Making DX and IT investment decisions: An integrated approach using IS Success Model, IT Business Value, Real Options, J-Curve, and RBV/VRIO

1. Introduction

In my work I regularly speak with people at many companies and government bodies, and lately I have sensed a sharp rise in interest in adopting AI. Much of it seems driven by a sense of alarm at how quickly the technology is advancing.
Yet when it comes to actually running a DX project, organizations differ enormously in how they make the investment decision and how they take it through internal approval.
Some companies set KPIs, run one proof of concept after another, and verify the benefits with care; at others, adoption is decided on an executive's gut feeling.
No DX investment can ever be guaranteed to succeed, but ways of thinking and approaches that maximize the odds of success have been taking shape through a body of knowledge that has accumulated since Adam Smith published his theory of investment more than 250 years ago. My impression, however, is that most people have yet to put that knowledge to full use.
In this article I want to lay out the fundamental thinking behind DX investment, the frameworks for making investment decisions, and practical approaches, all in light of the latest research.

2. Most DX Investments Fail

The data and the case studies on failure

Investing in DX and IT is an indispensable strategy for any modern enterprise, yet a wide range of surveys and studies point to a harsh reality: most of these investments fail to deliver the results that were expected.

• Only 16% of companies achieve full success Very few companies manage to convert their DX efforts into sustained performance gains. In one survey, only 16% of respondents said their digital transformation had "improved performance and equipped them to sustain the change over the long term." Even in high-tech and telecommunications the success rate did not exceed 26%, and in traditional industries such as oil and gas, automotive, infrastructure, and pharmaceuticals it fell to a sobering 4–11% (McKinsey & Company, 2018).
• 70% of transformations miss their targets, wasting enormous sums Another set of survey data finds that roughly 70% of digital transformations fall short of their original objectives. Of these, 44% land in a "worry zone," having created some value but missed their targets and produced little lasting change, while a further 26% fall into a "woe zone," delivering less than 50% of the targeted value and no sustainable change at all. The result is a massive loss of money, time, and organizational effort (Boston Consulting Group, 2020).
• How "organizational inertia" and "existing strengths" blocked transformation: the case of Kodak There are many cases in which the business model and core capabilities that once made a company successful turn into "core rigidities" that stand in the way of DX. Kodak is the textbook example. Confronted with the disruptive wave of digital photography, the company was held back by structural inertia—its highly optimized production processes and established customer relationships—and missed its chance to transform fundamentally, eventually filing for bankruptcy in 2012. It is a classic failure: even when management understands the value of digital, tangible and intangible assets deeply embedded in the organization act as shackles, and the investment never bears fruit (Vial, 2019).
• Over-emphasis on technology and derailment by the "human dimension" (employee resistance) Most DX failures stem not from the technology itself but from neglect of organizational culture and human factors—the operating model, processes, and employee mindset. When new digital tools are rolled out, strong resistance from employees intent on protecting existing workflows, along with "innovation fatigue," is a frequent result. Numerous studies point out that when the benefits of the change are not made visible to those on the front line and the effort amounts to nothing more than a top-down technology deployment, the resulting pushback prevents new ways of working from taking hold, and the DX investment ends in failure (Boston Consulting Group, 2020; Vial, 2019).

In short, DX and IT investments fail not because the technology is immature, but because of complex human and structural factors: organizational culture, psychological resistance from employees and middle managers, and an attachment to the existing business.

Why they fail

Many companies are pushing ahead with DX (digital transformation) and IT investments, but most fail to deliver the expected results. The causes go well beyond technical problems and are deeply rooted in organizational, psychological, and economic factors.

