Infrastructure or culture? Identifying determinants of consumer payments digitalization

This study examines cross-country differences in the pace of cash displacement by focusing on national-level infrastructural and cultural characteristics associated with non-cash payment adoption. Using data for 71 countries over the period 2012–2023, the analysis adopts a macro-level perspective and shifts attention from individual adoption decisions to aggregate outcomes observed at the country level. To address severe multicollinearity in highly correlated cross-country data, the study employs regularized regression techniques, specifically the Least Absolute Shrinkage and Selection Operator (LASSO), to identify robust correlates of payment digitalization. The results indicate that cash displacement is associated with a selective set of national characteristics rather than a broad range of uniformly relevant determinants. In particular, the presence of nationwide account-to-account payment systems, higher levels of individualism, and lower uncertainty avoidance emerge as stable correlates of faster cash displacement, alongside economic development measured by GDP per capita. Several commonly discussed infrastructural and cultural factors, including POS terminal density and multiple Hofstede dimensions, are not retained under regularization, suggesting that their relevance is weaker or context-dependent at the aggregate level. By providing a cross-country perspective on payment digitalization, the study contributes to management research on digital transformation and institutional context. Drawing on a Technology Acceptance Model (TAM)-informed macro-level framework, the findings highlight that the transition away from cash is shaped by a limited number of infrastructural and cultural conditions whose importance becomes visible only when examined at the country level, and demonstrate the value of combining theory-informed frameworks with regularized estimation techniques in comparative management research. • This study examines cross-country differences in the pace of cash displacement using a macro-level perspective. • The analysis identifies a selective set of infrastructural and cultural characteristics associated with non-cash payment adoption. • Nationwide account-to-account payment systems, individualism, and uncertainty avoidance emerge as stable correlates of faster cash displacement. • Regularized regression (LASSO) is used to address severe multicollinearity in country-level data and isolate robust associations. • The findings show that payment digitalization is context-dependent and shaped by national institutional and cultural environments rather than uniform determinants.

Authors

Institutions

Publication Details

Journal
Computers in Human Behavior Reports
Published
2026-02-23
DOI
https://doi.org/10.1016/j.chbr.2026.100979
Primary Topic
Technology Adoption and User Behaviour
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Infrastructure or culture? Identifying determinants of consumer payments digitalization

Dominika Bosek-Rak, Daniel Kaszyński, Agata Chutnik
Computers in Human Behavior Reports
Technology Adoption and User Behaviour
article

Infrastructure or culture? Identifying determinants of consumer payments digitalization

Dominika Bosek-Rak, Daniel Kaszyński, Agata Chutnik
article en

Abstract

This study examines cross-country differences in the pace of cash displacement by focusing on national-level infrastructural and cultural characteristics associated with non-cash payment adoption. Using data for 71 countries over the period 2012–2023, the analysis adopts a macro-level perspective and shifts attention from individual adoption decisions to aggregate outcomes observed at the country level. To address severe multicollinearity in highly correlated cross-country data, the study employs regularized regression techniques, specifically the Least Absolute Shrinkage and Selection Operator (LASSO), to identify robust correlates of payment digitalization. The results indicate that cash displacement is associated with a selective set of national characteristics rather than a broad range of uniformly relevant determinants. In particular, the presence of nationwide account-to-account payment systems, higher levels of individualism, and lower uncertainty avoidance emerge as stable correlates of faster cash displacement, alongside economic development measured by GDP per capita. Several commonly discussed infrastructural and cultural factors, including POS terminal density and multiple Hofstede dimensions, are not retained under regularization, suggesting that their relevance is weaker or context-dependent at the aggregate level. By providing a cross-country perspective on payment digitalization, the study contributes to management research on digital transformation and institutional context. Drawing on a Technology Acceptance Model (TAM)-informed macro-level framework, the findings highlight that the transition away from cash is shaped by a limited number of infrastructural and cultural conditions whose importance becomes visible only when examined at the country level, and demonstrate the value of combining theory-informed frameworks with regularized estimation techniques in comparative management research. • This study examines cross-country differences in the pace of cash displacement using a macro-level perspective. • The analysis identifies a selective set of infrastructural and cultural characteristics associated with non-cash payment adoption. • Nationwide account-to-account payment systems, individualism, and uncertainty avoidance emerge as stable correlates of faster cash displacement. • Regularized regression (LASSO) is used to address severe multicollinearity in country-level data and isolate robust associations. • The findings show that payment digitalization is context-dependent and shaped by national institutional and cultural environments rather than uniform determinants.

Computers in Human Behavior ReportsVol. 21
SGH Warsaw School of Economics (PL)
Openalex Percentile: Top 49%
Technology Adoption and User Behaviour
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.