Introduction

Learning objectives

After this session, you should be able to:

• Distinguish IT resources from digital resources and explain their strategic relevance.
• Explain how dynamic capabilities relate to IT-enabled value creation.
• Describe IS business value along its key dimensions (form, location, meaning).
• Articulate why usage is the critical link between IT investment and business value.
• Apply a structured approach to measuring and communicating IT value.

Introduction

Necessity of definitions

Figure 1: He should have defined what “fastest growing” (not) means …

To gain mutual understanding, core terms need to be defined. For this lecture, we need to—at least—figure out what IT and business value means. These two concepts form the foundation of the entire course: without clarity on what we mean by IT resources and how value is conceptualized, any discussion of how IT creates business value remains vague.

IT resources

IT resources are the foundational technological components that organizations leverage.
These include:

• IT assets are tangible or intangible technological components that can be bought or built (e.g., hardware, software, data repos) (Melville et al., 2004a)
• IT capabilities refers to the ability to mobilize and deploy IT assets in combination with other resources (e.g., IT skills or IT management abilities) (Wade & Hulland, 2004)
• IT-enabled resources are organizational resources that are enhanced or created through the application of IT (e.g, IT-enhanced business processes) (Wade & Hulland, 2004)

To create IT-enabled resources organizations need to integrate IT assets, IT capabilities and complementary organizational resources in a way that they create synergies.

The distinction between IT assets, IT capabilities, and IT-enabled resources is fundamental to understanding how technology creates value in organizations.

IT assets are the most tangible layer — the hardware, software, databases, and network infrastructure that an organization purchases or builds. On their own, however, assets are inert. A powerful server sitting idle creates no value; a sophisticated software tool that nobody knows how to use is a sunk cost.

IT capabilities bridge this gap. They represent the organizational ability to actually do something with IT assets. This includes technical skills (the ability to configure, maintain, and develop systems), managerial abilities (the capacity to plan, prioritize, and govern IT investments), and relational competencies (the skill to work across organizational boundaries to align technology with business needs). IT capabilities are what turn static assets into functioning systems.

IT-enabled resources represent the highest level of integration. These emerge when IT assets and capabilities are combined with other organizational resources — business process knowledge, domain expertise, customer relationships, organizational culture — to create something that is more than the sum of its parts. An IT-enhanced supply chain, for example, combines logistics software (asset), data analytics skills (capability), and deep knowledge of supplier relationships (complementary resource) into an integrated capability that competitors cannot easily replicate.

The concept of complementary organizational resources deserves emphasis because it is frequently the reason that identical IT investments produce vastly different results in different organizations. Two companies might purchase the same ERP system, but the one that also invests in process redesign, employee training, and change management will extract significantly more value. Research consistently shows that the returns on IT investment depend heavily on these complementary investments (Melville et al., 2004a), a point we will return to when discussing the IT value creation process later in this session.

From IT to digital assets

Figure 2: IT resources, IT-enabled resources, and digital resources (Piccoli et al., 2022, p. 2296)

The concept of digital resources represents an important evolution beyond traditional IS resources. While IT assets and capabilities focus on what technology an organization owns and can do with it, digital resources shift attention to the strategic value that emerges when technology components are designed for combination and exchange.

Piccoli et al. (2022, p. 2296) define digital resources through three distinguishing characteristics:

Modularity means that digital resources function like standardized building blocks. Each resource works independently but is specifically designed to be combined with other digital components to create larger, more complex systems. A payment processing API, for instance, is a self-contained module that can be integrated into e-commerce platforms, mobile apps, or websites without requiring fundamental changes to either side.

Encapsulated value means that a digital resource has no inherent worth on its own — its value derives entirely from the assets or capabilities it contains and the demand for those capabilities. A machine learning model for predicting customer churn, for example, is valuable only because companies need that prediction capability. Without demand, the model is just code.

Programmatic interface means that digital resources are accessed through software rather than physical interaction. They have a defined set of rules (an API) that governs how other systems can interact with them. Instead of physically handling a document, for example, a cloud storage service exposes commands to upload, download, or modify files programmatically.

Figure 3: IT resources, IT-enabled resources, and digital resources (Piccoli et al., 2022, p. 2296)

Within this framework, digital assets are the foundational building blocks — digital components with value-creating properties governed by well-defined property rights. Digital capabilities refer to an organization’s ability to mobilize and deploy these digital assets in combination with other resources, enabling it to conceptualize and execute digital initiatives.

The practical implication is significant: organizations that think in terms of digital resources design their technology investments for modularity, recombination, and ecosystem participation — not just internal optimization.

Dynamic capabilities

The resource-based view supports that firms may achieve a competitive advantage based on their bundles of resources and capabilities (Peteraf et al., 2013; Wade & Hulland, 2004).

