Employee Mindsets and Reconfiguration Capabilities: A Behavioral Study on Digital Transformation Readiness

Introduction

Digital transformation has moved from a peripheral information-technology initiative to a central strategic imperative across virtually every industry, as organizations combine information, computing, communication, and connectivity technologies to reconfigure how value is created and delivered (Vial, 2019). The strategic significance of digital transformation extends beyond technology adoption because digitally enabled change increasingly requires organizations to reconsider established structures, routines, capabilities, and employee roles. A large body of information-systems and strategy scholarship has therefore approached digital transformation as an organizational-level phenomenon in which firms sense disruptive digital opportunities, seize them through strategic investment, and transform internal structures to exploit emerging opportunities (Teece, 2007; Warner & Wäger, 2019).

This sensing–seizing–reconfiguring sequence represents a central conceptual foundation of dynamic capabilities theory, which defines dynamic capability as the firm's ability to integrate, build, and reconfigure internal and external competences to address rapidly changing environments (Teece et al., 1997). Within this perspective, reconfiguration constitutes the stage through which previously established resources, routines, knowledge, and skills are recombined into new configurations capable of supporting changing strategic requirements (Eisenhardt & Martin, 2000; Zollo & Winter, 2002). Although reconfiguration is frequently conceptualized at the organizational level, it does not occur in the abstract. It is ultimately enacted through the accumulated behavior of employees who must relearn tasks, adopt unfamiliar technologies, modify established work practices, and sometimes abandon routines in which they may have developed substantial psychological and professional investment.

This individual-level dimension has become increasingly important as digital transformation moves from infrastructure deployment toward continuous organizational adaptation. The microfoundations movement in strategy argues that macro-level capabilities cannot be fully understood without specifying the individual-level choices, cognitions, interactions, and behaviors that aggregate into organizational capabilities (Felin, Foss, Heimeriks, & Madsen, 2012; Felin, Foss, & Ployhart, 2015).

From this perspective, organizational reconfiguration depends not only on managerial decisions or technological investments but also on whether employees are willing and able to modify their behaviors in response to changing organizational requirements. Despite this theoretical emphasis, empirical work that operationalizes an individual-level, psychologically grounded predictor of reconfiguration behavior remains comparatively scarce. The digital-transformation literature has instead tended to treat employee readiness as a residual implementation variable rather than as a theoretically specified construct capable of explaining variation in individual contributions to organizational reconfiguration. This limitation is consequential because two organizations with comparable technological resources may experience substantially different transformation outcomes if their employees differ in their willingness to learn, respond to feedback, experiment with unfamiliar approaches, and adapt established routines.

Organizations therefore routinely underestimate the behavioral cost of digital transformation. Case evidence and practitioner commentary repeatedly indicate that when employees lack the appropriate orientation toward organizational change, digital initiatives can magnify rather than correct existing organizational weaknesses, as summarized in the digitalreadiness literature and the review by Höyng and Lau (2023).

At the same time, dynamic capabilities research has articulated reconfiguration primarily as a firm-level construct without developing a sufficiently specified individual-level analogue.

This creates a conceptual gap between what strategy theory requires organizations to accomplish—continuous reconfiguration—and what behavioral and organizationalchange research identifies as determinants of employees' willingness and ability to participate in that process. The resulting problem is not simply a lack of employee acceptance of technology; rather, it concerns the deeper behavioral mechanisms through which employees contribute to the modification of organizational routines, capabilities, and practices during digital transformation. Understanding these mechanisms is particularly important because digital transformation frequently requires employees to operate under conditions of uncertainty, acquire new competencies, respond to changing performance expectations, and relinquish previously successful routines.

Three interconnected research gaps motivate the present study.

First, the dynamic capabilities literature has theorized reconfiguration extensively at the firm level (Teece, 2007; Helfat et al., 2007) but has only progressively unpacked its microfoundations (Felin et al., 2012), and comparatively little work connects those microfoundations to an established psychological construct such as mindset. The absence of such a connection limits understanding of how individual psychological orientations may contribute to the behavioral realization of organizational dynamic capabilities. Second, the mindset literature, particularly research on implicit theories of ability, has demonstrated important relationships between individuals' beliefs about the malleability of ability and their learning behavior, persistence, response to feedback, and willingness to engage with challenges (Dweck, 2006).

However, this literature has predominantly been applied to individual performance, motivation, education, and leadership contexts rather than to organizational-level strategic renewal processes such as digital transformation. Consequently, the potential role of mindset orientation in enabling employees to participate in organizational reconfiguration remains insufficiently developed. Third, the technology-acceptance and organizational-readiness-for-change literatures provide established instruments for assessing perceptions such as usefulness, ease of use, organizational change efficacy, and readiness (Davis, 1989; Weiner, 2009), while research on resistance to change provides a complementary perspective on dispositional resistance and behavioral responses to organizational change (Oreg, 2003). Yet these streams have rarely been integrated into a single nomological network that simultaneously incorporates mindset orientation, resistance to change, technology acceptance beliefs, and change efficacy to explain variance in individual-level reconfiguration contribution. Bringing these perspectives together can therefore provide a more comprehensive explanation of why employees differ in their capacity and willingness to support digitally induced organizational change.

The motivation for integrating these perspectives is both theoretical and practical. Academically, incorporating mindset theory into the microfoundations of dynamic capabilities can extend both literatures by providing dynamic capabilities theory with a psychologically grounded individual-level construct while giving mindset theory a strategically consequential organizational outcome. Such integration shifts the analytical focus from treating reconfiguration exclusively as a managerial or organizational capability toward examining the employeelevel mechanisms through which that capability is enacted. It also provides an opportunity to distinguish psychological orientation from more proximal technology-acceptance and change-readiness perceptions, thereby clarifying whether mindset contributes explanatory value beyond conventional technology-adoption constructs. Practically, if growth mindset and its behavioral correlates are found to predict reconfiguration-relevant behavior, organizations undertaking digital transformation could consider psychologically informed change-management interventions alongside conventional technical and infrastructural investments. Potential interventions could include mindset-oriented training, participative goal-setting, feedback mechanisms, learningoriented development programs, and organizational practices designed to strengthen employees' willingness to experiment and adapt. Such an approach could help organizations address the behavioral dimension of transformation rather than assuming that technology deployment automatically produces corresponding changes in employee behavior. It may also provide organizations with a structured basis for identifying areas in which employees require additional support during periods of substantial technological and organizational change.

