title: Understanding gender differences in m-health adoption: a modified theory of reasoned action model.
abstract: BACKGROUND
  Mobile health (m-health) services are becoming increasingly popular in healthcare, but research on m-health adoption is rare. This study was designed to obtain a better understanding of m-health adoption intention.
  
  
  METHODS
  We conducted an empirical research of a 481-respondent sample consisting of 44.7% women and 55.3% men and developed a modified theory of reasoned action (TRA) model by incorporating the nonlinearities between attitude and subjective norms and the moderating effect of gender.
  
  
  RESULTS
  The results indicate that, based on the study population in China: (1) facilitating conditions, attitude, and subjective norms are significant predictors of m-health adoption intention; (2) the model including the nonlinearities enhances its explanatory ability; (3) males enjoy a higher level of m-health adoption intention compared with females; (4) the modified TRA model can predict men's behavior intention better than that of women; and (5) males have an Edgeworth-Pareto substitutability between attitude and subjective norms in predicting m-health adoption intention.
  
  
  CONCLUSIONS
  Thus, we found gender differences in m-health adoption from the perspective of social psychology.
authors.affiliation: 1 School of Management, Harbin Institute of Technology , Harbin, China .
authors.forename: Xiaofei
authors.initials: X
authors.lastname: Zhang
authors.forename: Xitong
authors.initials: X
authors.lastname: Guo
authors.forename: Kee-Hung
authors.initials: KH
authors.lastname: Lai
authors.forename: Feng
authors.initials: F
authors.lastname: Guo
authors.forename: Chenlei
authors.initials: C
authors.lastname: Li
mesh_terms: D000328:Adult; D002681:China; D005260:Female; D007722:Health Knowledge, Attitudes, Practice; D006801:Humans; D007256:Information Systems; D033182:Intention; D008297:Male; D008875:Middle Aged; D008960:Models, Psychological; D012737:Sex Factors; D017216:Telemedicine / Q000706:statistics & numerical data*