual organization improvement ARM
process (by the author Inspired by the learning cycle of
continuous improvement (Tunnicliffe et al, 2021).
The first stage, Assess, focuses on initial
information gathering aimed at surfacing tacit
knowledge and structuring organizational insight into
actionable recommendations. This foundation guides
the second stage, Remediate, which involves the
formalization and implementation of proposed
changes derived from the assessment phase. Finally,
the Monitor (Third) stage encompasses the
operational rollout, the definition of performance
metrics, and the ongoing evaluation of effectiveness,
often triggering a return to the Assess phase for
additional data collection and refinement. The three
stages repeated iteratively as fresh data continually
refined each cycle.
At the outset, we convened with the General
Manager and the engagement sponsor to agree on our
methodology, action plan, and desired outcomes. This
kickoff meeting framed the initiative as a formal
improvement effort, fostered trust across the
organization, and created a safe space for open
dialogue (Table 1 for the action plan details).
Table 1: Action Research - Organizational development
Activities Calendar.
TIMELINE ACTIVITIES
WEEK 1
Launch Stage 1 - Assess: (Initial
Assessment)
WEEK 2 Perform initial operational assessment
WEEK 3
Review Employees Personality Tests
conducted by HR upon hire in order to realign
team structures as required.
WEEK 5
Focus on R&D organization - Conduct Team
Climate survey and 1:1 exploratory interview
and produce detailed transcripts
WEEK 9 Complete Assessment and Issue report
WEEK 10 Review report with stakeholders
WEEK 13
Communicate the report’s findings to the
teams and review the recommendations for
final feedback
WEEK 16
Launch Stage 2 - Remediate: Formalize the
changes proposed in Phase 1
WEEK 40
Complete activities for transformation (see
stage 2 activities)
WEEK 42
Review progress with stakeholders and decide
on next action. Through interviews with
middle managers and an assessment by HR
and General manager.
WEEK 43
Launch Stage 3 - Monitor: Operationalize
the changes and assess further organizational
needs. It was determined that another
extension is required to anchor the changes
and assess further organizational needs of
operating, business and functional units.
WEEK 62
Contract was terminated early due to the
completion of the duties required and the
promotion of the essential GM stakeholder to
a new position. Another contract was setup for
other objectives of strategy and corporate level
development.
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3.4 Assess (Stage 1)
During Stage 1 (Assess) we partnered with the GM
and R&D director to conduct an in-depth review of
the R&D division, then planned to extend the process
to other departments by defining interdepartmental
metrics that would feed into companywide
performance evaluations.
In Week 5, we dedicated a full day to capturing
each of the 16 R&D team members’ “voice of the
customer” through individual, 30-minute,
conversational interviews. Participants were prepared
to share (1) their current tasks and priorities, (2)
projects underway, and (3) challenges they wanted
addressed. We probed deeper on recurring themes,
distilled the main improvement ideas, and validated
them with R&D leadership.
All interview transcripts were retained to
preserve the authenticity of their input. Transcripts
were retained to safeguard organizational memory
and validate insights with leadership. We triangulated
findings using transcript coding, HR personality
assessments (reviewed post-analysis to avoid bias),
and alignment checks between challenges and self-
proposed solutions, marking initial knowledge
structuring efforts.
We then conducted data analysis in three phases.
First, we coded each transcript for team
assignment, role, responsibilities, and feedback,
supplementing it with my observational notes.
Second, we reviewed employee personality
assessments from HR—conducted at hire—to
contextualize team dynamics, deliberately analyzing
our interview data first to avoid bias. Third, we
compared informants’ reported issues with their own
suggested remedies, using this alignment as a proxy
for willingness to change. Finally, we clustered
feedback into key observations and action items.
In summary (Table 2), team members cited poor
communication and visibility, particularly around
feature handoffs between PMO, R&D, and
Operations.
The assessment revealed lack in role clarity. The
R&D director had grown the team reactively, leading
to duplicated responsibilities across two product‐
focused subgroups, quality and delivery problems,
cost overruns, and elevated risk to customer
satisfaction and reputation.
Process handoff issues were reported. Process
handoff issues between PMO, R&D and Operations
were complicated with the lack of knowledge
management and documentation: there was no clear
insight regarding what features are currently being
implemented and how they work, leading to surprises
with the clients. Processes covering release
management lacked defined timelines and scope
controls, resulting in surprise deliverables and
inconsistent quality.
Table 2: Points of feedback from the data collection.
Observation Feedback from the data collection
Communication
and reporting
issues
“Total lack of communication from the
R&D team”
“Lack of visibility from upper
management regarding all activities
with the R&D team.”
