Pedro Simões Coelho

Towards business intelligence systems success: Effects of maturity and culture on analytical decision making

The information systems (IS) literature has long emphasized the positive impact of information provided by business intelligence systems (BIS) on decision-making, particularly when organizations operate in highly competitive environments. Evaluating the effectiveness of BIS is vital to our understanding of the value and efficacy of management actions and investments. Yet, while IS success has been well-researched, our understanding of how BIS dimensions are interrelated and how they affect BIS use is limited. In response, we conduct a quantitative survey-based study to examine the relationships between maturity, information quality, analytical decision-making culture, and the use of information for decision-making as significant elements of the success of BIS. Statistical analysis of data collected from 181 medium and large organizations is combined with the use of descriptive statistics and structural equation modeling. Empirical results link BIS maturity to two segments of information quality, namely content and access quality. We therefore propose a model that contributes to understanding of the interrelationships between BIS success dimensions. Specifically, we find that BIS maturity has a stronger impact on information access quality. In addition, only information content quality is relevant for the use of information while the impact of the information access quality is non-significant. We find that an analytical decision-making culture necessarily improves the use of information but it may suppress the direct impact of the quality of the information content.

Prevalence of rheumatic and musculoskeletal diseases and their impact on health-related quality of life, physical function and mental health in Portugal: results from EpiReumaPt– a national health survey

Objectives To estimate the national prevalence of rheumatic and musculoskeletal diseases (RMDs) in the adult Portuguese population and to determine their impact on health-related quality of life (HRQoL), physical function, anxiety and depression. Methods EpiReumaPt is a national health survey with a three-stage approach. First, 10 661 adult participants were randomly selected. Trained interviewers undertook structured face-to-face questionnaires that included screening for RMDs and assessments of health-related quality of life, physical function, anxiety and depression. Second, positive screenings for ≥1 RMD plus 20% negative screenings were invited to be evaluated by a rheumatologist. Finally, three rheumatologists revised all the information and confirmed the diagnoses according to validated criteria. Estimates were computed as weighted proportions, taking the sampling design into account. Results The disease-specific prevalence rates (and 95% CIs) of RMDs in the adult Portuguese population were: low back pain, 26.4% (23.3% to 29.5%); periarticular disease, 15.8% (13.5% to 18.0%); knee osteoarthritis (OA), 12.4% (11.0% to 13.8%); osteoporosis, 10.2% (9.0% to 11.3%); hand OA, 8.7% (7.5% to 9.9%); hip OA, 2.9% (2.3% to 3.6%); fibromyalgia, 1.7% (1.1% to 2.1%); spondyloarthritis, 1.6% (1.2% to 2.1%); gout, 1.3% (1.0% to 1.6%); rheumatoid arthritis, 0.7% (0.5% to 0.9%); systemic lupus erythaematosus, 0.1% (0.1% to 0.2%) and polymyalgia rheumatica, 0.1% (0.0% to 0.2%). After multivariable adjustment, participants with RMDs had significantly lower EQ5D scores (β=−0.09; p<0.001) and higher HAQ scores (β=0.13; p<0.001) than participants without RMDs. RMDs were also significantly associated with the presence of anxiety symptoms (OR=3.5; p=0.006). Conclusions RMDs are highly prevalent in Portugal and are associated not only with significant physical function and mental health impairment but also with poor HRQoL, leading to more health resource consumption. The EpiReumaPt study emphasises the burden of RMDs in Portugal and the need to increase RMD awareness, being a strong argument to encourage policymakers to increase the amount of resources allocated to the treatment of rheumatic patients.