Building on these theoretical foundations, the present study pursues four principal research objectives. The first objective (RO1) is to develop an integrative theoretical model linking employee mindset orientation to individual-level reconfiguration contribution and digital transformation readiness. The second objective (RO2) is to specify measurable constructs and hypotheses connecting growth mindset, resistance to change, technology-acceptance beliefs, and change efficacy to reconfiguration-relevant behavior. The third objective (RO3) is to design a rigorous and reproducible quantitative methodology encompassing instrumentation, sampling, and analytic procedures suitable for empirically testing the proposed model in digitally transforming organizations. The fourth objective (RO4) is to articulate a proposed evaluation framework, including illustrative or simulated results, demonstrating how model fit, reliability, validity, and hypothesis tests would be reported once real data are collected. The explicit distinction between proposed evaluation procedures and actual empirical findings is maintained to ensure that simulated outputs are not interpreted as evidence derived from observed organizational data.

The study contributes to the literature in several interconnected ways. First, it provides a conceptual integration of dynamiccapabilities microfoundations, implicit theories of ability, technology acceptance, and organizational readiness-forchange theory within a single testable model of individual reconfiguration contribution. This integration provides a mechanism-oriented perspective on how employee-level psychological and behavioral characteristics may contribute to the realization of organizational reconfiguration during digital transformation. Second, it develops a formally specified reflective measurement model and structural model, including mathematical representations suitable for analysis using partial least squares structural equation modeling (PLS-SEM) or covariance-based structural equation modeling. This formalization provides a transparent basis for operationalizing the proposed constructs and examining their measurement and structural relationships. Third, the study proposes a concrete and reproducible research design incorporating adapted validated scales, a defined sampling framework, and a statistical testing plan that future researchers can implement directly using appropriate organizational data. Fourth, it provides a transparently labeled illustrative evaluation demonstrating procedures for interpreting reliability, discriminant validity, model relationships, and path significance without presenting simulated results as unearned empirical findings. Together, these contributions establish a methodological and theoretical foundation for future empirical research examining the behavioral microfoundations of organizational reconfiguration in digitally transforming environments.

The remainder of the paper is organized as follows. Section 2 reviews the literature on dynamic capabilities and reconfiguration, microfoundations, employee mindset, technology acceptance, resistance to change, and digital transformation readiness and synthesizes the relevant literature in a comparative table. Section 3 develops the theoretical and conceptual framework and states the hypotheses derived from the proposed relationships. Section 4 presents the mathematical and measurement-model specification, including the formal representation of the constructs and their structural relationships. Section 5 details the proposed methodology, including sampling, instrumentation, data-collection procedures, and analytic procedures. Section 6 describes the proposed dataset characteristics and the requirements for empirical implementation. Section 7 presents the evaluation framework together with an illustrative and clearly labeled simulated result set. Section 8 discusses interpretation, theoretical and practical contribution, and deployment considerations. Section 9 addresses explainability considerations associated with the measurement and structural models. Section 10 discusses ethics, governance, and dataprotection considerations relevant to employee-level digitaltransformation research. Section 11 addresses robustness and validity, including potential threats to measurement and structural inference. Section 12 states the study's limitations.

Section 13 outlines future research directions, including empirical validation and cross-contextual testing. Finally, Section 14 concludes the paper by synthesizing its theoretical, methodological, and practical implications.

Literature Review

Dynamic Capabilities and the Reconfiguration Construct

Teece, Pisano, and Shuen (1997) defined dynamic capabilities as the firm's ability to integrate, build, and reconfigure internal and external competences to address rapidly changing environments, positioning the concept as a corrective to the static resource-based view of the firm. Eisenhardt and Martin (2000) subsequently argued that dynamic capabilities are best understood as identifiable, learnable processes — such as product development or alliancing routines — that exhibit common features across firms even though their specific manifestations are path-dependent. Zollo and Winter (2002) added a learning-theoretic dimension, proposing that dynamic capabilities emerge from deliberate learning mechanisms (experience accumulation, knowledge articulation, and knowledge codification) rather than from ad hoc adaptation alone. Teece (2007) later organized dynamic capabilities into three clusters of microfoundations — sensing, seizing, and transforming (reconfiguring) — with reconfiguration referring specifically to the continuous alignment and realignment of tangible and intangible assets to sustain evolutionary fitness.

Helfat et al. (2007) extended this typology into a full volume treatment of strategic change, emphasizing that reconfiguration is costly and that organizations must develop repeatable processes for accomplishing it efficiently.

Applied specifically to digital transformation, Warner and Wäger (2019) conducted a qualitative study of incumbent firms and proposed a nine-microfoundation process model describing how organizations build dynamic capabilities for DT, concluding that digital transformation is best understood as an ongoing process of strategic renewal in which agility functions as the core mechanism for reconfiguring the business model, the collaborative approach, and organizational culture. This literature establishes reconfiguration as strategically consequential but leaves its individual-level enactment largely unspecified — a gap addressed by the microfoundations literature reviewed next.

Microfoundations of Dynamic Capabilities

Felin, Foss, Heimeriks, and Madsen (2012) argued that collective phenomena such as capabilities and routines cannot be fully explained without reference to individual-level components, choices, agency, characteristics, and cognition, and to the social processes and structures through which individual actions aggregate into organizational outcomes.

Felin, Foss, and Ployhart (2015) extended this argument into a broader review of the microfoundations movement in strategy and organization theory, contending that relating macro-level variables to other macro-level variables produces limited insight into the mechanisms through which managers and employees actually enact strategic change. This literature supplies the theoretical warrant for treating an individual psychological disposition — such as mindset — as a legitimate microfoundation of a macro-level capability such as reconfiguration, provided that the individual-level construct is clearly defined, validly measured, and theoretically linked to the aggregate outcome it is claimed to underpin.

Employee Mindset: Implicit Theories of Ability

Dweck's (2006) theory of implicit beliefs distinguishes between a fixed mindset, in which personal attributes such as ability and intelligence are viewed as largely immutable, and a growth mindset, in which such attributes are viewed as malleable and developable through effort, strategy, and feedback. Individuals with a growth orientation tend to interpret challenges as opportunities for development rather than as threats to a fixed self-concept, to persist longer in the face of setbacks, and to seek out corrective feedback rather than avoid it. Because digital transformation initiatives routinely require employees to abandon well-practiced routines, tolerate temporary incompetence with new tools, and accept critical feedback during a learning curve, mindset theory offers a plausible individual-level mechanism for explaining heterogeneity in employees' willingness to engage with reconfiguration-relevant tasks. Although mindset research has been applied extensively to educational and leadership contexts, its systematic incorporation into strategy-level microfoundations research on digital transformation remains limited, which is the specific gap this paper targets.

