A Conceptual Model and Empirical Validation Agenda

Dr Riaan Steenberg
30 August 2026

Abstract

Innovation is generally evaluated by comparing anticipated benefits with direct investment costs. This treatment assumes that an approved innovation moves more or less directly from selection to value creation. In practice, potentially valuable innovations frequently lose value during implementation through internal delay, rework, disruption, low adoption, governance failure, capability gaps, and continued dependence on legacy systems. Existing research explains many of these barriers but does not provide a consistent project-level method for translating them into monetary value loss while distinguishing implementation failure from poor strategic selection, external shocks, necessary investment, and sunk expenditure. This conceptual paper develops the Frictional Cost Calculation Framework (FCCF) to address that gap. The framework separates three constructs: organisational innovation friction, defined as the internally controllable conditions that impede implementation; frictional value loss, defined as the discounted value gap between a feasible-efficient implementation and actual implementation; and friction-adjusted innovation value, defined as realised innovation value relative to the status quo or another strategic alternative. The central identity is that friction-adjusted innovation value equals potential innovation value less frictional value loss. The framework further separates strategic selection loss from execution friction, decomposes frictional value loss into excess cost and benefit leakage, and provides rules for treating delay, adoption shortfall, cannibalisation, legacy assets, switching costs, risk, and public value. A sequential mixed-methods validation programme is proposed, combining evidence synthesis, retrospective case calibration, psychometric development, project-level financial modelling, multilevel analysis, predictive validation, and stochastic frontier estimation. The FCCF contributes to innovation and implementation theory by operationalising organisational friction as measurable value leakage and offers practitioners a disciplined method for identifying where innovation value is destroyed.

Keywords: innovation implementation; organisational friction; frictional cost; value realisation; organisational readiness; X-efficiency; shareholder value; public value; benefit leakage; strategic selection

1. Introduction

Innovation is central to organisational adaptation, competitiveness, service quality, and long-term survival. This is especially true in service settings, where innovation often requires simultaneous changes to technology, employee behaviour, customer interaction, workflows, governance, and knowledge systems. Service innovation is therefore not reducible to the acquisition of a new technology or the launch of a new product; it frequently involves the reconfiguration of multiple interdependent characteristics of the service system (Gallouj & Weinstein, 1997). Dynamic capabilities theory similarly emphasises that value depends not only on recognising opportunities but on seizing them and reconfiguring organisational assets and routines to exploit them (Teece, 2007).

Yet the adoption of an innovation does not ensure its effective implementation. Klein and Sorra (1996) distinguish the decision to adopt an innovation from the quality and consistency of its subsequent use. This distinction is critical because an innovation may be strategically attractive in an approved business case while failing to generate the expected value after approval. Delayed decisions, duplicated work, unstable sponsorship, inadequate capability, conflicting incentives, resistance, technical debt, parallel legacy systems, and low user adoption can progressively erode the economic case. These losses are often spread across budgets, departments, and periods and are therefore obscured by conventional project reporting.

The original idea underlying this paper is that innovation should not automatically be treated as value adding. New innovation may displace earlier investments, cannibalise existing services, force a change in strategic direction, and introduce risks that exceed the expected return. However, the reverse is also true: continuing an existing course has an opportunity cost, and prior expenditure should not be allowed to create a sunk-cost bias in favour of continuation. The analytical challenge is therefore not merely to list innovation barriers. It is to distinguish at least four questions:

1. Was the proposed innovation economically preferable to continuing the existing course?
2. Was the best available strategic alternative selected?
3. How much value was lost while implementing the selected alternative?
4. What proportion of that loss was attributable to internal, controllable organisational factors rather than external shocks or inherent project difficulty?

Existing theories address parts of this problem. Transaction cost economics explains the costs of coordinating and governing exchanges (Williamson, 1985). Organisational ecology explains structural inertia and the difficulty of changing established routines (Hannan & Freeman, 1984). Research on exploration and exploitation describes the tension between developing new possibilities and extracting returns from established competencies (March, 1991). Organisational change research explains readiness, resistance, implementation climate, and recipient responses (Klein & Sorra, 1996; Oreg et al., 2011; Piderit, 2000; Weiner, 2009). Corporate finance and real-options approaches provide methods for valuing expected cash flows, uncertainty, delay, flexibility, and abandonment (Amram & Kulatilaka, 1999; Dixit & Pindyck, 1994; Rappaport, 1986). What remains insufficiently developed is an integrative method that converts internal implementation impediments into a defensible value gap without double-counting investment, risk, cannibalisation, and legacy loss.

This paper develops the Frictional Cost Calculation Framework (FCCF) as a conceptual response. Its central argument is that innovation creates potential value, not automatic realised value. The value eventually realised is a function of strategic selection quality and implementation efficiency. The FCCF therefore defines frictional cost not as every cost associated with change, but as the discounted monetary value of avoidable excess resource consumption and unrealised benefits attributable to organisational impediments, measured relative to a feasible and risk-adjusted implementation benchmark.

The paper makes five contributions. First, it differentiates organisational innovation friction, frictional value loss, and friction-adjusted innovation value. Second, it separates strategic selection loss from execution friction. Third, it develops an internally consistent calculation framework that decomposes value leakage into excess cost and benefit shortfall. Fourth, it proposes a falsifiable model for testing whether internal factors explain more frictional value loss than external factors. Fifth, it sets out a staged programme for construct, financial, predictive, and cross-sector validation in banking, education, government, and IT services.

The guiding research questions are:

RQ1. How can organisational innovation friction be distinguished from necessary innovation investment, strategic selection error, external shocks, sunk expenditure, and legacy transition costs?

RQ2. Which internal cultural, governance, structural, capability, and legacy factors contribute to frictional value loss?

RQ3. Through what mechanisms do internal organisational factors produce excess cost, implementation delay, disruption, and benefit leakage?

RQ4. How can frictional value loss be calculated relative to a feasible implementation counterfactual?

RQ5. Do internal organisational factors explain more variation in frictional value loss than external environmental factors after project characteristics are controlled?

RQ6. Is the FCCF generalisable across different service sectors and across private- and public-value settings?

2. Theoretical foundations

2.1 Innovation, adoption, implementation, and value realisation

Innovation research encompasses the generation, adoption, implementation, diffusion, and routinisation of new products, services, processes, organisational forms, and business models. These stages should not be collapsed. A decision to adopt establishes an intention to use an innovation; implementation concerns the transition from that intention to consistent and appropriate use. Klein and Sorra (1996) argue that implementation effectiveness depends on the implementation climate and the fit between the innovation and users' values. This provides a direct theoretical bridge between organisational context and realised innovation outcomes.

Damanpour's (1991) meta-analysis demonstrates that organisational determinants of innovation are multidimensional and context dependent. Crossan and Apaydin (2010) similarly show that organisational innovation involves interacting leadership, managerial, and business-process dimensions. These traditions provide strong explanations of why innovation activity varies, but common outcome measures – adoption, number of innovations, perceived success, or organisational performance – do not isolate the financial effect of implementation friction.

The distinction between technical success and value realisation is particularly important. A project may deliver software that functions technically while failing to generate expected benefits because users avoid it, parallel procedures continue, decisions remain slow, or business processes were never redesigned. Conversely, a project may exceed its original budget yet still create value if the additional expenditure was necessary to capture a substantially larger opportunity. Cost variance alone is therefore not a sufficient measure of innovation friction.

