Owner Identity and Firm Performance: Evidence from European Companies

Empirical evidence of the distribution of firms by owner identity for a set of European countries reveals substantial differences. Using the sensitivity of a firm’s sales to demand shocks as a measure of risk-taking behavior, the paper explores if owner identity affects the willingness of the firms to seize market opportunities. Consistent with a hypothesis of risk-avoidance behavior, family-owned companies appear to underreact to changes in market demand. Conversely, industrial and nonconcentrated family-owned firms appear more prone to deal with venturing risk, especially in the case of fast-growing companies or demand changes in nondomestic markets.owner identity, SMEs, risk aroidance, family firms.

Family Succession and Firm Performance: Evidence from Italian Family Firms

This article contributes to the growing empirical literature on family firms by studying the impact of the founder–chief executive officer (CEO) succession in a sample of Italian firms. We contrast firms that continue to be managed within the family by the heirs to the founders with firms in which the management is passed on to outsiders. Family successions, that is, successions by the founder’s heirs, are further analyzed by assessing the impact of the sectoral intensity of competition on the post-succession performance. This analysis also addresses the endogeneity in the timing of the CEO succession by controlling for a pure mean-reversion effect in the firm’s performance. We find that the maintenance of management within the family has a negative impact on the firm’s performance, and this effect is largely borne by the good performers, especially in the more competitive sectors. These results indicate that there is no inherent superiority of the family-firm structure and emphasize the importance of conducting an analysis of governance in a variety of institutional settings.Family successions; Family firms; Founder-run firms

Enhancing the predictive performance of ensemble models through novel multi-objective strategies: evidence from credit risk and business model innovation survey data

This paper proposes novel multi-objective optimization strategies to develop a weighted ensemble model. The comparison of the performance of the proposed strategies against simulated data suggests that the multi-objective strategy based on joint entropy is superior to other proposed strategies. For the application, generalization, and practical implications of the proposed approaches, we implemented the model on two real datasets related to the prediction of credit risk default and the adoption of the innovative business model by firms. The scope of this paper can be extended in ordering the solutions of the proposed multi- objective strategies and can be generalized for other similar predictive task

A New Model Averaging Approach in Predicting Credit Risk Default

none2siThe paper introduces a novel approach to ensemble modeling as a weighted model average technique. The proposed idea is prudent, simple to understand, and easy to implement compared to the Bayesian and frequentist approach. The paper provides both theoretical and empirical contributions for assessing credit risk (probability of default) effectively in a new way by creating an ensemble model as a weighted linear combination of machine learning models. The idea can be generalized to any classification problems in other domains where ensemble-type modeling is a subject of interest and is not limited to an unbalanced dataset or credit risk assessment. The results suggest a better forecasting performance compared to the single best well-known machine learning of parametric, non-parametric, and other ensemble models. The scope of our approach can be extended to any further improvement in estimating weights differently that may be beneficial to enhance the performance of the model average as a future research direction.openParitosh Navinchandra Jha; Cucculelli MarcoJha, Paritosh Navinchandra; Cucculelli, Marc

Environmental regulation and green productivity growth: Evidence from Italian manufacturing industries

Environmental policy is at the core of the current research debate and policy action. Few studies have discussed the impact of environmental regulation on productivity growth at industry level, and the empirical evidence on this issue is still controversial. Based on panel data on thirteen Italian manufacturing industries from 1995 to 2017, this study analyzes the effect of environmental policies on sectoral productivity by measuring the adjusted productivity growth using the Malmquist-Luenberger index. The main result of this analysis is that environmental regulation has no negative effect in most of the sample industries. A bootstrapping approach has been used to assess the robustness of estimated results

Business model innovation and digital technology: The perspective of incumbent Italian small and medium-sized firms

The aim of the article was to conduct an explorative study on the relationship between business model innovation and digital technologies in incumbent small and medium-sized enterprises (SMEs)

Tourism Destination Competitiveness in Italy: a Stakeholders’ Perspective

The paper aims at evaluating destination competitiveness of one the world’s leading tourism countries from a stakeholders’ perspective. A survey questionnaire has been submitted to 550 tourism stakeholders from a number of outstanding Italian destinations. Destination competitiveness was analysed at national level, as well as according to type, size and geographic localization of the destinations. Empirical analysis shows that tourism policy, destination management and general infrastructures are the weakest features of the Italian tourism system. They contribute to explain the gap that has emerged between Italy and its main competitors in the last two decades. Unexploited tourism potentials have been found in smaller and inland destinations and in the less developed southern part of the countr