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.     

Exact and approximate D-optimal designs in polynomial regression

If the sample sizen is large enough, then the exact polynomial regression designs obtained by rounding the weights of the approximate D-optimal design to integral multiples of 1/n are D-optimal. This was shown by alaevskiî (1966) and Gaffke (1987). In this note, an efficient algorithm to determine the minimum sample sizen _d for a polynomial model of degreed is derived from a condition given by Huang (1987). Under an additional assumption we show that the conditions of Gaffke and Huang are equivalent; we verify the additional assumption for polynomial degreed40.     

Chebyshev interpolation for parametric option pricing

Recurrent tasks such as pricing, calibration and risk assessment need to be executed accurately and in real time. We concentrate on parametric option pricing (POP) as a generic instance of parametric conditional expectations and show that polynomial interpolation in the parameter space promises to considerably reduce run-times while maintaining accuracy. The attractive properties of Chebyshev interpolation and its tensorized extension enable us to identify broadly applicable criteria for (sub)exponential convergence and explicit error bounds. The method is most promising when the computation of the prices is most challenging. We therefore investigate its combination with Monte Carlo simulation and analyze the effect of (stochastic) approximations of the interpolation. For a wide and important range of problems, the Chebyshev method turns out to be more efficient than parametric multilevel Monte Carlo. We conclude with a numerical efficiency study.     

R&D: the missing link between corporate social performance and financial performance?

Management Review Quarterly - The relationship between corporate social performance (CSP) and financial performance (CFP) has been analyzed for decades. Despite these efforts, the results remain...     

Do casinos pay their customers to become risk-averse? Revising the house money effect in a field experiment

The house money effect predicts that individuals show increased risk-seeking behavior in the presence of prior windfall gains. Although the effect’s existence is widely accepted, experimental studies that compare individuals’ risk-taking behavior using house money to individuals’ risk-taking behavior using their own money produce contradictory results. This experimental field study analyzes the gambling behavior of 917 casino customers who face real losses. We find that customers who received free play at the entrance showed not higher but significantly lower levels of risk-taking behavior during their casino visit, expressed through lower average wagers. This study thus provides field evidence against the house money effect. Moreover, as a result of lower levels of risk seeking, endowed customers yield better economic results in the form of smaller own-money losses when leaving the casino.     

The taxation of bonuses and its effect on executive compensation and risk‐taking: Evidence from the UK experience

This paper explores the effects of a bonus tax adopted in the UK in December 2009 on the compensation structure of executives and on risk‐taking behavior in the financial sector. Excessive bonuses are blamed for encouraging risk taking and are regarded as one of the pull factors of the financial crisis. The British government attempted to reduce bonuses and accordingly bank risk taking by means of a special tax on cash‐based bonuses. Using a comprehensive dataset on executive compensation, we show that the introduction of the bonus tax decreased the net cash bonuses awarded to directors by about 40%, accompanied, however, by a simultaneous increases in other forms of pay leaving total compensation as well as risk levels unaffected.     

Der Luxus »intellektueller Boheme« Zum Tod von Alphons Silbermann (1909–2000)

Ohne Zusammenfassung     

Destructive behavior in a Fragile Public Good game

Socially destructive behavior in a public good environment–like damaging public goods–is an underexposed phenomenon in economics. In an experiment we investigate whether such behavior can be influenced by the very nature of an environment. To that purpose we use a Fragile Public Good (FPG) game which puts the opportunity for destructive behavior (taking) on a level playing field with constructive behavior (contributing). We find substantial evidence of destructive decisions, sometimes leading to sour relationships characterized by persistent hurtful behavior. While positive framing induces fewer destructive decisions, shifting the selfish Nash towards minimal taking doubles its share to more than 20%.     

Verification of Web Service Compositions: An Operationalization of Correctness and a Requirements Framework for Service-oriented Modeling Techniques

Web service compositions coordinate Web services of different enterprises. They are expected to constitute the foundation of service-oriented architectures, to improve business processes as well as to foster intra- and inter-organizational integration. Especially in inter-organizational contexts, quality of service referring to non-functional requirements and conformance to functional requirements are becoming vital properties. With Web service compositions being asynchronous and distributed systems, the latter property – which is also called correctness – can be shown best by verification. This paper examines from a system-theoretic perspective how correctness can be operationalized for Web service compositions. It also proposes a requirements framework for service-oriented modeling techniques so that correctness can be shown by verification and Web service compositions can be modeled intuitively. In order to show the framework’s principle applicability, an example approach is analyzed with respect to the corresponding requirements.