Technology Acceptance and Individual Digital

Readiness Davis's (1989) technology acceptance model (TAM) proposes that perceived usefulness and perceived ease of use are the two principal determinants of an individual's acceptance and use of information technology, with ease of use theorized as a partial antecedent of usefulness. TAM has since become one of the most widely applied frameworks in information systems research for predicting individual technology-adoption behavior. In the specific context of organizational digital transformation, Höyng and Lau (2023) developed an Employee Digitalization Acceptance Model grounded in TAM and personal-resource theory, showing that personal resources (including proactive personality) and job resources (such as empowering leadership and trust in management) operate through perceptions of digital tools to shape employees' intentional digital readiness. Their finding that growth mindset showed a counter-intuitive negative direct association with intentional readiness in their specific sample is a reminder that the mindset–readiness relationship is unlikely to be simple or universally positive, and that mediating and boundary conditions (such as perceived usefulness, organizational support, and resistance to change) must be modeled explicitly rather than assumed away — a design implication incorporated into the framework developed in Section 3. Perceived usefulness may influence whether employees recognize digital technologies as contributing meaningfully to task performance and organizational objectives. Perceived ease of use can similarly reduce the cognitive and procedural barriers associated with adopting unfamiliar digital systems. These beliefs may interact with employees’ existing psychological orientations toward learning, adaptation, and organizational change. Accordingly, technology acceptance should be considered alongside dispositional and contextual factors rather than treated as an independent determinant of readiness.

Organizational support may strengthen positive technology perceptions by providing training, resources, guidance, and opportunities for employee participation. Resistance to change may, in contrast, weaken the translation of favorable technology perceptions into observable transformation-related behaviors.

Integrating these mechanisms provides a more nuanced basis for examining how individual psychological characteristics and technology-related beliefs jointly contribute to digital transformation readiness. This perspective also suggests that employees’ readiness may depend on the alignment between technological demands and the resources available to support adaptation

Resistance to Change

Oreg (2003) developed and validated the Resistance to Change (RTC) scale, identifying four reliable factors — routine seeking, emotional reaction to imposed change, cognitive rigidity, and short-term focus — that together capture a dispositional, traitlike tendency to resist change across contexts. Because dispositional resistance to change is conceptually adjacent to, but distinct from, a fixed mindset (the former concerns general attitude toward change, the latter concerns beliefs about the malleability of personal ability), the two constructs are treated as related but separable predictors in the framework proposed below.

Organizational Readiness for Change

Weiner (2009) proposed a theory of organizational readiness for change in which readiness is conceptualized as a shared psychological state comprising change commitment (organizational members' shared resolve to implement a change) and change efficacy (their shared belief in their collective capability to do so), with change efficacy shaped by task knowledge, resource availability, and situational factors.

Holt, Armenakis, Harris, and Feild (2006) similarly emphasized that readiness for change is a multi-dimensional and multi-level construct requiring assessment of both individual and organizational-level components. These constructs provide validated, well-cited measurement anchors for the “readiness” side of the proposed model and connect naturally to reconfiguration: change efficacy is, in effect, an employee's belief that reconfiguration is achievable, while change commitment reflects willingness to expend the effort reconfiguration requires.

Absorptive Capacity as a Behavioral Antecedent

Cohen and Levinthal (1990) introduced absorptive capacity — the ability of an organization (and, at the cognitive level, of an individual) to recognize the value of new external information, assimilate it, and apply it to productive ends — as a function of prior related knowledge. Because digital reconfiguration frequently requires employees to absorb unfamiliar technical knowledge, absorptive capacity is treated in this paper as a contextual boundary condition that may amplify or dampen the effect of growth mindset on reconfiguration contribution, rather than as a primary focal construct.

Synthesis and Literature Comparison

Table 1 synthesizes the reviewed streams thematically. Across streams, three patterns are evident. First, the dynamic capabilities literature is strong on strategic logic but weak on individual-level measurement. Second, the psychological literatures (mindset, resistance to change, TAM, readiness for change) offer validated instruments but have rarely been combined into a single nomological network oriented toward a strategy-level outcome such as reconfiguration capability.

Third, where digital-readiness studies have begun to combine these elements empirically (e.g., Höyng & Lau, 2023), findings are context-specific (a single German energy firm) and have not been generalized into a formal, portable measurement model.

Table 1: Literature Comparison Stream Key Source(s) Core Construct(s) Contribution Limitation Relevant to This Study Dynamic capabilities Teece et al. (1997); Teece (2007); Eisenhardt & Martin (2000) Sensing, seizing, reconfiguring Establishes reconfiguration as strategically central Firm-level; individual mechanisms unspecified Deliberate learning Zollo & Winter (2002) Experience accumulation, knowledge codification Explains capability evolution over time Organizational learning routines, not individual disposition Microfoundations Felin et al. (2012, 2015) Individual choice, cognition, interaction Provides warrant for individual-level theorizing Largely conceptual; few validated psychological measures applied DT-specific dynamic capability Warner & Wäger (2019) Nine microfoundations of DT capability Process model of strategic renewal via DT Qualitative; not quantified at individual level Implicit theories of ability Dweck (2006) Growth vs. fixed mindset Validated individualdifference predictor of learning behavior Rarely linked to strategylevel organizational outcomes Technology acceptance Davis (1989); Höyng & Lau (2023) Perceived usefulness, ease of use, digital readiness Predicts technology adoption behavior Findings on mindset's role are mixed and contextbound Dispositional resistance Oreg (2003) Routine seeking, emotional reaction, rigidity, short-term focus Validated trait measure of resistance Not previously integrated with mindset or DC theory Readiness for change Weiner (2009); Holt et al.

(2006) Change commitment, change efficacy Validated organizational readiness constructs Developed mainly in healthcare implementation contexts Absorptive capacity Cohen & Levinthal (1990) Recognize, assimilate, apply external knowledge Explains capacity to learn from new information Originally firm-level; individual-level operationalization varies

Research Gap Revisited

No identified study jointly models dispositional mindset, technology acceptance beliefs, dispositional resistance to change, and organizational readiness constructs as predictors of an individual-level reconfiguration-contribution outcome that is explicitly framed as a microfoundation of firm-level dynamic capability. Section 3 develops this integrative model.

