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Total productive maintenance consists of strategies and procedures that aim to guarantee the entire functioning of machines in a production process so that production is not interrupted and no loss of quality in the final product occurs. Planned maintenance is one of the eight pillars of total productive maintenance, a set of tools considered essential to ensure equipment reliability and availability, reduce unplanned stoppage and increase productivity. This study aims to analyze the influence of statistical reliability on the performance of such a pillar.

In this study, we utilized a multi-method approach to rigorously examine the impact of statistical reliability on the planned maintenance pillar within total productive maintenance. Our methodology combined a detailed statistical analysis of maintenance data with advanced reliability modeling, specifically employing Weibull distribution to analyze failure patterns. Additionally, we integrated qualitative insights gathered through semi-structured interviews with the maintenance team, enhancing the depth of our analysis. The case study, conducted in a fertilizer granulation plant, focused on a critical failure in the granulator pillow block bearing, providing a comprehensive perspective on the practical application of statistical reliability within total productive maintenance; and not presupposing statistical reliability is the solution over more effective methods for the case.

Our findings reveal that the integration of statistical reliability within the planned maintenance pillar significantly enhances predictive maintenance capabilities, leading to more accurate forecasts of equipment failure modes. The Weibull analysis of the granulator pillow block bearing indicated a mean time between failures of 191.3 days, providing support for optimizing maintenance schedules. Moreover, the qualitative insights from the maintenance team highlighted the operational benefits of our approach, such as improved resource allocation and the need for specialized training. These results demonstrate the practical impact of statistical reliability in preventing unplanned downtimes and informing strategic decisions in maintenance planning, thereby emphasizing the importance of your work in the field.

In terms of the originality and practicality of this study, we emphasize the significant findings that underscore the positive influence of using statistical reliability in conjunction with the planned maintenance pillar. This approach can be instrumental in designing and enhancing component preventive maintenance plans. Furthermore, it can effectively manage equipment failure modes and monitor their useful life, providing valuable insights for professionals in total productive maintenance.

Scholars increasingly focus on digital transformation as a key strategy for incumbent firms to gain competitive advantages. Meeting digital transformation commitments presents challenges, requiring the application and the reconfiguration of dynamic capabilities. To address this need, this research proposes a framework of dynamic capabilities and its microfoundations to assess the opportunities and challenges regarding digital transformation, involving three dimensions: Digital sensing, digital seizing, and digital reconfiguring.

This study employed a descriptive qualitative empirical approach, encompassing a sample of eight companies. Data triangulation was achieved through a combination of in-depth semi-structured interviews and secondary data.

The research provides evidence that data-driven culture fosters digital transformation and proposes the following new microfoundations: “Analytics for the customer experience journey” and “Digital analytics innovation management”, the internal barriers “Leadership without digital skills” and “Lack of strategic human resources management for digital transformation”, and the internal enablers “Defined strategy for digital transformation” and “Data-driven culture”. Finally, based on empirical results, it was possible to gather clues that link dynamic capabilities with digital maturity.

The application of the proposed framework in companies enables them to develop a roadmap for the digital transition oriented towards their business and management strategies.

This paper contributes to the literature on dynamic capabilities for digital transformation by proposing new theoretical constructs that unearth their microfoundations, barriers, and enablers.

Brazil is characterized by the inexistence of a more robust system of guarantees and rules to minimize risks and protect agents in energy futures contracts. In this sense, this study aims to answer the question of how a centralized clearing agent can compute safety margin requirements to help reduce the systemic risk of the energy futures contracts market in Brazil.

The intermediate steps and specific objectives are to analyze the volatility behavior, identify the autoregressive conditional heteroscedasticity effects and model the variance of the return series. Based on this, the authors calculate the value-at-risk and conditional value-at-risk metrics for the energy futures contracts. As a robustness test, the authors added a peak over threshold methodology from extreme values theory.

In general, monthly products require margins because of their higher variance. With the asymmetrical distribution of returns, the authors needed to consider different maintenance margins for the long and short positions. It was also shown that two guarantee margins were required to secure the contracts as follows: the initial margin and the maintenance margin. The three factors that defined the size of the maintenance margin the volatility, skewness and kurtosis of the return series.

The contribution of this study lies in promoting the understanding of the risk dimensions of the energy derivatives market in Brazil and it offers concrete recommendations for how to mitigate this risk through market mechanisms and structures. Similar arrangements can be applied to other emerging markets.

