Performance measurement and management systems for dealing with strategies in uncertain ecosystems

Abstract

PurposeAlthough there is a growing research stream on Performance Measurement and Management Systems (PMMS) in Ecosystems literature, current research offers limited theoretical insights into how PMMS deal with two types of strategies in uncertain ecosystems: ecosystem-based strategy – EBS (at the focal firm level) and ecosystem strategy – ES (at the ecosystem level). This study aims at identifying how PMMS are employed to deal with different types of strategies in uncertain ecosystems.Design/methodology/approachThe authors employed an inductive, rich multiple case approach in five focal firms with platform ecosystems. Data collection involved multiple sources of information (primary and secondary data), combing retrospective and longitudinal perspectives. Data analysis combined replication and comparison logic with coding.FindingsThis study identifies four major distinctive dimensions of Ecosystem PMMS under uncertainty: (1) Integrative Performance (considering the different ecosystem actors’ performance), (2) Interdependence Performance (mutual, yet not necessarily convergent amongst ecosystem partners), (3) Regulative Performance (paradoxical in nature, having to cope with both flexibility and stability) and finally (4) Phased Learning Performance (non-linear).Research limitations/implicationsOur primary contribution is a new framework for PMMS literature: a performance measurement and management system for dealing with strategies in ecosystems. This framework enables managing performance regarding both types of strategies (EBS and ES) and their interplay in uncertain ecosystems.Practical implicationsThe ecosystem management requires focal firms to measure and manage the overall ecosystem’s performance, and it varies according to the type of strategy adopted in each case. Our framework provides dimensions that guide firms to build and implement PMMS for an ecosystem consistent with the ES. Therefore, it may improve performance, especially in uncertain business contexts.Originality/valueThe findings enrich PMMS literature in an ecosystem context related to the ES in uncertain environments.