The dynamic capabilities (DC) view argues that firms have to evolve their resource and capability base in order to ensure a sustained competitive advantage (Peteraf et al., 2013).

It distinguishes between two types of resources.

• ordinary or operational capabilities, which allow firms to survive in the present by supporting existing operations
• dynamic capabilities, which are directed towards strategic change of ordinary capabilities and resources

DCs represent an organization’s ability to purposefully create, extend, or modify its resource base to address rapidly changing environments (Teece, 2014).

IS and dynamic capabilities

The relationship between IT and dynamic capabilities is more nuanced than a simple cause-and-effect logic. Steininger et al. (2022, p. 454) map out three distinct roles that IT plays in relation to dynamic capabilities, each with different strategic implications.

Figure 4: Nomological net of DC in IS research based on Steininger et al. (2022, p. 454)

IT as an enabler of dynamic capabilities. In this role, IT provides the infrastructure and tools that make dynamic capabilities possible or more effective. A business intelligence platform that aggregates market data, for instance, enables executives to sense emerging market changes. Cloud computing provides the scalability needed for rapid resource reallocation when seizing opportunities. The technology itself is not the capability, but it creates the conditions under which capabilities can develop and operate.

IT embedded in dynamic capabilities. Here, technology becomes an integral, inseparable part of the capability itself. Consider a digital product development platform that fuses customer feedback, design tools, and production processes into one cohesive workflow. The capability of rapid product innovation cannot be separated from the technology — they are co-constituted. Similarly, algorithmic decision-making systems embedded in resource allocation processes represent a capability that is inherently technological.

IT as a result of dynamic capabilities. In this role, new IT solutions emerge because an organization has exercised its dynamic capabilities. When a firm senses a market opportunity and reconfigures its resources to address it, the outcome might be a proprietary algorithm, a custom digital platform, or a new digital product. The IT artifact is the output of the sensing-seizing-transforming process, not its input.

These three roles are not mutually exclusive. In practice, IT often plays multiple roles simultaneously or sequentially. A cloud platform might enable the capability to experiment with new digital services, those services might have IT embedded in their delivery processes, and the organizational learning from delivering them might result in entirely new IT capabilities. Recognizing this complexity is essential for understanding how IT investments translate into sustained competitive advantage.

IT as an enabler of DCs

When IT functions as an enabler, it provides the foundation or platform that makes dynamic capabilities possible or enhances their effectiveness. IT systems provide the infrastructure, tools, and information access that organizations need to develop and deploy their dynamic capabilities.

Examples:

• A business intelligence platform that aggregates market data, enabling executives to better sense market changes
• Cloud computing infrastructure that provides the scalability and flexibility needed for rapid resource reallocation
• Advanced data analytics tools that enhance an organization’s ability to identify emerging patterns and opportunities
• Enterprise collaboration tools that facilitate knowledge sharing across organizational boundaries, supporting sensing capabilities

IT embedded in DCs

When IT is embedded in dynamic capabilities, it becomes an integral component of the capability itself, inseparable from the processes and routines that constitute the capability. The capability itself incorporates digital technology as a core element, where the technology and organizational processes are fused together.

Examples:

• Digital product development platforms that integrate customer feedback, design tools, and production processes into a cohesive capability
• Algorithmic decision-making systems that are embedded in a firm’s resource allocation processes
• Digital twins that are integrated into manufacturing operations, enabling continuous monitoring and optimization
• CRM systems that are embedded in customer relationship management capabilities, combining data, processes, and customer interactions

IT as a result of DCs

When IT is a result of dynamic capabilities, new technological solutions emerge from an organization’s ability to sense opportunities, mobilize resources, and transform its operations. The application of dynamic capabilities leads to the creation of new IT assets, systems, or capabilities as organizations adapt to changing environments.

Examples:

• A new proprietary algorithm developed through a firm’s innovation capability responding to market changes
• Custom digital platforms that result from an organization reconfiguring its resources to address emerging opportunities
• Internal IT systems that evolve based on organizational learning and transformation capabilities
• Digital products or services that emerge from an organization’s sensing and seizing of market opportunities

Value

The productivity paradox

We see the computer age everywhere except in the productivity statistics. Robert Solow, 1987

Despite massive IT investments, empirical evidence for productivity gains remained elusive for decades — a phenomenon known as the IT productivity paradox (Brynjolfsson, 1993).

This paradox motivates a central question of this course: Under what conditions do IT investments actually create business value — and how can we ensure they do?

The productivity paradox is one of the most influential puzzles in IS research. Throughout the 1980s and early 1990s, organizations invested heavily in information technology, yet macroeconomic data showed no corresponding increase in productivity. Nobel laureate Robert Solow captured this disconnect in his famous quip in 1987.