Theoretical and Conceptual Framework

The proposed framework treats Individual Reconfiguration Contribution (IRC) — an employee's self-reported engagement in behaviors that support the recombination of tasks, tools, and routines during digital transformation (e.g., voluntarily learning new digital tools, proposing workflow changes, mentoring colleagues on new systems) — as the individual-level analogue of firm-level reconfiguration capability, consistent with the microfoundations logic of Felin et al. (2012). Digital Transformation Readiness (DTR) is modeled as a proximal, more attitudinal outcome (a state of psychological preparedness) that both results from, and reinforces, IRC.

Constructs

i. Growth Mindset (GM): dispositional belief that ability and competence can be developed (Dweck, 2006), measured as a continuum with fixed mindset as the low pole.

ii. Perceived Usefulness (PU) and Perceived Ease of Use (PEOU): beliefs about digital tools introduced during transformation (Davis, 1989).

iii. Dispositional Resistance to Change (RTC): trait-level resistance comprising routine seeking, emotional reaction, cognitive rigidity, and short-term focus (Oreg, 2003).

iv. Change Commitment (CC) and Change Efficacy (CE): organizational readiness constructs reflecting resolve and perceived collective capability (Weiner, 2009).

v. Individual Reconfiguration Contribution (IRC): the focal microfoundation outcome, proposed as this study's central theoretical contribution.

vi. Digital Transformation Readiness (DTR): the downstream, more attitudinal criterion variable.

Hypotheses

i. H1: Growth mindset (GM) is positively associated with perceived usefulness (PU) of digital tools.

ii. H2: Growth mindset (GM) is negatively associated with dispositional resistance to change (RTC).

iii. H3: Dispositional resistance to change (RTC) is negatively associated with change efficacy (CE).

iv. H4: Perceived usefulness (PU) and perceived ease of use (PEOU) are positively associated with change efficacy (CE).

v. H5: Change commitment (CC) and change efficacy (CE) are positively associated with individual reconfiguration contribution (IRC).

vi. H6: Individual reconfiguration contribution (IRC) is positively associated with digital transformation readiness (DTR).

vii. H7: Growth mindset (GM) has a positive indirect effect on IRC that is sequentially mediated by PU/PEOU and CE (i.e., mindset operates on reconfiguration contribution not directly, but through technology beliefs and readiness states).

Hypothesis H7 is deliberately specified as an indirect-only pathway in the baseline model, consistent with the cautionary finding of Höyng and Lau (2023) that growth mindset's association with digital readiness outcomes need not be direct or uniformly positive once mediating technology beliefs are accounted for; the model therefore treats a possible direct GM→IRC path as an exploratory, not a hypothesized, relationship to be tested post hoc.

Conceptual Path Diagram

Figure 1 presents the full hypothesized path model described in Sections 3.1–3.2 as a directed diagram, and Table 2 (below) lists the same paths in tabular form for cross-reference.

Figure 1. Proposed conceptual and structural path model linking growth mindset, technology beliefs, resistance to change, and organizational readiness constructs to individual reconfiguration contribution and digital transformation readiness. Solid arrows denote hypothesized direct paths (H1–H6); the dashed arrow denotes the hypothesized sequential mediation path (H7).

Table 2 lists the same eight structural paths shown in Figure 1, together with their governing hypothesis and expected sign, for direct cross-reference during measurement-model specification (Section 4) and hypothesis testing (Section 7). The table also clarifies the directional relationships proposed among the principal constructs within the conceptual model. This alignment facilitates systematic interpretation of the hypothesized relationships and supports consistency between the theoretical framework and subsequent empirical analysis.

Table 2: Proposed Structural Paths Path Antecedent Consequent Hypothesis Expected Sign P1 Growth Mindset (GM) Perceived Usefulness (PU) H1 + P2 Growth Mindset (GM) Resistance to Change (RTC) H2 − P3 Resistance to Change (RTC) Change Efficacy (CE) H3 − P4 Perceived Usefulness (PU) Change Efficacy (CE) H4 + P5 Perceived Ease of Use (PEOU) Change Efficacy (CE) H4 + P6 Change Commitment (CC) Reconfiguration Contribution (IRC) H5 + P7 Change Efficacy (CE) Reconfiguration Contribution (IRC) H5 + P8 Reconfiguration Contribution (IRC) DT Readiness (DTR) H6 +

Mathematical and Measurement-Model Specification

All constructs are specified as reflective latent variables, consistent with standard practice for attitudinal and dispositional measures in behavioral strategy research (Hair, Hult, Ringle, & Sarstedt, 2022). Equations are presented for the measurement model, the structural model, and the psychometric criteria used to evaluate them. This specification ensures that each construct is represented through observable indicators that capture its underlying theoretical dimension. The resulting measurement structure provides a consistent foundation for assessing construct reliability, validity, and their relationships within the structural model.

Equation 1 — Reflective Measurement Model 𝑥𝑖𝑗 = 𝜆𝑖𝑗 𝜉𝑖 + 𝜀𝑖𝑗 where x_ij is the j-th observed indicator of latent construct ξ_i, λ_ij is the standardized outer loading, and ε_ij is measurement error. Each construct (GM, PU, PEOU, RTC, CC, CE, IRC, DTR) is measured with multiple reflective indicators adapted from the validated scales cited in Section 2 (Dweck, 2006; Davis, 1989; Oreg, 2003; Weiner, 2009).

Equation 2 — Structural (Inner) Model 𝜂𝑘 = 𝛴𝑚𝛽𝑘𝑚𝜂𝑚 + 𝛴𝑛𝛾𝑘𝑛𝜉𝑛 + 𝜁𝑘 where η_k denotes an endogenous latent construct (e.g., CE, IRC, DTR), β_km captures paths from other endogenous constructs, γ_kn captures paths from exogenous constructs (e.g., GM), and ζ_k is the structural disturbance term for construct k. Applied to the model in Section 3, the IRC equation specializes to: 𝐼𝑅𝐶 = 𝛾1 · 𝐶𝐶 + 𝛾2 · 𝐶𝐸 + 𝜁𝐼 𝑅𝐶 and the CE equation specializes to: 𝐶𝐸 = 𝛾3 · 𝑃𝑈 + 𝛾4 · 𝑃𝐸𝑂𝑈 + 𝛾5 · 𝑅𝑇𝐶 + 𝜁𝐶 𝐸 Equation 3 — Composite (Construct) Reliability 𝜌𝑐 = (𝛴𝜆𝑖)²/[(𝛴𝜆𝑖)² + 𝛴𝑉𝑎𝑟(𝜀𝑖)] Composite reliability (ρc) values above 0.70 are conventionally treated as acceptable for confirmatory research (Hair et al., 2022).

