Exploring the role of market learning capability in competitive strategy

It has been argued that a firm's capacity to learn from its market is a source of both innovation and competitive advantage. However, past research has failed to conceptualize market-focused learning activity as a capability having the potential to contribute to competitive advantage. Prior innovation research has been biased toward technological innovation. However, there is evidence to suggest that both technological and non-technological innovations contribute to competitive advantage reflecting the need for a broader conceptualization of the innovation construct. Past research has also overlooked the critical role of entrepreneurship in the capability building process. Competitive advantage has been predominantly measured in terms of financial indicators of performance. In general, the literature reflects the need for comprehensive measures of organizational innovation and competitive advantage. This paper examines the role of market-focused learning capability in organizational innovation-based competitive strategy. The paper contributes to the strategic marketing theory by developing and refining measures of entrepreneurship, market-focused learning capability, organizational innovation and sustained competitive advantage, testing relationships among these constructs.

Quantitation of alternatively spliced NMDA receptor NR1 isoform mRNA transcripts in human brain by competitive RT-PCR

The N-methyl-D-aspartate (NMDA)-selective subtype of ionotropic glutamate receptor is of importance in neuronal differentiation and synapse consolidation, activity-dependent forms of synaptic plasticity, and excitatory amino acid-mediated neuronal toxicity [Neurosci. Res. Program, Bull. 19 (1981) 1; Lab. Invest. 68 (1993) 372]. NMDA receptors exist in vivo as tetrameric or pentameric complexes comprising proteins from two families of homologous subunits, designated NR1 and NR2(A-D) [Biochem. Biophys. Res. Commun. 185 (1992) 826]. The gene coding for the human NR1 subunit (hNR1) is composed of 21 exons, three of which (4, 20 and 21) can be differentially spliced to generate a total of eight distinct subunit variants. We detail here a competitive RT-PCR (cRT-PCR) protocol to quantify endogenous levels of hNR1 splice variants in autopsied human brain. Quantitation of each hNR1 splice variant is performed using standard curve methodology in which a known amount of synthetic ribonucleic acid competitor (internal standard) is co-amplified against total RNA. This method can be used for the quantitation of hNR1 mRNA levels in response to acute or chronic disease states, in particular in the glutamatergic-associated neuronal loss observed in Alzheimer's disease [J. Neurochem. 78 (2001) 175]. Furthermore, alterations in hNR1 mRNA expression may be reflected at the translational level, resulting in functional changes in the NMDA receptor. (C) 2003 Elsevier Science B.V. All rights reserved.

Quantitation of human brain GABAA receptor isoforms by competitive RT-PCR

We have developed a competitive RT-PCR assay, adapted from Lewohl et al. [Brain Res. Brain Res. Protoc. 1 (1997) 347]. for the quantitation of GABA, receptor beta isoforms in human brain using an internal standard that shares high sequence homology to the targets. The internal standard is identical to the beta(1) sequence except for a 61 bp deletion and the incorporation of a Hind III restriction enzyme site. Unlike traditional competitive RT-PCR, which requires a range of internal standard concentrations to be titrated against a constant amount of unknown, this method relies on a standard curve for quantitation of each sample and thus permits increased sample throughput. This method is suitable for the quantitation of beta(1), beta(2) and beta(3) isoforms of the GABA(A) receptor in human alcoholic and control brain. (C) 2003 Elsevier Science B.V. All rights reserved.

The case for emotional intelligence in organizational research

Since its popularization by Goleman (1995), the concept of emotional intelligence has been the subject of ongoing controversy, so it is understandable that the model we proposed, which includes emotional intelligence as a moderator variable, would attract its share of criticism.

Is emotional intelligence training for leaders justified?

