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.

Purpose: Given the rise of big data and use of analytics, this study explores how the performance measurement systems research field has incorporated dynamic aspects and digitalization prevalent in rapidly changing and competitive environments. Design/methodology/approach: In the study we conduct a software supported and iterative systematic literature review of the performance management field together with a keyword network analysis. The meta-analysis provided a quantitative method that identified gaps and directions. Findings: The findings suggest there is a link between performance measurement systems and dynamics, but the link is weak, and the main research does not incorporate dynamic elements. There is an observable research community interest in managing information for informed decisions through digitalization. However, two broad research perspectives on performance management systems in highly dynamic and competitive environments were identified, one group exploring dynamic conditions, and the other digital technologies and optimization. Researchers positioning themselves in the dynamic subgroup are likely take an organizational or human capital perspective when exploring applications of performance management systems, while the other group mainly pursues research within artificial intelligence, automatization, and optimization. There is limited research with coverage of both groups, highlighting a notable gap in the existing research. Originality/value: This paper contributes to the understanding of performance measurement and management research in dynamic and digital environments.

Digitalization of manufacturing is once again on the industry application research agenda and Digital Manufacturing plays a fundamental role in this process. However, there is a lack of commonality in the literature about the purpose of Digital Manufacturing. The purpose of this paper is to analyze the concept and application domain of Digital Manufacturing considering the increasingly established Industry 4.0 paradigm. Based on a content analysis concepts are framed, and new technological characteristics identified. The paper contributes to a better understanding of the future challenges that companies face by positioning Digital Manufacturing conceptually and delimiting its application domain.

The manufacturing industry is shifting, entering a new era with smart and connected devices. The fourth industrial revolution is promising increased growth and productivity by the Smart Factory and within the enabling technologies is Augmented Reality (AR). At the same time as the technology is introduced, errors in manufacturing are a problem which is affecting the productivity and the quality. This research aims to find the main causes of human errors in assembly lines and thereafter explores whether augmented reality is an appropriate tool to be used in order to address those issues. Based on a literature review that identified and characterized a preliminary set of root causes for human errors in assembly lines, these causes were empirically studied in an exercise that covered an in-depth case study in a multinational automotive company. Data in form of interviews and deviation- reports have been used to identify the causing factors and the result showed that the main causes of human errors are the amount of thinking, deciding and searching for information which affected the cognitive load of the operator and in result their performance. Several interviews with experts in augmented reality allowed to verify if AR technology would be feasible to solve or mitigate the found causes. Besides that, in repetitive manual assembly operations, AR is better used showing the process in order to train new operators. At the same time for experienced operators, AR should show information only when an error occurs and when there is a need of taking an active choice. Nevertheless, while theoretically able to managing human error when fully developed, the desired application makes the augmentation of visual objects redundant and increasingly complex for solving the identified causes of errors which questions the appropriateness of using AR systems. Furthermore, the empirical findings showed that for managing human errors, the main bottleneck of an AR system is the systems artificial intelligence capabilities.

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.