The Erosion of Agency: How Automated Financial Nudging Impacts Long-term Economic Resilience in Digital Finance.

We implement a prospective, individual-level randomized controlled trial (RCT) with a longitudinal design spanning six months (July–December 2026). The study evaluates the behavioral and cognitive impacts of algorithmic choice architecture in digital finance, specifically examining how varying degrees of automated financial decision-making (automated nudging) affect personal financial agency and long-term economic resilience.

A sample of N=750 participants will be selected and randomly assigned to one of three experimental arms (1:1:1 allocation ratio), stratified by baseline age and educational attainment:

Arm A: Manual Management (Control Group): Participants manage their personal budgets and liquidity entirely manually through a standardized digital finance interface. They receive standard transaction records and balance updates but do not receive automated behavioral nudges, suggestions, or autonomous execution features. This arm measures baseline financial agency and organic adaptation.

Arm B: Hybrid/Nudge Management (Active Choice Treatment): Participants interact with an interface embedded with real-time digital behavioral nudges (e.g., proactive alerts on spending thresholds, reminders of financial goals, and context-dependent choice framing). Crucially, the system requires active user intervention and explicit confirmation to execute any budget reallocations or micro-savings adjustments, thereby preserving cognitive engagement and the decision-making loop.

Arm C: Fully Automated Management (Algorithmic Delegation Treatment): Participants delegate operational financial control to an algorithmic optimizer. The system autonomously executes micro-savings, schedules transfers, and reallocates discretionary spending based on predefined parameters without requiring step-by-step user confirmation or active choice. This arm isolates the impact of "automated nudging" and structural algorithmic reliance.

Longitudinal Monitoring & Exogenous Shocks:
Over the 24-week period, high-frequency granular behavioral data and subjective financial states will be captured twice weekly using the Experience Sampling Method (ESM) via mobile devices. To empirically assess resilience, two unexpected, exogenous macroeconomic price shocks (staged inflationary simulations that increase utility costs or essential transaction fees within the platform's simulated ecosystem) will be introduced: the first at the end of July (Week 4) and the second in September (Week 12). These shocks will isolate differences in adaptation latencies, perception errors regarding account balances, and real-time budgetary adjustments across the three arms.

2026-07-01

2026-12-23

We define three primary, high-frequency outcome variables (endpoints) to measure the structural erosion of financial agency and economic resilience:

1. Balance Awareness Error (Perception Deviation):
Measured as the absolute mathematical difference between the participant's subjectively reported account balance during an Experience Sampling Method (ESM) prompt and their true, objective system balance at that exact timestamp: Error = Absolute_Value(Reported_Balance - True_Balance). This continuous variable operationalizes financial salience and cognitive detachment induced by automated delegation.

2. Response Latency to Financial Stress Events:
Defined as the exact time elapsed (measured in seconds via system logs) from the precise moment an automated platform alert or exogenous shock notification is pushed to the participant's mobile device, until the user actively engages with the interface to review, override, or acknowledge the financial event. This metric captures behavioral inertia, automation bias, and cognitive friction under pressure.

3. Real-time Budgetary Adjustment Capacity (Shadow Liquidity Index 2.0):
A composite operational index that aggregates a participant's high-frequency spending contractions, reallocations toward non-discretionary commitments, and active micro-saving preservation behaviors immediately following the two introduced exogenous price shocks. It quantifies organic economic adaptation versus artificial stability.

Construction and Operationalization of Primary Outcome Measures

1. Shadow Liquidity Index 2.0 (SLI 2.0) - Composite Economic Resilience Measure:
The SLI 2.0 operationalizes real-time household economic adaptation and budget elasticity immediately following the introduction of exogenous price shocks (t = Shock_1, Shock_2). It is a normalized composite index bounded between 0 and 1, constructed dynamically from three underlying behavioral streams captured via system logs and high-frequency Experience Sampling Method (ESM) prompts:

* Discretionary Expenditure Contraction (DEC): The percentage reduction in non-essential expenditures relative to the individual's baseline spending average. Formulated as a normalized value: (DEC_individual - DEC_minimum) / (DEC_maximum - DEC_minimum).
* Budgetary Reallocation Efficiency (BRE): A binary score (1 or 0) indicating whether funds were actively diverted to cover increased fixed outlays within 48 hours of a shock, avoiding overdraft or payment failure.
* Micro-Saving Maintenance Rate (SMR): The proportion of planned automated or manual savings goals that remained funded. Formulated as a normalized value: (SMR_individual - SMR_minimum) / (SMR_maximum - SMR_minimum).

The index is mathematically specified using a weighted linear combination, where weights (w1, w2, w3) are derived using Principal Component Analysis (PCA) to avoid arbitrary bias, ensuring the highest variance of adaptation behavior is captured:
SLI = [w1 * Normalized_DEC] + [w2 * BRE] + [w3 * Normalized_SMR]

A value approaching 1 represents optimal organic economic resilience (proactive and autonomous budget stabilization), while a value near 0 denotes structural rigidity and financial vulnerability.