Brynjolfsson (1993) examined this paradox systematically and identified several possible explanations. First, traditional productivity measures may simply fail to capture the benefits of IT — particularly improvements in quality, variety, timeliness, and convenience that do not show up in output-per-hour statistics. Second, the benefits of IT may take years to materialize because organizations need time to reorganize processes, develop skills, and learn how to use new systems effectively. Third, IT investments may redistribute value within an industry (from laggards to leaders) without increasing aggregate productivity.

Later research largely resolved the paradox by showing that IT does create value — but only under specific conditions. Organizations that combine IT investments with complementary changes in business processes, organizational structure, and human capital consistently outperform those that simply deploy technology without broader organizational transformation. This insight underpins the entire logic of this course: IT investment is necessary but not sufficient for value creation. The organizational context — how technology is adopted, used, and embedded in business processes — determines whether the investment pays off.

IS business value

Value created by IT is—in a business context—usually referred to as IT/IS business value.

IS business value is the impact of investments in particular IS assets on the multidimensional performance and capabilities of economic entities at various levels, complemented by the ultimate meaning of performance in the economic environment. Schryen (2013, p. 141)

The ultimate meaning of performance refers to what is subsequently derived if the outcome is exploited, e.g.

• impact on performance of a workflow management system: faster business processes
• the ultimate meaning: is dependent on the use of gained time and the extent to which competitors have speeded up their processes

The concept of IT/IS business value is deceptively complex. At first glance, it seems straightforward: invest in technology, measure the returns. But Schryen (2013, p. 141) offers a definition that reveals important subtleties.

His definition highlights three aspects that are easily overlooked. First, value is multidimensional — it manifests as cost savings, revenue growth, customer satisfaction, innovation capacity, and many other forms simultaneously. Reducing it to a single financial metric always loses information. Second, value occurs at multiple levels — individual, team, department, organization, and ecosystem. An IT system might make individual employees more productive, improve team coordination, enable new organizational capabilities, and strengthen ecosystem partnerships all at once. Third, and most importantly, there is a critical distinction between direct performance impact and its ultimate meaning.

This last point deserves emphasis. Consider a workflow management system that speeds up business processes by 30%. The direct impact is clear: faster processes. But the ultimate business meaning depends entirely on context. If the time saved is used productively — for instance, to serve more customers or to invest in innovation — the value is substantial. If the time savings simply creates idle capacity, the value is minimal. And if every competitor has achieved similar speed improvements, the competitive advantage is neutralized entirely.

This context-dependence is what makes IT value determination so challenging — and why the productivity paradox persisted for so long. The same technology investment can create vastly different business value depending on how the organization exploits its effects and how the competitive landscape responds. Historical perspective matters here as well: the perception of IT value has evolved from “cost of doing business” in the mainframe era, through “strategic enabler” in the client-server period, to “foundation for digital transformation” today.

Taxonomy of IS business value

Schryen (2013) proposes a two-dimensional framework that organizes IS business value into four distinct types, creating a matrix that serves as a practical lens for comprehensive value assessment.

Figure 5: Taxonomy of IS business value types and examples according to Schryen (2013)

The first dimension distinguishes tangible from intangible value. Tangible value can be measured in financial or operational terms — cost reductions, productivity gains, increased market share. Intangible value resists direct quantification but is often strategically more significant — organizational learning, brand reputation, decision-making quality.

The second dimension separates internal from external value. Internal value improves operations and capabilities within the organization. External value enhances the organization’s position in its market and its relationships with customers, partners, and competitors.

Combining these dimensions yields four quadrants. Internal tangible value includes measurable process improvements such as reduced error rates, faster transaction processing, and decreased inventory holding costs. Internal intangible value encompasses harder-to-measure capabilities like enhanced organizational learning, improved information accessibility, better decision-making quality, and increased organizational flexibility. External tangible value shows up in market metrics such as increased market share, higher customer retention rates, and improved sales conversion. External intangible value shapes long-term positioning through enhanced brand reputation, improved customer trust, better industry partnerships, and greater strategic flexibility.

The framework is particularly useful because it reveals how value types reinforce each other. Internal intangible value (e.g., better knowledge management) often enables external tangible value (e.g., higher sales through better customer insights). Value also migrates between quadrants over time — internal process improvements eventually translate into external market benefits. Organizations that focus their IT value assessment on only one or two quadrants — typically internal tangible, because it is easiest to measure — inevitably miss a significant portion of the value their investments create.

Internal tangible value

Internal tangible value refers to measurable improvements in internal operations and financial metrics.

• Directly observable within the organization
• Quantifiable in financial or operational terms
• Often appears in performance reports

Examples:

• Cost reductions in business processes
• Improved employee productivity
• Reduced error rates
• Faster transaction processing times
• Decreased inventory holding costs

Internal intangible value

Internal intangible value refers to non-monetary improvements within the organization that enhance capabilities.

