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.     

The effects of economic news on commodity prices

We assess how commodity prices respond to macroeconomic news and show that commodities have been relatively insensitive to such news over daily frequencies between 1997 and 2009 compared to other financial assets and major exchange rates. Where commodity prices are influenced by news, there is a pro-cyclical bias and these sensitivities have risen as commodities have become increasingly financialized. However, models based on news still do a relatively poor job of forecasting commodity prices at daily frequencies. We also find some asymmetries in how commodity prices respond to news, most notably for gold, which alone among commodities acts as a safe-haven when “bad” economic news emerges.     

Bids as a Vehicle of (Mis)Information: Collusion in English Auctions with Affiliated Values

In an English auction, a bidder’s strategy depends on the prices at which his competitors drop out, because these convey information on the value of the object on sale. A ring of colluding bidders can strategically manipulate the information transmitted through its members’ bids, in order to mislead other bidders into bidding less aggressively and thus allow a designated bidder to bid more aggressively. Collusion increases the probability that the ring wins the auction and reduces the price it pays. The presence of a ring harms other bidders (as well as the seller) and reduces efficiency.     

Understanding employee preferences for investing in employer stock: evidence from France

In this article, we broaden the focus of existing research on employee stock purchase plans by analysing employee preferences for investing in employer stock as a construct distinct from actual investment behaviour. In our analysis of original survey data in a sample of 900 employees in four French companies, we find that employee preferences are influenced by two common cognitive heuristics (representativeness and familiarity), organisational commitment, the perceived quality of corporate communications about these plans and perceived managerial commitment to employee ownership. We did not find, however, that risk aversion, turnover intentions or perceived employee involvement in decision making influenced preferences for investing in employer stock. Our findings have both theoretical and practical implications for understanding and operating these types of employee benefit plans, which are becoming more common across the globe.     

EXTREME VALUE THEORY IN FINANCE: A SURVEY

Extreme value theory is concerned with the study of the asymptotic distribution of extreme events, that is to say events which are rare in frequency and huge in magnitude with respect to the majority of observations. Statistical methods derived from it have been employed increasingly in finance, especially for risk measurement. This paper surveys some of those main applications, namely for testing different distributional assumptions for the data, for Value‐at‐Risk and Expected Shortfall calculations, for asset allocation under safety‐first type constraints, and for the study of contagion and dependence across markets under conditions of stress.     

Innovation and ICT use in the EU: an analysis of regional drivers

Innovation and the use of information and communication technologies (ICT) are key areas for the European Cohesion Policy. Studies available at the regional level explore innovation and ICT use incorporating different variables, while those investigating ICT use mainly focus on that of households. This paper investigates the existence of common factors for innovation and ICT at the regional level, considering not only ICT use by households but also that of firms. Using factorial and spatial regression analysis, we find that although ICT use and innovation are mainly explained by different variables, knowledge-intensive services and the percentage of population aged 15–64 are common to all of them. In addition, ICT use by households and ICT use by firms are basically driven by different factors. Government quality is the main factor explaining ICT use at home, while ICT use by firms is mostly driven by employment in knowledge-intensive services. R&D is the main variable explaining innovation. We also find positive spatial effects, which are particularly notable in ICT use by households. The results stress the importance of promoting specialization strategies based on the knowledge economy that may contribute to creating synergies between innovation and ICT use in different environments.     

The impact of peer ratings on cooperation: The role of information and cost of rating

In this study, we experimentally analyze the effectiveness of payoff‐irrelevant peer‐to‐peer ratings as a cooperation enforcement device in a finitely repeated public goods game setting. We run two treatments that differ in the amount of information on own and others' received rating points provided to the players, whereas, in a third treatment, we analyze peer approval when assigning ratings to others is costly. In particular, we wonder whether, even under anonymity and in the absence of reputational concerns, (a) players rate others' contribution decisions in the expected direction and (b) the peer rating mechanisms under study foster cooperation and welfare. Our findings reveal that, in the two costless rating treatments, peer rating concerns lead to higher contributions and efficiency, compared to our control. Introducing a small fixed cost for assigning rating points results in a very high percentage of subjects deciding not to rate others' behavior, so that cooperation cannot be enforced.     

Political disagreement in the classroom: testing cultural theory through structured observation

The cultural theory pioneered by Dame Mary Douglas has been tested with a range of research methods, but it has not yet been made subject to a ‘structured observation’. This method has been developed in psychology and management studies, and is especially useful for testing cultural theory’s prediction that fatalistic, hierarchical, egalitarian, and individualistic ways of perceiving and justifying tend to emerge in group debates about pressing social and environmental issues. We present the results of a structured observation of this prediction. Groups of high school students (aged 17–19) were asked for their opinions concerning three to five ‘wicked’ (i.e., highly complex) problems, and to discuss how to resolve them. Each utterance was coded according to the rationalities proposed by cultural theory. The results confirm cultural theory’s hypothesis that all four specific ways of defining, perceiving and resolving a wicked problem emerge when a number of people debate such an issue. We also discuss how Douglas’ cultural theory can be further developed and tested. Finally, we use our study to outline how the method of structured observation can contribute to political culture research in general.     

THE ECONOMIC AND BEHAVIORAL CONSEQUENCES OF ONLINE USER REVIEWS

Online user reviews have become an increasingly relevant informational tool during product search and adoption. Recent surveys have shown that consumers trust and rely on online reviews more than they do on website recommendations and experts opinions. As a new way of driving consumer purchasing intentions, online user reviews have therefore come under scrutiny by researchers. The objective of this paper is to offer an overview of the literature regarding the impact of online user reviews on economic indicators (e.g., sales, marketing strategies) and on consumer behavior. Furthermore, following the growing interest of academics and professionals alike on the topic, the present work provides an exploratory analysis of the consequences of online reviews on individual rating behavior – empirical regularities showed that online rating distributions tend to be concentrated on extreme values, possibly because of rating biases. As consumers and firms incorporate the heuristic cues from such distributions into their decision-making processes, biased ratings might lead to suboptimal choices. This overview presents established results (e.g., the impact of volume on product sales) and insights as issues for future research.     

Modeling node heterogeneity in latent space models for multidimensional networks

Multidimensional network data can have different levels of complexity, as nodes may be characterized by heterogeneous individual-specific features, which may vary across the networks. This article introduces a class of models for multidimensional network data, where different levels of heterogeneity within and between networks can be considered. The proposed framework is developed in the family of latent space models, and it aims to distinguish symmetric relations between the nodes and node-specific features. Model parameters are estimated via a Markov Chain Monte Carlo algorithm. Simulated data and an application to a real example, on fruits import/export data, are used to illustrate and comment on the performance of the proposed models.