2. Balance Awareness Error (Cognitive Salience):
This variable captures the exact cognitive detachment of the user. During random ESM intervals, users are prompted: "What is your current account balance within a +/-5% margin?" The outcome variable is constructed as the absolute log difference between the subjectively estimated balance (Estimated_Balance) and the true system balance (True_Balance) recorded by the platform at time t:
Awareness_Error = Absolute_Value( ln(Estimated_Balance + 1) - ln(True_Balance + 1) )
Log transformation is applied to normalize the distribution and prevent scale distortion from high-income outliers.

3. Response Latency:
Extracted directly from backend timestamp metadata, this is calculated as a continuous variable in seconds: User_Interaction_Timestamp - Shock_Notification_Timestamp. This measures structural inertia and reliance on the automated interface.

We define three continuous and categorical secondary outcome variables (endpoints) to capture cognitive, psychological, and behavioral mechanisms underlying the intervention:

1. Financial Self-Efficacy Score:
A continuous measure adapted from Bandura's self-efficacy frameworks, tracking the participant's subjective belief in their capability to execute autonomous financial planning and withstand unexpected economic strain.

2. Subjective Cognitive Load Score:
A continuous index adapted from the NASA-TLX scale, measuring the mental effort, frustration, and psychological friction experienced by the participant during financial interface interactions and shock events.

3. Price Recall Accuracy (Financial Memory):
A continuous metric measuring the absolute mathematical percentage error between a participant's recalled transaction price or current essential goods cost and the objective historical system logs. This isolates the "Financial Cognitive Atrophy" effect across the arms.

Construction and Operationalization of Secondary Outcome Measures

1. Financial Self-Efficacy and Agency Construct Validation:
Subjective financial self-efficacy is constructed as a latent continuous variable derived from high-frequency psychometric items embedded within the Experience Sampling Method (ESM) prompts. Participants rate their perceived control on a 1-to-7 Likert scale responding to prompts such as: "How confident do you feel right now in your ability to manage an unexpected expense?" These scores are aggregated longitudinally using Structural Equation Modeling (SEM) to validate construct stability over time and cross-referenced across experimental arms.

2. Subjective Cognitive Load Index:
Constructed via a weighted average of three core dimensions from an adapted mobile NASA-TLX instrument: Mental Demand (how much mental activity was required), Frustration Level (how insecure, discouraged, or stressed the user felt), and Performance (how successful the user feels in executing their budgeting goals). Each dimension is measured on a continuous 0-to-100 visual analog scale within the mobile financial interface following the introduced exogenous price shocks.

3. Price Recall Accuracy:
This metric operationalizes active consumer attention and market salience. At random post-transaction ESM intervals, participants are asked to recall the exact price of their last transaction or the estimated cost of platform utility fees. The outcome variable is constructed as: Price_Recall_Error = Absolute_Value( (Recalled_Price - True_Price) / True_Price ). A higher value denotes severe price memory degradation, serving as an empirical proxy for the learned helplessness and behavioral passivity induced by fully automated decision-making.

Experimental Design Structure

1. Type of Study and Allocation Framework:
This study is structured as a prospective, longitudinal, individual-level randomized controlled trial (RCT) spanning 24 weeks (6 months) from July to December 2026. Randomization will be executed with a 1:1:1 allocation ratio across three independent experimental arms: Arm A (Manual Management / Control), Arm B (Hybrid/Nudge Treatment), and Arm C (Fully Automated Management / Treatment).

2. Randomization Strategy:
Randomization will occur at the individual level immediately following the baseline data collection (T0). To ensure covariate balance across the experimental arms, we will implement a stratified block randomization strategy. The stratification factors are baseline age cohorts and educational attainment levels, which are critical predictors of financial literacy and digital interface adoption. The randomization sequence will be generated via a computer algorithm in RStudio, ensuring full concealment from both the participants and the field researchers until the moment of assignment.

3. Sample Size, Power Analysis, and Attrition Target:
Based on an a priori power calculation for longitudinal panel models, we established a target power of 80% (1 - Beta = 0.80), a significance level of 5% (Alpha = 0.05), and a Minimum Detectable Effect Size (MDES) of f^2 = 0.15. The statistical power framework requires a minimum of 160 active participants per experimental arm, equating to 480 effective subjects. To aggressively safeguard against potential panel attrition, the trial will recruit an initial sample size of N = 750 participants. This structure absorbs a projected 30% attrition rate over the 6-month timeline while retaining approximately 500 final effective participants, comfortably exceeding the power threshold. Furthermore, the high-frequency measurement design (48 total points per individual via twice-weekly Experience Sampling Method prompts) drastically enhances intra-individual precision and statistical power.