Process
handoff issues
Process handoff issues between PMO,
R&D and Operations: no insight
regarding what features are currently
being implemented and how they work,
leading to surprises with the clients
Diminished
ability to deliver
Release management misalignment with
desired product objectives and customer
expectations: no time scope and
expected release date of new features.
Quality issues
“Quality assurance reviews are not
effective: serious lack of quality, due to
the fact that some features get
implemented without documentation
and in-depth analysis of the feature”.
Role clarity
Empowerment of PMO is lacking -
Ambiguous role and authority of PM
regarding all R&D functions - no
respect for deadlines, with no heads up
regarding any causes of delays
By the end of Week 9, we compiled our
assessment into a report outlining our findings and the
recommended next steps.
In Week 10, the GM convened stakeholders to
review and refine the report.
We performed an initial, SWOT analysis,
highlighting strengths (S), challenges (Weaknesses),
leadership imperatives (Opportunities), and priority
fixes (Threats). This analysis is a strategic planning
tool used to identify and evaluate an organization's
internal Strengths and Weaknesses, alongside
external Opportunities and Threats, to inform
decision-making and goal setting.
By Week 13, we presented the finalized SWOT-
driven recommendations. in a feedback session.
These high‐level delivery process proposals aim to
improve client engagement, streamline operations,
and support sustainable growth.
3.5 Formalize the Organization
(Stage 2)
Beginning in Week 16, we collaborated with middle
management and HR to translate the assessment
findings into concrete interventions.
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Remediation activities were organized into repeating
cycles of action and feedback, ensuring that each
iteration was built upon the last. Below is a summary
of the principal initiatives:
First, we facilitated a RACI analysis across the
organization to delineate responsibilities, eliminate
overlaps, and sharpen accountability—particularly
distinguishing between “solution development” and
“product packaging,” with the PMO orchestrating
their intersection. The RACI workshops clarified
responsibilities and surfaced latent organizational
knowledge. A RACI matrix stands for Responsible,
Accountable, Consulted, and Informed is a structured
responsibility assignment tool used in project
management to clarify roles and streamline decision-
making across tasks. It helps prevent ambiguity by
explicitly mapping each activity to stakeholders
based on their level of involvement.
Under the existing R&D director, the original
group split into Integration (requirements and
design), Customization (development and
configuration), and Delivery (QA and release
management). Each pod adopted two-week Agile
sprints to accelerate feedback and defect detection.
Agile pods accelerated knowledge loops through
biweekly retrospectives and sprint-based reviews.
Based on the RACI outcomes, a decision was
made to add two new roles to close apparent gaps.
One role was a Solutions Architect to formalize
design rigor before client handoff, and a dedicated
PMO lead to smooth engagement transitions and
reinforce project governance. In partnership with HR,
we redesigned job profiles into three tiers (e.g.,
Junior, Mid-level, Senior Developer) and accelerated
hiring for both new and backfilled roles. This tiered
structure clarified career paths and fostered cross-
disciplinary skill building.
We then established a formal communication
plan between the R&D team and the PMO, and
project managers were empowered to own scope,
timelines, and customer satisfaction throughout
solution delivery. The addition of a Solutions
Architect role institutionalized design knowledge and
ensured rigor prior to handoffs. The PMO Lead role
served as a conduit for codified best practices and
client-facing insights.
3.6 Operationalize Change (Stage 3)
From Week 43 onward, we maintained an ongoing
cycle of observation and adjustment to guide a
sustainable transformation. The latter requires more
than strategic intention, it demands robust change
governance frameworks that align institutional
structures, stakeholder engagement, and adaptive
learning processes. This is particularly critical in
dynamic environments where transformation efforts
often falter due to fragmented leadership or lack of
accountability (Rieg et al., 2021).
Hence, building on the assessment, we set out to
formalize our improvements. We convened a cross-
functional steering committee which met biweekly to
track progress and resolve roadblocks.
Two formal committees were established: a
Change Control Board to vet all internal and external
modifications, and a Design Review Committee to
ensure adherence to standards and client
requirements. These forums were structured to meet
regularly an embed learning and continuous
improvement. KM was embedded directly into
delivery processes and formalized through the Design
Review Committee and Change Control Board,
establishing knowledge as a governance asset.
We codified an end-to-end delivery process
aligned with the new team structure, supplemented by
practices of change management to support adoption.
After reviewing options, we configured Jira
(integrated into a KM platform, Confluence) to
automate workflow tracking, deliverable reporting,
and quality dashboards, thus reducing manual status
updates and enhancing visibility across stakeholders.