Equation 4 — Average Variance Extracted 𝐴𝑉𝐸 = (𝛴𝜆𝑖²)/𝑛 Average variance extracted (AVE) values above 0.50 indicate that a construct explains more variance in its indicators than measurement error does, and are used together with the Fornell– Larcker criterion — under which the square root of a construct's AVE should exceed its correlations with other constructs — to assess discriminant validity (Fornell & Larcker, 1981).

Equation 5 — Heterotrait–Monotrait Ratio 𝐻𝑇𝑀𝑇 = (𝑚𝑒𝑎𝑛𝑜𝑓ℎ𝑒𝑡𝑒𝑟𝑜𝑡𝑟𝑎𝑖𝑡 − ℎ𝑒𝑡𝑒𝑟𝑜𝑚𝑒𝑡ℎ𝑜𝑑𝑐𝑜𝑟𝑟𝑒𝑙𝑎𝑡𝑖𝑜𝑛𝑠) /(𝑚𝑒𝑎𝑛𝑜𝑓𝑚𝑜𝑛𝑜𝑡𝑟𝑎𝑖𝑡 − ℎ𝑒𝑡𝑒𝑟𝑜𝑚𝑒𝑡ℎ𝑜𝑑𝑐𝑜𝑟𝑟𝑒𝑙𝑎𝑡𝑖𝑜𝑛𝑠) An HTMT value below the conservative threshold of 0.85 provides additional evidence of discriminant validity between conceptually adjacent constructs such as growth mindset and resistance to change (Oreg, 2003), which, while theoretically distinct, are expected to correlate negatively.

Equation 6 — Coefficient of Determination and Effect Size 𝑅²𝜂 = 1 − [𝑉𝑎𝑟(𝜁𝜂 )/𝑉𝑎𝑟(𝜂)]; 𝑓² = (𝑅²𝑖𝑛𝑐𝑙𝑢𝑑𝑒𝑑 − 𝑅²𝑒𝑥𝑐𝑙𝑢𝑑𝑒𝑑) /(1 − 𝑅²𝑖𝑛𝑐𝑙𝑢𝑑𝑒𝑑) R² quantifies the proportion of variance in an endogenous construct explained by its predictors; f² quantifies the incremental contribution of a specific predictor, with 0.02, 0.15, and 0.35 conventionally interpreted as small, medium, and large effects, respectively.

Equation 7 — Mediation (Indirect Effect) Test 𝐼𝑛𝑑𝑖𝑟𝑒𝑐𝑡𝑒𝑓𝑓𝑒𝑐𝑡(𝑎 · 𝑏) = 𝛾(𝐺𝑀 → 𝑃𝑈) × 𝛽(𝑃𝑈 → 𝐶𝐸); 𝑇𝑜𝑡𝑎𝑙𝑒𝑓𝑓𝑒𝑐𝑡 = 𝐷𝑖𝑟𝑒𝑐𝑡 + 𝛴(𝐼𝑛𝑑𝑖𝑟𝑒𝑐𝑡𝑝𝑎𝑡ℎ𝑠) Hypothesis H7 is tested via bootstrapped confidence intervals (5,000 resamples) around the product-of-coefficients estimate

Proposed Methodology

No primary data collection has been conducted for this paper.

The methodology below is presented as a fully specified, reproducible research design intended for prospective execution; all subsequent references to “results” prior to Section 7 are explicitly proposed or illustrative and are labeled as such.

Figure 2 summarizes the six-stage methodological workflow detailed in Sections 5.1–5.8.

of each indirect path from GM to IRC, following standard PLSSEM mediation procedures (Hair et al., 2022).

Figure 2: Proposed methodological workflow, from research design through robustness checks.

Research Design

A quantitative, cross-sectional, explanatory survey design is proposed, consistent with the correlational and predictive nature of the hypotheses in Section 3. A single-wave design is proposed for the baseline study, with a two-wave time-lagged extension recommended in Section 13 to strengthen causal inference.

Population and Sampling

The target population is employees (non-managerial and firstline supervisory staff) in organizations that are actively undergoing a documented digital transformation initiative (e.g., ERP migration, CRM digitization, automation of client-facing processes), such as firms in the business-process-outsourcing, financial-services, and telecommunications sectors. A minimum sample of 250–300 respondents is proposed, informed by PLS-SEM minimum sample-size heuristics (10 times the largest number of structural paths directed at a single construct) and by power-analysis conventions for detecting medium effect sizes (f² ≈ 0.15) at α = .05 and power = .80.

Purposive sampling of organizations, followed by proportionate stratified sampling of employees within participating organizations, is recommended to ensure variation in tenure, digital exposure, and job function.

Data Collection Instrument

A self-administered structured questionnaire is proposed, comprising: (a) demographic and organizational-context items; (b) an adapted subset of Dweck's implicit-theories-of-ability items to operationalize GM; (c) Davis's (1989) six-item usefulness and six-item ease-of-use scales, reworded to reference the specific digital tools introduced by the respondent's organization; (d) a shortened form of Oreg's (2003) 17-item Resistance to Change scale; (e) Weiner's (2009)informed change-commitment and change-efficacy items, adapted from the Organizational Readiness for Implementing Change (ORIC) logic; and (f) a purpose-built IRC scale and a DTR scale, each requiring content-validation (expert panel review and pilot testing) prior to full deployment, since no previously validated instrument exists for either construct. All items are proposed on 5- or 7-point Likert-type response formats. The instrument should also be designed to minimize respondent burden while retaining sufficient item coverage to support reliable measurement.

Data Preprocessing

• Screening for straight-lining, excessive missingness (>15% per respondent), and completion-time outliers.

• Reverse-scoring of negatively worded resistance-tochange items.

• Multiple imputation or listwise deletion for missing data, contingent on missingness mechanism diagnostics (Little's MCAR test).

• Assessment of univariate and multivariate normality to confirm suitability of the variance-based (PLS-SEM) estimator, which does not require multivariate normality.