• Lack of "complementary investment" in intangible assets When adopting a general-purpose technology such as AI or a new IT system, the visible spending—buying hardware and software—does not by itself create real value. Complementary investment in intangible assets is essential: building new business processes, retraining employees, and redesigning the organizational structure. When this complementary investment and the learning period it requires are neglected, the company falls into the "productivity J-curve," in which productivity actually declines in the early stages of adoption, and the investment is judged a failure before its results have a chance to appear (Brynjolfsson et al., 2021).
• Inadequate "governance" and lax project management Most IT project failures arise not from flaws in the technology but from dysfunctional IS (information systems) governance: underestimating project effort, failing to manage stakeholders effectively, and managing risk inadequately (Schryen, 2013). Another decisive factor in derailed transformations is a leadership team that lacks an agile governance mindset—the willingness to respond flexibly to changing conditions and quickly remove obstacles on the ground—and therefore cannot respond appropriately to risk (Boston Consulting Group, 2020).
• Organizational inertia and the failure of change management The strengths a company builds through past success can become "core rigidities" when the environment shifts, giving rise to an "organizational inertia" that obstructs transformation. On top of this, the introduction of new digital technology provokes strong resistance from middle managers seeking to protect their existing turf (the "frozen middle") and from front-line employees suffering innovation fatigue after wave upon wave of change. When change management is not robust enough to overcome these psychological and structural barriers, digitalization fails to take root in the organization and the effort ends in failure (Boston Consulting Group, 2020; Vial, 2019).
• Behavioral economics: "cognitive biases" and distorted investment decisions Research in behavioral economics shows that human cognitive biases distort the ex-ante evaluation of DX and IT investments (such as cost-benefit analysis) and lead to failure. Examples include availability bias (recalling only the most salient cases), status quo bias (a preference for keeping things as they are), and optimism bias (unjustifiably high forecasts of project outcomes). On top of these, "strategic bias" is common: project champions deliberately understate costs and overstate benefits in order to win internal approval, and excessive investment proceeds on the basis of an unrealistic plan (Boardman et al., 2018).

In sum, DX and IT investments fail not because of technical immaturity but through a tangle of interrelated factors: psychological biases at the evaluation stage, underinvestment in the complementary organizational changes, resistance on the front line, and the absence of governance.
The sections that follow set out the knowledge and approaches needed to avoid these failures.

3. What Is Investment in the First Place?

Let us begin by sorting out the different types of investment and approaches to it, drawing on several perspectives: economics, finance, human capital, and public policy.

• The microeconomic view: intertemporal resource allocation and optimal consumption In microeconomics, investment is defined as the intertemporal allocation of resources—deciding when to invest, and in what—whereby present consumption is sacrificed and resources committed in order to raise future income or utility (satisfaction). The theory of optimal investment that laid the foundations of this field establishes a "separation theorem": an investor undertakes productive real investment up to the point where net present value (NPV) is maximized, and then optimizes the timing of his or her own consumption by borrowing and lending in the financial markets. In other words, the investment decision and the consumption-timing decision can be considered separately (Hirshleifer, 1958). (Because this assumes perfect capital markets, adjustments are needed in practice.)
• The macroeconomic view: accumulation of physical capital and market value Macroeconomics treats investment from an economy-wide perspective, as the accumulation of physical capital—machinery, factories, infrastructure—that raises a nation's productive capacity. Keynes argued that investment takes place when the expected return on a new capital asset under consideration (machinery, an IT system, real estate, and so on)—the marginal efficiency of capital, or IRR—exceeds the market rate of interest, that is, the rate at which the same sum could be borrowed from a bank (Keynes, 1936). Tobin's (1969) "q theory" goes further, explaining that when the ratio of a firm's market value (equity market capitalization plus the market value of debt) to the replacement cost of its existing capital (the cost of building the same firm again from scratch) exceeds one (q > 1), the firm has a strong incentive to undertake new real investment and expand its capital stock. When q > 1 a firm invests in itself; when q < 1 it forgoes new investment and acquires existing companies instead. Superstar firms such as Apple, NVIDIA, and Microsoft have q values of 5–10 or more, which is why they invest so aggressively in themselves.
• The strategic-management view: flexibility under uncertainty and "real options" In strategic management, the purpose of investment is to build competitive advantage and cope with environmental uncertainty. Investments in IT and new businesses in particular tend to generate sunk costs that are hard to recover once committed, which is why the concept of "real options"—the right to flexibly scale up or exit an investment as future conditions unfold—is so critically important (Trigeorgis & Reuer, 2017). Seen this way, IT investment is not simply the purchase of a system but a strategic allocation of resources that strengthens the organization's dynamic capabilities and flexibility and enables it to adapt to a changing environment (Drnevich & Croson, 2013).
• The finance view: the pursuit of "risk and return" in the market In finance, investment refers chiefly to the allocation of funds to financial assets such as stocks and bonds. It is defined as the act of bearing uncertainty (risk) in pursuit of a commensurate reward (return). Market participants hold their own "investment philosophy" regarding market inefficiencies and investor behavior—how efficient the market is, why stocks become mispriced, where one can beat other investors, and which risks matter—and on that basis deploy investment strategies such as value investing, growth investing, or arbitrage to optimize their portfolios (Damodaran, n.d.).
• The human capital and intangible assets perspective: "dynamic complementarity" and the productivity "J-curve" In today's economy, the focus of investment has shifted decisively from physical equipment toward "intangible assets" such as knowledge, skills, and organizational culture. Investment in "human capital" through education and training has been shown to exhibit "dynamic complementarity": the earlier the investment is made, the more it raises the efficiency of subsequent learning and the productivity of later investment (early investment increases the productivity of later investment itself; put simply, people with a solid foundation gain the most from additional investment) (Heckman & Mosso, 2014). Moreover, investment in new general-purpose technologies such as IT and artificial intelligence (AI) delivers genuine value only when accompanied by complementary investment in intangibles, such as redesigning business processes and retraining staff. As a result, productivity temporarily declines in the early phase of adoption because of learning and adjustment costs, before rising sharply over the long term—a pattern known as the "productivity J-curve" (Brynjolfsson et al., 2021).
• The public investment perspective: correcting "market failures" and maximizing net social benefit Public investment by governments and similar bodies aims not to pursue the financial profit of a private firm but to maximize the welfare of society as a whole (social surplus). It is undertaken to correct "market failures" such as environmental problems and the under-provision of public goods. Such investments are evaluated using cost-benefit analysis (CBA): a project is deemed socially desirable, and therefore worth implementing, when its "net social benefit"—the social benefits it generates (measured by people's willingness to pay) minus its social costs (opportunity costs)—is positive (Boardman et al., 2018).