• Difficult to measure directly
• Creates organizational capabilities and knowledge assets
• Often serves as foundation for other types of value

Examples:

• Enhanced organizational learning
• Improved information accessibility
• Better decision-making quality
• Increased employee satisfaction
• Improved organizational flexibility

External tangible value

External tangible value refers to measurable improvements in market position and customer relationships.

• Quantifiable impacts on market performance
• Observable through market metrics
• Directly impacts competitive position

Examples:

• Increased market share
• Higher customer retention rates
• Improved sales conversion rates
• Reduced customer acquisition costs
• Premium pricing ability

External intangible value

External intangible value refers to non-monetary improvements in external relationships and positioning.

• Difficult to quantify but strategically significant
• Influences long-term competitive position
• Creates strategic options and opportunities

Examples:

• Enhanced brand reputation
• Improved customer trust
• Better industry partnerships
• Greater strategic flexibility
• Increased barriers to competition

Strategic implications

Schryen (2013)’s matrix is particularly valuable because it emphasizes:

1. Value complementarity: The four types of value often reinforce each other (e.g., internal intangible value like knowledge management capabilities can enable external tangible value like increased sales)
2. Measurement challenges: Organizations need different metrics and approaches to capture value in each quadrant
3. Temporal dynamics: Value often migrates between quadrants over time (e.g., internal process improvements eventually translating to external market benefits)
4. Strategic balance: Organizations should pursue a portfolio of IS investments that create value across all four quadrants

Usage as the missing link

Soh & Markus (1995) proposed a process model that addresses a fundamental question: How exactly do IT investments become business value? Their model is historically significant because it helped resolve the productivity paradox — the puzzling observation that massive IT spending did not seem to translate into measurable productivity gains.

The model identifies three sequential processes, each with its own cause-effect logic:

Figure 6: The process model of IS value creation (Soh & Markus, 1995, p. 37)

The IT conversion process transforms financial expenditure into IT assets. This is not automatic — the quality of management decisions, vendor selection, and implementation practices all determine whether money spent on IT actually produces usable technological assets.

The IT use process transforms IT assets into organizational impacts. This is where the model’s central insight lies: technology that is purchased but not effectively used creates no value. The critical path runs through actual adoption and effective usage — not just deployment or availability.

The competitive process transforms IT-driven impacts into organizational performance. Even when IT produces real operational improvements, their effect on overall performance depends on competitive dynamics. If all competitors achieve similar improvements, no individual firm gains a relative advantage.

The model’s lasting contribution is the identification of usage as the essential missing link. Before Soh and Markus, researchers often looked directly at the relationship between IT spending and organizational performance, finding inconsistent results. The model explains why: without effective use, the causal chain is broken. This insight shifted IS research toward studying technology adoption, user behavior, and the organizational conditions that promote effective use — topics that remain central to the field today.

Adoption and use

Figure 7: Unified Theory of Acceptance and Use of Technology (UTAUT) (Venkatesh et al., 2003)

The Unified Theory of Acceptance and Use of Technology (UTAUT) by Venkatesh et al. (2003) provides a well-established framework for understanding why people adopt (or reject) new technologies. It is relevant here because, as the Soh and Markus model demonstrates, usage is the critical link between IT investment and value creation. If users do not adopt a system, no value is realized — regardless of the system’s technical quality.

UTAUT identifies four direct determinants of behavioral intention and use. Performance expectancy captures the degree to which users believe the technology will help them do their jobs better. Effort expectancy reflects how easy the system is perceived to be. Social influence accounts for the pressure from colleagues, supervisors, and organizational norms. Facilitating conditions capture the organizational and technical infrastructure that supports use — training, help desks, compatible systems.

These effects are moderated by age, gender, experience, and whether use is voluntary. The moderators matter practically: a system that is well-received by experienced, tech-savvy early adopters may still fail with the broader user population if effort expectancy is too high or facilitating conditions are inadequate.

For IT value management, UTAUT implies that organizations must actively manage adoption — through training, change management, user-centered design, and visible leadership support — rather than assuming that deployment equals use.

Effective use

Figure 8: A model for effective use and its effect on performance according to Burton-Jones & Grange (2013)

Key for Figure 8: Solid arrows reflect primary paths; dashed arrows reflect secondary paths; boxed arrows reflect necessary causality (i.e., x enables but does not determine y). Bolding on “user,” “system” and “task” reflect the relative emphasis on each element of use (bold reflects more emphasis).

While UTAUT explains whether users adopt a technology, the Theory of Effective Use by Burton-Jones & Grange (2013, p. 636) addresses a deeper question: Are users getting value from the system once they are using it?

The theory defines effective use as “using a system in a way that helps attain the goals of using the system” and breaks it down into three dimensions that build on each other.

Transparent interaction is the first requirement. Users must be able to access the system’s representations without being impeded by the interface. If a user struggles with confusing navigation, slow response times, or unclear visual layouts, their interaction with the system is opaque rather than transparent — and the subsequent dimensions cannot be achieved.