For the FCCF, innovation value realisation is defined as the extent to which the organisation captures the economic, operational, educational, social, or public benefits that made the innovation preferable to an identified alternative. The preferred outcome measure depends on context. Commercial organisations may use enterprise or shareholder value; private educational institutions may combine financial value and educational performance; public organisations may use net social value or cost-effectiveness. The framework is therefore anchored in organisational value realisation, with shareholder value as one application rather than the universal outcome.

2.2 Organisational friction and X-efficiency

The term friction has a useful economic antecedent. Leibenstein (1966) introduced X-efficiency to explain why organisations using similar resources may operate at different levels of efficiency. Rather than assuming that firms automatically minimise costs, X-efficiency recognises internal variation in effort, motivation, coordination, and managerial effectiveness. Leibenstein (1969) later linked organisational or frictional equilibria to the rate of innovation. This suggests that friction may be understood as the distance between observed performance and an attainable organisational efficiency frontier.

The FCCF extends this insight from the organisation or production-unit level to the innovation-project level. It proposes that, given a selected innovation and a feasible set of resources, there is a range of implementation outcomes. The efficient benchmark is not a perfectly frictionless ideal. It is the value that could reasonably have been achieved by a competent organisation facing the same material external conditions. Actual performance below that benchmark constitutes an implementation value gap. The portion of that gap attributable to controllable organisational impediments is frictional value loss.

This formulation has three advantages. First, it avoids classifying every implementation cost as friction. Necessary training, process redesign, data migration, customer communication, or risk control remains part of the efficient cost of implementation. Second, it requires an explicit counterfactual. Without a benchmark, an observed cost or delay cannot be identified as excess. Third, it allows the benchmark to be estimated by reference-class forecasting or frontier methods rather than relying solely on an optimistic original business case.

2.3 Resistance, readiness, and implementation climate

Resistance is frequently invoked as an explanation for failed change, but treating resistance as a single negative employee trait is theoretically weak. Piderit (2000) reconceptualises responses to change as multidimensional, incorporating cognitive, affective, and intentional components and allowing for ambivalence. Oreg et al. (2011), reviewing six decades of quantitative research, organise change-recipient reactions around individual characteristics, internal context, change process, perceived benefit or harm, change content, reactions, and consequences. These findings suggest that resistance is produced partly by the organisation and the way change is designed and governed.

Organisational readiness offers a more precise mediating mechanism. Weiner (2009) defines readiness as a shared psychological state consisting of change commitment and change efficacy. When readiness is high, organisational members are more likely to initiate change, persist, cooperate, and invest effort. Shea et al. (2014) developed the Organisational Readiness for Implementing Change instrument and provided initial evidence of its factor structure, reliability, and construct validity. The FCCF therefore treats readiness as a mediator between distal organisational conditions and implementation behaviour rather than as a monetary cost category.

Implementation climate is related but distinct. It reflects the extent to which organisational policies, practices, and expectations support, reward, and require the use of an innovation (Klein & Sorra, 1996). A strong climate can convert strategic intention into consistent use, whereas a weak or contradictory climate permits symbolic adoption, workarounds, and partial utilisation. Psychological safety may further affect whether staff disclose emerging implementation problems before they become expensive failures (Edmondson, 1999).

2.4 Structural inertia, path dependence, and legacy value

Organisations accumulate routines, systems, contracts, identities, and political commitments that stabilise operations but constrain redirection. Hannan and Freeman (1984) describe structural inertia as a consequence of both internal arrangements and external commitments. March (1991) explains the persistent tension between exploiting established competencies and exploring new possibilities. Dynamic capabilities theory reframes the issue as the capacity to sense opportunities, seize them, and reconfigure assets (Teece, 2007).

Legacy systems and prior innovations therefore play two different roles. Economically, the relevant comparison is the future value of continuing the legacy path versus the future value of changing course. Behaviourally, historical expenditure and organisational identity may create escalation of commitment, defensive routines, or reluctance to decommission old systems. The first belongs in the strategic valuation; the second is an internal source of friction. The FCCF explicitly separates these roles.

2.5 Shareholder value, public value, and strategic counterfactuals

Traditional shareholder-value analysis compares the present value of expected future cash flows with the capital required to produce them (Rappaport, 1986). Real-options approaches add the value of flexibility to defer, stage, expand, contract, pivot, or abandon an investment under uncertainty (Amram & Kulatilaka, 1999; Dixit & Pindyck, 1994). These approaches are necessary for innovation valuation, but they do not in themselves identify the organisational mechanisms causing a deviation between feasible and realised value.

The counterfactual is central. An innovation should not be evaluated against zero; it should be evaluated against the most relevant alternative, often continuation of the existing path. Similarly, the implementation outcome should not be compared only with the original budget; it should be compared with a feasible-efficient implementation path. This produces two distinct value gaps:

• Strategic selection loss: value lost because the organisation selected an inferior alternative.
• Frictional value loss: value lost because the selected alternative was implemented inefficiently.

The distinction prevents poor strategy from being mislabelled as employee resistance or execution failure, and prevents a potentially valuable innovation from being rejected merely because its implementation was poor.

3. Construct development and boundaries

The FCCF consists of three core constructs and two supplementary attribution concepts.

3.1 Organisational innovation friction

Organisational innovation friction (OIF) is the degree to which internally controllable cultural, governance, structural, capability, and legacy-related conditions impede the timely, coordinated, and sustained implementation of an innovation.

OIF is an explanatory construct. It is not the amount of money lost. Nor is it synonymous with resistance. It includes conditions such as unclear decision rights, unstable sponsorship, fragmented accountability, weak cross-functional coordination, capability gaps, overloaded resources, technical debt, and failure to decommission legacy routines.

The higher-order OIF construct should initially be modelled as a formative composite, not a purely reflective scale. Cultural friction, governance friction, capability gaps, and legacy dependence are non-interchangeable causes. An organisation could display severe legacy friction but little cultural resistance, or strong readiness but weak resource capacity. Removing one domain changes the meaning of the overall construct. This is consistent with the distinction between causal and effect indicators developed by Bollen and Lennox (1991) and Jarvis et al. (2003).

A standardised project-level friction score may be calculated as:

$$OIF_{i} = \sum_{k = 1}^{K}w_{k}z_{ik}$$

where z_ik is the standardised score for friction domain k on project i, w_k is an empirically or normatively derived weight, and ∑w_k = 1. The composite is useful for prediction and portfolio comparison, but it does not replace the monetary calculation of frictional value loss.

3.2 Frictional value loss

Frictional value loss (FVL) is the discounted monetary value of avoidable excess resource consumption and unrealised benefits attributable to organisational impediments, measured relative to a feasible and risk-adjusted implementation benchmark.

FVL is a calculated outcome, not a perceptual latent variable. It includes both excess cost and benefit leakage. It may be positive even when the project is within its approved budget if adoption and benefits are materially below what a feasible implementation could have achieved. It may also be small when a project exceeds its original budget if the original estimate was unrealistic and the revised expenditure was necessary to capture value.

3.3 Friction-adjusted innovation value

Friction-adjusted innovation value (FAIV) is the realised value of the actual innovation path relative to the status quo or another specified alternative after implementation friction has occurred. It can be expressed as enterprise value, shareholder value, net social value, or cost-effectiveness depending on the organisation's purpose.

3.4 Strategic selection loss and external shock cost

Strategic selection loss (SL) is the value lost by choosing an inferior strategic alternative before implementation efficiency is considered.

External shock cost (ESC) is the value loss produced directly by material environmental changes outside the reasonable control of the implementing organisation, such as an unforeseen legal prohibition, macroeconomic discontinuity, or exogenous supply interruption. Internal response may amplify or mitigate such a shock; that amplification remains part of organisational friction or an internal-external interaction.

