Az SAP-rendszer fejlesztése egy műanyag-feldolgozó vállalatnál (Development of SAP system in a plastic manufacturing company)

Az SAP-rendszernek a kontrollingszervezetek működésére gyakorolt pozitív hatása nem titok. A visszacsatolás lehetőségének biztosításával alkalom nyílik a szervezetek tevékenységének követésére, ellenőrzésére, felülbírálására. A logisztika mint az egész vállalatot átszövő rendszer, működésének nyomon követése is létfontosságúvá vált, hiszen összetettségéből kifolyólag jellegzetességei, színvonala az egész rendszerre hatást gyakorol. A logisztikai rendszer és folyamatainak figyelemmel kísérésére a logisztikai kontrollingrendszer nyújt megoldást, visszacsatolási pontjain keresztül. A műanyag-feldolgozó vállalat esetében a szervezeti SAP-rendszer logisztikai kontrollingterületének fejlesztésétől várják a szervezeti hatékonyság emelkedését és a jobb színvonal elérését. _____ Positive effect of the SAP system on the operation of controlling organisations has not been a secret. Opportunity of the feedback will be possible to track, control, override operation of the organisations. The logistic controlling system provides a solution through feedback points for monitoring the logistic system and processes. In the case of plastics manufacturing company the increasing of organisational efficiency and achievement of a better standard is anticipated from development of logistic controlling area of the organisational SAP system.

Mining and Energy Boom, Dutch Disease and Informality in Colombia: a DSGE Approach

The paper develops a Dynamic Stochastic General Equilibrium (DSGE) model, which assesses the macroeconomic and labor market effects derived from simulating a positive shock to the stochastic component of the mining-energy sector productivity. Calibrating the model for the Colombian economy, this shock generates a whole increase in formal wages and a raise in tax revenues, expanding total consumption of the household members. These facts increase non-tradable goods prices relative to tradable goods prices, then real exchange rate decreases (appreciation) and occurs a displacement of productive resources from the tradable (manufacturing) sector to the non-tradable sector, followed by an increase in formal GDP and formal job gains. This situation makes the formal sector to absorb workers from the informal sector through the non-tradable formal subsector, which causes informal GDP to go down. As a consequence, in the net consumption falls for informal workers, which leads some members of the household not to offer their labor force in the informal sector but instead they prefer to keep unemployed. Therefore, the final result on the labor market is a decrease in the number of informal workers, of which a part are in the formal sector and the rest are unemployed.

The impact of gender diversity on performance in services and manufacturing organizations

We present three competing predictions of the organizational gender diversity-performance relationship: a positive linear prediction, a negative linear prediction, and an inverted U-shaped curvilinear prediction. The paper also proposes a moderating effect of industry type (services vs. manufacturing). The predictions were tested using archival quantitative data with a longitudinal design. The results show partial support for the positive linear and inverted U-shaped curvilinear predictions as well as for the proposed moderating effect of industry type. The results help reconcile the inconsistent findings of past research. The findings also show that industry context can strengthen or weaken gender diversity effects.

The gender diversity-performance relationship in services and manufacturing organizations

Empirical findings on the link between gender diversity and performance have been inconsistent. This paper presents three competing predictions of the organizational gender diversity-performance relationship: a positive linear prediction derived from the resource-based view of the firm, a negative linear prediction derived from self-categorization and social identity theories, and an inverted U-shaped curvilinear prediction derived from the integration of the resource-based view of the firm with self-categorization and social identity theories. This paper also proposes a moderating effect of industry type (services vs. manufacturing) on the gender diversity-performance relationship. The predictions were tested in publicly listed Australian organizations using archival quantitative data with a longitudinal research design. The results show partial support for the positive linear and inverted U-shaped curvilinear predictions as well as for the proposed moderating effect of industry type. The curvilinear relationship indicates that different proportions of organizational gender diversity have different effects on organizational performance, which may be attributed to different dynamics as suggested by the resource-based view and self-categorization and social identity theories. The results help reconcile the inconsistent findings of past research that focused on the linear gender diversity-performance relationship. The findings also show that industry context can strengthen or weaken the effects of organizational gender diversity on performance.

High-Order Concept Associations Mining and Inferential Language Modeling for Online Review Spam Detection

Despite many incidents about fake online consumer reviews have been reported, very few studies have been conducted to date to examine the trustworthiness of online consumer reviews. One of the reasons is the lack of an effective computational method to separate the untruthful reviews (i.e., spam) from the legitimate ones (i.e., ham) given the fact that prominent spam features are often missing in online reviews. The main contribution of our research work is the development of a novel review spam detection method which is underpinned by an unsupervised inferential language modeling framework. Another contribution of this work is the development of a high-order concept association mining method which provides the essential term association knowledge to bootstrap the performance for untruthful review detection. Our experimental results confirm that the proposed inferential language model equipped with high-order concept association knowledge is effective in untruthful review detection when compared with other baseline methods.

Text mining and probabilistic language modeling for online review spam detecting

In the era of Web 2.0, huge volumes of consumer reviews are posted to the Internet every day. Manual approaches to detecting and analyzing fake reviews (i.e., spam) are not practical due to the problem of information overload. However, the design and development of automated methods of detecting fake reviews is a challenging research problem. The main reason is that fake reviews are specifically composed to mislead readers, so they may appear the same as legitimate reviews (i.e., ham). As a result, discriminatory features that would enable individual reviews to be classified as spam or ham may not be available. Guided by the design science research methodology, the main contribution of this study is the design and instantiation of novel computational models for detecting fake reviews. In particular, a novel text mining model is developed and integrated into a semantic language model for the detection of untruthful reviews. The models are then evaluated based on a real-world dataset collected from amazon.com. The results of our experiments confirm that the proposed models outperform other well-known baseline models in detecting fake reviews. To the best of our knowledge, the work discussed in this article represents the first successful attempt to apply text mining methods and semantic language models to the detection of fake consumer reviews. A managerial implication of our research is that firms can apply our design artifacts to monitor online consumer reviews to develop effective marketing or product design strategies based on genuine consumer feedback posted to the Internet.