Abstract
Purpose The purpose of this paper is to identify patterns of project risk management (PRM) practices' adoption, and provides empirical evidence concerning the importance (and key attributes) of organizational PRM maturity to the use of risk-related practices and project performance. Design/methodology/approach The research involved two phases: interviews with five project managers, and a worldwide survey of project managers that resulted in the analysis of 865 valid questionnaire responses. Cluster analysis was used to classify PRM practices' use, factor analysis to detect the structure of the relationship between the variables measuring PRM practices' use and a multiple regression analysis (with canonical correlation) to further reveal the different degrees to which PRM practices and organizational maturity are associated. Findings The identified patterns of risk practices' adoption indicate that different contexts of organization PRM maturity and project complexity influence practices selection. The PRM practices related with targets (e.g. time-phased budget plan) are the most used, and those related to tools and techniques (e.g. S-curve) are the least used. Additionally, the obtained results confirm that organizational PRM maturity influences risk practices' usage, moderated by project complexity, and organizational PRM maturity influences project performance. Originality/value Empirical methods were used to investigate the relationship between organizational PRM maturity and a large set of PRM practices with project complexity as a moderator. Gaps in the use of PRM practices (i.e. areas where more PRM knowledge and training are needed) were identified. Finally, this work identifies the attributes of organizational maturity with implications in practices' usage and project performance.

Abstract
This article serves to show how things are changing when it comes to business and social networking. Nowadays, it is hard to find a business that does not have an account on any social network, and we can safely say that social media is a crucial aspect for any business enterprise - to sell their products, to be seen and, obviously, to make more money. We talk about the possibility to mix innovation, business strategy and social media. To complement this research, we focused our work on a Portuguese start-up - Strain - that intends to prove marketing is changing and that it actually welcomes the change, with social networks at the base of their business. They intend to use online influencers to promote the image of a brand, free of charge, and earn discounts on it. It is, indeed, a win-win situation, where each of the three parts (the company itself, clients and Strain) is a winner one way or the other.

Abstract

Abstract
This work aims to develop an embedded system and mobile application within Internet of Things (IoT) context, which allow the evaluation of control techniques widely used in the industry, for both remote configuration and monitoring of environment inside storage silos in laboratory scale. The developed system contributes to the validation of the recent released KNoT meta platform and extends its application to agricultural area. Preliminary results for on-off and PID temperature control suggest its feasibility for remote transmission of setpoint data to local controllers and data acquisition for monitoring by a mobile application. © Springer Nature Switzerland AG 2019.

Abstract

Abstract
Preferential sampling in time occurs when there is stochastic dependence between the process being modeled and the times of the observations. Examples occur in fisheries if the data are observed when the resource is available, in sensoring when sensors keep only some records in order to save memory and in clinical studies, when a worse clinical condition leads to more frequent observations of the patient. In all such situations the observation times are informative on the underlying process. To make inference in time series observed under Preferential Sampling we propose, in this work, a numerical method based on a Laplace approach to optimize the likelihood and to approximate the underlying process we adopt a technique based on stochastic partial differential equation. Numerical studies with simulated and real data sets are performed to illustrate the benefits of the proposed approach. © 2019 SEIO