3.5 Boundary rules

Table 1 sets out the principal boundaries required to prevent double-counting.

Table 1. Construct boundaries in the FCCF

Item	Treatment in the FCCF	Reason
Necessary innovation investment	Included in the feasible-efficient cost path, not classified as friction	The innovation cannot be implemented without it
Avoidable rework, duplicate activity, prolonged parallel operation	Included in excess implementation cost	These resources exceed a feasible-efficient requirement
Historical sunk expenditure	Reported separately; excluded from a forward-looking decision unless it creates a future cash consequence	Past expenditure does not change the relative future value of alternatives
Future legacy cash flows	Included in the status-quo and innovation paths	They affect the economic comparison from the decision date onward
Cannibalisation	Reflected in the cash-flow paths; not added as a separate cost if already modelled	Separate addition would double-count the same revenue displacement
Accounting write-off	Included only to the extent that it creates tax, financing, covenant, disposal, or other cash consequences	Accounting recognition is not identical to economic value loss
Risk and uncertainty	Incorporated through expected cash flows, scenarios, discounting, and real-option value	A separate risk-cost line would usually double-count risk
Poor strategic choice	Classified as strategic selection loss, not implementation friction	It occurs before or independently of execution efficiency
Direct exogenous shock	Classified as external shock cost	The central hypothesis concerns internally controllable causes
Weak response to an external shock	Classified as organisational friction or an interaction effect	The external event does not determine the quality of internal response

4. The Frictional Cost Calculation Framework

4.1 The three-path valuation model

The FCCF compares three paths at a common decision date:

• S: continuation of the status quo or existing innovation;
• E: implementation of the selected innovation under feasible-efficient conditions; and
• A: the actual or currently forecast implementation outcome.

For any path j ∈ {S, E, A}, value is calculated as:

Equation (1): General path value

$$V_{j} = \sum_{t = 0}^{T}\frac{\mathbb{E}\left( B_{j,t} – C_{j,t} \right)}{\left( 1 + r_{j} \right)^{t}} + OV_{j}$$

where B_j, t is the benefit in period t, C_j, t is the relevant cost, r_j is the risk-consistent discount rate, T is the evaluation horizon, and OV_j is real-option or residual value not already contained in the cash-flow series. All alternatives should be valued in consistent real or nominal terms and from the same organisational or societal perspective.

The feasible-efficient path is not the original plan by definition. It is an evidence-based estimate of what a competent implementation could reasonably have achieved under the material conditions that prevailed. It may be constructed using the approved business case, independent expert estimates, reference-class data, comparable projects, stage-gate forecasts, and observed external events. Reference-class forecasting is particularly important where initial project estimates are affected by optimism or strategic misrepresentation (Flyvbjerg, 2006).

4.2 Potential innovation value

Potential innovation value measures whether the selected innovation is economically preferable to continuing the existing course under feasible implementation.

Equation (2): Potential innovation value

PIV = V_E - V_S

If PIV > 0, the selected innovation is potentially value creating. If PIV < 0, the selected innovation is economically inferior to the status quo even before implementation friction is considered.

4.3 Frictional value loss

Frictional value loss is the difference between feasible-efficient and actual value for the same selected innovation.

Equation (3): Frictional value loss

FVL = V_E - V_A

This equation isolates execution loss. It does not compare different innovations, and it does not assume that all variance from the original plan is friction.

4.4 Friction-adjusted innovation value

The value actually created by the innovation relative to the status quo is:

Equation (4): Friction-adjusted innovation value

FAIV = V_A - V_S

Substitution of Equations (2) and (3) yields the central FCCF identity:

Equation (5): Central FCCF identity

FAIV = PIV - FVL

The identity provides a direct interpretation. An innovation can have positive potential value but negative realised value where frictional value loss exceeds potential innovation value. Conversely, a project may sustain material friction and still create value if the underlying opportunity is sufficiently strong.

A value-realisation efficiency ratio can be calculated for projects with PIV > 0:

Equation (6): Value-realisation efficiency

$$VRE = \frac{FAIV}{PIV} = 1 – \frac{FVL}{PIV}$$

A VRE of 1 indicates that the full potential advantage over the status quo was realised; a value between 0 and 1 indicates partial realisation; a value below 0 indicates that implementation friction converted a potentially superior innovation into a value-destroying outcome. Values above 1 are possible where actual performance exceeds the feasible benchmark and should trigger review of the benchmark.

4.5 Decomposing frictional value loss

Let net path value equal discounted benefits less discounted costs. FVL can then be decomposed into excess cost and benefit leakage:

Equation (7): Excess-cost and benefit-leakage decomposition

$$FVL = \sum_{t = 0}^{T}\frac{\left( C_{A,t} – C_{E,t} \right) + \left( B_{E,t} – B_{A,t} \right)}{(1 + r)^{t}}$$

Define:

Equation (8): Present value of excess implementation cost

$$EC = \sum_{t = 0}^{T}\frac{C_{A,t} – C_{E,t}}{(1 + r)^{t}}$$

and:

Equation (9): Present value of benefit leakage

$$BL = \sum_{t = 0}^{T}\frac{B_{E,t} – B_{A,t}}{(1 + r)^{t}}$$

Therefore:

Equation (10): Frictional value-loss composition

FVL = EC + BL

Examples of excess cost include avoidable rework, duplicated processes, unnecessarily prolonged consultancy, additional remediation caused by poor requirements, repeated training caused by inadequate initial preparation, avoidable employee turnover, and the extended operation of parallel systems. Benefit leakage includes delayed revenue, delayed savings, reduced capacity, lost contribution margin, low user adoption, lower service quality, customer or student attrition, and benefits that were never achieved.

Two supplementary ratios support portfolio comparison:

Equation (11): Excess-cost ratio

$$ECR = \frac{EC}{PV\left( C_{E} \right)}$$

Equation (12): Benefit-leakage ratio

$$BLR = \frac{BL}{PV\left( B_{E} \right)}$$

These ratios should be reported together with absolute value. Ratios alone can exaggerate small projects and obscure the strategic significance of large losses.

4.6 Separating delay from adoption shortfall

Delay and adoption failure frequently affect the same benefit stream and can therefore be double-counted. The FCCF uses a sequential counterfactual. Let B_E be the efficient benefit path, B_D the benefit path after incorporating observed delay but assuming full eventual adoption, and B_A the actual benefit path.

Equation (13): Delay-related benefit leakage

$$BL_{delay} = \sum_{t = 0}^{T}\frac{B_{E,t} – B_{D,t}}{(1 + r)^{t}}$$

Equation (14): Adoption-related benefit leakage

$$BL_{adoption} = \sum_{t = 0}^{T}\frac{B_{D,t} – B_{A,t}}{(1 + r)^{t}}$$

Thus:

Equation (15): Sequential benefit-leakage decomposition

BL = BL_delay + BL_adoption + BL_other

The ordering of components should be fixed in the study protocol. Where several causes jointly affect the same benefit stream, event-level attribution or Shapley decomposition can be used to allocate the combined effect without counting it more than once.

4.7 Strategic selection loss

Where the organisation considered several alternatives, the best feasible alternative is:

Equation (16): Best feasible alternative

V_best^FE = max_{a ∈ 𝒜}V_a^FE

where 𝒜 includes the status quo and all credible alternatives, and the superscript FE denotes feasible-efficient implementation.

