Managing customer knowledge through the use of big data analytics in tourism research

The use of big data is growing in relevance and importance in tourism management research. Companies operating in this industry are exploiting big data analytics and developing systems to manage customer knowledge and provide the best service in the right place at the right time. This paper aims to provide a systematic literature review to present issues associated with the use of big data in tourism and identify future research directions on the topic. To achieve this aim, this paper develops a citation network analysis methodology to drive the content analysis and explore the content of 109 selected papers. The findings of this review highlight that although there is an increasing number of contributions on the topic, there are yet some issues that require to be further developed. In particular, the paper identifies research gaps and consequent research questions that represent an agenda for both researchers and practitioners.     

Macroeconomic determinants of stock volatility and volatility premiums

How does stock market volatility relate to the business cycle? We develop, and estimate, a no-arbitrage model, and find that (i) the level and fluctuations of stock volatility are largely explained by business cycle factors and (ii) some unobserved factor contributes to nearly 20% to the overall variation in volatility, although not to its ups and downs. Instead, this “volatility of volatility” relates to the business cycle. Finally, volatility risk-premiums are strongly countercyclical, even more than stock volatility, and partially explain the large swings of the VIX index during the 2007–2009 subprime crisis, which our model captures in out-of-sample experiments.     

Trade Elasticity,Gravity and Trade Liberalisation: Evidence from the Food Industry

We present an econometric investigation of the trade effect produced by the elimination of tariffs in 18 food sectors for a large sample of developing and developed countries. The standard CES monopolistic competition trade model and the gravity equation were used to estimate trade substitution elasticities, exploring their sensitivity to different estimation methods. Using these elasticities, we simulate the trade effect of the elimination of tariffs, dealing with the problem of uncertainty in the estimated values. Results point to a significant variation in the elasticities estimated by different econometric methods, suggesting that the Poisson pseudo‐maximum‐likelihood estimator significantly inflates their magnitude. Simulation results indicate that trade liberalisation will strongly increase food exports especially from high income and emerging countries, leading to a general loss of market share by developing countries. The simulated trade flows obtained from the econometric approach are quite close to current evidence based on computable general equilibrium models.     

The Signal of Applying for a Job Under a Vacancy Referral Scheme

We investigate the signalling effect related to participation in active labor‐market programs. To this end, we conduct an experiment in which human resources professionals make hiring decisions concerning fictitious job candidates who apply either under a job‐vacancy referral system or directly. We provide first causal evidence for a substantial adverse effect of referral on the probability of being hired. In addition, we find that employers perceive referred candidates as being less motivated than other candidates.     

The effect of data transformation on common cycle,cointegration, and unit root tests: Monte Carlo results and a simple test

Cointegration, common cycle, and related tests statistics are often constructed using logged data, even without clear reason why logs should be used rather than levels. Unfortunately, it is also the case that standard data transformation tests, such as those based on Box–Cox transformations, cannot be shown to be consistent unless assumptions concerning whether variables I(0)$I(0)$ or I(1)$I(1)$ are made. In this paper, we propose a simple randomized procedure for choosing between levels and log-levels specifications in the (possible) presence of deterministic and/or stochastic trends, and discuss the impact of incorrect data transformation on common cycle, cointegration and unit root tests.     

R&D, patents and stock return volatility

Recent finance literature highlights the role of technological change in increasing firm specific (idiosyncratic) and aggregate stock return volatility, yet innovation data is not used in these analyses, leaving the direct relationship between innovation and stock return volatility untested. The paper investigates the relationship between volatility and innovation using firm level patent data. The analysis builds on the empirical work by Mazzucato (Rev Econ Dyn 5:318–345, 2002; J Evol Econ 13(5):491–512, 2003) where it is found that stock return volatility is highest during periods in the industry life-cycle when innovation is the most ‘radical’. In this paper we ask whether firms which invest more in innovation (more R&D and more patents) and/or which have more important innovations (patents with more citations) experience more volatility in their returns. Given that returns should in theory be higher, on average, for higher risk stocks, we also look at the effect of innovation on the level of returns. To take into account the competition between firms within industries, firm returns and volatility are measured relative to the industry average. We focus the analysis on firms in the pharmaceutical industry between 1974 and 1999. Results suggest that there is a positive and significant relationship between volatility, R&D intensity and the various patent related measures—especially when the innovation measures are filtered to distinguish the very innovative firms from the less innovate ones.     

Credit Counseling and Mortgage Termination by Low-Income Households

Published research on credit counseling and mortgage termination is surprisingly scarce, despite substantial growth in this industry. While the purpose of counseling is to assist low-income borrowers to improve their handling of debt and thereby reduce default, counseling may also improve the borrowers understanding of their financial position and thus induce optimal mortgage termination. Using a competing-risks framework, we study the effects on default and prepayment of a counseling program implemented in several Midwest states. We find weak evidence of that the default hazard was lower for graduates of the counseling program, but that their default behavior was more optimal. The prepayment hazard was higher for counseled borrowers, but their prepayment behavior was not more optimal. Overall, counseling seems to affect the lenders profits, but the net effect should be evaluated both in terms of prepayment and default.