Although 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.

The 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.

This 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).

Our 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.

The 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.

The findings enrich PMMS literature in an ecosystem context related to the ES in uncertain environments.

The link between theory and practice in innovation studies still has some gaps, despite scholars’ efforts to illustrate and identify them in real-world situations. To fully understand the dynamics of the innovation ecosystem, it is crucial to consider key actors and their roles and recognize their impact on ecosystem outcomes. Therefore, this paper seeks to discuss how analytical structures of innovation ecosystems address the dynamics of actors and their contribution to the ecosystem outcomes.

A research protocol was developed to query the Web of Science database to identify analytical structures of innovation ecosystems based on pre-established criteria.

The dynamics of actors interfere with their contribution to value creation. That is, the actor changes his contribution. Therefore, depending on the ecosystem's value proposition, the activities and dynamics of the actors change over time.

It contributes to advancing the discussion of innovation ecosystems, addressing insights into the dynamics of actors in different analytical structures. The essay proposal considers innovation ecosystems' evolutionary aspects, value propositions and exchange. In addition, the importance of orchestration in the various stages of the ecosystem is highlighted.

Accessing and sharing dispersed knowledge in ecosystems is neither easy nor automatic. In ecosystems, focal firms should purposely create the right conditions and act to deal with dispersed knowledge. This study aims to investigate how focal firms manage dispersed knowledge in ecosystems characterized by a set of autonomous, heterogeneous, yet interdependent actors involved in experimentation under uncertainty.

Following a conceptual framework based on preceding literature, this study conducted a broad qualitative case study of 6 firms and 12 projects, with 43 semi-structured interviews to identify the patterns of actions associated with dispersed knowledge management (KM) in ecosystems. This paper combines coding and multiple case comparisons to examine the processes and strategies used by the firms to strategically manage dispersed knowledge in ecosystems.

This paper proposes a framework that articulates a new type of orchestration (dispersed knowledge orchestration) and offers a new set of dispersed knowledge strategies (transfer, modularity and circular) for ecosystems.

Innovation and knowledge managers play the roles of dispersed knowledge orchestrators. The study offers guidance on how focal firms should carefully use a particular set of approaches (e.g. integrative theorization) including a portfolio of dispersed knowledge strategies in ecosystems.

Current literature on KM and ecosystem management offers a limited understanding of how organizations manage dispersed knowledge in ecosystems. The research provides three major original contributions. First, the framework contributes to broadening the current understanding of ecosystem orchestration by identifying the micro-foundations of dispersed knowledge orchestration: integrative theorization, nurturing distributed sensemaking and a new chapter for ecosystem governance (i.e. dispersed knowledge governance). Moreover, the framework proposes a new type of strategy, the dispersed knowledge strategy. Finally, by exploring the interplay between the micro-foundations of dispersed knowledge orchestration and dispersed knowledge strategy, the results contribute to a multi-level approach in the field.

Entrepreneurs' pivot decisions are poorly understood. The purpose of this paper is to review the existing literature on pivot decisions to identify the different conceptualizations, research streams and main theoretical building blocks and to offer a baseline framework for future studies on this phenomenon.

A systematic literature review of 86 peer-reviewed papers published between January 2008 and October 2020, focusing on the pivot decision in startups, was performed through bibliometric, descriptive and content analyses.

The literature review identifies four research streams concerning the pivot concept – pivot design, cognitive, negotiation and environmental perspectives. Building on previous studies, this paper provides a refined definition of a pivot that bridges elements from the four research streams identified: a pivot comprises strategic decisions made after a failure (or in the face of potential failure) of the current business model and leads to changes in the firm's course of action, resource reconfiguration and possible modifications of one or more business model elements. This study proposes a framework that elaborates the pivot literature by identifying four stages of the pivot process addressed in the existing literature: recognition, generating options, seizing and testing and reconfiguration.

This study provides a comprehensive review, enabling researchers to establish a baseline for developing future pivot research. Furthermore, it improves the conceptualization of pivots by summarizing prior definitions and proposing a refined definition that places decision-making and judgment at its center. That introduces new contextual and behavioral elements, contributing to a better understanding of how entrepreneurs assess alternative courses of action and envision possible outcomes to redirect a venture after failure.