If alternative c was selected, strategic selection loss is:

Equation (17): Strategic selection loss

SL = V_best^FE - V_c^FE

Execution friction for the selected alternative is:

Equation (18): Selected-path frictional value loss

FVL_c = V_c^FE - V_c^A

Total value loss relative to the best available path is therefore:

Equation (19): Total value loss

TVL = SL + FVL_c = V_best^FE - V_c^A

This decomposition is important for governance. Strategic selection loss points to deficiencies in sensing, analysis, portfolio choice, or board decision-making. FVL points to implementation capability. Combining them would prevent targeted remediation.

4.8 Legacy assets, cannibalisation, and pivot decisions

Historical expenditure on an existing innovation is relevant to lifecycle accountability but is normally sunk at the current decision date. It should not be inserted as a new cost of changing direction. The future cash flows of the legacy path remain relevant and are included in V_S. Under the innovation path, remaining legacy revenue, cannibalisation, decommissioning cost, disposal proceeds, contractual exit cost, and dual-running cost should be included in V_E and V_A as appropriate.

A pivot from an existing innovation to a new path at time τ is justified where:

Equation (20): Pivot value

ΔV_pivot, τ = V_new, τ^FE - SC_τ - V_old, τ^continue > 0

where SC_τ is the incremental switching cost from time τ onward. Expenditure before τ is separately disclosed. This prevents escalation of commitment while preserving accountability for value already destroyed.

4.9 Internal versus external attribution

The total implementation value gap is:

Equation (21): Total implementation value gap

TIVG = V_E - V_A

For empirical testing it can be partitioned as:

Equation (22): Attribution of the implementation value gap

TIVG = OFC + ESC + INT + ε

where OFC is organisational frictional cost, ESC is directly external shock cost, INT is the incremental effect of interactions between external shocks and internal response, and ε is unexplained value loss. This structure makes the internal-dominance hypothesis falsifiable. It does not define all unexplained loss as internal.

A practical event ledger can allocate each documented friction event e across internal domains and external causes. Let FVL_e be the present value of the loss caused by event e, and let attribution weights satisfy:

Equation (23): Event-attribution weights

$$\sum_{k = 1}^{K}a_{ek} + a_{e,ext} + a_{e,int} = 1$$

where a_ek is the contribution of internal domain k, a_e, ext is the direct external contribution, and a_e, int is the internal-external interaction contribution. Dimension-specific loss is then:

Equation (24): Domain-specific frictional value loss

$$FVL_{k} = \sum_{e = 1}^{E}a_{ek}FVL_{e}$$

Attribution weights should be supported by contemporaneous records, independent coding, and sensitivity analysis. They should not be assigned retrospectively by a single project sponsor without corroboration.

4.10 Public-value extension

For public-sector and non-profit organisations, benefits may include social outcomes that do not accrue as cash to the implementing entity. The same logic can be used with net social value:

Equation (25): Public-sector friction-adjusted innovation value

FAIV_public = NSV_A - NSV_S

where NSV includes monetised social benefits and costs from the stated analytical perspective. Where monetisation would be artificial or ethically inappropriate, the FCCF should report cost and effectiveness separately, for example cost per additional successful student, cost per service transaction, or cost per quality-adjusted outcome. Friction remains observable through excess resource use and lost effectiveness even when a single monetary total is not defensible.

5. Worked numerical example

Consider a service organisation assessing a digital innovation over six years at a 10% discount rate. The status quo remains profitable but declines. Under feasible-efficient implementation, the innovation requires a large initial investment and then creates substantially stronger cash flows. Actual implementation incurs extra cost in year 0, produces a year-1 disruption, and achieves lower benefits thereafter because of delay and incomplete adoption. All amounts are in millions of currency units.

Table 2. Illustrative net cash-flow paths

Year	Status quo (S)	Feasible-efficient innovation (E)	Actual innovation (A)
0	0	-120	-140
1	40	20	-5
2	38	65	35
3	35	90	70
4	32	110	95
5	30	120	110
6	28	130	120

The calculated present values are:

V_S = 150.35,    V_E = 242.54,    V_A = 137.90

Potential innovation value is:

PIV = 242.54 - 150.35 = 92.19

Frictional value loss is:

FVL = 242.54 - 137.90 = 104.65

Friction-adjusted innovation value is:

FAIV = 137.90 - 150.35 = -12.46

The identity is confirmed:

FAIV = PIV - FVL = 92.19 - 104.65 = -12.46

The example reveals a result that conventional budget reporting can obscure. The innovation was potentially superior to the status quo by 92.19 million, but implementation friction destroyed 104.65 million of value. The realised project was therefore 12.46 million worse than continuing the legacy path. This does not imply that the innovation idea was inherently poor. It implies that the organisation failed to realise a sufficiently valuable opportunity. A governance response should focus on the causes of implementation friction rather than conclude automatically that innovation itself was a mistake.

6. Causal model and hypotheses

The FCCF proposes a causal sequence from internal organisational antecedents through readiness and implementation climate to observable friction events, financial value loss, and reduced value realisation. Figure 1 summarises the model.

The antecedent domains are set out in Table 3. They should be measured before or during implementation, while proximal events and financial consequences should be drawn wherever possible from operational and financial records.

Table 3. Internal antecedents, mediators, manifestations, and indicators

Level	Domain	Illustrative content	Potential indicators
Distal antecedent	Culture and behaviour	Fear of failure, ambivalence, defensive routines, not-invented-here behaviour, symbolic compliance	Change-attitude items, psychological safety, workaround frequency, issue-reporting behaviour
Distal antecedent	Governance and decision rights	Unclear sponsorship, slow decisions, conflicting objectives, scope churn, weak escalation	Decision latency, sponsor turnover, unresolved escalations, approval layers, change requests
Distal antecedent	Structure and coordination	Silos, excessive handoffs, fragmented accountability, duplicated responsibilities	Handoff count, meeting load, cross-unit dependencies, rework hours, role ambiguity
Distal antecedent	Capability and resource adequacy	Skill gaps, insufficient capacity, competing priorities, weak implementation knowledge	Vacancy periods, workload variance, training gaps, consultant dependence, resource shortfall
Distal antecedent	Legacy and path dependence	Technical debt, contract lock-in, old routines, data constraints, parallel systems	Interface count, dual-running period, decommissioning delay, maintenance burden, data remediation
Mediator	Organisational readiness	Shared commitment and collective efficacy	ORIC change-commitment and change-efficacy measures
Mediator	Implementation climate	Extent to which implementation is expected, supported, and rewarded	Policy-practice consistency, reinforcement, management follow-through, implementation priority
Proximal manifestation	Delay and decision friction	Time lost before activities or benefits can occur	Schedule slippage, decision days, blocked-task days, time-to-value
Proximal manifestation	Rework and coordination load	Repeated or duplicated activity	Rework hours, defects, repeated training, duplicated data entry, avoidable meetings
Proximal manifestation	Disruption and adoption shortfall	Temporary performance loss and incomplete use	Downtime, service backlog, active-use rate, process compliance, benefit-realisation rate
Financial outcome	Frictional value loss	Excess cost plus benefit leakage	Calculated project-level present value

The following hypotheses translate the conceptual framework into testable statements.

H1: Multidimensionality. Organisational innovation friction is a higher-order formative construct comprising cultural, governance, structural, capability, and legacy-related domains.

H2: Financial consequence. Higher organisational innovation friction is positively associated with frictional value loss.

$$\frac{\partial FVL}{\partial OIF} > 0$$

H3: Internal-factor dominance. Internal organisational factors explain greater incremental and out-of-sample variation in frictional value loss than external environmental factors after project size, novelty, complexity, and duration are controlled.

H4: Readiness mediation. Organisational readiness mediates the relationship between internal organisational antecedents and proximal friction events.

