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The purpose of this paper is to analyze the effects of inflationary shocks on inequality, using data of selected countries of the Middle East and North Africa (MENA).

Inflationary shocks were measured as deviations from core inflation, based on a genetic algorithm. Bayesian quantile regression was used to estimate the impact of inflationary shocks in different levels of inequality.

The results showed that inflationary shocks substantially affect countries with higher levels of inequality, thus suggesting that the detrimental impact of inflation is exacerbated by the high division of classes in a country.

The study contributes to the literature about the relationship between inflation and inequality by proposing that not only the sustained increase in prices but also the inflationary shocks – the deviations from core inflation – contribute to the generation of inequality. Also, to the best of the authors knowledge, the relationship between inflation shocks and inequality in the MENA region has never been analyzed before, thus creating a research gap to provide additional empirical evidence about the sources of inequality. Additionally, the authors contribute with a methodological approach to measure inflationary shocks, based on a semelparous genetic algorithm.

The purpose of this study is to propose a methodological approach for modeling catastrophic consequences caused by black swan events, based on complexity science, and framed on Feyerabend’s anarchistic theory of knowledge. An empirical application is presented to illustrate the proposed approach.

Thom’s nonlinear differential equations of morphogenesis are used to develop a theoretical model of the impact of catastrophes on international business (IB). The model is then estimated using real-world data on the performance of multinational airlines during the SARS-CoV-2 (COVID-19) pandemic.

The catastrophe model exhibits a remarkable capability to simultaneously capture complex linear and nonlinear relationships. Through empirical estimations and simulations, this approach enables the analysis of IB phenomena under normal conditions, as well as during black swan events.

To the best of the author’s knowledge, this study is the first attempt to estimate the impact of black swan events in IB using a catastrophe model grounded in complexity theory. The proposed model successfully integrates the abrupt and profound effects of catastrophes on multinational corporations, offering a critical perspective on the theoretical and practical use of complexity science in IB.

In this study, three models were empirically compared, the DeLone and McLean model, the Seddon model and the Modified Seddon model, by measuring the impact of a business intelligence system (BIS) in companies in Peru. After that, the mediators and dependent constructs were analysed to determine if they were behaving properly (a good level of variance explanation and significant relations with others constructs). The study used a sample of 104 users of the BIS, from companies in several important economic sectors, in a quasi-voluntary context and with six constructs: information quality, system quality, service quality, system dependence (system use), user satisfaction and perceived usefulness (individual impact).

To interpret the results, the authors used structural equations. The idea was to look for the best fit and explanations for the outcomes. The main difference in these models is that the DeLone and McLean model considers system dependence (system use) as a part of information system success, but in the Seddon model, it is a consequence of it.

The Seddon model seems to show the best fit and explanation for the outcomes. After that, a review of the system use construct was realised, because of its limited variance explained and the few significant relations with other constructs, to improve its explanation power in future research.

It is estimated that the sample includes more than 15 per cent of all the companies that use a BISs in Peru, so the size of the sample is adequate, but it is not entirely random and therefore limits the generalizability of outcomes. Besides that, a sample size that is bigger could be better for the sake of making a more detailed analysis, permitting the use of some items with less power, or the use of another statistical procedure for structural equations such as the Asymptotical Distribution Free, permitting a more detailed analysis (Hair et al., 2006).

Business intelligence (BI), one of the most important components of information systems (IS), is playing a very relevant role in business in this time of high competition, high amounts of data and new technology. Currently, companies feel pressured to respond quickly to change and complicated conditions in the market, needing to make the correct tactical, operational and strategic decisions (Chugh and Grandhi, 2013). BI is one of the most important drivers of the decade (Gartner, 2013). Big companies of IS are creating special units specialised in BI, helping companies become more efficient and effective in daily operations.

Purpose – The purpose of this chapter is to retell the narratives of a preservice teacher and a teacher educator as they lived a story of critical literacy and curriculum-making as a curriculum of lives.

Approach – The chapter presents a year-long narrative inquiry centered on the revisioning of curriculum for an undergraduate literacy course for preservice teachers.

Findings – The researcher broadened her understanding of teacher and teacher educators as curriculum makers to include preservice teachers as curriculum makers. As preservice teachers in the literacy course were invited to reflect on their own literacy backgrounds, several crucial narratives emerged that shaped new understandings for the researcher/teacher educator and drew her into her own curriculum-making with moral purpose. One preservice teacher began a journey of narrative authority and curriculum-making as a curriculum of lives in a subsequent field experience, even through the mire of political pressure in schools.

Research implications – The preservice teacher's retelling featured children who discovered newfound understandings of social justice through literary ways of knowing and critical literacy events. She developed new understandings of how to help public school students value and define their literacies and their life events, all of which folded back into the undergraduate literacy course.

Value – Teacher educators can be encouraged to walk in relationship with their preservice teachers, valuing human experiences and lives as curriculum rather than relenting to top-down, politically driven, outside curriculum.

The purpose of this paper is to contribute understanding tendencies of the Italian demand for craft beer. More in details, it seeks at exploring consumers’ awareness about craft beer, their attitudes, habits and behaviors. It also aims at assessing the impact of each and all these features on consumers’ choice. At last, the paper frames different consumers’ profiles.

The analysis is based on an online survey associated with a choice experiment designed for assessing willingness to buy (WTB) and willingness to pay (WTP) for craft vs industrial beer. Factors influencing consumers’ expenditure for craft beer are explored via an OLS estimation of a simple regression model. Afterwards, different consumers’ profiles are depicted via cluster analysis.

The survey shows that, overall, Italian consumers appreciate craft beers and are interested in this market. Socio-demographic characteristics impact this inclination but have no significant influence on the results of the choice experiment. Market knowledge, preferences and consumption habits influence consumer spending. Five different consumer profiles emerge.

The research proposes a combined original methodology for assessing beer consumers’ features and their impacts on beer WTB and WTP. The complementary perspectives adopted provide new insights on craft beer demand. Results are of interest for craft brewery managers and for the design of policies aimed at promoting and expanding the sector. Enlarging the sample and improving its representativeness would allow for more general results.