Algorithm-based　decision　support　systems　are　increasingly　employed　across　various　decision　environments.　This　article　investigates　how　algorithmic　support　influences　the　human　decision-making　process　from　a　neuro-information　systems　(neuro-IS)　perspective.　The　goal　of　this　study　is　twofold:　first,　to　elucidate　the　cognitive　mechanisms　behind　the　adoption　of　algorithmic　advice,　and　second,　to　draw　managerial　insights　from　these　cognitive　mechanisms.　Utilizing　electroencephalography　to　measure　human　brain　activity　during　decision-making　with　and　without　algorithmic　support,　the　study　finds　that　the　presence　of　algorithmic　support　significantly　influences　a　series　of　decision-making　processes.　Specifically,　it　impacts　information　conflict　processing　(observed　in　beta-band　activity　at　the　early　stage　of　decision-making),　decision-making　itself　(observed　in　theta-band　activity　at　the　late　stage　of　decision-making),　and　reward　prediction　error　processing　(observed　in　theta-band　activity　at　the　outcome　evaluation　stage).　Furthermore,　there　is　a　significant　correlation　between　the　changes　in　subjects＇　neural　activity　due　to　the　introduction　of　algorithmic　support　and　their　levels　of　compliance　with　the　algorithmic　advice　during　decision-making.　Additionally,　the　results　show　that　neural　measures　outperform　self-reported　measures　in　explaining　and　predicting　algorithm　compliance.　This　study　highlights　the　roles　of　cognitive　and　emotional　factors　in　shaping　algorithm　compliance　and　yields　practical　implications　for　designing　and　promoting　the　usage　of　algorithmic　support　to　achieve　higher　compliance　levels.　Overall,　by　observing　the　relationship　between　neural　processes　and　behavioral　changes,　this　study　leverages　the　complementary　value　of　neurophysiological　methods　to　offer　new　managerial　insights　into　the　design　of　IS,　thereby　contributing　to　the　development　of　the　neuro-IS　discipline.