How information-sharing values influence the use of information systems

We examine the effects of information-sharing values on BIS success dimensions relationships.Information use depends on information quality, but not on system quality.An increase in information-sharing values is reflected in increased information quality.Information-sharing values are not directly linked to information use.Information-sharing values negatively affect the information quality-information use link. Although the constituents of information systems (IS) success and their relationships have been well documented in the business value of information technology (IT) and strategic IS literature, our understanding of how information-sharing values affect the relationships among IS success dimensions is limited. In response, we conduct a quantitative study of 146 medium and large firms that have implemented a business intelligence system in their operations. Our results highlight that in the business intelligence systems context information-sharing values are not directly linked to IT-enabled information use, yet they act as significant moderators of information systems success dimensions relationships.

Likelihood and PLS Estimators for Structural Equation Modeling: An Assessment of Sample Size, Skewness and Model Misspecification Effects

This chapter aims to contribute to a better understanding of partial least squares (PLS) and maximum likelihood (ML) estimators’ properties, through the comparison and evaluation of these estimation methods for structural equation models with latent variables based on customer satisfaction data. Although PLS is a well-established tool to estimate structural equation models, more work is still needed in order to better understand its properties and relative merits when compared to likelihood methods. Despite the controversy over these two estimation techniques, their complexity makes any analytical comparison very difficult to be made. Therefore, it constitutes a fertile ground for conducting simulation studies. This chapter continues the research of Vilares et al. [Comparison of likelihood and PLS estimators for structural equation modelling: a simulation with customer satisfaction data. In: Vinzi, W.E., Chin, W.W., Henseler, J., Wang, H. (eds.) Handbook of Partial Least Squares. Concepts, Methods and Applications, pp. 289–307. Springer Handbooks of Computational Statistics, Springer (2010)], which has compared PLS and ML estimators using Monte Carlo simulation within three different frameworks (symmetric data, skewed data and formative blocks). It also continues to generate the data according to the ECSI (European Customer Satisfaction Index) model with the assumption that the coefficients of the structural and measurement models are known. This new chapter introduces the effect of sample size and includes two different simulations. The first one is conducted in a context of both symmetric data and skewed response data. This simulation is conducted for the sample sizes n = 50, 100, 150, 250, 500, 1,000 and 2,000 and uses reflective blocks. A second simulation includes the presence of model misspecifications (omissions of an existent path) for a sample size of 250 observations and symmetric data. In all simulations the ability of each method to adequately estimate the inner model coefficients and indicator loadings is evaluated. The estimators are analysed in terms of bias and dispersion (standard deviation). Results have shown that overall PLS estimates are generally better than covariance-based estimates. This is particularly true when the data is asymmetric, when estimating the model for smaller sample sizes and for the inner model structure.

Assessing different uncertainty measures of EBLUP: a resampling-based approach

The empirical best linear unbiased prediction approach is a popular method for the estimation of small area parameters. However, the estimation of reliable mean squared prediction error (MSPE) of the estimated best linear unbiased predictors (EBLUP) is a complicated process. In this paper we study the use of resampling methods for MSPE estimation of the EBLUP. A cross-sectional and time-series stationary small area model is used to provide estimates in small areas. Under this model, a parametric bootstrap procedure and a weighted jackknife method are introduced. A Monte Carlo simulation study is conducted in order to compare the performance of different resampling-based measures of uncertainty of the EBLUP with the analytical approximation. Our empirical results show that the proposed resampling-based approaches performed better than the analytical approximation in several situations, although in some cases they tend to underestimate the true MSPE of the EBLUP in a higher number of small areas.

The Impact of Quality Information Provided by Business Intelligence Systems on the Use of Information in Business Processes

The main purpose of introducing business intelligence systems in a firm is to increase the quality of information available to knowledge workers at various organizational levels. However, quality information is of little value to firms if it has not been used in firm’s business processes. Literature suggests the use of information mainly helps organizations in two ways, namely in managing their business processes and in making decisions. The quantitative analysis carried out on data from Slovenian medium and large organizations further shows that information quality differently impacts the two uses of information with impact on business process management being stronger.