Model Estimation Strategy

Partial least squares structural equation modeling (PLS-SEM) is proposed as the primary estimator, using the two-stage procedure recommended by Hair et al. (2022): (1) assessment of the measurement model (indicator loadings, composite reliability, AVE, discriminant validity via Fornell–Larcker and HTMT criteria); followed by (2) assessment of the structural model (path coefficients, R², f², Q² predictive relevance via blindfolding, and bootstrapped significance testing of direct and indirect effects). PLS-SEM is preferred over covariance-based SEM given the prediction-oriented nature of the research questions and the inclusion of a formative-adjacent, newly developed construct (IRC) alongside established reflective scales.

Common Method Bias Controls

Because all constructs are measured via self-report at a single point in time, common method bias is a material threat to validity. Procedural remedies (temporal separation of predictor and criterion blocks within the questionnaire, respondent anonymity assurances, and psychological separation of measurement scales) and statistical remedies (Harman's singlefactor test and a full collinearity variance inflation factor assessment) are proposed, following Podsakoff, MacKenzie, Lee, and Podsakoff (2003). The statistical assessment should be interpreted jointly with the procedural safeguards rather than relying on any single diagnostic criterion. The measurement model can additionally be examined for unusually high interconstruct correlations that may indicate potential methodrelated inflation. Where feasible, marker-variable or latentmethod-factor approaches may be considered as supplementary robustness checks for common method effects.

Statistical Testing Plan

• Descriptive statistics and correlation matrix for all study variables.

• Measurement-model assessment (loadings ≥ 0.70, composite reliability ≥ 0.70, AVE ≥ 0.50, HTMT < 0.85).

• Structural-model assessment: path coefficients with bootstrapped 95% confidence intervals; R² and f² for endogenous constructs; Q² via blindfolding for predictive relevance.

• Mediation analysis for H7 using bias-corrected bootstrapped indirect effects.

Ablation-Style Model Comparison

Because IRC is a newly proposed construct rather than an established one, an ablation-style comparison of nested structural models is proposed rather than a machine-learning ablation in the conventional sense: (Model A) direct GM→IRC path only; (Model B) full mediated model without RTC; (Model C) full proposed model including RTC. Comparing R²(IRC) and AIC-equivalent information criteria for PLS path models (e.g., via the model-selection criteria proposed for PLS-SEM) across these three nested specifications will indicate the incremental explanatory contribution of the resistance-to-change pathway.

Proposed Dataset Characteristics

No dataset has been collected or analyzed for this paper. Table 3 specifies the proposed dataset that a future confirmatory study would need to assemble; all entries are recommended/proposed, not actual.

Table 3: Proposed Dataset Specification (Recommended, Not Yet Collected) Element Specification Source Notes Sampling frame Employees in ≥3 organizations undergoing active DT initiatives HR partnership / organizational gatekeepers Cross-sector variation recommended (BPO, finance, telecom) Target sample size 250–300 usable responses Primary survey Based on PLS-SEM 10times rule and power

analysis

Unit of analysis Individual employee Self-report Nested within organization for MGA Constructs measured GM, PU, PEOU, RTC, CC, CE, IRC, DTR (8 latent variables) Adapted validated scales + 2 new scales IRC and DTR scales require pilot validation Administration mode Online, anonymous, self-administered Qualtrics/Google Forms equivalent Reduces socialdesirability bias relative to in-person Timing Single wave (baseline); two-wave extension recommended T1 predictors; T2 (3 months later) IRC/DTR Improves causal inference over pure cross-section

Evaluation Framework and Illustrative Results

This section presents a Proposed Evaluation Framework together with an Illustrative, Simulated Result Set generated solely to demonstrate how the model in Sections 3–4 would be interpreted once genuine data are collected. The values below are synthetic, internally consistent numbers constructed for demonstration purposes; they are not derived from any survey, experiment, organization, or participant, and must not be cited as empirical findings. The simulated values are included only to illustrate the expected structure and interpretation of the proposed evaluation procedures. Actual conclusions should be based exclusively on results obtained from systematically collected and appropriately validated empirical data.

Illustrative Measurement-Model Assessment (Simulated)

Table 4: Illustrative Reliability and Validity Statistics (Simulated — Not Real Data) Construct No. of Items (illustrative) Composite Reliability (illustrative) AVE (illustrative) Growth Mindset (GM) 4 0.86 (simulated) 0.61 (simulated) Perceived Usefulness (PU) 6 0.91 (simulated) 0.65 (simulated) Perceived Ease of Use (PEOU) 6 0.89 (simulated) 0.60 (simulated) Resistance to Change (RTC) 8 (short form) 0.84 (simulated) 0.55 (simulated) Change Commitment (CC) 5 0.87 (simulated) 0.58 (simulated) Change Efficacy (CE) 5 0.88 (simulated) 0.60 (simulated) Reconfiguration Contribution (IRC) 6 (new scale) 0.85 (simulated) 0.56 (simulated) DT Readiness (DTR) 5 0.90 (simulated) 0.64 (simulated) In this illustrative set, all simulated composite reliability values exceed the conventional 0.70 threshold and all simulated AVE values exceed 0.50, which — were these numbers real — would indicate acceptable convergent validity and reliability for every construct (Hair et al., 2022; Fornell & Larcker, 1981). These simulated values therefore illustrate the measurement-quality criteria that would be applied to the constructs during empirical validation. In an actual study, reliability and validity would need to be established using observed indicator data rather than predefined or simulated values. Researchers should also examine indicator loadings to determine whether individual measures adequately represent their respective latent constructs.

