>>> # where_records_ordered_by: date_newest, date_oldest, global_cited_by_highest,
>>> # global_cited_by_lowest, local_cited_by_highest,
>>> # local_cited_by_lowest, first_author_a_to_z,
>>> # first_author_z_to_a, source_title_a_to_z,
>>> # source_title_z_to_a
>>> from techminer2.packages.networks.co_occurrence.index_keywords import DocumentsByClusterMapping
>>> documents_by_cluster = (
... DocumentsByClusterMapping()
... #
... # FIELD:
... .having_terms_in_top(20)
... .having_terms_ordered_by("OCC")
... .having_term_occurrences_between(None, None)
... .having_term_citations_between(None, None)
... .having_terms_in(None)
... #
... # NETWORK:
... .using_clustering_algorithm_or_dict("louvain")
... .using_association_index("association")
... #
... # DATABASE:
... .where_root_directory_is("example/")
... .where_database_is("main")
... .where_record_years_range_is(None, None)
... .where_record_citations_range_is(None, None)
... .where_records_match(None)
... .where_records_ordered_by("date_newest")
... #
... .run()
... )
>>> print(len(documents_by_cluster))
4
>>> print(documents_by_cluster[0][0])
UT 1346
AR Gracia D.B., 2019, IND MANAGE DATA SYS, V119, P1411
TI Artificial Intelligence in FinTech: understanding robo-advisors adoption
among customers
AU Gracia D.B.; Casaló-Ariño L.V.; Flavián C.
TC 225
SO Industrial Management and Data Systems
PY 2019
AB purpose : considering THE_INCREASING_IMPACT of ARTIFICIAL_INTELLIGENCE ( AI
) on FINANCIAL_TECHNOLOGY ( FINTECH ) , THE_PURPOSE of THIS_PAPER is to
propose A_RESEARCH_FRAMEWORK to better understand ROBO_ADVISOR_ADOPTION by
A_WIDE_RANGE of POTENTIAL_CUSTOMERS . it also predicts that
PERSONAL_AND_SOCIODEMOGRAPHIC_VARIABLES ( FAMILIARITY with ROBOTS , AGE ,
GENDER and COUNTRY ) moderate THE_MAIN_RELATIONSHIPS .
DESIGN_METHODOLOGY_APPROACH : DATA from A_WEB_SURVEY of 765 north american ,
british and PORTUGUESE_POTENTIAL_USERS of ROBO_ADVISOR_SERVICES confirm
THE_VALIDITY of THE_MEASUREMENT_SCALES and provide THE_INPUT for
STRUCTURAL_EQUATION_MODELING and MULTISAMPLE_ANALYSES of THE_HYPOTHESES .
FINDINGS : CONSUMERS_ATTITUDES toward ROBO_ADVISORS , together_with
MASS_MEDIA and INTERPERSONAL_SUBJECTIVE_NORMS , are found to be
THE_KEY_DETERMINANTS of ADOPTION . THE_INFLUENCES of PERCEIVED_USEFULNESS
and ATTITUDE are slightly higher for USERS with A_HIGHER_LEVEL of
FAMILIARITY with ROBOTS . in_turn , SUBJECTIVE_NORMS are significantly more
relevant for USERS with A_LOWER_FAMILIARITY and for CUSTOMERS from
ANGLO_SAXON_COUNTRIES . PRACTICAL_IMPLICATIONS : BANKS and OTHER_FIRMS in
THE_FINANCE_INDUSTRY should DESIGN_ROBO_ADVISORS to be used by
A_WIDE_SPECTRUM of CONSUMERS . MARKETING_TACTICS applied should consider
THE_CUSTOMER_LEVEL of FAMILIARITY with ROBOTS . ORIGINALITY_VALUE :
THIS_RESEARCH identifies THE_KEY_DRIVERS of ROBO_ADVISOR_ADOPTION and
THE_MODERATING_EFFECT of PERSONAL_AND_SOCIODEMOGRAPHIC_VARIABLES . it
contributes to UNDERSTANDING_CONSUMERS_PERCEPTIONS regarding
THE_INTRODUCTION of AI in FINTECH . 2019 , EMERALD_PUBLISHING limited .
DE ARTIFICIAL_INTELLIGENCE; FINANCE; ROBO_ADVISORS; ROBOTS; TECHNOLOGY_ADOPTION
ID FINANCE; FINTECH; INTELLIGENT_ROBOTS; ROBOTS; SALES;
DESIGN_METHODOLOGY_APPROACH; PERCEIVED_USEFULNESS; POTENTIAL_CUSTOMERS;
RESEARCH_FRAMEWORKS; ROBO_ADVISORS; SOCIO_DEMOGRAPHIC_VARIABLES;
STRUCTURAL_EQUATION_MODELING; TECHNOLOGY_ADOPTION; ARTIFICIAL_INTELLIGENCE