Investment, then, is not merely the spending of money; it is "the allocation of resources across time with the aim of creating future value and coping with uncertainty." As the discussion above shows, DX and IT investment is a form of investment that touches on microeconomics, macroeconomics, business strategy, and human capital alike.

4. How to evaluate DX investment 

The limits of evaluating DX investment on simple ROI

When it comes to evaluating corporate DX and IT investment, a large body of literature points out the limits of relying on conventional, static financial metrics such as simple ROI (return on investment).

• Complementary investment in intangibles and early undervaluation due to the "J-curve effect" The general-purpose technologies (GPTs) at the heart of DX, such as AI and IT, do not generate value simply by being installed; they deliver their true effects only when accompanied by "complementary investment in intangible assets," such as redesigning business processes and developing employee skills. Because accumulating these intangibles and learning to use them takes time, a "J-curve effect" emerges in which costs come first and productivity appears to dip temporarily during the early phase of adoption. Short-term ROI therefore undervalues the dramatic growth potential that lies ahead (Brynjolfsson et al., 2021).
• The time lag before results materialize (lag effects) Numerous empirical studies show that information systems (IS) and digital investments take years to translate into actual financial results and performance improvements, as organizations must first adapt and learn (lag effects). Consequently, attempting to measure ROI over a short window immediately after investing can lead to the mistaken conclusion that the investment has failed (Schryen, 2013).
• The missing "real options" (the value of flexibility) under uncertainty Conventional ROI and net present value (NPV) calculations assume deterministic forecasts of future cash flows. In highly uncertain DX investments, however, there is substantial value in simply holding the right (a real option) to start with a small initial investment and then flexibly scale up, change course, or exit as circumstances evolve. The value of this strategic flexibility—"keeping future options open"—cannot be captured within a simple ROI framework (Drnevich & Croson, 2013; Trigeorgis & Reuer, 2017).
• The difficulty of measuring multidimensional "net impacts" and internal capabilities The success of DX investment is not limited to financial returns such as direct revenue growth or cost reduction. Equally important outcomes include hard-to-quantify, non-financial, and intangible strengthening of internal capabilities: better decision-making, the cultivation of an agile organizational culture, and higher customer and employee satisfaction. An ROI assessment that ignores these dramatically understates the overall net benefit—the "net impact"—that a system delivers to the organization and to society as a whole (DeLone & McLean, 2016; Schryen, 2013).
• A mismatch with value creation built on network effects and ecosystems Modern digital business strategy aims not merely at internal operational efficiency but at harnessing network effects through platforms and building business ecosystems in collaboration with external partners. This new mechanism of value creation—dynamic, exponential, and extending beyond the boundaries of the firm—simply does not fit within a conventional ROI framework that measures only linear, short-term returns on capital invested inside the company (Bharadwaj et al., 2013).

In short, because DX investment involves the long-term accumulation of intangible assets, the preservation of strategic flexibility in the face of environmental change, and the construction of new network-based business models, there are inherent limits to how accurately its value can be measured by ROI alone, a single financial metric.

