Dynamic managerial capabilities: Scale development and validation

Studies on dynamic managerial capabilities still have a rather theoretical nature, and there is great difficulty in finding a valid, reliable instrument to measure this construct. The present study contributes to solving this problem: It aims to develop and validate a scale to measure dynamic managerial capabilities. In this investigation, we develop a scale for three factors related to dynamic managerial capabilities: human capital, managerial cognition (already described in the literature), and relationship networks. The prominent finding in this research is the factor that is referred to here as relationship networks.     

Exploring sources and outcomes of trust and commitment to nonprofit organizations: The case of Amnesty International Portugal

The interactions between customers and organizations have been regarded as a core aspect in cocreating and developing new products and practices. The current study explores these interactions in the context of citizens and nonprofit organizations. The study comprises two parts: (a) a qualitative approach using interviews and the analysis of social networks to better understand the organization's social media marketing practices and corresponding outcomes and (b) a quantitative approach to analyze antecedents and outcomes of trust and being committed to participate in a nonprofit organization such as Amnesty International. From the qualitative approach, the findings highlight that Amnesty International uses social media (Facebook) to share information, and citizens tend to consider sharing posts, but they do not express new ideas or interact with the organization. These findings contribute to structure the model analyzed in the second stage of the research. Regarding the qualitative approach, the results show that organizational credibility is the most significant driver of trust followed by communicative ability. Affectively commitment to participation and relationship perception act as outcomes of overall commitment.     

Antecedents and outcomes of digital influencer endorsement: An exploratory study

The advertising and marketing literature have established that celebrity endorsements constitute an effective way to enhance attitudes toward brands and increase purchase intents. However, there are no relevant studies on digital influencers. This study addresses the research gap by examining the effect of digital influencers' attractiveness and the effect of the congruence between a digital influencer and a brand on consumer attitudes and purchase intentions. By applying structural equation modeling to a sample of 307 followers of digital influencers, a model was tested and group analysis was performed for two categories (“entertainment & video games” and “fashion & beauty”). The results show that both brand attitudes and purchase intentions are influenced by the digital influencer's attractiveness (which includes both likeability and familiarity) and by the congruence between the digital influencer and the brand. This study makes several contributions to both theory and practice, which are highlighted in this paper.     

Statistical Decision Problems and Bayesian Nonparametric Methods

This paper considers parametric statistical decision problems conducted within a Bayesian nonparametric context. Our work was motivated by the realisation that typical parametric model selection procedures are essentially incoherent. We argue that one solution to this problem is to use a flexible enough model in the first place, a model that will not be checked no matter what data arrive. Ideally, one would use a nonparametric model to describe all the uncertainty about the density function generating the data. However, parametric models are the preferred choice for many statisticians, despite the incoherence involved in model checking, incoherence that is quite often ignored for pragmatic reasons. In this paper we show how coherent parametric inference can be carried out via decision theory and Bayesian nonparametrics. None of the ingredients discussed here are new, but our main point only becomes evident when one sees all priors—even parametric ones—as measures on sets of densities as opposed to measures on finite-dimensional parameter spaces.     

Economic mobility along the business cycle. The case of Peru

The performance of Latin American countries in reducing poverty and expanding the middle class has been remarkable. By taking a close look at the Peruvian experience, we examine how this aggregate behavior relates to business cycle conditions and whether different population groups share this behavior. We find that social mobility is cyclical; it decreases in recessions but increases with strong economic growth. The reduction in poverty in Peru appears to be the result of a sustained increase in the poverty exit rate together with a prolonged decrease in the poverty entry rate. These results hold among heterogeneous groups and are particularly marked for households regarded as initially disadvantaged.     

Flexible exchange rates as shock absorbers

In this paper we analyze empirically the effect of terms of trade shocks on economic performance under alternative exchange rate regimes. We are particularly interested in investigating whether terms of trade disturbances have a smaller effect on growth in countries with a flexible exchange rate arrangement. We also analyze whether negative and positive terms of trade shocks have asymmetric effects on growth, and whether the magnitude of these asymmetries depends on the exchange rate regime. We find evidence suggesting that terms of trade shocks get amplified in countries that have more rigid exchange rate regimes. We also find evidence of an asymmetric response to terms of trade shocks: the output response is larger for negative than for positive shocks. Finally, we find evidence supporting the view that, after controlling for other factors, countries with more flexible exchange rate regimes grow faster than countries with fixed exchange rates.     

Improving an outpatient clinic utilization using decision analysis-based patient scheduling

This study presents a predictive model to be used in scheduling patients in an urban outpatient clinic. The model is based upon actual patient characteristics from a physical therapy clinic within an urban health and wellness center situated in a public university. A number of reported patients' characteristics such as age, education level, distance from the clinic, historical attendance records, etc. were examined to determine if they significantly impacted the patients' missing scheduled appointments (no-shows.) Decision tree analysis was used to develop a model that assessed the likelihood of a patient's no-show, using key patient characteristics and attendance records. Such a model can be used to assist with scheduling patients in an outpatient clinic, while attempting to increase the clinic's overall utilization. Four tree growing criteria were examined to develop the model with the strongest predictive power. Predictive power of each method was assessed by using the entire dataset as well as using split sampling. The results were then compared with those of a Bayesian networks model and a neural networks model. In addition, the trade-off between the selected decision tree model's predictive power versus simplicity of the associated classification rules was examined. We also assessed the impact of various levels of overbooking on the clinic's utilization when using patients' schedules based on the predictive model.     

Monetary policy rules with PID control features: evidence from the UK,USA and EU

ABSTRACT

This paper considers the extent to which the monetary policy operations of three major central banks can be regarded as an application of Proportional-Integral-Derivative (PID) control rules. The paper outlines the general PID framework and estimates a series of dynamic models to identify how interest rate policy adjustments are affected by the rate of inflation and the level of macroeconomic activity. The paper examines data for the UK, the USA and the Eurozone. The results suggest that the PID rules can provide a useful theoretical and empirical framework for estimating central bank responses to the inflation and macroeconomic activity variables by improving the explanatory power of the Taylor rule model and determining the effect of the parameters.