H5: Implementation-climate mediation. Implementation climate mediates the relationship between governance arrangements and sustained innovation use.

H6: Governance mitigation. Clear decision rights, stable sponsorship, and effective learning mechanisms weaken the relationship between cultural resistance and frictional value loss.

H7: Legacy amplification. Legacy dependence strengthens the positive relationship between organisational friction and implementation delay or excess cost.

H8: Value-realisation consequence. Frictional value loss is negatively associated with friction-adjusted innovation value.

$$\frac{\partial FAIV}{\partial FVL} < 0$$

H9: Cross-sector validity. The conceptual domains and causal sequence are substantially generalisable across banking, education, government, and IT services, although domain weights and value measures vary by sector.

7. Proposed empirical validation programme

7.1 Research design

The appropriate design is a sequential mixed-methods framework-development and validation study. The paper should not claim that the FCCF has already been empirically validated. Retrospective cases can establish face validity, content validity, pattern consistency, and financial traceability; prospective project data are required for strong predictive validity.

The proposed programme has five phases:

1. evidence synthesis and construct-domain development;
2. retrospective case calibration;
3. instrument and measurement-model development;
4. project-level financial and statistical validation; and
5. prospective and cross-sector replication.

A scoping review may be preferable during the initial mapping stage because the relevant literature spans innovation, organisational change, implementation science, project governance, finance, public management, service innovation, and efficiency analysis. PRISMA-ScR can guide reporting of the scoping review (Tricco et al., 2018). If the study proceeds to a narrower systematic review with defined effect questions, PRISMA 2020 should be used (Page et al., 2021). PRISMA is a reporting guideline, not a substitute for a protocol, search strategy, quality appraisal, or synthesis method.

7.2 Phase 1: Evidence synthesis and construct development

The first phase should identify candidate antecedents, mediators, proximal manifestations, financial consequences, boundary conditions, and competing constructs. Databases should include Scopus, Web of Science, Business Source, ABI/INFORM, PsycINFO, ERIC, and relevant public-administration and implementation-science sources. Search terms should combine concepts such as innovation implementation, organisational friction, implementation failure, resistance, readiness, inertia, rework, adoption, benefit realisation, switching costs, legacy systems, cost overrun, and value leakage.

Construct development should follow explicit inclusion and exclusion rules. Necessary implementation effort must be excluded from friction; internal causes should be separated from external shocks; and perceptual measures should not be treated as monetary outcomes. Expert review can assess content validity and identify missing domains. The initial item pool should deliberately over-sample each conceptual domain before reduction.

7.3 Phase 2: Retrospective case calibration

Case studies should be selected for theoretical replication rather than anecdotal prominence (Eisenhardt, 1989; Yin, 2018). A useful initial design would include successful, partially successful, and failed innovations across banking, education, government, and IT services. Cases should vary in size, novelty, governance form, and legacy dependence. Inclusion should require, at minimum:

• an identifiable innovation decision and implementation period;
• a documented status quo or strategic alternative;
• planned and actual cost or resource data;
• planned and actual timing;
• evidence of adoption or benefit realisation;
• contemporaneous records concerning internal and external causes; and
• sufficient information to construct at least a partial counterfactual.

Each case should be coded by more than one researcher using a common event codebook. The analysis should establish temporal ordering: antecedent conditions should be observed before the friction event, and the event should precede the financial consequence. Pattern matching should test whether the same mechanisms recur across sectors. Negative cases – projects with substantial apparent friction but little value loss, or low measured friction but poor outcomes – are especially important for refining boundary conditions.

Retrospective cases can validate whether FCCF categories are observable and whether value loss can be traced. They cannot by themselves establish population-level effect sizes or predictive validity.

7.4 Phase 3: Measurement-model development

The measurement model should distinguish formative and reflective components. The five OIF domains are initially formative because they define the composite and need not be interchangeable. Organisational readiness and implementation climate can be treated as reflective latent variables where the indicators represent manifestations of an underlying shared state. Frictional value loss is calculated from project records and should not be estimated as a reflective survey construct.

Scale development should include cognitive interviews, expert assessment, pilot testing, exploratory factor analysis for reflective subscales, and confirmatory modelling in an independent sample. Reliability should not be evaluated solely with Cronbach's alpha; composite reliability or omega, test-retest stability, and within-project agreement are also relevant. Because readiness is a shared project or organisational property, individual scores should be aggregated only where within-group agreement and between-group differentiation are defensible.

Convergent validity can be examined against established readiness, resistance, implementation climate, psychological safety, and project-governance measures. Discriminant validity must be demonstrated against project complexity, general job dissatisfaction, technology anxiety, and perceived external uncertainty. Criterion validity should be tested against objective delay, rework, adoption, cost, and benefit outcomes.

7.5 Phase 4: Project-level financial dataset

The preferred unit of analysis is the innovation project or programme, with repeated periods nested within projects and projects nested within organisations and sectors. Table 4 identifies the minimum dataset.

Table 4. Minimum project-level FCCF dataset

Category	Required variables
Project profile	Sector, organisation, innovation type, strategic objective, novelty, complexity, interdependence, scale, duration
Decision alternatives	Status quo, selected path, credible rejected alternatives, decision date, valuation perspective
Feasible-efficient baseline	Expected costs, benefits, adoption path, timing, residual value, and documented benchmark method
Actual costs	Technology, internal labour, training, transition, consultancy, remediation, rework, dual-running, operating cost
Actual timing	Planned and actual milestones, decision latency, go-live, stabilisation, time to target adoption
Benefits	Planned and actual revenue, cost savings, avoided loss, productivity, capacity, quality, service, educational, or social outcomes
Adoption	Eligible users, active users, use intensity, process compliance, workarounds, decommissioning of old routines
Internal antecedents	Culture, governance, structure, capability, legacy dependence, readiness, implementation climate
External conditions	Regulation, macroeconomic change, vendor failure, supply interruption, political change, demand shock
Friction events	Date, description, duration, direct cost, benefit impact, causal evidence, attribution weights, coding confidence
Governance updates	Business-case revisions, stage-gate decisions, pivot, pause, cancellation, benefit-restoration action

The efficient benchmark should be frozen for each reporting period and revised only through a documented governance process. Otherwise, the benchmark may be retrospectively altered to explain away poor performance. Analyses should report results under alternative plausible benchmarks to demonstrate sensitivity.

7.6 Multilevel explanatory and predictive models

A baseline multilevel model is:

Equation (26): Multilevel FCCF model

$$FVL_{ij} = \beta_{0} + \sum_{k = 1}^{K}\beta_{k}OIF_{k,ij} + \sum_{q = 1}^{Q}\gamma_{q}X_{q,ij} + u_{j} + \epsilon_{ij}$$

where i denotes the project; j the organisation; OIF_k, ij internal friction domain k; X_q, ij external shock or project-control variable q; u_j the organisation-level random effect; and ϵ_ij project-level unexplained variation. The internal-domain vector can include culture, governance, structure, capability, and legacy dependence, while the control vector can include external shocks, complexity, project size, novelty, and duration. If observations are longitudinal, project and period effects can be added and time-varying friction events modelled directly.

Mediation can be tested by modelling readiness and implementation climate between antecedents and proximal events, followed by the path from events to FVL. However, causal language should be used cautiously unless the design establishes temporal precedence and addresses confounding. Repeated measurement is preferable to a single retrospective survey.

Predictive validity should be assessed separately from explanatory fit (Shmueli, 2010). The model should be trained on one set of projects and tested on held-out or later projects. Useful metrics include cross-validated R², mean absolute error, root mean squared error, calibration, and the ability to identify projects that will cross defined FVL or VRE thresholds.