Defining success in DX investment

The success of corporate DX and IT investment is not a one-dimensional question of whether a new system was rolled out on schedule; it is defined by a set of metrics that are both multidimensional and multilayered.

• Improved financial and market performance (capturing economic value) The most common and objective definition of IT investment success is an improvement in financial metrics such as ROI (return on investment), ROA (return on assets), and revenue growth, together with better market performance in terms of share price and enterprise value. In the DX context specifically, successful digital leaders are reported to achieve 1.8 times the profit growth and more than twice the growth in enterprise value of digital laggards, making the amount of economic value created against concrete targets a key criterion of success (BCG, 2020; Liang et al., 2010; Schryen, 2013).
• More efficient business processes and higher productivity (creating internal value) Another key definition of success is the internal value generated when high-quality systems and information are put to proper use by their users. Concretely, this includes cost reductions, more efficient and automated business processes, and faster customer response and decision-making. Raising employee "productivity" and improving operational efficiency through IT investment is regarded as the central success metric at the operational level (DeLone & McLean, 2016; Schryen, 2013; Vial, 2019).
• Stronger organizational capabilities and embedded "sustainable change" Success is also defined as IT resources doing more than replacing tools: strengthening the organization's internal capabilities (process integration and knowledge management) and its external capabilities (building relationships with customers and suppliers). In DX in particular, true success is not a temporary boost in performance but a state in which agile ways of working and a digital mindset have taken root in the organizational culture, producing "sustainable change" that allows the organization to keep adapting to a shifting environment (BCG, 2020; Liang et al., 2010).
• Establishing competitive advantage through new business models In the digital age, success is defined by "new value creation and capture" that breaks existing frameworks. Specifically, the success criteria for digital business strategy emphasize transforming the customer experience and establishing distinctive competitive advantage and strategic differentiation through the digitalization of products and services, the construction of multi-sided platforms, and the use of insights drawn from vast volumes of data and information (Bharadwaj et al., 2013; Vial, 2019).
• Creating "net impacts" (net benefits) that extend from individuals to society as a whole The success of an information system begins with improvements in individual user satisfaction and job performance, but its effects progressively widen. In its broadest definition, ultimate success means that IT investment benefits not only individuals, workgroups, and organizations, but also delivers positive "net impacts" (net benefits) to industry and society at large—improving consumer welfare, making markets more efficient, and creating new jobs (DeLone & McLean, 2016).

Thus the success of DX and IT investment is defined not merely by short-term financial returns, but by the creation of comprehensive value (net impact) that encompasses improved operational efficiency, the organization's capacity for transformation, the construction of new business models, and a positive impact on society.

How to evaluate DX investment properly

To evaluate corporate DX and IT investment properly, organizations need a multidimensional, long-term framework rather than a reliance on short-term financial metrics such as ROI.

• Multidimensional evaluation based on "IS Success Models" Measuring the success of an information system by a single criterion, such as whether it was delivered within budget, is inadequate. According to DeLone and McLean's "IS Success Model," a system should be evaluated comprehensively across six interrelated dimensions: three quality dimensions—the technical "system quality," the "information quality" of its outputs, and the "service quality" provided by the IT department and others—together with "use" by users, "user satisfaction," and, ultimately, "net impacts." Rather than depending on any one metric, it is essential to combine these multiple perspectives into a well-rounded assessment. (DeLone & McLean, 2016; Petter et al., 2008)
• Hierarchical, context-sensitive evaluation in "IT Business Value" When assessing how IT investment generates business value (IT Business Value), it is risky to assume that the investment will feed directly into the company's financial results. The process-oriented evaluation models supported by much of the research call instead for a hierarchical assessment: IT investment first improves "process performance"—faster operations, better customer service, and so on—and these improvements in turn mediate the eventual effect on "firm and organizational performance" (financial and market results). Because these outcomes are also heavily shaped by environmental factors at the firm, industry, and national levels, the evaluation must be designed with context in mind. (Schryen, 2013)
• Evaluating "organizational capabilities" through the resource-based view (RBV) From a resource-based view (RBV) perspective, IT resources on their own do not generate financial returns directly; performance improves only when IT strengthens the organization's "capabilities." Evaluation must therefore go beyond the system implementation itself and measure how much it has strengthened "internal capabilities" such as knowledge sharing within the organization, as well as "external capabilities" such as the ability to adapt to the market. (Liang et al., 2010)
• Long-term evaluation that accounts for the "J-curve effect" and time lags To assess the true value of a DX investment, you need to account for the time lag (lag effects) between the investment and the emergence of results. When a new general-purpose technology (GPT) such as AI is introduced, complementary investments in intangible assets—redesigning business processes, employee learning, and so on—are required. As a result, productivity temporarily dips in the early stages of adoption and then rises sharply afterward: the "productivity J-curve." Because measuring over a short window risks undervaluing the investment, value creation should be evaluated from a long-term perspective. (Brynjolfsson et al., 2021; Schryen, 2013)
• Valuing flexibility under uncertainty with "real options" For DX investments in fast-changing, uncertain environments, simply holding the right (a real option) to start small and then flexibly decide to scale up or exit as you learn more about the situation is itself a major source of value. Rather than relying solely on NPV (net present value) or ROI based on fixed cash-flow forecasts, it is more appropriate to also factor in the value of strategic flexibility—keeping future options open and pursuing upside opportunities while containing downside risk. (Trigeorgis & Reuer, 2017)