These mechanisms provided operational
structure and a sustainable change platform, allowing
the organization to store, share, and act on its
collective insight. Leveraging the same Atlassian
suite, we built a centralized repository for design
documents, best practices, and feedback loops,
enabling faster decision-making and safeguarding
institutional memory. The deployment of Jira and
Confluence created a living repository for design
assets, delivery templates, and continuous feedback,
bridging departmental silos and enhancing real-time
decision-making.
Throughout this phase, we addressed individual
resistance through one-on-one coaching and
customized adoption plans. As role clarity improved
and new hires completed training, team cohesion
strengthened. On Week 40 the organization was
realigned with measurable performance gains.
During Week 42 progress review, stakeholders
elected to extend the engagement briefly to
consolidate these changes into a sustainable plan for
the next product-line rollout.
Our initiative then focused on reinforcing KM
through performance metrics, coaching, and adaptive
monitoring. In collaboration with senior stakeholders,
we defined clear organizational objectives and
designed measurement methods to track progress
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against both top-down and cross-functional targets.
Performance dashboards and centralized
documentation created a feedback-rich environment
that supported iterative refinement and innovation.
Team performance goals were tied to metrics
reflecting cross-team achievements, reinforcing a
culture of shared success. These metrics were
incorporated into regular performance appraisals,
ensuring continuous monitoring and improvement.
Critical information became instantly accessible
through the knowledge repository, improving
onboarding and sustaining project momentum as the
organization scaled. Embedding knowledge
management practices created real-time feedback
loops and performance dashboards that fueled
innovation, collaboration, and iterative refinement. A
centralized knowledge repository made critical
information instantly accessible and kept
documentation up to date.
By Week 60, the company had won two
substantial government contracts, requiring the
addition of roughly 70 new employees. The prior
organizational transformation strengthened the firm’s
dynamic capabilities, enabling it to absorb this rapid
growth with minimal disruption. The structured KM
tools enabled transition of knowledge to the new team
and powered their productivity.
3.7 Celebrating Success
Our intervention markedly altered the company’s
performance trajectory. Within 90 days, a previously
stalled development initiative was revived,
reputational risk was mitigated, customer confidence
was restored, and the firm recovered $2 million in lost
revenue.
On Week 62, we formally closed the action
research engagement—with a celebratory “cake
ceremony”, having met our transformation
objectives.
Although the formal OD activities concluded, the
organization continued to pursue strategic and
corporate goals under a continuous‐improvement
ethos. The organization had realized significant gains,
reviving stalled projects, recapturing revenue,
restoring stakeholder confidence, and winning major
contracts. The final celebration marked not just the
completion of a formal OD engagement, but the
emergence of a knowledge-led enterprise.
While classic OD principles provided a
framework for change, it was the intentional layering
of KM, from tacit insight capture to automated
repositories and governance integration, that
ultimately powered adaptability, alignment, and
innovation. KM transformed scattered data points
into a strategic resource, elevating both human and
organizational potential.
In the weeks that followed, the GM was
promoted to oversee the holding company,
underscoring the lasting impact of our organizational
transformation effort.
4 CONCLUSION
Our approach was built upon the foundational
elements proven to drive successful organizational
transformation (Beckhard, 1969): steadfast
leadership engagement, a clearly articulated vision,
structured change‐management processes, a
customer‐centric and adaptive culture, seamless
technology integration, ongoing improvement cycles,
with decision making grounded in data.
4.1 Strategic KM as Catalyst for
Startup Growth
Success in OD hinges on the startup’s ability to
translate evolving insights into scalable practices. As
demonstrated in our case study, embedding robust
KM practices and empowering the human capital
were strategic imperatives.
Startups that prioritize early investment in KM
frameworks and choose enabling tools with intention
set themselves apart. Proper KM empowers teams to
harness institutional knowledge, reinforce
accountability, and sustain adaptive capacity as scale
increases. With a unified knowledge base powering
decision-support tools, automation, and feedback
mechanisms, startups transform insight into action,
continuously.
Selecting appropriate KM tools, those aligned
with the organization’s values, workflows, and
culture, ensures that knowledge capture and retrieval
are frictionless. Whether through integrated platforms
for real-time collaboration (e.g., Notion, Confluence),
advanced tagging and semantic search engines, or
lightweight consensus and annotation layers for
distributed decision-making, these tools shape how
effectively organizations learn and adapt.
