Interpersonal Sensitivity and Information Sharing During Layoffs : Implications for Job Seekers

Layoffs have become an increasingly common cost reduction strategy implemented by organizations. In addition to affecting those who lose their jobs or remain with the organization after a reduction, layoffs may also affect individuals outside the organization. A systems perspective on layoffs takes into account the various stakeholders who are affected by such an action beyond those traditionally studied. Job applicants are one group of stakeholders for which research on the implications of layoffs is lacking. The purpose of this dissertation was to examine the specific organizational justice factors of interpersonal sensitivity and information sharing in a layoff and their effects on subsequent attitudes and behaviors of future job seekers. After being presented with one of four fictitious newspaper articles that described details about a layoff, participants were asked to respond to a survey containing questions regarding general attitudes toward organizations as well as thoughts specifically regarding the target organization and the way it managed the layoffs. Specific relationships were hypothesized to exist between the justice factors and organizational attractiveness, organizational relation expectations, and procedural fairness. Results indicated that the attitudes and reactions of participants toward organizations varied based on the levels of interpersonal sensitivity and information sharing. Moreover, the justice factors interacted to influence subsequent outcomes. Although they did not moderate the fairness-outcome relationships as hypothesized, the individual difference variables of equity sensitivity and employment goals did have significant main effects as well as some moderating effects. Lastly, organizational relation expectations did partially mediate fairness-outcome relationships as predicted. This study represents an important step in advancing the limited literature on layoffs and job seekers, and illustrates that the effects of layoffs have implications beyond those individuals directly affected. There are several implications for research, including a further illustration of the complexity of the fairness-outcome relationship as a result of mediating and moderating effects. This study also bridges several different areas of organizational research, namely, layoffs, applicant reactions, organizational image, and recruitment, and highlights opportunities to further explore and integrate these diverse lines of inquiry. Additional implications are discussed for future research as well as management practice

Improving MRO order processing by means of advanced technological diagnostics and data mining approaches

Production planning based on uncertain load information may lead to low schedule adherence or low capacity utilization. Thus, maintenance, repair and overhaul (MRO) service providers are striving to improve their business processes to achieve high logistics efficiency. To estimate repair expenditures and material demands as early as possible, different approaches may be pursued. In this paper, the advancement of technological diagnostics to enable condition assessment without prior disassembly and the use of data mining to generate reliable forecasts are discussed. Thereby, the potential for planning MRO order processing is focused using the example of aircraft engines and rail vehicle transformers

Soot nanoparticle sizing in counterflow flames using in-situ particle sampling and differential mobility analysis verified with two-colour time-resolved laser-induced incandescence

The emphasis of this work is on the development of an intrusive particle sampling system to track the evolution of soot nanoparticles in ethylene counterflow diffusion flames. The overarching objective is to determine the mobility size distributions in a spatially resolved manner using the developed probe system coupled with differential mobility analysis, i.e., a scanning mobility particle sizer (SMPS). The probe system involves a tailor-made quartz probe, gas supply, pressure control periphery, and a traverse system enabling a precise positioning along the flame axis. In preliminary experiments, the dilution ratio of the quartz probe as function of boundary conditions as well as particle losses during intrusive particle sampling are studied. To demonstrate the capability of the developed particle sampling system, results from ethylene counterflow diffusion flames with different fuel mass fractions and strain rates are presented and compared with results derived by non-intrusive laser-based diagnostics, i.e., two-colour time-resolved laser induced incandescence (2C-TiRe-LII). Results of these experiments indicate that the particle sampling system is capable of tracking the development of particle size distributions – independent of the distribution function, i.e., mono-, bi- or multimodal shape – in counterflow flames. Likewise, the agreement between soot volume fractions and particle size distributions measured via intrusive particle sampling coupled with differential mobility analysis and non-intrusive laser-based 2C-TiRe-LII is excellent at varying the fuel mass fractions and strain rates of the ethylene counterflow flames

3D Printing of Piezoelectric Barium Titanate-Hydroxyapatite Scaffolds with Interconnected Porosity for Bone Tissue Engineering

The prevalence of large bone defects is still a major problem in surgical clinics. It is, thus, not a surprise that bone-related research, especially in the field of bone tissue engineering, is a major issue in medical research. Researchers worldwide are searching for the missing link in engineering bone graft materials that mimic bones, and foster osteogenesis and bone remodeling. One approach is the combination of additive manufacturing technology with smart and additionally electrically active biomaterials. In this study, we performed a three-dimensional (3D) printing process to fabricate piezoelectric, porous barium titanate (BaTiO3) and hydroxyapatite (HA) composite scaffolds. The printed scaffolds indicate good cytocompatibility and cell attachment as well as bone mimicking piezoelectric properties with a piezoelectric constant of 3 pC/N. This work represents a promising first approach to creating an implant material with improved bone regenerating potential, in combination with an interconnected porous network and a microporosity, known to enhance bone growth and vascularization

Architektur vernetzter Systeme. Seminar

This technical report comprises student papers within assignments for the seminar ``Architektur vernetzter Systeme\u27\u27. It took place at the Institute of Telematics of the University of Karlsruhe in summer 1999. Main topics for discussion contained CORBA extensions, tools and systems for mobile applications and also optimization concepts for large distributed systems

Discovering functional gene expression patterns in the metabolic network of Escherichia coli with wavelets transforms

BACKGROUND: Microarray technology produces gene expression data on a genomic scale for an endless variety of organisms and conditions. However, this vast amount of information needs to be extracted in a reasonable way and funneled into manageable and functionally meaningful patterns. Genes may be reasonably combined using knowledge about their interaction behaviour. On a proteomic level, biochemical research has elucidated an increasingly complete image of the metabolic architecture, especially for less complex organisms like the well studied bacterium Escherichia coli. RESULTS: We sought to discover central components of the metabolic network, regulated by the expression of associated genes under changing conditions. We mapped gene expression data from E. coli under aerobic and anaerobic conditions onto the enzymatic reaction nodes of its metabolic network. An adjacency matrix of the metabolites was created from this graph. A consecutive ones clustering method was used to obtain network clusters in the matrix. The wavelet method was applied on the adjacency matrices of these clusters to collect features for the classifier. With a feature extraction method the most discriminating features were selected. We yielded network sub-graphs from these top ranking features representing formate fermentation, in good agreement with the anaerobic response of hetero-fermentative bacteria. Furthermore, we found a switch in the starting point for NAD biosynthesis, and an adaptation of the l-aspartate metabolism, in accordance with its higher abundance under anaerobic conditions. CONCLUSION: We developed and tested a novel method, based on a combination of rationally chosen machine learning methods, to analyse gene expression data on the basis of interaction data, using a metabolic network of enzymes. As a case study, we applied our method to E. coli under oxygen deprived conditions and extracted physiologically relevant patterns that represent an adaptation of the cells to changing environmental conditions. In general, our concept may be transferred to network analyses on biological interaction data, when data for two comparable states of the associated nodes are made available