Discriminant validity should subsequently be assessed to establish that conceptually distinct constructs remain empirically distinguishable. Together, these procedures would provide a comprehensive basis for evaluating the adequacy of the proposed measurement model before structural relationships are interpreted

Illustrative Structural-Model Results (Simulated)

Table 5: Illustrative Path Coefficients and Hypothesis Outcomes (Simulated — Not Real Data) Hypothesis Path β (illustrative) Illustrative 95% CI Simulated Outcome H1 GM → PU 0.34 (simulated) [0.22, 0.46] Supported (illustrative) H2 GM → RTC −0.29 (simulated) [−0.41, −0.16] Supported (illustrative) H3 RTC → CE −0.31 (simulated) [−0.44, −0.18] Supported (illustrative) H4a PU → CE 0.27 (simulated) [0.14, 0.39] Supported (illustrative) H4b PEOU → CE 0.19 (simulated) [0.05, 0.32] Supported (illustrative) H5a CC → IRC 0.33 (simulated) [0.20, 0.45] Supported (illustrative) H5b CE → IRC 0.28 (simulated) [0.15, 0.40] Supported (illustrative) H6 IRC → DTR 0.52 (simulated) [0.41, 0.62] Supported (illustrative) H7 GM → IRC (indirect, via PU/PEOU→CE) 0.06 (simulated) [0.02, 0.11] Supported (illustrative, small effect) In this illustrative demonstration, R²(CE) ≈ 0.34, R²(IRC) ≈ 0.29, and R²(DTR) ≈ 0.27 (all simulated), which would represent moderate explanatory power by conventional behavioral-science benchmarks were the data genuine. These figures are provided strictly to illustrate the reporting format and interpretive logic that a future confirmatory study should follow; they carry no evidentiary weight regarding the actual relationships proposed in Section 3. These simulated values demonstrate how the proportion of variance explained by the endogenous constructs could be presented in a future empirical analysis. The interpretation of explanatory power should ultimately be based on observed data, validated measurement models, and statistically estimated structural relationships.

Future studies should therefore assess the stability of R² values across samples, organizational contexts, and alternative model specifications. Such validation would help determine whether the proposed relationships provide consistent explanatory value beyond the illustrative patterns presented in this section.

Figure 3: Illustrative (simulated) standardized path coefficients with 95% bootstrapped confidence intervals, corresponding to Table 5.

These values are synthetic and are shown only to demonstrate the intended reporting format; they are not empirical findings.

Figure 4: Illustrative (simulated) explained variance (R²) for each endogenous construct, corresponding to the R² values reported in the text above. These values are synthetic and are not empirical findings.

Illustrative Ablation-Style Model Comparison (Simulated)

Table 6: Illustrative Nested-Model Comparison (Simulated — Not Real Data) Configuration Includes Direct GM→IRC Includes RTC Path R²(IRC) (illustrative) Model A Yes No 0.18 (simulated) Model B No (mediated only) No 0.24 (simulated) Model C (proposed full model) No (mediated only) Yes 0.29 (simulated) The illustrative pattern (Model C > Model B > Model A) is constructed to demonstrate the intended interpretive claim — that the resistance-to-change pathway and full mediation structure would add explanatory value over a naive direct-effect specification — and again should not be read as an empirical finding.

Discussion

The framework developed in this paper repositions employee mindset from an individual-difference variable studied mainly in educational and leadership psychology into a candidate microfoundation of a strategically consequential organizational capability.

If the hypothesized pattern were empirically confirmed — with growth mindset operating primarily indirectly through technology beliefs and reduced resistance to change, rather than directly — this would have two important implications. First, it would suggest that organizations cannot rely on “mindset” alone as a lever for driving reconfiguration behavior; interventions would need to simultaneously target perceived usefulness of specific tools (through better change communication and demonstrable early wins) and dispositional resistance (through participative, low-threat exposure to change) for mindset-related benefits to materialize in observable reconfiguration behavior. Second, the small hypothesized indirect effect size for H7 is consistent with the caution raised by Höyng and Lau (2023), whose empirical findings suggested that mindset's relationship to digital readiness outcomes is neither large nor uniformly positive, reinforcing the need for the fully mediated specification proposed here rather than a simpler direct-effects model.

Compared with prior dynamic-capabilities scholarship, which has theorized reconfiguration primarily through qualitative case evidence (Warner & Wäger, 2019) or at a highly abstract conceptual level (Teece, 2007), the present framework offers a quantifiable, falsifiable specification that can be directly tested with standard organizational-survey methods. Compared with prior mindset scholarship, which has rarely been connected to strategy-level outcomes, the framework offers mindset theory a clearly specified organizational criterion variable (IRC) and a plausible causal pathway (via technology beliefs and change efficacy) rather than a bare correlational claim.

Practically, the framework implies that human-resource and change-management functions should be involved from the earliest stages of DT planning, not solely at the rollout stage, since the proposed model suggests that dispositional and attitudinal variables measurable well before go-live (mindset, baseline resistance to change) already condition how employees will respond to specific tools. Diagnostic use of the IRC and DTR scales, once validated, could allow organizations to identify units or individuals at higher risk of disengagement and to sequence support resources accordingly.

Trade-offs and unexpected considerations are also worth noting. A strong organizational emphasis on “growth mindset” framing can, if implemented carelessly, produce a superficial or performative culture in which employees feel pressured to display enthusiasm for change they do not genuinely feel — a risk noted in the broader mindset literature's caution against treating mindset as a simple slogan rather than a genuine belief system. The proposed model's inclusion of dispositional resistance to change as a distinct, non-pathologized construct is intended to guard against this risk by acknowledging that some resistance reflects legitimate situational concerns rather than a deficient mindset.

Explainability and Interpretability Considerations

Although the proposed model is a structural equation model rather than a black-box predictive algorithm, interpretability remains important for practitioner uptake. Standardized path coefficients and their bootstrapped confidence intervals (Table 5, once populated with real data) provide a directly interpretable, effect-size-scaled account of each hypothesized relationship — arguably a stronger transparency property than post hoc explanation methods such as SHAP or LIME offer for opaque machine-learning models. Total-effects decomposition (direct plus indirect paths, per Equation 7) further allows practitioners to see which levers (mindset-oriented training versus tool-usability improvements versus participative goalsetting for change efficacy) contribute most to the ultimate outcome, DTR. A limitation of this transparency is that it is only as trustworthy as the measurement model underlying it: a poorly validated IRC or DTR scale would produce interpretable but substantively misleading path estimates, which is why Sections 5.3 and 11 emphasize scale-validation and robustness procedures before any causal interpretation is drawn.

Ethics, Privacy, and Governance Considerations

A future empirical implementation of this design would need to secure institutional ethics review prior to data collection, given that the study involves human participants and workplacesensitive attitudinal data (including resistance-to-change items that could, if mishandled, be used punitively against individual employees).

i. Informed consent: participants must be informed of the study's purpose, voluntary nature, and their right to withdraw without penalty.

ii. Anonymity and confidentiality: individual-level responses should never be shared with supervisors or used in performance evaluation; only aggregated, deidentified results should be reported to participating organizations.

iii. Data minimization: only variables directly relevant to the stated hypotheses should be collected; demographic variables should be limited to those needed for stratification and control.

iv. Data security: survey data should be stored on accesscontrolled, encrypted institutional infrastructure, consistent with applicable data-protection regulation in the jurisdictions where data collection occurs.

v. Power-sensitivity: because resistance-to-change and mindset items could be misread by managers as a covert performance audit, researchers should proactively communicate to both employees and management that the study is diagnostic and developmental, not evaluative of individuals.

vi. Algorithmic/analytic governance: because the proposed model could, in principle, be adapted into an internal HR analytics tool for flagging “resistant” employees, any operational deployment beyond academic research should include human-oversight safeguards and an explicit prohibition on using IRC or RTC scores as grounds for adverse employment action.