Representational fidelity requires that the information the system presents faithfully reflects reality. A dashboard showing outdated sales data, a risk model built on flawed assumptions, or an AI tool generating hallucinated content all fail on this dimension. Users may interact smoothly with the system but receive unreliable information.

Informed action is the ultimate dimension: users take better decisions or actions based on the faithful representations they obtain. This is where system use translates into actual performance improvement. A manager who receives accurate, timely data (representational fidelity) through a well-designed interface (transparent interaction) and then adjusts strategy accordingly is demonstrating informed action.

The practical implication is that organizations cannot simply track whether a system is used — they must assess whether it is used effectively. This requires attention to interface design, data quality, and the decision-making context in which the system is embedded.

Value determination

Challenges

According to Kohli & Grover (2008), organizations struggle to articulate the full business value of IT beyond cost savings.

Challenges of IT value determination and subsequent realization include:

• IT investments are often viewed as cost centers rather than value creators
• Traditional metrics fail to capture full spectrum of IT value
• Stakeholders have different value perceptions as compared to IT professionals

The challenges that Kohli & Grover (2008) identify go beyond mere measurement difficulty. They reflect a fundamental tension between how IT professionals and business stakeholders think about technology.

The cost center perception persists because IT costs are immediate and visible — hardware invoices, license fees, consultant rates — while benefits are distributed across the organization and often materialize only in the future. This asymmetry creates a structural bias: costs are always attributable; benefits rarely are.

The failure of traditional metrics stems from the fact that most IT benefits are second-order effects. IT rarely creates value directly. Instead, it enables business capabilities that then create value. A CRM system does not generate revenue — sales teams using CRM data more effectively do. This indirection makes it difficult to attribute value to the IT investment specifically, as complementary investments in training, process redesign, and change management are typically required as well (recall the role of complementary organizational resources discussed earlier).

The divergence between stakeholder and IT professional perspectives is perhaps the most consequential challenge. IT teams naturally evaluate investments in technical terms — system availability, processing speed, data throughput. Business stakeholders care about outcomes — revenue growth, cost reduction, risk mitigation. When these perspectives are not bridged, IT investments are judged by the wrong criteria, and genuine value goes unrecognized.

Kohli and Grover also note that organizations tend to focus on efficiency metrics not because these are the most important, but because they are the easiest to measure. This measurement convenience bias systematically undervalues the strategic and transformative dimensions of IT investment.

Business-focused value definition

CIOs must first understand how business stakeholders define value. Usually it is based on their highest priority business objectives. Cannon (2020)

This implies that

• value is defined from the business perspective;
• technical capabilities must be translated to business outcomes;
• and success depends on alignment with strategic objectives

The insight from Cannon (2020) represents a paradigm shift in how IT value is framed. Traditionally, IT departments defined value in their own terms: system uptime, transaction throughput, storage capacity. These metrics are meaningful to technologists but opaque to business leaders.

A business-first approach requires translating technical capabilities into business outcomes. The translation is not trivial — it requires IT leaders to understand business strategy deeply enough to articulate connections that are often implicit. Consider these examples:

• A technical achievement of “99.99% system availability” translates to “uninterrupted customer service capability” for business stakeholders.
• “Reduced data latency” becomes “faster response to market changes.”
• “Increased automation” means “fewer operational errors and higher customer satisfaction.”

In each case, the same IT achievement is reframed in terms of what the business stakeholder cares about most. This reframing is not spin — it is essential communication. IT leaders who cannot make this translation will consistently struggle to secure investment and organizational support, regardless of the technical merit of their proposals.

Primary business objectives

The three objectives that Cannon (2020) identifies — revenue creation, cost optimization, and risk mitigation — provide a practical classification for the value that IT creates. Each objective corresponds to different stakeholder priorities, measurement approaches, and time horizons.

Revenue and value creation is the objective most directly linked to business growth. In commercial enterprises, this manifests as increased market share, better customer acquisition and retention, and the development of new products and services. Revenue-oriented IT value is often the hardest to attribute directly to specific investments, but it receives the most executive attention — particularly during growth periods. A digital customer experience platform that demonstrably increases conversion rates is a clear example.

Cost optimization is the most established and measurable value dimension. It encompasses automation, process standardization, and efficiency improvements across the organization. The key distinction is between value-destroying cost-cutting (which erodes capabilities) and strategic optimization (which frees resources for higher-value activities). A cloud migration that reduces infrastructure costs while simultaneously increasing operational flexibility exemplifies the latter.

Risk mitigation has gained prominence as digital dependence has grown. Cybersecurity incidents, regulatory penalties, supply chain disruptions, and operational downtime all carry quantifiable financial consequences. Yet risk mitigation value is consistently undervalued — until a negative event occurs. An identity and access management system that reduces breach probability has clear value, but organizations often struggle to justify the investment in the absence of a recent incident.

