Knowledge management for sustainability in operations

Abstract

The purpose of this paper is to examine the effect of Knowledge Management (KM) as a platform to enable sustainability in firms’ operations and to provide recommendations for managers to integrate sustainable operations into their business strategies. The urgency to create and implement sustainable operations is also argued. The conceptual framework is based on the hypothesis that KM contributes to the achievement of more suitable operations. To test this hypothesis, a model of structural equations was developed with data collected from 345 SMEs. Empirical evidence supported the hypothesis, indicating that KM represents an important alternative to implementing sustainability. The results suggest that managers need to implent KM, as they will enable a better understanding and awareness regarding the global dangerous impacts from unsustainable operations mainly focused on sales and cost reduction. For this reason, this research provides evidence that KM offers an alternative impulse on the quest for more sustainable operations.     

Hazardous Times for Monetary Policy: What Do Twenty‐Three Million Bank Loans Say About the Effects of Monetary Policy on Credit Risk‐Taking?

We identify the effects of monetary policy on credit risk‐taking with an exhaustive credit register of loan applications and contracts. We separate the changes in the composition of the supply of credit from the concurrent changes in the volume of supply and quality, and the volume of demand. We employ a two‐stage model that analyzes the granting of loan applications in the first stage and loan outcomes for the applications granted in the second stage, and that controls for both observed and unobserved, time‐varying, firm and bank heterogeneity through time*firm and time*bank fixed effects. We find that a lower overnight interest rate induces lowly capitalized banks to grant more loan applications to ex ante risky firms and to commit larger loan volumes with fewer collateral requirements to these firms, yet with a higher ex post likelihood of default. A lower long‐term interest rate and other relevant macroeconomic variables have no such effects.     

Living with zombie companies: Do we know where the threat lies?

This paper shows the existence of extreme types of zombie firm, i.e. companies with negative equity that continue to do business despite having lost their entire equity. We explain how these firms are measured and how the riskier ones are defined with different determinants. Using a Spanish sample from 2010 to 2014 an index called the EZIndex is developed that includes four dimensions of the extreme zombie problem: extension, contagion, recovery signs and immediacy. The paper contributes to zombie theory on the one hand by developing a method for ranking zombie firms based on risks and changes over time, and on the other hand by using a log-linear model to detect the riskiest corporate profiles out of all these risky firms. It demonstrates significant implications that need to be considered by the competent authorities not only in terms of their impact as a whole but also in regard to the particular profile of extreme zombie firms: they are less regulated, large and located in regions with large business fabrics.     

Constructing 1-rotational NRDFs through an optimization approach: new (46,9,8), (51,10,9) and (55,9,8)-NRBDs

In this paper we formulate the problem of constructing 1-rotational near resolvable difference families as a combinatorial optimization problem where a global optimum corresponds to a desired difference family. Then, we develop an algorithm based on scatter search in conjunction with a tabu search to construct many of these difference families. In particular, we construct three new near resolvable difference families which lead to an equal number of new 1-rotational near resolvable block designs with parameters: (46,9,8), (51,10,9) and (55,9,8). Our results indicate that this conjunction outperforms both scatter search and tabu search.     

Estimation of the Hurst parameter in some fractional processes

We propose to estimate the Hurst parameter involved in fractional processes via a method based on the Karhunen–Loève expansion of a Gaussian process. We specifically investigate the cases of the fractional Brownian motion, the fractional Ornstein–Uhlenbeck family and the fractional Brownian bridge. We numerically compare our results with the ones obtained by the maximum-likelihood method, which show the validity of our proposal.     

Deterministic versus stochastic seasonal fractional integration and?structural breaks

This paper considers a general model which allows for both deterministic and stochastic forms of seasonality, including fractional (stationary and nonstationary) seasonal orders of integration, and also incorporating endogenously determined structural breaks. Monte Carlo analysis shows that, in the case of a single break, the suggested procedure performs well even in small samples, accurately capturing the seasonal properties of the series, and correctly detecting the break date. As an illustration, the model is estimated using four US series (output, consumption, imports and exports). The results suggest that the seasonal patterns of these variables have changed over time: specifically, in the second subsample the systematic component of seasonality becomes insignificant, whilst the degree of persistence increases.     

Asymptotic confidence intervals in ridge regression based on the Edgeworth expansion

Ridge Regression techniques have been found useful to reduce mean square errors of parameter estimates when multicollinearity is present. But the usefulness of the method rest not only upon its ability to produce good parameter estimates, with smaller mean squared error than Ordinary Least Squares, but also on having reasonable inferential procedures. The aim of this paper is to develop asymptotic confidence intervals for the model parameters based on Ridge Regression estimates and the Edgeworth expansion. Some simulation experiments are carried out to compare these confidence intervals with those obtained from the application of Ordinary Least Squares. Also, an example will be provided based on the well known data set of Hald.     

“Shelf-Ready” Print Serials Acquisitions

The focus of this installment of “The Balance Point” is “shelf-ready” print serials acquisitions, including functions associated with traditional consolidation services (ordering, receiving, check-in, labeling, claiming and batch shipments), and the newer capabilities of uploading check-in data automatically into library systems. Featured authors discuss traditional vendor consolidation services, pilot projects and experiments in the pursuit of what they define as genuinely “shelf-ready” periodicals. The authors view obtaining “shelf-ready” print journal issues as an effective and efficient means of managing print serials operations while coping with the demands of managing digital resource acquisitions with limited financial resources.     

A step-by-step algorithm for combining diagnostic tests

Combining data of several tests or markers for the classification of patients according to their health status for assigning better treatments is a major issue in the study of diseases such as cancer. In order to tackle this problem, several approaches have been proposed in the literature. In this paper, a step-by-step algorithm for estimating the parameters of a linear classifier that combines several measures is considered. The optimization criterion is to maximize the area under the receiver operating characteristic curve. The algorithm is applied to different simulated data sets and its performance is evaluated. Finally, the method is illustrated with a prostate cancer staging database.     

Risk: For Whom? Representations of Mining Activity by Different Social Actors in the Molango Manganese District of Hidalgo,Mexico

Previous studies have shown high levels of manganese exposure and neurocognitive damage in the population living in the mining zone in Molango, Mexico. One of the objectives of the Intersectoral Group on Environmental Management for the mining district has been to provide public participation in the risk management plan. To achieve this, it is important to know how the different social actors represent the mining activity. The objectives of this study were to characterize the social representations of the mining activity by different social actors. A qualitative design was used based on in‐depth interviews of residents, public officials, and a mining company representative. The analysis was conducted according to themes for each group of actors. Essentially, distinct social representations of the different mining activities were identified. Residents viewed mining activities as synonymous with contamination and, therefore, as having affected all areas of their environment, health, and daily life. These activities were seen as a collective risk. The public officials and the mining company held that there was no evidence of harm and saw mining activities as a generator of regional development. Harm to health and the environment were seen as a stance taken by the communities in order to obtain economic benefits from the company. These images of the “other” are shaped by social, political, and cultural factors. They make it difficult for the actors to reach cooperative agreements and thereby affect progress on the risk management plan. Decisionmakers need to take these differences into account when promoting social participation.