The ongoing discussion regarding blockchain technologies is focused primarily on cryptocurrencies, but blockchain features and functionalities have developed beyond financial instruments. As the technologies provide new functionalities, the associated value proposition changes as well. This study explores the relationship between blockchain technologies and their underlying value drivers. Four identified distinct blockchain stages of increased maturity are analyzed and discussed. This covers the evolutionary technology types focused on transactions, smart contracts, decentralized applications, and the introduction of artificial intelligence supporting decentralized decision making. In addition, we address management issues around appropriate blockchain adoption using a blockchain value driver-focused framework that gives decision makers actionable questions and recommendations. We provide practitioners with a method for assessing suitable blockchain adoption that addresses the specific value creation associated with a given organizational strategy. For academics, we critically identify and assess the characteristics of the blockchain stages and their strategy implications and provide a structured approach conceptualizing blockchain technology evolution.

Companies are adopting several new technologies that form the pillars of Industry 4.0 production framework, of which Digital Manufacturing (DM) stands out by combining conventional manufacturing technologies with digital techniques. These are used to assist in the design and analysis of the product and manufacturing processes. The adoption of digital manufacturing is partly about technological change, but it also entails significant organizational issues, which often are overlooked by managers. The purpose of this study is to identify the key factors that enable or prevent DM implementation, considering the production paradigm of Industry 4.0. Based on a literature review that identified a preliminary list of key factors, the appropriateness of these factors is empirically tested and refined in a two-fold approach: an in-depth pilot case in a multinational automotive company that is adopting DM technologies, and a survey of 113 users, managers, implementers and researchers working on digital manufacturing and Industry 4.0. The study identified 24 key factors to be considered when firms implement DM. These are categorized into technical, organizational, project based and external factors. The findings also indicate how each factor should be considered, and that they cannot be generalized due to cultural differences inherent to each individual company. As such, this research contributes to the current research debate by identifying the critical factors to be considered when conceiving and applying models for planning and executing DM implementation.

Complexity negatively impacts the process of continually improving performance management systems (PMSs). The extant PMS literature considers complexity to be a result of the external environment rather than a user response to that environment. However, this paper argues that organizations generally face internal complexity when adopting PMSs. Introducing PMSs into an organization can have varied effects in those organizations based on the complexity of an organization's associated members and its interactions. This study aims to understand the emergence of complexities while implementing and using PMSs in organizations. From the complexity theory perspective, four system properties (ontological, teleological, genetic and functional) are used to understand complexity in PMSs. The paper builds on a systematic literature review consisting of 76 papers and analyses them in the light of exploring sources of complexity when implementing and using PMSs. From the outset, complexity is understood to be a result of the conflict between existing organizational practices and mechanisms and the organizational controls associated with PMSs. The key findings abstracted six sources of complexity in this study: role, task and procedural types of complexity associated with the social dimension, and methodological, analytical and technological types of complexity associated with the technical dimension. The study findings contribute to the current discussion regarding why PMSs typically lag and are not responsive and resilient in emerging contexts. While understanding and exploring all organizational controls that moderate a PMS is useful, organizations should construct the necessary capabilities, depending on their context and adapt to the changes associated with PMSs.

The purpose of this paper is to explore turnaround performance as a resultant from both Collaborative Decision Making (CDM) processes and collaborative measures. This paper presents how CDM operates in the Turnaround Process (TAP) to propose a new method for managing the collaborative turnaround performance of all actors by predicting the most critical indicators. To achieve this, data from a CDM airport is used. Sample data of 6500 observations, taken from turnaround movements handled in 2014 at Madrid-Barajas Airport, were obtained from three separate databases and analyzed separately (in three databases). To predict turnaround performance, this paper also introduces a predictor dependent variable called "star values" as a measure of minimal delay conditions in order to predict time performance. The analysis shows that the proposed method unveils a new approach in determining how collaborative performance can be measured in the TAP and the predicted key performance indicators, which shows variations in the predicted CDM indicators. Results challenge managers and policymakers to find which improvements can be enacted for better usage of airport infrastructures and resources for optimum use as well as enhanced TAP. In terms of theory use and extension, the study reveals how CDM is an essential element in the literature on air traffic management.