7.7 Testing internal-factor dominance

The central internal-dominance hypothesis should be tested through nested and out-of-sample model comparison rather than by comparing raw coefficients with different scales.

Let:

M₀ = project controls only

M₁ = M₀ + external factors

M₂ = M₁ + internal organisational factors

Support for H3 requires a material and replicable improvement from M₁ to M₂ in explanatory and predictive performance. Appropriate evidence includes incremental R², likelihood-ratio tests, information criteria, out-of-sample error reduction, dominance analysis, relative-importance analysis, and Shapley decomposition of explained variation. Robustness tests should alter the counterfactual benchmark, discount rate, event-attribution weights, and treatment of interaction effects.

7.8 Frontier-based validation

A stochastic frontier provides an advanced way to estimate feasible implementation performance from a reference class rather than relying entirely on management's plan. Adapted from Aigner et al. (1977), project value can be expressed as:

Equation (27): Stochastic value frontier

V_i = f(X_i; β) + v_i - u_i

where f(X_i; β) is the attainable value conditional on project characteristics, v_i is symmetric random noise, and u_i ≥ 0 is one-sided inefficiency. The estimated inefficiency term provides a statistical approximation of project-level value lost relative to the frontier.

Following the inefficiency-effects approach of Battese and Coelli (1995), the non-negative term can be modelled as:

Equation (28): Organisational determinants of inefficiency

u_i = δ₀ + δ₁Culture_i + δ₂Governance_i + δ₃Structure_i + δ₄Capability_i + δ₅Legacy_i + η_i

This approach creates a direct bridge between X-efficiency and the FCCF. It is best suited to a sufficiently large set of reasonably comparable projects with consistent value measures. Where sectors have fundamentally different outputs, separate frontiers or a meta-frontier approach will be required.

7.9 Validation criteria

The FCCF should be judged against multiple forms of validity rather than a single goodness-of-fit statistic.

Table 5. Validation criteria for the FCCF

Validity form	Core question	Evidence required
Face validity	Does the framework make sense to experienced participants?	Structured expert and practitioner review
Content validity	Are all material internal friction domains represented without construct contamination?	Literature map, expert ratings, item-domain agreement
Construct validity	Do the proposed reflective and formative structures behave as theorised?	Factor/composite modelling, convergent and discriminant evidence
Criterion validity	Does OIF correspond with objective implementation outcomes?	Association with delay, rework, adoption, EC, BL, and FVL
Incremental validity	Does FCCF add information beyond conventional project controls?	Comparison with budget variance, complexity, and risk models
Predictive validity	Does early OIF predict later FVL and FAIV?	Holdout and prospective project performance
Cross-sector validity	Is the framework meaningful across banking, education, government, and IT services?	Measurement invariance or sector-specific parameter comparison
Financial validity	Does the calculation reconcile to auditable costs and benefits?	Ledger traceability, valuation review, sensitivity analysis
Causal credibility	Are internal factors temporally and plausibly linked to loss?	Longitudinal data, case process tracing, alternative-explanation tests
Decision usefulness	Does use of the framework improve project governance or value realisation?	Intervention or comparative portfolio studies

8. Candidate cases and datasets

8.1 Candidate retrospective cases

Publicly documented cases can calibrate the framework but vary in their ability to support full financial calculation. Table 6 presents a candidate sampling frame.

Table 6. Candidate cases for retrospective FCCF calibration

Sector	Candidate case	Observable FCCF material	Principal limitation
Banking	TSB Bank IT migration programme	Governance and operational-risk findings; service disruption; regulatory penalties; customer redress; prolonged stabilisation	Full internal project cost and foregone revenue may not be public
Government health	NHS National Programme for IT	Planned versus delivered systems; expenditure; reduced scope; delays; interoperability cost; benefit shortfall	Programme boundary and changing objectives complicate the counterfactual
Government security	FBI Virtual Case File	Requirements instability, management continuity, oversight, integration, schedule and cost evidence	Public records emphasise failure causes more than monetised user benefits
Government digital service	HealthCare.gov initial implementation	Contract-cost growth, schedule slips, changing requirements, oversight gaps, delayed functionality	Later recovery benefits require longitudinal reconstruction
Education	LAUSD Instructional Technology Initiative	School readiness, infrastructure, training, device use, implementation process, and adoption evidence	A complete efficient-versus-actual financial benefit path is difficult to establish
IT services	Multi-project enterprise software portfolio	Detailed cost, schedule, defect, adoption, and benefit data where organisational access is granted	Public cases rarely disclose expected and realised commercial benefits

Official audit and regulatory records show why these cases are useful. The TSB migration case links governance and risk-management failings to service disruption, customer redress, and regulatory penalties (Financial Conduct Authority & Prudential Regulation Authority, 2022). The UK National Audit Office reported that the original vision for the NHS programme would not be realised, that fewer systems were being delivered without a commensurate reduction in cost, and that delivered systems were not producing all intended benefits (National Audit Office, 2011). The US Department of Justice Office of the Inspector General traced the FBI project's delay and cost growth to requirements, management, oversight, investment practices, scheduling, integration, and issue resolution (US Department of Justice Office of the Inspector General, 2005, 2006). The Government Accountability Office documented cost growth, delayed functionality, changing requirements, and oversight gaps in the initial HealthCare.gov implementation (US Government Accountability Office, 2014). These cases are suitable for mechanism calibration and event-ledger development, but statistical validation requires a larger, consistently coded sample.

8.2 Public and official datasets

No single public dataset appears to contain all FCCF variables. The most promising strategy is data linkage.

The Eurostat Community Innovation Survey is designed to support analysis of innovation activities, drivers, barriers, expenditure, and outcomes across business sectors (Eurostat, n.d.). It is valuable for enterprise-level relationships but generally lacks project-level planned-versus-actual cash flows and event-level implementation data.

The United States Federal IT Dashboard publishes planned, projected, and actual costs and planned and projected dates for federal IT investments (General Services Administration, 2022). This can support cost and schedule outcome construction, especially when linked to Government Accountability Office, Inspector General, or agency reports that describe governance and implementation causes.

The South African Business Innovation Survey 2019-2021 provides enterprise responses on innovation activity in South Africa and includes service-sector coverage (Human Sciences Research Council, 2024). It can support local context, sector comparison, and barrier analysis. As with the Community Innovation Survey, project-level FVL requires supplementary financial, timing, adoption, and governance records.

Internal organisational portfolios remain the strongest source for full validation. Approved business cases can provide planned costs and benefits; finance systems can provide actual expenditure; operational systems can provide adoption and service outcomes; governance records can provide decision latency, scope change, and sponsorship stability; and longitudinal readiness surveys can provide early measures of internal conditions.

9. Theoretical implications

The FCCF advances innovation theory in four ways. First, it shifts attention from innovation activity to innovation value realisation. The existence of an innovation, a launch, or a completed project is not treated as evidence that value was created. This directly incorporates the implementation distinction advanced by Klein and Sorra (1996).

Second, the framework extends X-efficiency to innovation implementation. Rather than treating inefficiency as a general property of a firm, it identifies an innovation-specific value frontier and measures the gap between feasible and actual performance. This permits organisational variables to be linked to a monetary consequence.

Third, the framework separates strategy quality from implementation quality. Innovation research frequently studies antecedents of adoption or performance without identifying whether failure arose because the wrong alternative was selected or because a potentially sound alternative was poorly executed. The SL + FVL decomposition addresses that ambiguity.

