Purpose　-　The　purpose　of　this　paper　is　to　propose　a　novel　prospect-based　two-sided　matching　decision　model　for　matching　supply　and　demand　of　technological　knowledge　assisted　by　a　broker.　This　model　enables　the　analyst　to　account　for　the　stakeholders＇　psychological　behaviours　and　their　impact　on　the　matching　decision　in　an　open　innovation　setting.　Design/methodology/approach　-　The　prospect　theory　and　grey　relational　analysis　are　used　to　develop　the　proposed　two-sided　matching　decision　framework.　Findings　-　By　properly　calibrating　model　parameters,　the　case　study　demonstrates　that　the　proposed　approach　can　be　applied　to　real-world　technological　knowledge　trading　in　a　market　for　technology　(MFT)　and　yields　matching　results　that　are　more　consistent　with　the　reality.　Research　limitations/implications　-　The　proposed　model　does　not　differentiate　the　types　of　knowledge　exchanged　(established　vs　novel,　tacit　vs　codified,　general　vs　specialized)　(Ardito　et　al.,　2016,　Nielsen　and　Nielsen,　2009).　Moreover,　the　model　focuses　on　incorporating　psychological　behaviour　of　the　MFT　participants　and　does　not　consider　their　other　characteristics.　Practical　implications　-　The　proposed　model　can　be　applied　to　achieve　a　better　matching　between　technological　knowledge　suppliers　and　users　in　a　broker-assisted　MFT.　Social　implications　-　A　better　matching　between　technological　knowledge　suppliers　and　users　can　enhance　the　success　of　open　innovation,　thereby　contributing　to　the　betterment　of　the　society.　Originality/value　-　This　paper　furnishes　a　novel　theoretical　model　for　matching　supply　and　demand　in　a　broker-assisted　MFT.　Methodologically,　the　proposed　model　can　effectively　capture　market　participants＇　psychological　considerations.