Strategic I/O Psychology and the Role of Utility Analysis Models

In the 1990’s, the significance of human capital in organizations has been increasing,and measurement issues in human resource management have achieved significant prominence. Yet, I/O psychology research on utility analysis and measurement has actually declined. In this chapter we propose a decision-based framework to review developments in utility analysis research since 1991, and show that through lens of this framework there are many fertile avenues for research. We then show that both I/O psychology and strategic HRM research and practice can be enhanced by greater collaboration and integration, particularly regarding the link between human capital and organizational success. We present an integrative framework as the basis for that integration, and illustrate its implications for future research

Evaluating the effectiveness of explanations for recommender systems : Methodological issues and empirical studies on the impact of personalization

Peer reviewedPostprin

Selection of Software Product Line Implementation Components Using Recommender Systems: An Application to Wordpress

In software products line (SPL), there may be features which can be implemented by different components, which means there are several implementations for the same feature. In this context, the selection of the best components set to implement a given configuration is a challenging task due to the high number of combinations and options which could be selected. In certain scenarios, it is possible to find information associated with the components which could help in this selection task, such as user ratings. In this paper, we introduce a component-based recommender system, called (REcommender System that suggests implementation Components from selecteD fEatures), which uses information associated with the implementation components to make recommendations in the domain of the SPL configuration. We also provide a RESDEC reference implementation that supports collaborative-based and content-based filtering algorithms to recommend (i.e., implementation components) regarding WordPress-based websites configuration. The empirical results, on a knowledge base with 680 plugins and 187 000 ratings by 116 000 users, show promising results. Concretely, this indicates that it is possible to guide the user throughout the implementation components selection with a margin of error smaller than 13% according to our evaluation.Ministerio de Economía y Competitividad RTI2018-101204-B-C22Ministerio de Economía y Competitividad TIN2014-55894-C2-1-RMinisterio de Economía y Competitividad TIN2017-88209-C2-2-RMinisterio de Economía, Industria y Competitividad MCIU-AEI TIN2017-90644-RED

A study on text-score disagreement in online reviews

In this paper, we focus on online reviews and employ artificial intelligence tools, taken from the cognitive computing field, to help understanding the relationships between the textual part of the review and the assigned numerical score. We move from the intuitions that 1) a set of textual reviews expressing different sentiments may feature the same score (and vice-versa); and 2) detecting and analyzing the mismatches between the review content and the actual score may benefit both service providers and consumers, by highlighting specific factors of satisfaction (and dissatisfaction) in texts. To prove the intuitions, we adopt sentiment analysis techniques and we concentrate on hotel reviews, to find polarity mismatches therein. In particular, we first train a text classifier with a set of annotated hotel reviews, taken from the Booking website. Then, we analyze a large dataset, with around 160k hotel reviews collected from Tripadvisor, with the aim of detecting a polarity mismatch, indicating if the textual content of the review is in line, or not, with the associated score. Using well established artificial intelligence techniques and analyzing in depth the reviews featuring a mismatch between the text polarity and the score, we find that -on a scale of five stars- those reviews ranked with middle scores include a mixture of positive and negative aspects. The approach proposed here, beside acting as a polarity detector, provides an effective selection of reviews -on an initial very large dataset- that may allow both consumers and providers to focus directly on the review subset featuring a text/score disagreement, which conveniently convey to the user a summary of positive and negative features of the review target.Comment: This is the accepted version of the paper. The final version will be published in the Journal of Cognitive Computation, available at Springer via http://dx.doi.org/10.1007/s12559-017-9496-

Is there a regulatory trade-off between stability and performance? Evidence from italian banks.

Disentangling the direct causal effect that sanctions exert on bank performance from the indirect through default risk, we show that a trade-off exists for regulators between banks’ performance and stability in Italy. Two key findings provide evidence for the nontriviality of the return-risk nexus: (i) banks’ liquidations are concentrated at the lower-end of the profitability distribution, resulting in (attrition) biased estimates; (ii) the drop-out is informative since it depends on the unobserved measurements of profitability. Despite this evidence, while returns are affected by sanctions and regulatory requirements, default risk is not. However, looking at growth of gross loans, enforcement actions reduce default risk though at a cost of a significant fall in lending, creating a regulatory tradeoff. In fact, through loans’ growth, we account for the key dynamics of intermediaries’ soundness, namely higher profits and less non-performing loans