4. Econometric and Analytical Strategy:
The public data analysis plan relies on three highly parsimonious and complementary analytical frameworks to evaluate the temporal dynamics of the intervention:
* Fixed-Effects (FE) Panel Models: Used as the primary econometric strategy to control for time-invariant unobserved individual heterogeneity, isolating the clean causal impact of automated nudging over time.
* Latent Growth Curve Models (LGM): Deployed to parametrically model and map the individual and aggregate longitudinal trajectories of financial agency and resilience across the 24-week period, integrating the exogenous price shocks as time-dependent predictors.
* Causal Forest (Machine Learning Causal Inference): Utilized to optimize the estimation of Conditional Average Treatment Effects (CATE). This approach will systematically uncover treatment effect heterogeneity and identify specific vulnerable socio-economic or cognitive subgroups without risking arbitrary post-hoc specification mining.

Other complex structural or multi-group extensions, such as Structural Equation Modeling (SEM) for latent construct validation, will be restricted strictly to secondary verification to preserve model parsimony. Non-random attrition patterns will be verified using the Verbeek-Nijman test, complemented by Inverse Probability Weighting (IPW) adjustments if necessary. Potential extreme data volatility in behavioral latencies or expenditure entries will be managed using systematic winsorizing at the 1% and 99% thresholds.

Not available

The individual-level randomization will be conducted in-office by a computer using a reproducible pseudo-random number generator algorithm.

Specifically, the block-stratified randomization sequence will be pre-programmed and generated in RStudio using a secure seed for reproducibility. The final allocation to the three experimental arms (Arm A, Arm B, or Arm C) will be dynamically executed via an automated script embedded within the digital finance platform's backend infrastructure. This process occurs automatically and immediately after a participant's baseline (T0) demographic and psychometric survey is completed and locked, ensuring complete concealment from both field researchers and participants.

Individual.

Randomization is executed strictly at the individual level. Each participant who completes the baseline assessment is independently and randomly assigned to one of the three experimental arms (Arm A, Arm B, or Arm C). There is no cluster-level or multi-level hierarchical randomization in this trial design.

No

Not applicable. This study utilizes an individual-level randomization design and does not contain clusters.

750 individuals (initial planned sample size). Given the longitudinal design of this trial, which captures high-frequency panel data over a 24-week period using twice-weekly Experience Sampling Method (ESM) prompts, each participant is expected to contribute up to 48 distinct measurement points. Therefore, the maximum planned number of longitudinal observations under zero attrition is 36,000 observations (750 individuals * 48 measurement points). Accounting for our conservative 30% projected attrition rate, we anticipate a minimum effective final sample of approximately 500 individuals, yielding an effective dataset of at least 24,000 panel observations for the final fixed-effects and latent growth curve analysis.

250 individuals assigned to Arm A: Manual Management (Control Group).

250 individuals assigned to Arm B: Hybrid/Nudge Management (Active Choice Treatment Group).

250 individuals assigned to Arm C: Fully Automated Management (Algorithmic Delegation Treatment Group).

Total planned sample size: 750 individuals. This individual-level allocation ensures a perfect 1:1:1 balanced distribution at baseline across all three experimental arms.

Minimum Detectable Effect Size (MDES) Specifications 1. Statistical Power Framework and Parameters: With an initial sample size of N = 750 individuals and a conservative 30% projected attrition rate, the final analysis rests on a minimum effective sample size of N = 500 individuals (approximately 166 individuals per experimental arm). Utilizing a standard significance level (Alpha) of 0.05 and a target statistical power (1 - Beta) of 0.80 within a longitudinal fixed-effects panel framework, the Minimum Detectable Effect Size (MDES) for the primary outcomes is established at Cohens f^2 = 0.15 (equivalent to a Cohen's d of approximately 0.25 to 0.30 for standard cross-sectional pairwise comparisons). This characterizes a localized "small-to-medium" shifting effect, which is highly realistic for digital behavioral interventions. 2. MDES Expressed by Primary Outcome Variables: * Outcome 1: Balance Awareness Error (Unit: Currency units / Absolute deviation) - Standard Deviation (SD): Based on pilot data, the baseline pooled standard deviation for account balance perception error is approximately $450 MXN. - MDES in Absolute Units: 0.25 SD translates to a minimum detectable change of $112.50 MXN in absolute perception error. - MDES in Percentage: This represents a 25.0% change in standard deviation units, or an approximate 8.5% structural shift in average financial salience accuracy relative to the baseline group mean. * Outcome 2: Response Latency to Financial Stress (Unit: Seconds) - Standard Deviation (SD): The baseline pooled standard deviation for interface interaction response times under stress is approximately 120 seconds. - MDES in Absolute Units: 0.25 SD translates to a minimum detectable difference of 30.0 seconds in operational engagement. - MDES in Percentage: This represents a 25.0% change in standard deviation units, or an approximate 12.0% acceleration/deceleration in adaptation inertia across treatment arms. * Outcome 3: Shadow Liquidity Index 2.0 - SLI 2.0 (Unit: Index Score / Bounded 0 to 1) - Standard Deviation (SD): The estimated standard deviation for this composite household resilience index is 0.18 points. - MDES in Absolute Units: 0.25 SD translates to a minimum detectable shift of 0.045 points on the 0-to-1 scale. - MDES in Percentage: This represents a 25.0% change in standard deviation units, or an approximate 9.0% change in organic budgetary adjustment capacity following the exogenous price shocks.