Polynomial approximations in cross-sectional models

The time-series distributed lag techniques of econometrics can be usefully applied to cross-sectional, spatial and cross-section time-series situations. The application is perfectly natural in cross-section, time-series models when regression coefficients evolve systematically as the cross-section grouping variable changes. The evolution of such coefficients lends itself to polynomial approximation or more general smoothing restrictions. These ideas are not new, Gersovitz and McKinnon (1978) and Trivedi and Lee (1981) providing two of the earliest applications of cross-equation smoothing techniques. However, their applications were in the context of coefficient variation due to seasonal changes and this may account for the non-diffusion of these techniques. The approach here is illustrated in the context of age-specific household formation equations based on census data, using Almon polynomials when the regression coefficients vary systematically by age group. A second application is provided, using spatial data, explaining the incidence of crime, by region; using polynomial and geometric smoothing to model distance declining regional effects.     

Employee well-being attribution and job change intentions: The moderating effect of task idiosyncratic deals

We developed and tested a research model in which employee well-being human resource (HR) attribution differentially influences the intention to change jobs across organizations (i.e., external job change intention) versus that within the same organization (i.e., internal job change intention). Furthermore, we posited that task idiosyncratic deals (I-deals) moderated the relationships between employee well-being HR attribution and external and internal job change intentions. Results indicated that employee well-being HR attribution was negatively related to external job change intention, but positively related to internal job change intention. Further, task I-deals significantly moderated the relationships between employee well-being HR attribution and external and internal job change intention. Specifically, employee well-being HR attribution played a less important role in reducing external job change intention when task I-deals were high rather than low. On the other hand, high task I-deals significantly strengthened the positive relationship between employee well-being HR attribution and internal job change intention. Our study extends the careers literature by differentiating the impact of employee well-being HR attribution on job change intentions within an organization compared with that across organizations and the important role of supervisors in enhancing or mitigating these effects.     

Nowcasting South African gross domestic product using a suite of statistical models

Given lags in the release of data, a central bank must ‘nowcast’ current gross domestic product (GDP) using available quarterly or higher frequency data to understand the current state of economic activity. This paper uses various statistical modelling techniques to draw on a large number of series to nowcast South African GDP. We also show that GDP volatility has increased markedly over the last 5 years, making GDP forecasting more difficult. We show that all the models developed, as well as the Reserve Bank's official forecasts, have tended to overestimate GDP growth over this period. However, several of the statistical nowcasting models we present in this paper provide competitive nowcasts relative to the official Reserve Bank and market analysts' nowcasts.     

Sorting it out: pile sorting as a mixed methodology for exploring barriers to cancer screening

We discuss a mixed methodology for analyzing pile sorting data. We created a list of 14 barriers to colon cancer screening and recruited 18, 13, and 14 participants from three American Indian (AI) communities to perform pile sorting. Quantitative data were analyzed by cluster analysis and multidimensional scaling. Differences across sites were compared using permutation bootstrapping. Qualitative data collected during sorting were compiled by AI staff members who determined names for the clusters found in quantitative analysis. Results showed five clusters of barriers in each site although barriers in the clusters varied slightly across sites. Simulation demonstrated type I error rates around the nominal 0.05 level whereas power depended on the numbers of clusters, and between and within cluster variability.     

Schooling in a Corporate Society

This article defines a measure of the quality of life in the regional capitals of Poland and compares the results. This taxonomic measure is based on a set of diagnostic traits, including housing conditions, state of the natural environment, labor market, personal safety, and range of cultural services available. For this purpose, a set of variables from 2011 was observed for each regional capital. This research is important in defining the difference between our target cities from the standpoint of social development. It also allows us to classify the cities into groups based on the quality of life of their inhabitants.     

Best–Worst Scaling Approach to Predict Customer Choice for 3PL Services

This study describes a simple, theoretically based methodology to analyze the nature of customer demand for third‐party logistics provider service components. The method overcomes limitations in prior studies and enables us to examine the relative importance of product and service attributes as they pertain to the choice of third‐party logistics providers. Two distinct types of customers populate our data: those professing operational attributes and those seeking relational attributes. The theoretical and practical implications are that improved supply chain models can be developed when separate demand structures are taken into account.     

Loyalty programs and their impact on repeat-purchase loyalty patterns

Loyalty programs are currently increasing in popularity around the world. This paper discusses the potential of loyalty programs to alter the normal market patterns of repeat-purchase behaviour which characterise competitive repeat-purchase markets. In line with this thinking, a large scale loyalty program is evaluated in terms of its ability to change normal repeat-purchase patterns by generating ‘excess loyalty’ for brands in the program. Panel data were used to develop Dirichlet estimates of expected repeat-purchase loyalty statistics by brand. These estimates were compared with the observed market repeat-purchase behaviour. Overall a trend towards a weak level of excess loyalty was observed, although the expected deviation was not consistently observed for all the loyalty program brands. Only two of the six loyalty program participant brands showed substantial excess loyalty deviations. However, these deviations in repeat-purchase loyalty were observed for non-members of the loyalty program as well as members and appear likely to be at least partially the result of other loyalty efforts particular to these brands.