The balance between these three objectives shifts with market conditions, industry dynamics, and organizational maturity. A startup in a growth phase will weight revenue creation heavily; a regulated enterprise will prioritize risk mitigation. Sustainable value creation requires attention to all three.

Revenue/value creation

IT investments can contribute to firm performance through revenue enhancement mechanisms (Melville et al., 2004b), such as:

1. Market share growth
2. New revenue streams
3. Customer acquisition/retention

Metrics to quantify value potential are, e.g.:

• Digital revenue percentage (revenue from digital channel/product)
• Market penetration (contribution to market share)
• Customer experience (impact on NPS, satisfaction scores)
• Innovation rate (new products/services enabled; share of revenue)

Revenue and value creation through IT has evolved significantly. Early IT value focused primarily on automation and cost reduction. Digital transformation has shifted the emphasis toward revenue growth and business model innovation. Today, IT enables market responsiveness and customer intimacy, facilitates product and service innovation, creates new market opportunities, provides competitive intelligence through information assets, and enables fundamental business model transformation.

Consider the example of a mid-sized retailer implementing an AI-powered recommendation engine. The digital revenue percentage captures how much online revenue is attributable to digital channels. Market penetration metrics track whether the recommendation engine is attracting new customer segments. Customer experience scores (such as Net Promoter Score) reveal whether personalized recommendations increase satisfaction. And the innovation rate — measuring the share of revenue from digitally-enabled products or services — shows whether the investment is opening genuinely new value streams or merely optimizing existing ones.

Advanced organizations are developing IT value maps that trace the causal chain from specific IT capabilities to revenue impacts, with intermediate indicators at each step. These maps make the value creation logic explicit and testable, rather than relying on assumptions about how technology investment translates into business growth.

Cost optimization

Strategic IT investments yield cost benefits through process optimization rather than simple cost-cutting (Mithas et al., 2011), such as:

• Programmatic reduction of costs (vs. arbitrary cuts)
• Efficiency improvements across business processes
• Resource utilization and productivity gains
• Elimination of technical debt and redundancies

Options to quantify the scope of cost optimizations are, e.g.:

• Labor/capital productivity (output per employee/labor hour and asset productivity)
• Process efficiency (reduced cycle time, increased throughput, fewer errors)
• Unit cost reduction (cost per transaction, service, or output)

Cost optimization through IT represents a mature value dimension with established practices. The critical distinction is between tactical cost-cutting — which can harm organizational capabilities — and strategic optimization, where IT investments fundamentally improve how work gets done.

Mithas et al. (2011) show that cost benefits often emerge from second-order effects rather than direct substitution. Replacing a manual process with software is a first-order effect. The second-order effect occurs when the resulting data visibility enables further process improvements that were previously invisible. Process optimization that combines IT changes with business process reengineering consistently yields greater returns than either approach alone.

Resource utilization improvements extend to both human resources and technology assets. Cloud computing, for example, allows organizations to match computing capacity to actual demand rather than provisioning for peak loads — a shift from capital expenditure to operational expenditure that fundamentally changes the cost structure. Technical debt elimination creates long-term cost advantages by reducing the maintenance burden that accumulates when organizations defer system upgrades and architectural improvements.

Industry benchmarks provide useful reference points for cost optimization: IT spending as a percentage of revenue (which varies significantly by industry), revenue per employee, and operational cost ratios such as cost-to-serve. These benchmarks help organizations assess whether their IT investments are delivering competitive cost structures.

Risk mitigation

Investments in IT can significantly reduce the likelihood and financial impact of risks by enhancing security, compliance, resilience and operational stability. They mitigate, e.g.:

• Cyberattacks, fraud, and insider threats
• Regulatory fines and legal penalties
• Supply chain vulnerabilities and disruptions
• Operational downtime and disaster recovery

Options to quantify positive outcomes of IT investments on risk mitigation:

• Expected loss reduction (probability × impact) before and after controls
• Risk transfer valuation (cost of insurance premiums avoided)
• Compliance cost avoidance (fines, penalties, remediation costs)
• Business continuity value (avoided downtime costs)

Risk mitigation value has gained prominence in recent years due to converging trends: the escalation of cyber threats and high-profile breaches with severe business impact, expanding regulatory requirements such as the GDPR and industry-specific regulations, increasing organizational dependence on technology for critical business operations, and supply chain vulnerabilities highlighted by recent global disruptions.

Quantifying risk mitigation value requires a different approach than revenue or cost metrics. The core logic is counterfactual: what losses would the organization face without the investment? Frameworks like NIST CSF, ISO 27001, and FAIR (Factor Analysis of Information Risk) provide structured approaches to evaluating control effectiveness and estimating residual risk.

