The　online　review　fraud　process　is　an　event　in　which　a　reviewer　posts　fraudulent　reviews　on　an　e-commerce　platform　with　respect　to　a　review　target,　such　as　a　commodity　or　service.　Extant　studies　on　detecting　fraudulent　reviewers　often　rely　on　reviewer　behavioral　patterns　and　the　textual　content　of　reviews　while　ignoring　the　targets　being　reviewed.　Based　on　the　Goals-Plans-Action　theory,　we　examine　the　relative　importance　of　target　features　for　fraudulent　reviewer　detection　in　comparison　to　that　of　non-target　features.　Target　features　refer　to　the　features　derived　from　the　innate　information　related　to　the　reviewed　products　or　services　while　non-target　features　refer　to　the　features　derived　from　the　acquired　information　related　to　the　reviewed　products　or　services.　In　this　study,　we　analyze　a　sample　from　the　Yelp.com　dataset　of　restaurant　reviews　to　help　better　understand　the　importance　of　target　features　and　non-target　features.　The　results　suggest　that　using　the　combination　of　target　features　with　non-target　features　can　improve　the　performance　of　online　fraudulent　reviewer　detection　in　comparison　with　using　non-target　features　alone.　Moreover,　using　the　combination　of　target　features　with　nontarget　features　will　further　improve　the　performance　of　online　fraudulent　reviewer　detection　when　we　consider　the　non-target　features　as　conditioned　on　the　reviewed　target　features　rather　than　treating　them　as　independent　of　each　other.