Visual displays: analytical study and applications to graphs and real data

Principal axis methods such as principal component analysis (PCA) and correspondence analysis (CA) are useful for identifying structures in data through interesting planar graphic displays. However, some kinds of data sets can be dealt alternatively with PCA or CA. This paper focuses on methods, such as PCA and CA, and on visual displays. Our aim is to illustrate the implications for a potential user of selecting either method, and its advantages and disadvantages, from an applied point of view. This is a matter covered broadly in textbooks and elsewhere considering theoretical arguments. Our purpose is to contribute to the comparison between these methods, over the same data set, in order to illustrate them for the practitioner. In the first part of this paper we present a novel analytical study of a binary matrix associated with a non-oriented axis-symmetric graph and show that CA outperforms standardized PCA for the reconstitution and visualization of such kind of graphs. In the second part we present a case using real data dealing with the distribution of employees in different economic sectors for the countries of the European Union, analyzed by means of standardized PCA and two-way CA, in order to see the differences between the two methods in practice.     

GEM research: achievements and challenges

This article analyzes the content and evolution of research based on the Global Entrepreneurship Monitor (GEM) project. We conducted a rigorous search of articles published in journals within the Thomson Reuters’ Social Sciences Citation Index^® through an exploratory analysis focused on articles using GEM data. The main findings of this study reveal that the institutional approach is the most commonly used conceptual framework. Also, although there are still few academic publications using GEM data, the number of articles is increasing, as are opportunities for future research.     

Is the leadership style important in R&D departments in Spanish industrial companies? An empirical analysis

This paper aims to thoroughly look into the effects of the differences between the diverse leadership styles on innovative outputs (intensive, efficient and eco-efficient) in Spanish industrial companies. A questionnaire for the factories was sent and was received between the months of September 2013 and June 2014. A total of 142 valid questionnaires were received. Several regression models were used in the empirical analysis. The results show that charismatic leadership positively induces an intensive innovative output, at the same time as it improves firms’ eco-efficient innovative output. Likewise, the individualised consideration that characterises transformational leadership makes this leadership style produce positive results on the whole group of innovative outputs considered. At the other end of the leadership spectrum, the transactional style causes negative effects on the relationship between innovative activity and innovative outputs, and more specifically, on the efficient innovative output.     

ASSESSING THE EFFECT OF PUBLIC SUBSIDIES ON FIRM R&D INVESTMENT: A SURVEY

This survey examines the empirical literature on the relationship between public R&D subsidies and private R&D investment over the past five decades. The survey reveals a considerable heterogeneity of empirical results that cannot be explained fully by methodological issues. We aim to provide further explanations of the possible causes of that heterogeneity. In particular, we emphasise a set of issues that, in our view, are critical to understanding the potential effect of public R&D subsidies on private R&D spending. Special attention is paid to the dynamic aspects and composition of firm R&D, the constraints faced by the firm (such as financial constraints), and the amount and source of public subsidies. None of these issues have been investigated in depth. We formulate a set of research assumptions to guide future empirical research in this field.     

Non-technical skills for managing risk and performance in financial trading

Recent large-scale failures in financial institutions have been found to be caused, in-part, by human factors-related issues in financial trading. In other environments where risk management and performance are intertwined, a human factors approach is often adopted to understand how the ‘non-technical skills (NTS)’ (leadership (LD), decision-making (DM), situation awareness (SA), teamwork) of organisational actors influence outcomes. Yet, to date, there has been minimal application of human factors research in financial trading. This study (i) identifies ‘real-world’ (i.e. non-laboratory) research studies investigating the NTS important for performance in financial trading, (ii) examines and synthesises data on the NTS found to underpin good or poor performance and (iii) considers the quality and coverage of research investigating NTS in financial trading, and identifies potential areas for future research. Nineteen studies were identified through a systematic literature search and then content-analysed for associations between NTS and performance in financial trading. The review found a range of decision-making (e.g. heuristics and biases, intuitive DM, emotional regulation) and LD skills (e.g. setting standards, monitoring behaviour, encouraging speaking-up) to have been identified as important for managing risk and performance in financial trading environments. Furthermore, SA (e.g. information search and assessment strategies, vigilance, identifying ‘noise’ data) and teamwork (e.g. avoiding ‘role’ conflict, communication between traders) were found to be important, yet remain less explored within the literature, and should be the focus of future research. NTS appear essential for effective risk management within the financial sector, yet further field research is required to examine the context-relevant behaviours that underpin safe activity. This will facilitate the development of evidence-based systems for assessing and training NTS competencies.     

Fractional integration and cointegration in merger and acquisitions in the US petroleum industry

This article employs methodologies based on fractional integration and cointegration to analyse the time-series properties of merger and acquisitions (M&A) activity and crude oil prices in the US from 1980 to 2012. Our results indicate that an increase in the crude oil price produces a significant increase in the M&A data between 2 and 3 months after the initial shock.