Many organizations use scenario-based methods to estimate risk mitigation value. Tabletop exercises simulate incident response to identify gaps. Breach cost calculators estimate the financial impact of different attack scenarios. Cyber risk quantification models translate technical vulnerabilities into financial exposure. Business impact analyses map technology dependencies to revenue streams.

Some organizations now maintain risk registers with monetized risk exposure and track risk reduction as an explicit value metric for IT investments. This approach makes risk mitigation value visible and comparable to revenue and cost metrics — enabling more balanced investment decisions rather than defaulting to whichever value type is easiest to quantify.

Measurement framework

General remarks

Adopting a balanced scorecard approach (Kaplan & Norton, 2004), a measurement framework should consider following insights:

• Balanced approach (covering all three value objectives)
• Multi-level metrics (strategic, tactical, operational)
• Leading indicators (future value realization)
• Lagging indicators (confirming delivered value)

A robust measurement framework draws on the logic of the Balanced Scorecard (Kaplan & Norton, 2004) by insisting on multiple perspectives rather than reducing IT value to a single number.

A balanced approach means covering all three value objectives — revenue, cost, and risk — rather than defaulting to whichever is easiest to measure. Organizations that assess IT value only through cost savings systematically undervalue strategic investments.

Multi-level metrics recognize that IT value materializes differently at different organizational levels. Strategic metrics (market share, profitability) capture enterprise-level outcomes. Tactical metrics (productivity, quality) reflect business unit performance. Operational metrics (cycle time, error rates) track process-level improvements. A complete picture requires connecting all three levels into a coherent value hierarchy.

Leading indicators predict future value realization. They measure capability improvements, adoption rates, and process changes that are expected to produce business outcomes over time. Lagging indicators confirm that value has actually been delivered — revenue growth, cost reductions, reduced incident frequency. Both types are necessary. Leading indicators without lagging confirmation may reflect wishful thinking; lagging indicators alone provide no early warning when value realization is at risk.

The central implementation challenge is creating clear, evidence-based linkages between levels and between leading and lagging indicators — so that the measurement framework tells a coherent story about how IT investments create business value.

Critical assessment

Before assessing the business value of IT solutions, following questions should be considered:

1. What assumptions are we making about value?
2. What stakeholder perspectives might we be missing?
3. How can we validate our value hypotheses?
4. What data would strengthen our value assessment?

Rigorous IT value assessment requires questioning the assumptions that underlie any value projection. Four categories of critical questions help surface blind spots.

Assumptions about value are often implicit. What time horizon is assumed — will the investment pay off in months or years? How much of the observed value can genuinely be attributed to the IT investment rather than to concurrent organizational changes? What market conditions are assumed to hold? And is the relationship between the IT investment and the claimed outcome truly causal, or merely correlational?

Stakeholder perspectives are frequently incomplete. End users, customers, supply chain partners, regulators, and future stakeholders all experience IT investments differently. An ERP implementation that improves management reporting may simultaneously frustrate frontline employees with rigid workflows. Comprehensive value assessment accounts for the full range of affected parties.

Validation approaches turn value hypotheses into testable propositions. Pilot projects, A/B tests, reference benchmarks, and post-implementation reviews all provide evidence that can confirm or challenge projected benefits. The earlier validation occurs, the lower the cost of correcting course.

Supporting data strengthens any value assessment. Baseline metrics captured before implementation provide a point of comparison. Usage and adoption statistics reveal whether the preconditions for value creation (as identified by Soh and Markus) are being met. Performance data across multiple time periods shows whether value is growing, stable, or declining.

This critical thinking process should not be a one-time exercise at the investment decision point. It should be iterative throughout the IT investment lifecycle — from initial business case through implementation to post-deployment review.

Assessment types

Ward & Daniel (2006) advocates for a mixed-method approach to IT value assessment reflected by balanced IT value scorecards.

Quantitative:

• Financial metrics (ROI, NPV)
• Operational metrics (time, quantity)
• Customer metrics (retention rate)
• Risk metrics (incident reduction)

Qualitative:

• Strategic alignment
• Innovation enablement
• Capability enhancement
• Organizational learning

Communicating value

Tailored messages

Value messages need to be tailored to business stakeholders (Peppard, 2016), such as

C-Suite:

• Strategic alignment
• Competitive advantage
• Financial impacts
• Risk portfolio effects

Business unit leaders:

• Operational excellence
• User productivity
• Cost/benefit analysis
• Process improvements

End users:

• Usability improvements
• Time savings
• Quality of work life
• Capability enhancement

Recommendations

Built a rich business case based on the insights you have gained:

1. Identify and center on stakeholders’ highest priority objectives.
2. Provide evidence-based value projections.
3. Include both tangible and intangible benefits.
4. Address value realization timeline and dependencies.

Ward & Daniel (2006) provides a comprehensive toolkit for increasing the benefits that organizations realize from their IT investments. Several practices are particularly valuable for building business cases.