Fourth, the FCCF integrates organisational change constructs with finance while preserving their distinct levels of analysis. Readiness and implementation climate are mediators; culture, governance, structure, capability, and legacy are antecedents; delay, rework, disruption, and adoption are manifestations; and FVL is the calculated financial outcome. This ordering reduces construct contamination and creates testable temporal propositions.

10. Managerial and governance implications

The FCCF can be used at four decision points.

At selection, boards and executives compare the feasible value of the innovation with the status quo and credible alternatives. The analysis should disclose expected switching cost, legacy value, cannibalisation, and option value. This reduces the risk that innovation is approved because it appears modern or rejected because prior expenditure is emotionally salient.

At readiness assessment, management measures cultural, governance, structural, capability, and legacy conditions before committing fully. High predicted friction does not necessarily justify rejection. It may justify staged investment, redesign, additional capability, clearer decision rights, or a pilot that preserves the option to stop.

During implementation, the event ledger records where value is being lost. A delay is translated into the present value of deferred benefits; rework is translated into excess cost; adoption shortfall is translated into benefit leakage. This focuses governance on restoring value rather than merely explaining red traffic lights.

At post-implementation review, PIV, FVL, FAIV, SL, and VRE distinguish four possible judgements: good strategy and good execution; good strategy but poor execution; poor strategy despite competent execution; or both poor strategy and poor execution. Lessons can therefore be directed to the appropriate governance process.

The framework also changes mitigation priorities. If benefit leakage dominates FVL, additional adoption, process redesign, and capability support may have greater value than further cost cutting. If strategic selection loss dominates, the organisation should improve option generation and portfolio decision-making. If legacy amplification dominates, decommissioning and data architecture may be the highest-value interventions.

11. Limitations and boundary conditions

The FCCF faces several limitations. The first is counterfactual uncertainty. Feasible-efficient value is not directly observable, and different defensible benchmarks may produce different loss estimates. The solution is not to abandon the counterfactual but to disclose the benchmark method, use reference classes, and report sensitivity ranges.

Second, causal attribution is difficult. Internal and external causes interact, and retrospective participants may blame external events or employee resistance selectively. Longitudinal records, independent coding, negative-case analysis, and pre-specified attribution rules are required.

Third, project selection is endogenous. Organisations may attempt more difficult innovations when their capabilities are stronger, creating misleading simple correlations between capability and loss. Statistical models should control for project novelty and complexity and, where possible, exploit repeated projects, fixed effects, matched comparisons, or quasi-experimental variation.

Fourth, benefits may be multidimensional and delayed. Educational quality, public trust, resilience, learning, and option value may not be captured by near-term cash flows. The framework permits net social value and cost-effectiveness, but monetisation should not be forced where it would conceal important distributional or ethical issues.

Fifth, the framework could become administratively burdensome. A calculation system intended to measure friction may itself create friction. Implementation should therefore be proportional: a light event ledger and a small number of value measures for minor projects, and full counterfactual and longitudinal modelling for strategically material programmes.

Finally, the proposed construct requires empirical testing. The current paper establishes conceptual coherence and an operational research design; it does not claim statistical validation. Domain definitions, weights, functional forms, and sector-specific parameters may change after evidence is collected.

12. Conclusion

Innovation is neither automatically value creating nor inherently value destroying. It creates a set of possibilities whose value depends on the quality of strategic choice and the organisation's capacity to implement. Existing work explains innovation barriers, resistance, readiness, inertia, governance, risk, and value separately. The FCCF integrates these insights by defining organisational friction as a set of internally controllable antecedents and frictional value loss as the monetary consequence of implementation falling below a feasible benchmark.

The framework's central identity is simple:

FAIV = PIV - FVL

Its implications are substantial. A potentially superior innovation can become value destroying when implementation friction exceeds its advantage over the status quo. A project that underperforms may nevertheless reflect a sound strategic choice, while a smoothly delivered project may still be inferior to a better alternative. Separating potential value, selection loss, and implementation loss therefore improves both scholarly explanation and governance accountability.

The next step is empirical rather than rhetorical. Retrospective cases should calibrate the event categories and test financial traceability; project-level data should validate the measurement and value models; and prospective studies should determine whether early organisational friction predicts later excess cost, benefit leakage, and value realisation. If validated, the FCCF would provide a common language through which banking, education, government, and IT-service organisations can identify not only that innovation encountered barriers, but where, why, and how much value those barriers destroyed.

References

Aigner, D., Lovell, C. A. K., & Schmidt, P. (1977). Formulation and estimation of stochastic frontier production function models. Journal of Econometrics, 6(1), 21-37. https://doi.org/10.1016/0304-4076(77)90052-5

Amram, M., & Kulatilaka, N. (1999). Real options: Managing strategic investment in an uncertain world. Harvard Business School Press.

Armenakis, A. A., Harris, S. G., & Mossholder, K. W. (1993). Creating readiness for organizational change. Human Relations, 46(6), 681-703. https://doi.org/10.1177/001872679304600601

Battese, G. E., & Coelli, T. J. (1995). A model for technical inefficiency effects in a stochastic frontier production function for panel data. Empirical Economics, 20(2), 325-332. https://doi.org/10.1007/BF01205442

Bollen, K. A., & Lennox, R. (1991). Conventional wisdom on measurement: A structural equation perspective. Psychological Bulletin, 110(2), 305-314. https://doi.org/10.1037/0033-2909.110.2.305

Crossan, M. M., & Apaydin, M. (2010). A multi-dimensional framework of organizational innovation: A systematic review of the literature. Journal of Management Studies, 47(6), 1154-1191. https://doi.org/10.1111/j.1467-6486.2009.00880.x

Damanpour, F. (1991). Organizational innovation: A meta-analysis of effects of determinants and moderators. Academy of Management Journal, 34(3), 555-590. https://doi.org/10.2307/256406

Dixit, A. K., & Pindyck, R. S. (1994). Investment under uncertainty. Princeton University Press.

Edmondson, A. (1999). Psychological safety and learning behavior in work teams. Administrative Science Quarterly, 44(2), 350-383. https://doi.org/10.2307/2666999

Eisenhardt, K. M. (1989). Building theories from case study research. Academy of Management Review, 14(4), 532-550. https://doi.org/10.2307/258557

Eurostat. (n.d.). Community Innovation Survey: Microdata. Retrieved August 30, 2026, from https://ec.europa.eu/eurostat/web/microdata/collections-research/community-innovation-survey

Financial Conduct Authority, & Prudential Regulation Authority. (2022, December 20). TSB fined £48.65m for operational resilience failings. https://www.fca.org.uk/news/press-releases/tsb-fined-48m-operational-resilience-failings

Flyvbjerg, B. (2006). From Nobel Prize to project management: Getting risks right. Project Management Journal, 37(3), 5-15. https://doi.org/10.1177/875697280603700302

Flyvbjerg, B., Holm, M. S., & Buhl, S. (2002). Underestimating costs in public works projects: Error or lie? Journal of the American Planning Association, 68(3), 279-295. https://doi.org/10.1080/01944360208976273

Gallouj, F., & Weinstein, O. (1997). Innovation in services. Research Policy, 26(4-5), 537-556. https://doi.org/10.1016/S0048-7333(97)00030-9

General Services Administration. (2022, March 21). GSA launches modernized Federal IT Dashboard to enhance transparency and accountability in federal IT modernization. https://www.gsa.gov/about-gsa/newsroom/news-releases/gsa-launches-modernized-federal-it-dashboard-to-enhance-transparency-and-accountability-in-federal-it-modernization-03212022

