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In this paper, we seek to highlight how adherence to a dominant logic is an effortful activity. Using rhetorical analysis, we show that the use of rhetorical history provides a key mechanism by which organizations may convince audiences of adherence to a dominant logic, while also subverting or obscuring past adherence to a (currently) subordinate logic. We illustrate such use of rhetorical history by drawing on the case study of Ontario wine industry, where wineries use rhetorical history to demonstrate adherence to the logic of fine winemaking, while obscuring the industry’s past adherence to the now-subordinate and stigmatized logic of alcohol making. Implications for future research on institutional logics are discussed.

In this paper, we seek to highlight how adherence to a dominant logic is an effortful activity. Using rhetorical analysis, we show that the use of rhetorical history provides a key mechanism by which organizations may convince audiences of adherence to a dominant logic, while also subverting or obscuring past adherence to a (currently) subordinate logic. We illustrate such use of rhetorical history by drawing on the case study of Ontario wine industry, where wineries use rhetorical history to demonstrate adherence to the logic of fine winemaking, while obscuring the industry’s past adherence to the now-subordinate and stigmatized logic of alcohol making. Implications for future research on institutional logics are discussed.

This article focuses on the selenium (Se) content of 29 legumes and grains available in Mauritius. Selenium was determined using an ultraviolet/visible spectrophotometric method. Results showed that there was considerable variation in the Se content of the legumes and grains that varied from 0.009 ± 0.003μg/g in Dhall Mung (Phaseolus areus) to 1.380 ± 0.003μg/g in Gram Noir (Cicer arietinum). The overall mean Se content of the legumes and grains analysed was 0.231 ± 0.00μg/g. In conclusion, the present study tends to show that legumes and grains are important vehicles of Se intake in the Mauritian diet.

The student revolt of 1967 to 1974, which finally expired about 1978, retains its fascination and much of its significance in the twenty‐first century. But the seven or so years which preceded it are often passed over as simply a precursor, the incubation of a subsequent explosion; they deserve a higher status. The concentration of interest on the late 1960s and early 1970s arises from the driving role of students in the cultural revolution whose traumatic impact still echoes with us. As late as 2005 some commentators saw federal legislation introducing Voluntary Student Unionism as the culmination of struggles in the 1970s when Deputy Prime Minister Costello and Health Minister Abbott battled their radical enemies. Interest in these turbulent years at a popular, non‐academic level has produced a succession of nostalgic reminiscences. In the Sydney Morning Herald’s ‘Good Weekend’ for 13 December 2003 Mark Dapin pondered whether the Melbourne Maoists had changed their world views (‘Living by the Little Red book’.) In the Sydney University Gazette of October 1995 Andrew West asserted that the campus radicals of the 1960s and ‘70s had remained true to their basic beliefs (‘Not finished fighting’.) Some years later, in April 2003, the editor of that journal invited me to discuss ‘Where have all the rebels gone?’ My answer treated this as a twofold question: What has happened to the former rebels? Why have the students of today abandoned radicalism?

Critics say cryptocurrencies are hard to predict and lack both economic value and accounting standards, while supporters argue they are revolutionary financial technology and a new asset class. This study aims to help accounting and financial modelers compare cryptocurrencies with other asset classes (such as gold, stocks and bond markets) and develop cryptocurrency forecast models.

Daily data from 12/31/2013 to 08/01/2020 (including the COVID-19 pandemic period) for the top six cryptocurrencies that constitute 80% of the market are used. Cryptocurrency price, return and volatility are forecasted using five traditional econometric techniques: pooled ordinary least squares (OLS) regression, fixed-effect model (FEM), random-effect model (REM), panel vector error correction model (VECM) and generalized autoregressive conditional heteroskedasticity (GARCH). Fama and French's five-factor analysis, a frequently used method to study stock returns, is conducted on cryptocurrency returns in a panel-data setting. Finally, an efficient frontier is produced with and without cryptocurrencies to see how adding cryptocurrencies to a portfolio makes a difference.

The seven findings in this analysis are summarized as follows: (1) VECM produces the best out-of-sample price forecast of cryptocurrency prices; (2) cryptocurrencies are unlike cash for accounting purposes as they are very volatile: the standard deviations of daily returns are several times larger than those of the other financial assets; (3) cryptocurrencies are not a substitute for gold as a safe-haven asset; (4) the five most significant determinants of cryptocurrency daily returns are emerging markets stock index, S&P 500 stock index, return on gold, volatility of daily returns and the volatility index (VIX); (5) their return volatility is persistent and can be forecasted using the GARCH model; (6) in a portfolio setting, cryptocurrencies exhibit negative alpha, high beta, similar to small and growth stocks and (7) a cryptocurrency portfolio offers more portfolio choices for investors and resembles a levered portfolio.

