Literature on supply chain management has been recently stressing the need for cooperation and integration between suppliers and customers, highlighting the critical role played by the supplier in contributing to the overall performance of the purchaser. Consequently supplier selection is one of the purchasing manager's most critical tasks, as demonstrated by the number of articles that have dealt with vendor selection criteria and techniques in the light of the requirements of the new environment. The paper proposes four different vendor selection systems (VSSs) depending on the time frame (short-term versus long-term) and on the content (logistic versus strategic) of the cooperative customer/supplier relationships. More precisely, the suggested VSSs consider, in an analytic hierarchy process framework, different sets of measures, deriving from a non-conventional model of the supplier based on the dynamic system and on the resource-based approach.

Many authors have suggested including non‐financial measures, besides traditional cost measures, in manufacturing performance measurement systems, in order to control the correct implementation of the manufacturing strategy with respect to all competitive priorities (quality, timeliness, flexibility, dependability, etc.). But the use of non‐financial performance measures makes it difficult to assess and compare the overall effectiveness of each manufacturing department, in terms of support provided to the achievement of the manufacturing strategy, since to this aim it is necessary to integrate performance measures expressed in heterogeneous measurement units. Aims to show the potential of the analytical hierarchy process (AHP) for assessing and comparing the overall manufacturing performance of different departments. Does not report the detailed analytical description of the AHP but focuses on the practical problems and managerial implications related to its application to performance measurement, pointing out also its assumptions and limitations.

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The emergence of a sharing revolution is leading to a new societal system of collaboration enabled by digital technologies. Although sharing-based initiatives are re-shaping established organizational practices and innovating traditional business models (BMs), existing research fails in grasping the phenomenon's multiple facets. This study aims at making sense of the Sharing Economy (SE) by shedding light on how startups embed the social trend of sharing and leverage digital technologies to develop innovative BMs. We attempt to solve the current theory-practice misalignment by proposing an original framework, definition, and classification of SE startups. The study presents a cluster analysis on 196 SE startups. We argue that SE startups group into five clusters: (i) pseudo-sharing; (ii) gig economy; (iii) crowd-based economy; (iv) pooling economy; and (v) P2P rental. This study contributes to positioning the SE from both a conceptual and an empirical perspective, interpreting the SE phenomenon from the theoretical lenses of BM Innovation, and classifying it through the unit of analysis of startups, intended as empirical vehicles and manifestation of the phenomenon.

Purpose This paper aims to examine how competitive intelligence (CI) relates to the strategy formulation process of firms. Design/methodology/approach Due to the novelty of the phenomenon and to the depth of the investigation required to grasp the mechanisms and logics of CI, a multiple case study has been performed related to four companies located in Brazil that adopted CI practices within dedicated business units to inform and support strategic decision-making. Findings The authors provide detailed empirical evidence on the connection and use of CI practices throughout each stage of the strategy formulation process. Moreover, the study suggests that CI practices, despite their strategic relevance and diffusion, are still extensively adopted for tactical use. Originality/value This study sheds light on how CI practices may inform, support, and be integrated in the strategy formulation process, as few studies have done before.