When quality signals talk: Evidence from the Turin hotel industry

This study analyzes the relationship between quality signals and price setting through the application of hedonic price functions. The model proposals also include variables relating to land use policies and tourism development strategies. A simultaneous equation model endogenously estimates quality signals. This method is appropriate for analyzing emerging urban destinations, as characterized by the presence of an expanding hotel industry. The results are based on a dataset of 145 hotels in Turin, Italy. The empirical findings reveal that reputation-based quality signals help explain tariff levels. Price proposals also include a premium for quality assured hotels, defining some limits of the current hotel classification system. The empirical evidence has significant marketing implications for the hospitality industry’s competitiveness, since the results clarify the impact of quality signals on price level.     

On the determinants of data breaches: A cointegration analysis

Decisions in Economics and Finance - Cyber risks and particularly data breaches constitute one of the new frontiers of risk modeling for insurers across the world. We use the cointegration...     

Machine learning,artificial neural networks and social research

Quality & Quantity - Machine learning (ML), and particularly algorithms based on artificial neural networks (ANNs), constitute a field of research lying at the intersection of different...     

Conducting Mode 2 research in HRM: A phase‐based framework

Recent studies in the field of human resource management (HRM) have highlighted that current research is mostly performed and consumed by academics, and is driven by theoretical and disciplinary concerns rather than practical ones. This debate has invoked the need to produce more Mode 2 research in the HRM field, that is, research driven by practical problems that integrate collaborative efforts by academics and practitioners. Yet, guidelines on how academics and practitioners may implement Mode 2 research remain disjointed and incomplete. Our study provides a phase‐based collaborative‐based framework for the implementation of Mode 2 research in the HRM field, in ways that both academic rigor and practical relevance are achieved. Our framework is informed by a comprehensive review of previous Mode 2 research, within and outside the HRM field. The proposed framework details four macro‐phases: the codevelopment of research questions with practitioners; the design of collaborative spaces and mechanisms; the design and management of double‐loop iterative research processes; and finally the academic and practice legitimization of Mode 2 outcomes. Our framework has the objective to support HRM researchers and practitioners, as well as relevant institutions and gatekeepers in the design, implementation, education, and assessment of Mode 2 research.     

Narrow Replication of ‘A Spatio‐Temporal Model of House Prices in the Usa’ Using R

I narrowly replicate Holly et al.'s (Journal of Econometrics 2010; 158 (1): 160–173) analysis of the housing market in the USA, using the open source R software instead of the original ad hoc GAUSS routines. Their main findings are confirmed and most results are matched exactly. Attention is given to providing a self‐contained and fully reproducible analysis, exclusively using user‐level features available in the public domain. Copyright © 2014 John Wiley & Sons, Ltd.     

The effect of organizational culture on deviant behaviors in the workplace

Abstract

This study investigated the impact of organizational culture (OC) on deviant behaviors in the workplace (workplace deviant behaviors: WDB). We tested the hypothesis that different types of OC (according to the Competing Values Framework model) had an impact on WDB, in addition to the effect of Big Five personality traits. Survey research was undertaken with 954 employees of 30 enterprises in the public and private field, using a hierarchical model approach (HLM) to test the effects of four types of OC (Clan; Adhocracy; Market, Hierarchy) on WDB, over and above the effect of Five Personality traits. The HLM results partially supported our hypotheses, showing that the OC had a significant effect on WDB, with the adhocracy and clan cultures characterized by lower levels of WDB. Managerial implications about the importance of managing the OC are discussed.     

An alternative procedure for imputing missing data based on principal components analysis

This work entailed tackling the significant problem of missing data which was solved by identifying a new substitution procedure, following an empirical approach based on the analysis of the information contained in the entire set of data collected. This procedures offers a number of advantages compared to other techniques commonly mentioned in the statistical–methodological literature.     

A Backward Induction Experiment

This paper reports experiments with one-stage and two-stage alternating-offers bargaining games. Payoff-interdependent preferences have been suggested as an explanation for experimental results that are commonly inconsistent with players' maximizing their monetary payoffs and performing backward induction calculations. We examine whether, given payoff-interdependent preferences, players respect backward induction. To do this, we break backward induction into its components, subgame consistency and truncation consistency. We examine each by comparing the outcomes of two-stage bargaining games with one-stage games with varying rejection payoffs. We find and characterize systematic violations of both subgame and truncation consistency. Journal of Economic Literature Classification Numbers: C70, C78.     

Fitting dynamic factor models to non-stationary time series

Factor modelling of a large time series panel has widely proven useful to reduce its cross-sectional dimensionality. This is done by explaining common co-movements in the panel through the existence of a small number of common components, up to some idiosyncratic behaviour of each individual series. To capture serial correlation in the common components, a dynamic structure is used as in traditional (uni- or multivariate) time series analysis of second order structure, i.e. allowing for infinite-length filtering of the factors via dynamic loadings. In this paper, motivated from economic data observed over long time periods which show smooth transitions over time in their covariance structure, we allow the dynamic structure of the factor model to be non-stationary over time by proposing a deterministic time variation of its loadings. In this respect we generalize the existing recent work on static factor models with time-varying loadings as well as the classical, i.e. stationary, dynamic approximate factor model. Motivated from the stationary case, we estimate the common components of our dynamic factor model by the eigenvectors of a consistent estimator of the now time-varying spectral density matrix of the underlying data-generating process. This can be seen as a time-varying principal components approach in the frequency domain. We derive consistency of this estimator in a “double-asymptotic” framework of both cross-section and time dimension tending to infinity. The performance of the estimators is illustrated by a simulation study and an application to a macroeconomic data set.