Modelling persuasion strategies as predictors of task outcome has several\nreal-world applications and has received considerable attention from the\ncomputational linguistics community. However, previous research has failed to\naccount for the resisting strategies employed by an individual to foil such\npersuasion attempts. Grounded in prior literature in cognitive and social\npsychology, we propose a generalised framework for identifying resisting\nstrategies in persuasive conversations. We instantiate our framework on two\ndistinct datasets comprising persuasion and negotiation conversations. We also\nleverage a hierarchical sequence-labelling neural architecture to infer the\naforementioned resisting strategies automatically. Our experiments reveal the\nasymmetry of power roles in non-collaborative goal-directed conversations and\nthe benefits accrued from incorporating resisting strategies on the final\nconversation outcome. We also investigate the role of different resisting\nstrategies on the conversation outcome and glean insights that corroborate with\npast findings. We also make the code and the dataset of this work publicly\navailable at https://github.com/americast/resper.\n