Dietary Patterns Characterized by High Meat Consumption Are Associated with Other Unhealthy Life Styles and Depression Symptoms

Objective We aimed to identify dietary patterns (DPs) of Portuguese adults, to assess their socioeconomic, demographic, lifestyle determinants, and to identify their impact on health. Design EpiDoC 2 study included 10,153 Portuguese adults from the EpiDoC Cohort, a population-based study. In this study, trained research assistants using computer-assisted telephone interview collected socioeconomic, demographic, dietary, lifestyles, and health information from March 2013 to July 2015. Cluster analysis was performed, based on questions regarding the number of meals, weekly frequency of soup consumption, vegetables, fruit, meat, fish, dairy products, and daily water intake. Factors associated with DP were identified through logistic regression models. Results Two DPs were identified: the “meat dietary pattern” and the “fruit & vegetables dietary pattern.” After multivariable adjustment, women (OR = 0.52; p < 0.001), older adults (OR = 0.97; p < 0.001), and individuals with more years of education (OR = 0.96; p = 0.025) were less likely to adopt the “meat dietary pattern,” while individuals in a situation of job insecurity/unemployment (OR = 1.49; p = 0.013), Azores island residents (OR = 1.40; p = 0.026), current smoking (OR = 1.58; p = 0.001), daily alcohol intake (OR = 1.46; p = 0.023), and physically inactive (OR = 1.86; p < 0.001) were positively and significantly associated with “meat dietary pattern.” Moreover, individuals with depression symptoms (OR = 1.50; p = 0.018) and the ones who did lower number of medical appointments in the previous year (OR = 0.98; p = 0.025) were less likely to report this DP. Conclusion Our results suggest that unhealthy DPs (meat DP) are part of a lifestyle behavior that includes physical inactivity, smoking habits, and alcohol consumption. Moreover, depression symptoms are also associated with unhealthy DPs.

A Small Area Predictor under Area-Level Linear Mixed Models with Restrictions

A calibrated small area predictor based on an area-level linear mixed model with restrictions is proposed. It is showed that such restricted predictor, which guarantees the concordance between the small area estimates and a known estimate at the aggregate level, is the best linear unbiased predictor. The mean squared prediction error of the calibrated predictor is discussed. Further, a restricted predictor under a particular time-series and cross-sectional model is presented. Within a simulation study based on real data collected from a longitudinal survey conducted by a national statistical office, the proposed estimator is compared with other competitive restricted and non-restricted predictors.

Small area estimation of mean price of habitation transaction using time-series and cross-sectional area-level models

In this paper, a new small domain estimator for area-level data is proposed. The proposed estimator is driven by a real problem of estimating the mean price of habitation transaction at a regional level in a European country, using data collected from a longitudinal survey conducted by a national statistical office. At the desired level of inference, it is not possible to provide accurate direct estimates because the sample sizes in these domains are very small. An area-level model with a heterogeneous covariance structure of random effects assists the proposed combined estimator. This model is an extension of a model due to Fay and Herriot [5], but it integrates information across domains and over several periods of time. In addition, a modified method of estimation of variance components for time-series and cross-sectional area-level models is proposed by including the design weights. A Monte Carlo simulation, based on real data, is conducted to investigate the performance of the proposed estimators in comparison with other estimators frequently used in small area estimation problems. In particular, we compare the performance of these estimators with the estimator based on the Rao–Yu model [23]. The simulation study also accesses the performance of the modified variance component estimators in comparison with the traditional ANOVA method. Simulation results show that the estimators proposed perform better than the other estimators in terms of both precision and bias.

Model-Based Estimation of Unemployment Rates in Small Areas of Portugal

The high level of unemployment is a major problem in most European countries nowadays. Hence, the demand for small area labor market statistics has rapidly increased over the past few years. The Portuguese Labour Force Survey is the main source of official statistics at the macro level. However, it was not designed to produce reliable design-based statistics at the micro level due to small sample sizes. The goal of this article is to analyze the performance of model-based small area estimators to estimate the unemployment rate at micro level. Our results showed that the temporal estimator is the most suitable.