Hannan, M. T., & Freeman, J. (1984). Structural inertia and organizational change. American Sociological Review, 49(2), 149-164. https://doi.org/10.2307/2095567

Human Sciences Research Council. (2024). South African Business Innovation Survey, 2019-2021. https://hsrc.ac.za/about-cestii/measuring-innovation-capacity/business-innovation-survey/

Jarvis, C. B., MacKenzie, S. B., & Podsakoff, P. M. (2003). A critical review of construct indicators and measurement model misspecification in marketing and consumer research. Journal of Consumer Research, 30(2), 199-218. https://doi.org/10.1086/376806

Jensen, M. B., Johnson, B., Lorenz, E., & Lundvall, B. Å. (2007). Forms of knowledge and modes of innovation. Research Policy, 36(5), 680-693. https://doi.org/10.1016/j.respol.2007.01.006

Klein, K. J., & Sorra, J. S. (1996). The challenge of innovation implementation. Academy of Management Review, 21(4), 1055-1080. https://doi.org/10.5465/amr.1996.9704071863

Leibenstein, H. (1966). Allocative efficiency vs. "X-efficiency." American Economic Review, 56(3), 392-415.

Leibenstein, H. (1969). Organizational or frictional equilibria, X-efficiency, and the rate of innovation. Quarterly Journal of Economics, 83(4), 600-623. https://doi.org/10.2307/1885452

Leibenstein, H., & Maital, S. (1992). Empirical estimation and partitioning of X-inefficiency: A data-envelopment approach. American Economic Review, 82(2), 428-433.

March, J. G. (1991). Exploration and exploitation in organizational learning. Organization Science, 2(1), 71-87. https://doi.org/10.1287/orsc.2.1.71

Moore, M. H. (1995). Creating public value: Strategic management in government. Harvard University Press.

National Audit Office. (2008). The National Programme for IT in the NHS: Progress since 2006. https://www.nao.org.uk/reports/the-national-programme-for-it-in-the-nhs-progress-since-2006/

National Audit Office. (2011). The National Programme for IT in the NHS: An update on the delivery of detailed care records systems. https://www.nao.org.uk/reports/the-national-programme-for-it-in-the-nhs-an-update-on-the-delivery-of-detailed-care-records-systems/

OECD/Eurostat. (2018). Oslo manual 2018: Guidelines for collecting, reporting and using data on innovation (4th ed.). OECD Publishing. https://doi.org/10.1787/9789264304604-en

Oreg, S., Vakola, M., & Armenakis, A. (2011). Change recipients' reactions to organizational change: A 60-year review of quantitative studies. Journal of Applied Behavioral Science, 47(4), 461-524. https://doi.org/10.1177/0021886310396550

Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., … Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 372, n71. https://doi.org/10.1136/bmj.n71

Piderit, S. K. (2000). Rethinking resistance and recognizing ambivalence: A multidimensional view of attitudes toward an organizational change. Academy of Management Review, 25(4), 783-794. https://doi.org/10.5465/amr.2000.3707722

Putnick, D. L., & Bornstein, M. H. (2016). Measurement invariance conventions and reporting: The state of the art and future directions for psychological research. Developmental Review, 41, 71-90. https://doi.org/10.1016/j.dr.2016.06.004

Rappaport, A. (1986). Creating shareholder value: The new standard for business performance. Free Press.

Rogers, E. M. (2003). Diffusion of innovations (5th ed.). Free Press.

Shea, C. M., Jacobs, S. R., Esserman, D. A., Bruce, K., & Weiner, B. J. (2014). Organizational readiness for implementing change: A psychometric assessment of a new measure. Implementation Science, 9, 7. https://doi.org/10.1186/1748-5908-9-7

Shmueli, G. (2010). To explain or to predict? Statistical Science, 25(3), 289-310. https://doi.org/10.1214/10-STS330

Smith, W. K., & Lewis, M. W. (2011). Toward a theory of paradox: A dynamic equilibrium model of organizing. Academy of Management Review, 36(2), 381-403. https://doi.org/10.5465/amr.2009.0223

Teece, D. J. (2007). Explicating dynamic capabilities: The nature and microfoundations of sustainable enterprise performance. Strategic Management Journal, 28(13), 1319-1350. https://doi.org/10.1002/smj.640

Teece, D. J. (2018). Business models and dynamic capabilities. Long Range Planning, 51(1), 40-49. https://doi.org/10.1016/j.lrp.2017.06.007

Tricco, A. C., Lillie, E., Zarin, W., O'Brien, K. K., Colquhoun, H., Levac, D., Moher, D., Peters, M. D. J., Horsley, T., Weeks, L., Hempel, S., Akl, E. A., Chang, C., McGowan, J., Stewart, L., Hartling, L., Aldcroft, A., Wilson, M. G., Garritty, C., … Straus, S. E. (2018). PRISMA extension for scoping reviews (PRISMA-ScR): Checklist and explanation. Annals of Internal Medicine, 169(7), 467-473. https://doi.org/10.7326/M18-0850

US Department of Justice Office of the Inspector General. (2005). The Federal Bureau of Investigation's management of the Trilogy Information Technology Modernization Project (Audit Report 05-07). https://oig.justice.gov/archives/reports/FBI/a0507/index.htm

US Department of Justice Office of the Inspector General. (2006). The Federal Bureau of Investigation's pre-acquisition planning for and controls over the Sentinel Case Management System (Audit Report 06-14). https://oig.justice.gov/archives/reports/FBI/a0614/exec.htm

US Government Accountability Office. (2014). Healthcare.gov: Ineffective planning and oversight practices underscore the need for improved contract management (GAO-14-694). https://www.gao.gov/products/gao-14-694

Weiner, B. J. (2009). A theory of organizational readiness for change. Implementation Science, 4, 67. https://doi.org/10.1186/1748-5908-4-67

Williamson, O. E. (1985). The economic institutions of capitalism. Free Press.

Yin, R. K. (2018). Case study research and applications: Design and methods (6th ed.). Sage.

Appendix A. Friction-event ledger template

Table A1. Minimum event-ledger fields

Field	Description
Project and event ID	Unique identifiers linking the event to the project dataset
Event start and end	Dates defining duration and discounting period
Event description	Observable occurrence, not an inferred cause
Proximal manifestation	Delay, rework, disruption, coordination load, adoption shortfall, or other
Efficient counterfactual	What would reasonably have occurred without the event
Excess direct cost	Incremental labour, supplier, technology, remediation, or operating cost
Benefit effect	Deferred, reduced, or foregone benefit and calculation method
Internal-domain weights	Cultural, governance, structural, capability, and legacy contributions
External and interaction weights	Direct external contribution and internal-external interaction
Evidence source	Minutes, system log, financial ledger, survey, audit, interview, or observation
Coding confidence	High, moderate, or low, with reasons
Mitigation action	Action taken and expected value restored

Appendix B. Illustrative FCCF project dashboard

A project dashboard should report, at minimum:

• V_S, V_E, and V_A;
• potential innovation value (PIV);
• frictional value loss (FVL), split into excess cost and benefit leakage;
• friction-adjusted innovation value (FAIV);
• value-realisation efficiency (VRE);
• strategic selection loss (SL), where alternatives are available;
• organisational friction domain scores;
• readiness and implementation-climate scores;
• top friction events by present value;
• internal, external, and interaction attribution shares; and
• sensitivity of results to the counterfactual, discount rate, and benefit assumptions.

The dashboard should preserve drill-through to the underlying event ledger and financial evidence. Its purpose is not merely to display project status but to identify where intervention can restore the greatest remaining value.