移动智能推荐情境下网络成瘾行为扎根研究

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Grounded Study of Internet Addiction Behavior in the Context of Mobile Intelligent Recommendation

AbstractAI-driven recommendation algorithms have been widely deployedacross mobilesocial media platforms， enablingpersonalized informationdeliverythrough learning fromusers'historical behaviorsand interestpreferences. Theresultingmobile inteligentrecommendationecosystemunderscoresthedisruptiveapplicationsofAIintherecommendation domain.While mobile inteligentrecommendationsbettersatisfyusers'information needs，theyalso exacerbateaddictiveonlinebehaviors.This study employs grounded theory to explore theunderlying mechanisms influencing userinternetaddictionwithinmobileinteligentrecommendationcontexts.First，aninterviewprotocolwasdesigned，nd data werecollected through semi-structured interviews.Three-tiercoding Was thenconducted： during open coding，l32 initialconceptsand33basiccategories wereextracted;throughaxialcoding，lOprincipalcategories wereidentified;and duringselectivecoding，atheoreticalmodelofinternetaddictionmechanismsinmobileinteligentrecommendationscenarios wasconstructed.Tefindingsrevealthatpersonaliteracyehavioralbeliefs，exteralifluences，egativemotions， andreal-worldconditions exertdirectimpactsoninternetaddictionbehaviors.Meanwhile，theinformationqualitysystem quality，andservicequalityofintellgentrecommendations indirectlyinfluencebehavioralbeliefs throughmediatingeffectsof usersatisfaction，therebyaectingaddictivebehaviors.Thisresearch，toacertainextent，elucidates thesocietal impactsofAI'sdisruptiveapplicationsinrecommendationsystems，providing theoreticalreferencesforinterventionsand governance strategies addressing user internet addiction within mobile intelligent recommendation environments.

/wordsmobilesocialmedia;inteligent recommendation;disruptive application;internetaddiction; grounded th

随着人工智能（ArtificialIntelligence，AI）技术的快速发展和移动社交媒体的广泛使用，算法驱动的智能推荐系统已深度嵌入移动社交媒体平台，对人们获取信息的方式产生了颠覆性影响[1]。（剩余11704字）