Research On The Impact Mechanism Of The Continuance Intention Of M Health Driven By User Generated Content

Objective:Currently,the Chinese population is still facing serious health challenges,with the total number of deaths from non-communicable diseases increasing,and health risk factors are prevalent in the population,making it an important health risk for individuals.With the advent of the“Internet+Health”era,mobile health is now an important method and tool for continuous health management.With the support of the national policy environment,the mobile health industry is booming,but it is facing the threat of user abandonment and poor continuous use.Poor continued use of m Health reduces the efficiency of users’autonomous health management and makes it difficult to achieve effective and long-term improvement of public health status,while detrimental to the development of the m Health industry.It is important to investigate the impact factors and impact paths of mobile health continuance intention and propose strategies to improve mobile health to help the public establish a long-term self-health management model and promote the sustainable development of mobile health industry.In the current research on continuance intention of m Health,the impact factors are mainly derived from existing theoretical models or small sample surveys,which are subjective and have limitations such as low sample size,high cost and observer effect.In response to these limitations,scholars have called for actively using the massive data in user-generated content generated in the Internet to explore continuance intention,but there is a lack of such research.Therefore,this study will propose a user-generated content-driven research framework for continuance intention for m Health,address issues such as continuance intention for m Health,and under the guidance of this framework,explore the impact factors and paths of continuance and discontinuance intention for m Health apps,and provide strategic suggestions for the improvement of m Health based on the research results.Methods:(1)Under the perspective of health informatics,the DIKIW model,data-driven decision-making idea and Kano model were synthesized to conduct an in-depth analysis of the basic elements,information value-added process and research process in the study of user-generated content-driven continuance intention,and on this basis,a framework for the study of user-generated content-driven continuance intention for m Health was constructed.(2)Based on the polarization phenomenon of user satisfaction and continuance intention in user-generated content,combined with the two-dimensional pattern of user satisfaction and impact asymmetry,the discriminative model of impact attributes of factors was proposed based on the Kano model to analyze the differences in the impact of factors mined from user-generated content on user satisfaction and user dissatisfaction,and then determine the impact attributes of each factor.Based on the premise that user satisfaction and user dissatisfaction are important antecedent variables for continuance intention and discontinuance intention,the potential impact factors for continuance intention and discontinuance intention were determined by combining the impact attributes of the factors.(3)According to the constructed continuance intention research framework driven by user-generated content,user-generated content of m Health was collected,and the methods and techniques of natural language processing,text mining and statistical analysis were used to explore the potential impact factors of continuance intention and discontinuance intention from user-generated content,and the models of continuance intention and discontinuance intention of m Health are constructed,and the two theoretical models were validated by structural equations modelling.The impact factors and paths of m Health continuance intention and discontinuance intention were obtained.According to the frequency of users’use,users were divided to deep group and shallow group,and structural equation subgroup analysis was used to explore the differences of impact effects between different user use characteristic groups.(4)Based on the research results,combining the principles of human-computer interaction,this study proposed improvement strategies from two perspectives of m Health tools and m Health users respectively,to provide a reference basis for building a user-empowered long-term self-health management model.Results:(1)The basic elements of user-generated content-driven continuance intention research includes data base(data),user experience(information),impact factors(knowledge),impact mechanisms(intelligence),and improvement strategies(wisdom),and the research is essentially a data-driven decision-making process.(2)The framework for user-generated content-driven m Health continuance intention research consists of seven modules,including identifying research subjects,data collection,data pre-processing,topic identification based on topic modeling,impact effect analysis and attribute discrimination,constructing continuance intention models and discontinuance intention models,and developing m Health continuance intention improvement strategies.The framework can guide the study of m Health continuance intention based on user-generated content.(3)Impact attribute discrimination model analyzes the impact attributes of factors mined from user-generated content based on four indicators,including user satisfaction positive deviation influence significance(SPD_k),user satisfaction negative deviation influence significance(SND_k),user satisfaction distribution(RD_k)and influence effect asymmetry(D_k),and the analysis results lay the foundation for constructing a model of m Health continuance intention and a model of m Health discontinuance intention.(4)From the 191,619 user-generated contents of m Health apps obtained,five factors with significant direct or indirect effects on the continuance intention of m Health were mined,including expectation confirmation,perceived usefulness,recordability,tutorial and interface design aesthetics,some of which can indirectly promote continuance intention through the impact path of perceived usefulness or satisfaction.It was found that three factors,namely information failure,system failure,and service failure,had significant direct or indirect effects on the discontinuance intention of m Health,and some of these factors could indirectly contribute to the discontinuance intention through the impact path of dissatisfaction.The influence effect of the significant impact path has variability in different groups of user usage characteristics.(5)The current situation of impact factors was sorted out from the technical features,functional features and design marketing features of m Health tools,and strategies and specific implementation suggestions are given to improve the m Health.Secondly,this study divided m Health users into general users who receive health services and practitioners who provide health services from a broad level,and suggested providing personalized services for general users and actively inviting professionals to join the m Health platform.Conclusions:(1)The core of the study on m Health continuance intention driven by user-generated content is the acquisition,data processing,information extraction,knowledge mining and impact analysis of m Health user-generated content,which leads to the knowledge related to the user’s continuance intention.(2)The research framework of m Health continuance intention driven by user-generated content consists of seven modules.Using the above-mentioned research framework,the impact factors and influence paths of continuance intention and discontinuance intention can be mined from the huge amount of data in user-generated content,and improvement strategies for m Health can be provided based on the results.(3)The proposed discriminative model of influence attributes explores the differences between the influence of factors on satisfaction and dissatisfaction mined from user-generated content from the perspective of asymmetry,and thus identifies the potential impact factors of continuance intention and discontinuance intention of m Health.(4)The research framework and model proposed in this study can effectively mine the massive amount of data from user-generated content to reveal the impact factors on the continuance intention and discontinuance intention of m Health,and can reveal the impact paths of each factor.(5)The m Health improvement strategies proposed from three perspectives of m Health tools,and m Health users can provide a reference basis for helping users to build a self-health management model,improving the m Health platform and promoting the benign development of the m Health industry.