Robustness and Validity

Internal Validity

The single-wave cross-sectional design proposed as the baseline limits causal inference; common method bias (addressed in Section 5.6), and reverse causality (e.g., employees who already contribute to reconfiguration may retrospectively report higher change efficacy) are plausible alternative explanations for observed associations. The two-wave extension recommended in Section 13 partially addresses this.

External Validity

A purposive, cross-sector sample improves generalizability relative to a single-organization design (such as Höyng and Lau's 2023 single-firm study) but does not guarantee generalizability to organizations outside the sampled sectors, national contexts, or DT maturity levels.

Measurement and Construct Validity

The IRC and DTR scales are newly proposed and require rigorous content validation (expert panel review), exploratory factor analysis on a calibration subsample, and confirmatory assessment on a holdout subsample before substantive hypothesis testing proceeds, following standard scaledevelopment practice.

Sensitivity and Multi-Group Analysis

PLS-MGA across organizational sector, tenure bands, and digital-exposure levels (Section 5.7) is proposed to test whether path coefficients are stable across subgroups or whether the model requires sector-specific calibration.

Common Method and Non-Response Bias

Beyond the procedural and statistical remedies in Section 5.6, comparison of early versus late respondents (a standard proxy for non-response bias) is recommended once data collection occurs.

Limitations

i. No primary data have been collected; all quantitative results in Section 7 are explicitly simulated for illustrative purposes and cannot be used to draw substantive conclusions about the hypotheses.

ii. The Individual Reconfiguration Contribution (IRC) and Digital Transformation Readiness (DTR) scales are newly proposed and unvalidated; their psychometric properties are unknown until pilottested.

iii. A single-wave cross-sectional design, if executed as the baseline, cannot establish temporal precedence or rule out reverse causality.

iv. Self-report measurement of all constructs introduces risk of common method variance despite the procedural and statistical remedies proposed.

v. The proposed sampling strategy, being purposive rather than probabilistic, may limit statistical generalizability to the broader population of digitally transforming organizations.

vi. Cultural and institutional context (e.g., power distance, job-security norms) may moderate the mindset– resistance–readiness relationships in ways the baseline model does not explicitly capture.

vii. The mathematical specification assumes linear relationships among latent constructs; nonlinear or threshold effects (e.g., a resistance “tipping point”) are not modeled in the baseline specification.

Future Research Directions

i. Execute the proposed survey design (Section 5) in three or more organizations across at least two industry sectors to obtain the first empirical test of the model.

ii. Extend the design to a two-wave or three-wave longitudinal panel to test whether growth mindset measured at baseline predicts IRC and DTR trajectories over the course of a specific DT rollout, strengthening causal inference beyond the crosssectional baseline.

iii. Validate the newly proposed IRC and DTR scales through a dedicated scale-development study (item generation, expert content validation, exploratory and confirmatory factor analysis, and nomologicalnetwork testing) prior to large-scale deployment.

iv. Investigate managerial mindset and leadership behavior as a cross-level moderator of the employeelevel model, consistent with evidence that organizational-level mindset signals (communicated through policies and leader behavior) shape individual mindset expression.

v. Incorporate absorptive capacity (Cohen & Levinthal, 1990) as an explicit moderator of the PU/PEOU→CE paths to test whether employees with greater prior related digital knowledge translate technology beliefs into change efficacy more efficiently.

vi. Explore multi-informant designs (pairing employee self-report with supervisor-rated reconfiguration behavior) to reduce reliance on single-source selfreport data.

vii. Test the model's robustness in remote and hybrid work arrangements, where reconfiguration behaviors (e.g., adoption of collaboration tools) may follow different behavioral dynamics than in co-located settings.

Conclusion

This paper set out to address a specific and underexplored gap at the intersection of dynamic capabilities theory and behavioral organizational science: dynamic capabilities scholarship has theorized reconfiguration as a strategically decisive organizational process without adequately specifying its individual-level, psychological microfoundations, while mindset theory has established robust individual-difference effects without connecting them to strategy-level organizational outcomes such as digital transformation readiness. By integrating dynamic capabilities and microfoundations theory (Teece et al., 1997; Felin et al., 2012, 2015) with implicit theories of ability (Dweck, 2006), technology acceptance theory (Davis, 1989), dispositional resistance to change (Oreg, 2003), and organizational readiness-for-change theory (Weiner, 2009), this paper developed a fully specified, testable model connecting employee growth mindset to Individual Reconfiguration Contribution and, in turn, to Digital Transformation Readiness, with technology beliefs and change efficacy serving as the principal mediating mechanisms.

The research questions posed in Section 1.6 were addressed conceptually and methodologically rather than empirically: RQ1–RQ3 are answered through the hypothesized structural paths (H1–H7) and their theoretical justification, while RQ4 is addressed through the proposed multi-group and nested-model comparison strategy (Sections 5.7–5.8), which would allow future researchers to identify which combination of variables best differentiates high- from low-contribution employees.

Because no primary data were collected, the paper's central contribution is conceptual and methodological — a portable, psychometrically specified model and research design — rather than empirical; the illustrative results in Section 7 exist solely to demonstrate the intended analytic and reporting workflow and carry no evidentiary status.

Practically, the framework suggests that organizations undertaking digital transformation should treat employee mindset, technology usability perceptions, and dispositional resistance as measurable, addressable levers rather than as unmanageable individual differences, and should sequence change-management investment accordingly. Academically, the paper extends the microfoundations research program by supplying a validated psychological construct (mindset) with a plausible mechanism (mediated through technology beliefs and change efficacy) linking it to a strategically consequential capability (reconfiguration). The proposed empirical agenda in Section 13, if executed, would provide the first direct test of these relationships and a validated instrument (IRC, DTR) that future digital-transformation and dynamic-capabilities researchers could reuse.