Blockchain in Additive Manufacturing and its Impact on Supply Chains

Additive manufacturing (AM) appears to be a particularly attractive use case for blockchain. This research combines inductive in‐depth interviews with the Delphi method to explore what potentials blockchain technology in AM creates, which adoption barriers firms need to overcome, and how supply chains will be affected by the integration of these two potentially disruptive technologies. The results suggest opportunities that are related to intellectual property (IP) rights management, the monitoring of printed parts throughout their lifecycle, process improvements, and data security. The most important barriers for blockchain adoption in AM are an absence of blockchain‐skilled specialists on the labor market, missing governance mechanisms, and a lack of firm‐internal technical expertise. By addressing important limitations of AM, blockchain is expected to improve the competitiveness of AM in parts’ production, catalyzing the trend toward more decentralized manufacturing resulting in more agile, resilient, and flexible supply chains and reduced logistics costs. Beyond that, blockchain‐based AM platforms are expected to enhance supply chain visibility, drive supply chain digitalization, support supply chain finance, and contribute to the emergence of shared factory systems.     

Nonfinancial Considerations in Eco-Innovation Decisions: The Role of Family Ownership and Reputation Concerns

Societal pressures for greater sustainability can encourage firms to target part of their innovation activities at ecological initiatives (i.e., eco-innovation). Yet, depending on their value function, firms can respond differently to such pressures and exhibit variance in their eco-innovation activities. In this paper, we investigate the idea that a firm’s ownership structure may play a significant role in determining its engagement in eco-innovation. Specifically, we propose that ownership by family blockholders increases the value attached to the company’s reputation and that this, in turn, stimulates higher levels of eco-innovation. In other words, we model the company reputation motive as a key mediator in the relationship between family ownership and firm-level eco-innovation. To account for family firm heterogeneity, we also model the moderating role of owners’ intention to pass the business on to the next family generation (transgenerational intentions) and of the extent to which these owners reside in the firm’s local community (local embeddedness). As theoretical backdrop, our study builds on institutional theory and the mixed gamble logic. To test our hypotheses, we use a large sample of German firms and nonlinear moderated mediation regression analysis. Results reveal that family ownership is positively related to the introduction of eco-innovations by firms, in part because of the stronger emphasis being placed on the company’s reputation. We find that this effect is strongest when the owning-family has transgenerational intentions. As such, this study advances our understanding of firm-level drivers of eco-innovation. In view of the prevalence of family-owned firms and the mounting importance of ecological sustainability, it is valuable to extend knowledge on the contingent and indirect effect of family ownership on eco-innovation.     

Missing the Popular Vote: Pitfalls in US Democracy and Reform Proposals

Intereconomics - Only a few years ago, it was a widespread belief that globalisation would trigger processes of democratisation worldwide. However, even old and established democracies such as the...     

Effects of scale economies and production seasonality on optimal hub locations: the case of regional fresh produce aggregation

Interest in supporting local and regional food systems is rising and food hubs have attracted considerable attention among Federal, State, and local policymakers. This study explores the problem of endogenous hub location in fresh produce value chains in the Northeastern United States. To overcome limitations in the literature, we incorporate the effects of economies of scale and production seasonality into our models. Three experimental models are designed to examine the effects of alternatively applying yearly, quarterly, and monthly data on model solutions. We explicitly assess how interactions of scale economies and seasonality influence the structure and spatial attributes of an optimal regional produce aggregation hub system. The three models generate marketed different solutions and in many respects they lead to different conclusions about developing local/regional supply chains. The monthly model allows for production seasonality and actual hub operation cycle frequency and thus leads to more efficient hub solution with rich policy implications.     

Contagion of network products in small-world networks

We formulate a model in which agents embedded in an exogenous social network decide whether to adopt a new network product or not. In the theoretical part of the paper, we characterize the stochastically stable equilibria for complete networks and cycles. For an arbitrary network structure, we develop a novel graph decomposition method to characterize the set of recurrent communication states, which is a superset of stochastically stable equilibria of the adoption game presented in our model. In the simulation part, we study the contagion process of a network product in small-world networks that systematically represent social networks. We simulate a generalization of the Morris (Rev Econ Stud 67(1):57–78, 2000) Contagion model that can explain the chasm between early adopters and early majority. Our numerical analysis shows that the failure of a new network product is less likely in a highly cliquish network. In addition, the contagion process reaches to steady state faster in random networks than in highly cliquish networks. It turns out that marketers should work with mixed marketing strategies, which will result in a full contagion of a network product and faster contagion rates with a higher probability.