Results replication data: 16-year longitudinal analysis of emails using GPT models - testing three psychological theories

This study evaluated whether email language yields psychologically meaningful signals in Self‑Determination Theory, the Big Five, and Psychological Well‑Being. It utilized eleven LLM-based models: three based on GPT 3.5 Turbo with different prompting strategies; and eight based on AI agentic structure that acts as a teacher for knowledge-distilled multilingual transformers (students). These models are applied in a single study setting of 25,780 emails (2008–2024) from a senior executive with international experience. Privacy note Raw email texts are not included. Interested researchers can request the anonymized email corpus after the paper is published by contacting the corresponding author and signing a standard NDA, per the data‑use terms described in the manuscript. File inventory 1. emails_classification_all_models.csv (row = one email) What it is: Label outputs for every email, so you can build the monthly indices used as dependent variables in the regressions. The email body is not present; only labels, metadata, and model outputs are included. Row/column counts & coverage. 25,780 emails authored between 2008-01 and 2024-03. Total columns: 156. Key columns (metadata). Date — UTC timestamp string. WordCount — raw word count of the author’s original text (min 10, mean 66.5, max 11186). Note: although the manuscript analyzes only the first 300 words per email, this WordCount column reports the full length before that analysis‑time truncation. Label families and value spaces: • Self‑Determination Theory (SDT): Competence, Autonomy, Relatedness → values: Present, Struggle, Absent (neutral), or None (cannot be determined, neutral). • Psychological Well‑Being (PWB): Autonomy, Environmental Mastery, Personal Growth, Positive Relations with Others, Purpose in Life, Self‑Acceptance → values: Enhancing, Struggling, Maintaining (neutral), Not Applicable (neutral). • Big Five: Openness, Conscientiousness, Extraversion, Agreeableness, Neuroticism → values: High, Low, or None (cannot be determined, neutral). Column organization. Three GPT‑3.5 passes per theory (r1: zero‑shot, r2: few‑shot rater, r3: few‑shot evaluator) yield 15 Big Five, 18 PWB, and 9 SDT columns (total 42). Eight fine‑tuned multilingual transformer models contribute 56 separate‑encoder columns (four backbones × 14 dimensions) and 56 shared‑encoder columns (four backbones × 14 dimensions). 2. regression_monthly_data.csv (row = one month, key = y_m) What it is: The month‑level regressors used in the paper’s longitudinal specifications. Merge on y_m with monthly outcome indices computed from the per‑email file. Index column. y_m — string “YYYY‑MM”, covering 2009-10 through 2024-03 (inclusive). Total rows: 167. Variables and intended interpretation: income_index: monthly salary + consulting income; continuous ratio‑type index, current month value divided by full period monthly average (min ≈ 0.114, max ≈ 5.625). card_spending: [0,1] min‑max normalized credit‑card spending. abroad_far: dummy 0/1; months working in Central Asia. abroad_near: dummy 0/1; months working in another EU country. death_1_war: 1.0 in the month of father‑in‑law’s death (coincided with the start of Russia’s invasion of Ukraine), then 0.75 (t+1) and 0.5 (t+2); 0 otherwise. death_2: analogous three‑month decay for mother’s death. court_case: dummy 0/1; months with the inheritance court case. Big4_partner: dummy 0/1; months working as a Big Four partner. AI_company: dummy 0/1; months serving as a C‑level executive at an AI company. elections: dummy 0/1; months of the parliamentary campaign. covid_lockdowns: dummy 0/1; strict lockdown months in Poland (CSV uses plural 'covid_lockdowns', manuscript uses 'covid_lockdown'). no_receive: [0,1]; number of unique email recipients per month, min‑max scaled. avg_length: [0,1]; average email word count per month, min‑max scaled. Note: Regression results are reported in the paper with HAC‑robust SEs and BH‑FDR correction; they quantify contemporaneous covariation rather than causal effects.