Structured KM tooling also facilitates contextual
reliability modeling—preserving not just data, but the
reasoning and lived experience behind decisions. This
depth supports post-mortem analyses, accelerates
onboarding, and reinforces psychological safety,
critical for experimentation and creative problem-
solving. When embedded into governance structures
such as Change Control Boards or steering
Embedding Knowledge Management in R&D Capability Transformation in Software Startups
329

committees, KM ensures continuity, traceability, and
shared mental models. Decision-support systems
grounded in transparent knowledge access increase
stakeholder trust and reduce cognitive load across
roles.
As scaling demands faster pivots, KM becomes
the compass guiding teams through complexity,
enabling responsible risk-taking without sacrificing
coherence. Furthermore, fostering a knowledge-
sharing culture through inclusive storytelling, active
listening, and open feedback loops amplifies the
impact of OD efforts. From humble inquiry to cross-
functional retrospectives, the social dimension of KM
unlocks innovation that is emergent, co-created, and
meaningful.
Figure 3: Strategic KM as Catalyst for Startup Growth.
4.2 Contribution and Limitations
This study advances scholarship at the intersection of
knowledge management (KM), organizational
development (OD), and startup growth by
demonstrating how KM can be operationalized as a
dynamic capability enabler in resource-constrained
R&D environments. Despite these contributions, the
study’s findings should be interpreted considering
some limitations. The dual role of practitioner-
researcher inherent in action research introduces
potential bias in data interpretation and intervention
design, despite triangulation efforts across
interviews, artifacts, and performance dashboards.
To build on this work, we propose avenues for
scholarly inquiry. Mainly, this work sets the stage for
multi-case comparative research across diverse
startup sectors can illuminate boundary conditions for
KM-embedded OD effectiveness and reveal potential
industry-specific adaptations. Additionally,
experimental or quasi-experimental designs could
isolate the impact of KM strategy elements, such as
governance forums versus repository structures,
independent of technology platforms.
Nevertheless, our paper concretizes that by
embedding strategic KM into capability
transformation, startups could accelerate internal
growth but also position themselves as learning
organizations capable of driving ecosystem-wide
change.
REFERENCES
Adelman, C. (1993). Kurt Lewin and the origins of action
research. Educational Action Research, 1(1), 7–24.
Alavi, M., & Leidner, D. E. (2001). Knowledge
management and knowledge management systems:
Conceptual foundations and research issues. MIS
Quarterly, 107–136.
Badr, N. G. (2018). Enabling bimodal IT: Practices for
improving organizational ambidexterity for successful
innovation integration.
Beckhard, R. (1969). Organization development: Strategies
and models.
Bharadwaj, S. S., Chauhan, S., & Raman, A. (2015). Impact
of knowledge management capabilities on knowledge
management effectiveness in Indian organizations.
Vikalpa, 40(4), 421–434.
Cantamessa, M., Gatteschi, V., Perboli, G., & Rosano, M.
(2018). Startups’ roads to failure. Sustainability, 10(7),
2346.
Coghlan, D. (2019). Doing action research in your own
organization.
da Silva, L. S. C. V., Kaczam, F., de Barros Dantas, A., &
Janguia, J. M. (2021). Startups: A systematic review of
literature and future research directions. Revista de
Ciências Da Administração, 23(60), 118–133.
Garengo, P., & Bernardi, G. (2007). Organizational
capability in SMEs: Performance measurement as a key
system in supporting company development.
International Journal of Productivity and Performance
Management, 56(5/6), 518–532.
Giardino, C., Bajwa, S. S., Wang, X., & Abrahamsson, P.
(2015). Key challenges in early-stage software startups.
International Conference on Agile Software
Development, 52–63.
Nonaka, I. (2009). The knowledge-creating company. In
The economic impact of knowledge (pp. 175–187).
Routledge.
Okanović, M. Ž., Jevtić, M. V., & Stefanović, T. D. (2020).
Organizational changes in development process of
technology startups. International Conference of
Experimental and Numerical Investigations and New
Technologies, 203–219.
Ren, Y., & Argote, L. (2011). Transactive memory systems
1985–2010: An integrative framework of key
dimensions, antecedents, and consequences. Academy
of Management Annals, 5(1), 189–229.
KMIS 2025 - 17th International Conference on Knowledge Management and Information Systems
330

Rieg, N. A., Gatersleben, B., & Christie, I. (2021).
Organizational Change Management for Sustainability
in Higher Education Institutions: A Systematic
Quantitative Literature Review. Sustainability, 13(13),
7299. https://doi.org/10.3390/su13137299
Tunnicliffe, M., Brown, N., & Shekar, A. (2021). Rapid
Learning Cycles for project-based learning. REES
AAEE 2021 Conference: Engineering Education
Research Capability Development: Engineering
Education Research Capability Development, 1094–
1103.
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