Industry 4.0 technologies in the manufacturing sector: Are we sure they are all relevant for environmental performance?

This research contributes to the debate about the relevance of Industry 4.0 technologies in improving environmental performance in the manufacturing industry. We employed a qualitative–quantitative approach in which 19 Italian operations managers were interviewed and 260 managers responded to an online questionnaire. The effects of various technologies were ranked using ordinal regression. Comments and suggestions gave context to the quantitative results. Sensors, radio-frequency identification, artificial intelligence and analytics were found to be most relevant in improving environmental performance, whereas simulation software contributed moderately. Additive manufacturing, cobots, robots, automated mobile robots and automated guided vehicles had a negative effect, augmented reality had no effect and other technologies indirectly affected environmental performance. We also found a lack of knowledge and application as well as scepticism about technologies such as artificial intelligence and augmented reality. Finally, there was concern about the disposal of electrical and electronic waste produced by these technologies.     

Designing business models in circular economy: A systematic literature review and research agenda

The concept of circular economy is increasingly receiving attention in different domains, including strategic management, operations management, and technology management. It requires companies to design their business model (i.e., the value network, the relationships with the supply chain partners, and the value propositions towards customers) around a new concept of sustainable development that reduces consumption of natural resources and preserves the environment. However, extant research falls short in terms of explaining how companies design their business model according to the circular economy principles. Starting from this premise, the present paper provides a systematic review of the literature on the design of business models in the context of circular economy, aiming to offer an overview of the state of research and outline a promising research agenda.     

Approaching rainfall-based weather derivatives pricing and operational challenges

Review of Derivatives Research - This article approaches some of the current rainfall derivatives pricing and operational challenges through an empirical application to Comunidad Valenciana, Spain....     

Effects of redistributing policy support on farmers’ technical efficiency

The objective of this article is twofold: first, investigating the relationship between technical efficiency and decoupled direct payments of a sample of Italian farms prior to the application of the 2014–2020 Common Agricultural Policy reform; second, evaluating possible implications of alternative scenarios about distribution of direct payments on technical efficiency. To these aims, a stochastic frontier analysis is adopted. Results indicate that direct payments produce significant effects on technical efficiency in specialized farms, which received higher levels of support. However, effects are contrasting. Moreover, results show that redistribution of policy subsidies may negatively impact on technical efficiency to an extent depending upon the criterion of redistribution applied. Finally, some policy suggestions are given.     

Customer satisfaction and service domains: a further development of PROSERV

Quality & Quantity - This paper aims to represent that levels of customer satisfaction of tourist accommodation are an important prerequisite when the goal is building a loyal relationship with...     

From private label to co-branded credit card: an assessment of the impact of a change in value proposition over customer value perception and company value capture

Journal of Financial Services Marketing - Purpose: The aim of this paper is to evaluate the success of a value proposition over time, considering two aspects: customer’s perceived value and...     

Randomised Mixture Models for Pricing Kernels

Numerous kinds of uncertainties may affect an economy, e.g. economic, political, and environmental ones. We model the aggregate impact by the uncertainties on an economy and its associated financial market by randomised mixtures of Lévy processes. We assume that market participants observe the randomised mixtures only through best estimates based on noisy market information. The concept of incomplete information introduces an element of stochastic filtering theory in constructing what we term “filtered Esscher martingales”. We make use of this family of martingales to develop pricing kernel models. Examples of bond price models are examined, and we show that the choice of the random mixture has a significant effect on the model dynamics and the types of movements observed in the associated yield curves. Parameter sensitivity is analysed and option price processes are derived. We extend the class of pricing kernel models by considering a weighted heat kernel approach, and develop models driven by mixtures of Markov processes.     

Machine learning for pricing American options in high-dimensional Markovian and non-Markovian models

In this paper we propose two efficient techniques which allow one to compute the price of American basket options. In particular, we consider a basket of assets that follow a multi-dimensional Black–Scholes dynamics. The proposed techniques, called GPR Tree (GRP-Tree) and GPR Exact Integration (GPR-EI), are both based on Machine Learning, exploited together with binomial trees or with a closed form formula for integration. Moreover, these two methods solve the backward dynamic programing problem considering a Bermudan approximation of the American option. On the exercise dates, the value of the option is first computed as the maximum between the exercise value and the continuation value and then approximated by means of Gaussian Process Regression. The two methods mainly differ in the approach used to compute the continuation value: a single step of the binomial tree or integration according to the probability density of the process. Numerical results show that these two methods are accurate and reliable in handling American options on very large baskets of assets. Moreover we also consider the rough Bergomi model, which provides stochastic volatility with memory. Despite that this model is only bidimensional, the whole history of the process impacts on the price, and how to handle all this information is not obvious at all. To this aim, we present how to adapt the GPR-Tree and GPR-EI methods and we focus on pricing American options in this non-Markovian framework.