Benefits identification requires explicitly tracing the causal chain from technology changes to business changes to measurable benefits. This chain is rarely obvious and often involves intermediate steps that must be actively managed.

Benefits ownership assigns clear responsibility for realizing each projected benefit to a specific individual — typically a business manager, not an IT manager. This practice prevents the common situation where everyone assumes someone else is responsible for turning capability into value.

Benefits categorization recognizes that not all benefits are equally measurable. Ward and Daniel distinguish between financial benefits (directly quantifiable in monetary terms), quantifiable benefits (measurable but not in monetary terms), measurable benefits (observable through defined indicators), and observable benefits (recognized through qualitative assessment). Each category requires different evidence standards and communication approaches.

Benefits realization planning specifies the timeline and actions required to achieve projected benefits. It acknowledges that benefits do not materialize automatically upon system deployment — organizational changes, training, and process adaptation are typically required.

Benefits review closes the loop through post-implementation assessment of actual versus projected benefits. This practice builds organizational learning and improves the quality of future business cases.

Common pitfalls include overestimating benefits while underestimating the organizational effort required to realize them, focusing exclusively on financial metrics while ignoring strategic value, and failing to account for the change management work that turns technical deployment into effective business use. Modern approaches such as lean business cases and minimum viable products allow organizations to validate value incrementally rather than betting on a single large projection.

Key takeaways

1. Digital resources go beyond traditional IT assets — they are modular, encapsulate value, and are programmatically accessible.
2. Dynamic capabilities determine whether IT investments create sustained competitive advantage.
3. IS business value is multidimensional — tangible/intangible, internal/external — and always context-dependent.
4. Without effective use, even the best IT investment creates no value (the missing link).
5. Measuring and communicating value requires stakeholder-centered framing, not technical metrics.

Literature

Brynjolfsson, E. (1993). The productivity paradox of information technology. Communications of the ACM, 36(12), 66–77.

Burton-Jones, A., & Grange, C. (2013). From use to effective use: A representation theory perspective. Information Systems Research, 24(3), 632–658.

Cannon, J. W. (2020). How to measure the business value of IT: 5 measures to consider (Gartner Research G00723855). Gartner, Inc.

Kaplan, R. S., & Norton, D. P. (2004). Strategy maps: Converting intangible assets into tangible outcomes. Harvard Business School Press.

Kohli, R., & Grover, V. (2008). Business value of IT: An essay on expanding research directions to keep up with the times. Journal of the Association for Information Systems, 9(1), 23–39. https://aisel.aisnet.org/jais/vol9/iss1/1/

Melville, N., Kraemer, K., & Gurbaxani, V. (2004a). Information technology and organizational performance: An integrative model of IT business value. MIS Quarterly, 283–322.

Melville, N., Kraemer, K., & Gurbaxani, V. (2004b). Information technology and organizational performance: An integrative model of IT business value. MIS Quarterly, 28(2), 283–322. https://doi.org/10.2307/25148636

Mithas, S., Ramasubbu, N., & Sambamurthy, V. (2011). How information management capability influences firm performance. MIS Quarterly, 35(1), 237–256. https://doi.org/10.2307/23043496

Peppard, J. (2016). Unlocking the performance of the chief information officer (CIO). California Management Review, 58(1), 73–99. https://doi.org/10.1525/cmr.2015.58.1.73

Peteraf, M., Di Stefano, G., & Verona, G. (2013). The elephant in the room of dynamic capabilities: Bringing two diverging conversations together. Strategic Management Journal, 34(12), 1389–1410.

Piccoli, G., Rodriguez, J., & Grover, V. (2022). Digital strategic initiatives and digital resources: Construct definition and future research directions. Management Information Systems Quarterly, 46(4), 2289–2316.

Schryen, G. (2013). Revisiting IS business value research: What we already know, what we still need to know, and how we can get there. European Journal of Information Systems, 22(2), 139–169. https://doi.org/10.1057/ejis.2012.45

Soh, C., & Markus, M. L. (1995). How IT creates business value: A process theory synthesis. ICIS 1995 Proceedings, 4.

Steininger, D. M., Mikalef, P., Pateli, A., & Ortiz-de-Guinea, A. (2022). Dynamic capabilities in information systems research: A critical review, synthesis of current knowledge, and recommendations for future research. Journal of the Association for Information Systems, 23(2), 447–490.

Teece, D. J. (2014). A dynamic capabilities-based entrepreneurial theory of the multinational enterprise. Journal of International Business Studies, 45(1), 8–37.

Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 425–478.

Wade, M., & Hulland, J. (2004). The resource-based view and information systems research: Review, extension, and suggestions for future research. MIS Quarterly, 107–142.

Ward, J., & Daniel, E. (2006). Benefits management: Delivering value from IS and IT investments. John Wiley & Sons.