From private label to co-branded credit card: an assessment of the impact of a change in value proposition over customer value perception and company value capture

Journal of Financial Services Marketing - Purpose: The aim of this paper is to evaluate the success of a value proposition over time, considering two aspects: customer’s perceived value and...     

Family firm’s management composition: the role played by family members’ age in TMT and supervisor levels

Quality & Quantity - Family firms (FFs) are the backbone of entrepreneurial fabric in many countries. Management of such businesses is complex because of their features: the overlap between...     

Characterization of the Row Geometric Mean Ranking with a Group Consensus Axiom

An axiomatic approach is applied to the problem of extracting a ranking of the alternatives from a pairwise comparison ratio matrix. The ordering induced by row geometric mean method is proved to be uniquely determined by three independent axioms, anonymity (independence of the labelling of alternatives), responsiveness (a kind of monotonicity property) and aggregation invariance, which requires the preservation of group consensus, that is, the pairwise ranking between two alternatives should remain unchanged if unanimous individual preferences are combined by geometric mean.     

Stock market volatility forecasting: Do we need high-frequency data?

The general consensus in the volatility forecasting literature is that high-frequency volatility models outperform low-frequency volatility models. However, such a conclusion is reached when low-frequency volatility models are estimated from daily returns. Instead, we study this question considering daily, low-frequency volatility estimators based on open, high, low, and close daily prices. Our data sample consists of 18 stock market indices. We find that high-frequency volatility models tend to outperform low-frequency volatility models only for short-term forecasts. As the forecast horizon increases (up to one month), the difference in forecast accuracy becomes statistically indistinguishable for most market indices. To evaluate the practical implications of our results, we study a simple asset allocation problem. The results reveal that asset allocation based on high-frequency volatility model forecasts does not outperform asset allocation based on low-frequency volatility model forecasts.     

A multivariate distance nonlinear causality test based on partial distance correlation: a machine learning application to energy futures

This paper proposes a multivariate distance nonlinear causality test (MDNC) using the partial distance correlation in a time series framework. Partial distance correlation as an extension of the Brownian distance correlation calculates the distance correlation between random vectors X and Y controlling for a random vector Z. Our test can detect nonlinear lagged relationships between time series, and when integrated with machine learning methods it can improve the forecasting power. We apply our method as a feature selection procedure and combine it with the support vector machine and random forests algorithms to study the forecast of the main energy financial time series (oil, coal, and natural gas futures). It shows substantial improvement in forecasting the fuel energy time series in comparison to the classical Granger causality method in time series.     

Evolution of indigenous tourism among the Lacandon of Chiapas: an application of Weaver’s model

ABSTRACT

In this paper, Weaver’s six-stage indigenous tourism model is applied to the Lacandon Maya (Hach Winik) of Chiapas, Mexico. Based on a comprehensive review of the anthropological and historical literature on this indigenous group, combined with longitudinal ethnographic and collaborative research performed with tourism entrepreneurs, the Lacandon tourism experience is assessed from the pre-European period until present. By analysing a case study of indigenous tourism in Mexico, a developing country in another geographical region and with a different colonial past, this work supplements Weavers’ perspective. The results show that the fourth and fifth stages of Weaver’s model coincide in this case study, while the sixth stage is still incomplete. Although the Lacandon case has its peculiarities and bearing in mind that several different factors should be considered in the Latin American context, the model proves to be an interesting tool for indigenous tourism analysis in developing countries.     

The Great Recession,Austerity and Inequality: Lessons from Ireland

The advent of the Great Recession and the widespread adoption of fiscal austerity policies have heightened concern about inequality and its effects. We examine how the distribution of income in Ireland—a country which experienced one of the most severe economic contractions—has evolved over the years 2008 to 2013. Standard cross‐sectional analysis of the income distribution shows broad stability in the Gini coefficient and in decile shares, with one main exception: the share of the bottom decile fell sharply, with the largest fall in average incomes being for that group. Longitudinal analysis shows that the falls in the average income for the bottom decile were not due to decreasing income for those remaining in the bottom decile, but to falls in income from those initially located in higher deciles. The extent of redistribution through taxes and transfers increased strongly, as measured by the Reynolds‐Smolensky index, which rose from 0.20 before the onset of the crisis to 0.27 in 2013. Analysis indicates that about three‐quarters of this increased redistribution is due to automatic stabilisers and one‐quarter to discretionary policy changes.     

Capability and need: A framework for understanding why states create sovereign wealth funds

Over the past half‐century, roughly one‐quarter of states in the international system have created sovereign wealth funds (SWFs). As a case of sovereign states investing public wealth mostly in private markets across borders, it is not altogether clear why SWFs have proliferated to this extent. What explains their spread in recent decades? In this paper, I build on a multidisciplinary literature that conceives of SWFs as a type of insurance against external risk and argue that middle economic powers that are highly exposed in global trade and capital markets are the states most likely to establish these institutions. Such states possess both the capability to create an SWF of a size sufficient to insure against risk and the need for the insurance function of a SWF by virtue of their relatively vulnerable position in the global economy. To evaluate my argument, I rely on a data set consisting of all states in the international system from 1950 to 2012 including the 48 SWFs created during that period. I find that middle economic powers with high degrees of trade and capital openness are the states most likely to create SWFs.     

Using the Pack‐and‐a‐Half Rule to Eliminate Backroom Inventories in Retail Operations

In the retail industry, backroom inventories are typically associated with higher labor costs and greater operational complexity. Thus, retailers look for ways to eliminate backroom inventories. A heuristic used for this purpose is the pack‐and‐a‐half rule which suggests that the shelf space allocated for a product should be at least 50% larger than the case pack quantity in which the product is delivered. Despite its popularity among retailers, the pack‐and‐a‐half rule has been ignored in the academic literature. We introduce the pack‐and‐a‐half rule, assess its impact on a retailer's profits, identify cost, demand, and product characteristics driving this impact, and propose a modification. Based on an analysis of data obtained from a retailer on 1,986 SKUs in 20 categories, we find that the pack‐and‐a‐half rule decreases a retailer's profits, on average, by 10% when applied uniformly across all SKUs. Further, this decrease is significantly affected by product depth, product width, demand elasticity, case pack quantity, and inventory carrying cost. Finally, we develop a set of modifications based on these variables where the pack‐and‐a‐half rule is applied selectively and in a stepwise fashion. These modifications limit the decrease in a retailer's profits to a range between 6% and 7%.