In short, a sound evaluation of DX investment cannot stop at measuring short-term financial returns. It must integrate multiple perspectives—system quality, process improvement, stronger organizational capabilities, and strategic flexibility—over a long-term horizon.

5. How to Use the Framework for DX Investment Decisions and Execution

The most coherent approach, both academically and in practice, is to design DX investment decision-making as a stage-gate process that runs through the five templates in sequence, advancing to the next stage only once the criteria for the current one are met. The order of review is RBV/VRIO → IT Business Value → Real Options → J-Curve → IS Success Model.
Because the process relies on checklists in Excel or a similar tool, we recommend either finding templates online or generating them with AI.

A. Strategic-Level Screening: RBV/VRIO

Purpose

• Examine the rationale for the investment and its strategic significance

Steps

1. Identify the capabilities the proposed DX investment will develop or strengthen
2. Score each dimension of the VRIO framework (V/R/I/O) on a scale of 1 to 5
3. Based on the VRIO analysis, determine the competitive-advantage status (disadvantage / parity / temporary advantage / untapped potential advantage / sustained advantage)
4. Check the Sensing / Seizing / Transforming scores on the "Dynamic Capabilities" sheet
5. Identify the gap between strategic importance and the current VRIO position, and review the recommended actions

Pass criteria

• Classified as a "sustained competitive advantage" or an "untapped potential advantage," or classified as a competitive disadvantage but judged essential to build because its "strategic importance" is rated 4 or higher
• Contributes explicitly to at least one of the three dimensions of dynamic capabilities

If the investment does not pass

• Reaches only "competitive parity" → treat it as an efficiency investment (aimed at cost reduction) under a separate budget line rather than as a strategic investment
• "Disadvantage + low strategic importance" → do not proceed with the investment

B. Designing the Causal Mechanism: IT Business Value

Purpose: examine the causal pathways through which value is created and the preconditions for it
Steps:

1. Rate the enabling and inhibiting factors at three levels (the company itself / competitive environment / macro environment) on a scale of 1 to 5
2. Diagnose the readiness of IT resources (technology plus people) and complementary organizational resources
3. Identify the business processes that will be affected by process improvement and set improvement targets
4. Map the causal chain from resources → processes → organizational outcomes
5. Define KPIs for process outcomes and organizational outcomes

Pass criteria:

• The company's own context score averages 3.5 or higher (no strong inhibiting factors)
• Readiness of both IT resources and complementary resources is 3 or higher
• Investment is concentrated in areas where the total importance of business process improvements is high
• Weighted overall score of 3.0 or higher

Typical reasons for not passing:

• Low readiness of complementary organizational resources (processes, culture, KPIs) → redesign the investment as a package that includes complementary investments
• Low score for the company itself in the context assessment → secure management commitment and establish governance first
• The causal pathway lacks specificity, amounting to little more than "install IT and something will happen" → send the proposal back for rework

C. Valuation Under Uncertainty and Choice of Investment Structure: Real Options

Purpose: determine when to execute the investment under uncertainty
Steps:

1. Select the investment type (defer / staged investment / expand / abandon / switch)
2. Estimate the parameters: S (present value of cash flows if successful), K (investment amount), T (decision deferral period), and σ (uncertainty)
3. Use Black-Scholes for a simple valuation, or a binomial model for complex multi-stage decisions such as PoC → full-scale rollout
4. Run a sensitivity analysis to check how sensitive the result is to σ and T

Decision rules:

