Due　to　gradual　disappearance　of　global　demographic　dividend,　the　improvement　of　workforce　efficiency　becomes　increasingly　important.　It　has　been　commonly　recognised　that　workforce　motivation　can　largely　stimulate　the　workers,　especially　in　manufacturing　systems.　This　paper　investigates　a　worker　scheduling　problem　under　given　work　shifts　with　workforce　motivation　effects,　where　motivation　effects　are　characterised　by　motivation　coefficients　of　job　processing　times.　We　focus　on　the　situation　where　motivation　coefficients　are　uncertain　due　to　various　factors,　and　only　the　mean　vector　and　covariance　matrix　are　known.　The　objective　is　to　maximise　the　service　level,　measured　by　the　probability　of　ensuring　no　tardy　jobs.　We　first　propose　a　distributionally　robust　chance　constrained　formulation　with　a　probabilistic　objective　function.　Then　an　adapted　sample　average　approximation　(SAA)　method　and　a　heuristic,　based　on　an　approximated　mixed　integer　second-order　cone　programming　(MI-SOCP)　model　and　the　idea　of　problem　decomposition,　is　developed.　Numerical　results　show　that　the　decomposition-based　heuristic　is　more　efficient.　We　also　draw　some　managerial　insights.