Impact of Information Technology Investments on Firm Productivity in Peripherals Countries: The Case of Portugal

IS/IT investments are seen has having an enormous potential impact on the competitive position of the firm, on its performance, and demand an active and motivated participation of several stakeholder groups. The shortfall of evidence concerning the productivity of IT became known as the ‘productivity paradox’. As Robert Solow, the Nobel laureate economist stated “we see computers everywhere except in the productivity statistics”. An important stream of research conducted all over the world has tried to understand these phenomena, called in the literature as «IS business value» field. However, there is a gap in the literature, addressing the Portuguese situation. No empirical work has been done to date in order to understand the impact of Information Technology adoption on the productivity of those firms. Using data from two surveys conducted by the Portuguese National Institute of Statistics (INE), Inquiry to the use of IT by Portuguese companies (IUTIC) and the Inquiry Harmonized to (Portuguese) companies (accounting data), this study relates (using regression analysis) the amounts spent on IT with the financial performance indicator Returns on Equity, as a proxy of firm productivity, of Portuguese companies with more than 250 employees. The aim of this paper is to shed light on the Portuguese situation concerning the impact of IS/IT on the productivity of Portuguese top companies. Empirically, we test the impact of IT expenditure on firm productivity of a sample of Portuguese large companies. Our results, based on firm-level data on Information Technology expenditure and firm productivity as measured by return on equity (1186 observations) for the years of 2003 and 2004, exhibit a negative impact of IT expenditure on firm productivity, in line with “productivity paradox” claimants.

Improving understandability in consumer health information search: Uevora @ 2016 fire chis

This paper presents our work at 2016 FIRE CHIS. Given a CHIS query and a document associated with that query, the task is to classify the sentences in the document as relevant to the query or not; and further classify the relevant sentences to be supporting, neutral or opposing to the claim made in the query. In this paper, we present two different approaches to do the classification. With the first approach, we implement two models to satisfy the task. We first implement an information retrieval model to retrieve the sentences that are relevant to the query; and then we use supervised learning method to train a classification model to classify the relevant sentences into support, oppose or neutral. With the second approach, we only use machine learning techniques to learn a model and classify the sentences into four classes (relevant & support, relevant & neutral, relevant & oppose, irrelevant & neutral). Our submission for CHIS uses the first approach.