<p>Aggregation operators are intended to combine inputs that are typically expressed as membership degrees in fuzzy settings. For <i>q</i>-rung orthopair fuzzy (<i>q</i>-ROF) information, Yager proposed a usual way to construct aggregation operators by using dual pairs of basic ones. In this paper, following Yager’s construction approach, we consider aggregation operators of <i>q</i>-ROF values whose names begin with “Yager’s type”. First, some new aggregation operators are obtained by the weighted power mean operator and its dual on membership and nonmembership degrees separately. The prominent benefit of these operators is that they can avoid ineffectiveness when dealing with extreme values. Some special cases and limiting cases are listed to show relations with some existing mean operators, and some regular properties are examined such as idempotency, monotonicity and boundedness. Then, under <i>q</i>-ROF environment, a method for multi-criteria decision making is proposed based on the developed operators. Finally, the current method is illustrated by a practical problem of ranking products through online reviews, in which the sensitivity with respect to the parameters is examined, and the feasibility and efficiency are analyzed by comparing with other related methods. The experimental analysis demonstrates that our method provides an alternative way to aggregate <i>q</i>-ROF data with simple calculation and global continuity.</p>