Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Artificial Intelligence in FinTech: understanding robo-advisors adoption among customers
2019468 citationsDaniel Belanche, Luis V. Casaló et al.profile →
Service robot implementation: a theoretical framework and research agenda
2019436 citationsDaniel Belanche, Luis V. Casaló et al.profile →
Understanding influencer marketing: The role of congruence between influencers, products and consumers
2021334 citationsDaniel Belanche, Luis V. Casaló et al.profile →
Intention to use analytical artificial intelligence (AI) in services – the effect of technology readiness and awareness
2021248 citationsCarlos Flavián, Alfredo Pérez-Rueda et al.Journal of service managementprofile →
Examining the effects of robots' physical appearance, warmth, and competence in frontline services: The Humanness‐Value‐Loyalty model
2021247 citationsDaniel Belanche, Jeroen Schepers et al.Psychology and Marketingprofile →
Robots or frontline employees? Exploring customers’ attributions of responsibility and stability after service failure or success
2020220 citationsDaniel Belanche, Luis V. Casaló et al.Journal of service managementprofile →
Influencer marketing on TikTok: The effectiveness of humor and followers’ hedonic experience
2022183 citationsSergio Barta, Daniel Belanche et al.Journal of Retailing and Consumer Servicesprofile →
Building influencers' credibility on Instagram: Effects on followers’ attitudes and behavioral responses toward the influencer
2021154 citationsDaniel Belanche, Luis V. Casaló et al.Journal of Retailing and Consumer Servicesprofile →
Attitudes toward service robots: analyses of explicit and implicit attitudes based on anthropomorphism and construal level theory
2021144 citationsDaniel Belanche, Marta Flavián et al.profile →
Human versus virtual influences, a comparative study
202475 citationsDaniel Belanche, Luis V. Casaló et al.profile →
The dark side of artificial intelligence in services
202459 citationsDaniel Belanche, Russell W. Belk et al.profile →
Automated social presence in AI: Avoiding consumer psychological tensions to improve service value
202446 citationsCarlos Flavián, Russell W. Belk et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
hero ref
Countries citing papers authored by Daniel Belanche
Since
Specialization
Citations
This map shows the geographic impact of Daniel Belanche's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Daniel Belanche with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniel Belanche more than expected).
This network shows the impact of papers produced by Daniel Belanche. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Daniel Belanche. The network helps show where Daniel Belanche may publish in the future.
Co-authorship network of co-authors of Daniel Belanche
This figure shows the co-authorship network connecting the top 25 collaborators of Daniel Belanche.
A scholar is included among the top collaborators of Daniel Belanche based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with Daniel Belanche. Daniel Belanche is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Belanche, Daniel, et al.. (2021). Examining the effects of robots' physical appearance, warmth, and competence in frontline services: The Humanness‐Value‐Loyalty model. Psychology and Marketing. 38(12). 2357–2376.247 indexed citations breakdown →
6.
Flavián, Carlos, Alfredo Pérez-Rueda, Daniel Belanche, & Luis V. Casaló. (2021). Intention to use analytical artificial intelligence (AI) in services – the effect of technology readiness and awareness. Journal of service management. 33(2). 293–320.248 indexed citations breakdown →
Belanche, Daniel, Luis V. Casaló, Carlos Flavián, & Jeroen Schepers. (2020). Robots or frontline employees? Exploring customers’ attributions of responsibility and stability after service failure or success. Journal of service management. 31(2). 267–289.220 indexed citations breakdown →
Belanche, Daniel, Luis V. Casaló, & Carlos Flavián. (2012). Understanding the influence of social information sources on e-government adoption.. Information Research. 17.17 indexed citations
Belanche, Daniel, Luis V. Casaló, & Carlos Flavián. (2011). Adopción de servicios públicos online: un análisis a través de los modelos TAM y TPB. Revista Europea de Dirección y Economía de la Empresa. 20(4). 41–56.3 indexed citations
Belanche, Daniel, et al.. (1974). La "Isagoge de Ioannitius", introducción, edición, traducción y notas. 267–310.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive
bibliographic database. While OpenAlex provides broad and valuable coverage of the global
research landscape, it—like all bibliographic datasets—has inherent limitations. These include
incomplete records, variations in author disambiguation, differences in journal indexing, and
delays in data updates. As a result, some metrics and network relationships displayed in
Rankless may not fully capture the entirety of a scholar's output or impact.