With　the　digital　transformation　of　the　global　economy,　a　new　mode　of　knowledge　service　has　emerged　on　open　innovation　platforms　such　as　those　for　the　sharing　economy.　This　mode　is　the　paid　knowledge-sharing　service,　where　knowledge　providers　share　knowledge　with　only　those　who　have　paid　for　it.　Since　an　individual　customer＇s　purchases　are　influenced　by　others　around　them,　we　adopted　social　influence　theory　to　explain　sales　of　such　services　on　paid　knowledge-sharing　platforms.　A　machine　learning　approach　was　applied　to　analyze　27,223　text　reviews　from　the　Zhihu　Live　platform　(a　well-known　and　large-scale　open　knowledge　community　in　China).　Hierarchical　regression　models　were　built　to　verify　twelve　proposed　hypotheses　about　the　knowledge　providers,　knowledge　quality,　interaction　quality,　and　ratings.　The　results　confirm　the　positive　effect　on　sales　of　responsiveness　(a　dimension　of　interaction　quality),　and　the　negative　effect　on　sales　of　free　provider-driven　knowledge　contributions.　In　summary,　this　study　provides　a　comprehensive　framework　for　antecedent　factors　of　sales　of　knowledge-sharing　services.　By　introducing　to　knowledge　management　notions　from　the　field　of　e-commerce　(e.g.,　price,　quality),　this　study　broadens　the　understanding　of　the　free-to-paid　phenomenon　on　knowledge-sharing　platforms.　©　2024　The　Authors