Quantitative and qualitative examination of propositions concerning supervisor–subordinate convergence in descriptions of leader–member exchange (LMX) quality

Two studies were conducted to quantitatively and qualitatively examine supervisor–subordinate differences in perceptions concerning the quality of their LMX relationship. The results provided strong support for a lack of construct convergence between these two perspectives. Specifically, the findings suggest that leaders tend to focus more on task-oriented subdimensions and tend to give them greater weight when evaluating LMX relationships. However, subordinates are more oriented toward the social aspects of the relationship and perceive them as more important. This differential pattern was found to be generalizable across gender, race, and industry types.     

A Flexible Evaluative Framework for Order Picking Systems

This paper develops a novel framework to evaluate the integral performance of order picking systems with different combinations of storage and order picking policies. The warehousing literature on order picking mostly considers minimizing either elapsed time or distance as the sole objective, whereas warehouse managers in a supply chain have to look beyond single‐dimensional performance and consider trade‐offs among different criteria. Thus managers still need a unified and efficient framework to select a portfolio of appropriate order picking policies from a multi‐criteria and contextual perspective. Our framework—combining data envelopment analysis, ranking and selection, and multiple comparisons—provides an efficient methodology to simultaneously analyze several interrelated problems in order picking systems with multiple performance attributes, such as service levels and operational costs. We demonstrate our approach through comprehensive evaluations of order picking policies in three low‐level, picker‐to‐parts rectangular warehouses facing demand variations.     

ABC Classification: Service Levels and Inventory Costs

ABC inventory classifications are widely used in practice, with demand value and demand volume as the most common ranking criteria. The standard approach in ABC applications is to set the same service level for all stock keeping units (SKUs) in a class. In this paper, we show (for three large real life datasets) that the application of both demand value and demand volume as ABC ranking criteria, with fixed service levels per class, leads to solutions that are far from cost optimal. An alternative criterion proposed by Zhang et al. performs much better, but is still considerably outperformed by a new criterion proposed in this paper. The new criterion is also more general in that it can take criticality of SKUs into account. Managerial insights are obtained into what class should have the highest/lowest service level, a topic that has been disputed in the literature.     

Testing for Causal Effects in a Generalized Regression Model With Endogenous Regressors

A unifying framework to test for causal effects in nonlinear models is proposed. We consider a generalized linear‐index regression model with endogenous regressors and no parametric assumptions on the error disturbances. To test the significance of the effect of an endogenous regressor, we propose a statistic that is a kernel‐weighted version of the rank correlation statistic (tau) of Kendall (1938). The semiparametric model encompasses previous cases considered in the literature (continuous endogenous regressors (Blundell and Powell (2003)) and a single binary endogenous regressor (Vytlacil and Yildiz (2007))), but the testing approach is the first to allow for (i) multiple discrete endogenous regressors, (ii) endogenous regressors that are neither discrete nor continuous (e.g., a censored variable), and (iii) an arbitrary “mix” of endogenous regressors (e.g., one binary regressor and one continuous regressor).     

Temptation and Taxation

We study optimal taxation when consumers have temptation and self‐control problems. Embedding the class of preferences developed by Gul and Pesendorfer into a standard macroeconomic setting, we first prove, in a two‐period model, that the optimal policy is to subsidize savings when consumers are tempted by “excessive” impatience. The savings subsidy improves welfare because it makes succumbing to temptation less attractive. We then study an economy with a long but finite horizon which nests, as a special case, the Phelps–Pollak–Laibson multiple‐selves model (thereby providing guidance on how to evaluate welfare in this model). We prove that when period utility is logarithmic, the optimal savings subsidies increase over time for any finite horizon. Moreover, as the horizon grows large, the optimal policy prescribes a constant subsidy, in contrast to the well known Chamley–Judd result.     

The Determinants of Retail Productivity: A Critical Review of the Evidence

This paper discusses the literature on the established determinants of productivity in the retail sector. It also draws attention to some neglected strands of research which provide useful insights into strategies that could allow productivity enhancements in this area of the economy. To date, very few attempts have been made to integrate different specialisms in order to explain what drives productivity in retail. Here this paper rectifies this omission by putting together studies from economics, geography, knowledge management and employment studies. It is the authors’ view that quantitative studies of retail productivity should focus on total factor productivity in retailing as the result of competition/composition effects, planning regulations, information and communications technology, the multinational operation element and workforce skills. Further, the fact that retail firms possess advantages that are transferable between locations suggests that investment in strategies enhancing the transfer of explicit and tacit knowledge between and within businesses are crucial to achieve productivity gains.     

Relevance of Risk Predictions Derived from a Chronic Species Sensitivity Distribution with Cadmium to Aquatic Populations and Ecosystems

Criteria to protect aquatic life are intended to protect diverse ecosystems, but in practice are usually developed from compilations of single‐species toxicity tests using standard test organisms that were tested in laboratory environments. Species sensitivity distributions (SSDs) developed from these compilations are extrapolated to set aquatic ecosystem criteria. The protectiveness of the approach was critically reviewed with a chronic SSD for cadmium comprising 27 species within 21 genera. Within the data set, one genus had lower cadmium effects concentrations than the SSD fifth percentile‐based criterion, so in theory this genus, the amphipod Hyalella, could be lost or at least allowed some level of harm by this criteria approach. However, population matrix modeling projected only slightly increased extinction risks for a temperate Hyalella population under scenarios similar to the SSD fifth percentile criterion. The criterion value was further compared to cadmium effects concentrations in ecosystem experiments and field studies. Generally, few adverse effects were inferred from ecosystem experiments at concentrations less than the SSD fifth percentile criterion. Exceptions were behavioral impairments in simplified food web studies. No adverse effects were apparent in field studies under conditions that seldom exceeded the criterion. At concentrations greater than the SSD fifth percentile, the magnitudes of adverse effects in the field studies were roughly proportional to the laboratory‐based fraction of species with adverse effects in the SSD. Overall, the modeling and field validation comparisons of the chronic criterion values generally supported the relevance and protectiveness of the SSD fifth percentile approach with cadmium.     

Informed Public Preferences for Electricity Portfolios with CCS and Other Low‐Carbon Technologies

Public perceptions of carbon capture and sequestration (CCS) and other low‐carbon electricity‐generating technologies may affect the feasibility of their widespread deployment. We asked a diverse sample of 60 participants recruited from community groups in Pittsburgh, Pennsylvania to rank 10 technologies (e.g., coal with CCS, natural gas, nuclear, various renewables, and energy efficiency), and seven realistic low‐carbon portfolios composed of these technologies, after receiving comprehensive and carefully balanced materials that explained the costs and benefits of each technology. Rankings were obtained in small group settings as well as individually before and after the group discussions. The ranking exercise asked participants to assume that the U.S. Congress had mandated a reduction in carbon dioxide emissions from power plants to be built in the future. Overall, rankings suggest that participants favored energy efficiency, followed by nuclear power, integrated gasification combined‐cycle coal with CCS and wind. The most preferred portfolio also included these technologies. We find that these informed members of the general public preferred diverse portfolios that contained CCS and nuclear over alternatives once they fully understood the benefits, cost, and limitations of each. The materials and approach developed for this study may also have value in educating members of the general public about the challenges of achieving a low‐carbon energy future.