In this paper, we present the results of a qualitative study of subordinate perceptions of leaders. The study represents a preliminary test of a model based on Affective Events Theory, which posits that leaders who are seen to be effective shape the affective events that determine employees' attitudes and behaviours in the workplace. Within this framework, we argue that effective leaders ameliorate employees' hassles by providing frequent, small emotional uplifts. The resulting positive affective states are then proposed to lead to more positive employee attitudes and behaviours, and more positive regard for the leader. Importantly, leaders who demonstrate these ameliorating behaviours are likely to require high levels of emotional intelligence, defined in terms of the ability to recognise, understand, and manage emotions in self and others. To investigate this model, we conducted interviews and focus groups with 10 leaders and 24 employees. Results confirmed that these processes do indeed exist in the workplace. In particular, leaders who were seen by employees to provide continuous small emotional uplifts were consistently held to be the most effective. Study participants were especially affected by negative events (or hassles). Leaders who failed to deal with hassles or, worse still, were the source of hassles, were consistently seen to be less effective. We conclude with a discussion of implications for practicing managers, and suggest that our exploratory findings provide justification for emotional intelligence training as a means to improve leader perceptions and effectiveness. [Abstract from author]

An ecosystem of products and systems for ambient intelligence – the AAL4ALL users perspective

Developed societies are currently facing severe demographic changes: the world is getting older at an unprecedented rate. In 2000, about 420 million people, or approximately 7 percent of the world population, were aged 65 or older. By 2050, that number will be nearly 1.5 billion people, about 16 percent of the world population. This demographic trend will be also followed by an increase of people with physical limitations. New challenges will be raised to the traditional health care systems, not only in Portugal, but also in all other European states. There is an urgent need to find solutions that allow extending the time people can live in their preferred environment by increasing their autonomy, self-confidence and mobility. AAL4ALL presents an idea for an answer through the development of an ecosystem of products and services for Ambient Assisted Living (AAL) associated to a business model and validated through large scale trial. This paper presents the results of the first survey developed within the AAL4ALL project: the users’ survey targeted at the Portuguese seniors and pre-seniors. This paper is, thus, about the lives of the Portuguese population aged 50 and over.

Comparative analysis between statistical and artificial intelligence models in business failure prediction

A growing number of predicting corporate failure models has emerged since 60s. Economic and social consequences of business failure can be dramatic, thus it is not surprise that the issue has been of growing interest in academic research as well as in business context. The main purpose of this study is to compare the predictive ability of five developed models based on three statistical techniques (Discriminant Analysis, Logit and Probit) and two models based on Artificial Intelligence (Neural Networks and Rough Sets). The five models were employed to a dataset of 420 non-bankrupt firms and 125 bankrupt firms belonging to the textile and clothing industry, over the period 2003–09. Results show that all the models performed well, with an overall correct classification level higher than 90%, and a type II error always less than 2%. The type I error increases as we move away from the year prior to failure. Our models contribute to the discussion of corporate financial distress causes. Moreover it can be used to assist decisions of creditors, investors and auditors. Additionally, this research can be of great contribution to devisers of national economic policies that aim to reduce industrial unemployment.

Comparative Analysis between Statistical and Artificial Intelligence Models in Business Failure Prediction

A growing number of predicting corporate failure models has emerged since 60s. Economic and social consequences of business failure can be dramatic, thus it is not surprise that the issue has been of growing interest in academic research as well as in business context. The main purpose of this study is to compare the predictive ability of five developed models based on three statistical techniques (Discriminant Analysis, Logit and Probit) and two models based on Artificial Intelligence (Neural Networks and Rough Sets). The five models were employed to a dataset of 420 non-bankrupt firms and 125 bankrupt firms belonging to the textile and clothing industry, over the period 2003–09. Results show that all the models performed well, with an overall correct classification level higher than 90%, and a type II error always less than 2%. The type I error increases as we move away from the year prior to failure. Our models contribute to the discussion of corporate financial distress causes. Moreover it can be used to assist decisions of creditors, investors and auditors. Additionally, this research can be of great contribution to devisers of national economic policies that aim to reduce industrial unemployment.