• Option premium > 0 and expanded NPV > static NPV → deferral or staged investment is preferable
• Option premium from the staged-investment ROV > PoC investment amount → a PoC is recommended
• High σ × long T → the value of waiting is large

D. Simulating Long-Term Effects: J-Curve

Purpose: understand when the effects will materialize and how deep the trough will be
Steps:

1. Set the investment horizon (5–10 years), lags (1–3 years for intangibles, 1 year for measured investments), decay rate (around 15%), and effect multiplier
2. Enter the annual plan for measured investments and intangible investments (processes, people, organization, data)
3. Check the cumulative net, discounted cash flows, and NPV
4. Visualize the depth of the J-curve trough and the timing of recovery
5. Set KPIs for interim monitoring that measure the accumulation of intangible assets (e.g., process standardization rate, number of DX personnel, data volume)

Examples of how to frame this for senior management:

• "The cumulative net figure is negative through year 3. However, this is the J-curve described by Brynjolfsson-Rock-Syverson (2021) AEJ:Macro, and once intangible assets have accumulated, it will turn positive from year 4 onward."
• "If the interim KPIs are accumulating as planned, the academic literature supports the expectation that the financial effects will materialize with a lag."

E. Monitoring Execution and Driving Improvement: IS Success Model

Purpose: track implementation progress
Steps:

1. Conduct surveys at three points in time: baseline (pre-investment) / immediately after rollout (3 months) / after the system has taken root (6–12 months)
2. Collect responses from 10–30 frontline users plus IT and the head of the responsible department (rating ① system quality, ② information quality, ③ service quality, ④ use, ⑤ user satisfaction, and ⑥ net benefits on a 5-point scale)
3. Visualize imbalances with a six-dimension radar chart
4. Track the net-benefit KPIs on an ongoing basis

Dimensions for short-term evaluation vs. dimensions for long-term evaluation:

• Short term (3–6 months): look at ① system quality, ② information quality, ③ service quality, and ④ use
• Long term (12–24 months): look at ⑤ user satisfaction and ⑥ net benefits

6. Conclusion

As we have seen, DX and IT implementation projects—which are so often judged on gut feeling or simple ROI—can be given a much better chance of success when they are advanced through evaluation from multiple angles.

One limitation of this article is that, while the integration of the five frameworks offers a comprehensive set of perspectives, the resulting framework may give the impression of being time-consuming to apply. Creating a simplified version would likely make it accessible to a wider range of users, and we hope to take this up as a future topic.

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22. Challapally, A., Pease, C., Raskar, R., & Chari, P. (2025). The GenAI divide: State of AI in business 2025. MIT NANDA. Summary: A report analyzing the current state of the "GenAI divide"—the fact that most organizations adopting generative AI have yet to reach production deployment or scale—and the reasons behind it.
23. McKinsey & Company. (2018). Unlocking success in digital transformations. Summary: A report examining the low success rate of digital transformation and the factors shared by successful companies: organizational culture, leadership, and agile change management.
24. Petter, S., DeLone, W. H., & McLean, E. R. (2008). Measuring information systems success: Models, dimensions, measures, and interrelationships. European Journal of Information Systems, 17(3), 236-263. Summary: A qualitative review of more than 180 empirical studies based on the D&M IS Success Model, examining the relationships among constructs such as system quality and net benefits.
25. 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. Summary: Takes stock of prior research on the business value of IT and charts directions for future work, including the valuation of intangibles, lagged effects, and the challenge of organizational capabilities.
26. Schumpeter, J. A. (1934). Theorie der wirtschaftlichen entwicklung [The theory of economic development]. Summary: The classic "theory of economic development," which explains how new combinations, innovation, and the role of the entrepreneur drive economic development and business cycles.
27. Tobin, J. (1969). A general equilibrium approach to monetary theory. Journal of Money, Credit and Banking, 1(1), 15-29. Summary: Explains the mechanism behind real investment decisions in the macroeconomy using "Tobin's q," the ratio of a firm's market value to the replacement cost of its existing capital.
28. Trigeorgis, L., & Reuer, J. J. (2017). Real options theory in strategic management. Strategic Management Journal, 38(1), 42-63. Summary: A review paper examining corporate strategic decision-making under high uncertainty through the lens of "real options theory," which emphasizes the value of flexibility.
29. Vial, G. (2019). Understanding digital transformation: A review and a research agenda. Journal of Strategic Information Systems, 28(2), 118-144. Summary: Reviews the literature on digital transformation (DX) and models how digital technologies affect organizational structures and value-creation processes.