One of the tasks of the financial econometrics profession is building pro forma models that meet accounting standards and satisfy auditors. This paper undertook such activity by deploying traditional financial econometric methods and applying them to an emerging cryptocurrency asset class.

This paper attempts to contribute to the existing academic literature in three ways: Pro forma models for price forecasting: five established traditional econometric techniques (as opposed to novel methods) are deployed to forecast prices; Cryptocurrency as a group: instead of analyzing one currency at a time and running the risk of missing out on cross-sectional effects (as done by most other researchers), the top-six cryptocurrencies constitute 80% of the market, are analyzed together as a group using panel-data methods; Cryptocurrencies as financial assets in a portfolio: To understand the linkages between cryptocurrencies and traditional portfolio characteristics, an efficient frontier is produced with and without cryptocurrencies to see how adding cryptocurrencies to an investment portfolio makes a difference.

The growth of social media and the sharing economy is generating abundant unstructured image and video data. Computer vision techniques can derive rich insights from unstructured data and can inform recommendations for increasing profits and consumer utility – if only the model outputs are interpretable enough to earn the trust of consumers and buy-in from companies. To build a foundation for understanding the importance of model interpretation in image analytics, the first section of this article reviews the existing work along three dimensions: the data type (image data vs. video data), model structure (feature-level vs. pixel-level), and primary application (to increase company profits vs. to maximize consumer utility). The second section discusses how the “black box” of pixel-level models leads to legal and ethical problems, but interpretability can be improved with eXplainable Artificial Intelligence (XAI) methods. We classify and review XAI methods based on transparency, the scope of interpretability (global vs. local), and model specificity (model-specific vs. model-agnostic); in marketing research, transparent, local, and model-agnostic methods are most common. The third section proposes three promising future research directions related to model interpretability: the economic value of augmented reality in 3D product tracking and visualization, field experiments to compare human judgments with the outputs of machine vision systems, and XAI methods to test strategies for mitigating algorithmic bias.

The paper aims to explain why and how, in the USA, a very particular interpretation of economic liberalism, faring though different historical contexts, has generated, since the 1970s, a new kind of capitalism whose language, logic, legitimating paradigm and associated practices have become, thanks to “organic intellectuals” and active networks of power and influence, the “newspeak” and compass of chief executive officers from around the world, despite their always direst societal consequences.

Using history as a support to investigate the domestic and international relations contexts that bore financialized globalization, the paper is strongly located into political sociology. As such, and if we consider that political sociology is the “science of power”, the paper tries to identify precisely the networks of power and influence which transformed a specific interpretation of liberalism and business into a dominant paradigm and specific kind of capitalism, in the USA and the rest of the world. The approach helps to understand which sets of ideas and authors were deemed worth supporting by business and political networks of power and influence and how both sides drew on their reciprocal resources to transform their cosmogonies into dominant paradigms and real politics (corporate and States).

The paper provides a global but precise understanding of the complex processes that allowed some vested interests to impose their vision of economics and business on a domestic, then world, scale. It also questions the relevancy of that vision according to a presentation of the negative societal externalities the associated policies generated and according to the official investigations that have been conducted on the corporate and banking misdemeanors that it contributed to generate.

The paper illustrates a method of investigation that can be used to develop the “global view”, a prerequisite to making decisions in full knowledge of causes and consequences and thus a means to train future “globally responsible leaders”.

By revealing the hidden interests behind financialized globalization and the societal consequences of their power plays, the paper indirectly demonstrates the urgent need for an “alter‐economy” geared to meet the fundamental needs of societies and to preserve their natural environment in the long term.

The paper offers a different perspective on economics and business which is seldom presented in business schools where, owing to the discussed dominant ideology, politics is considered irrelevant to understand business and economics and where the latter are nearly always presented as vectors of good.

Natural collective phenomena, for example, the movement of crowds of pedestrians and the impressive nest formations of social insects, provide us with an existence proof that sophisticated constructions may be built by swarms of relatively simple artificial agents. The constructions often appear to have required impressive control and coordination – yet each agent in the collective does not appear to be provided with an internal world model or blue‐print for the complete construction. These macroscopic structures emerge as the consequence of interaction of agents, carrying out simple rules based upon the local state of the world, which includes the interaction between agents and the growing structure. In an attempt to understand the underpinning principles of structure formation in collectives of minimal mobile agents the paper focuses on an investigation of automata‐like agents in a two‐dimensional lattice. All agents start their evolution at the same site on the lattice. Every agent moves at random until it finds a neighbourhood it likes more than other neighbourhoods. The agents form a stationary structure of their immobile bodies. The paper focuses upon the parameterisation of the rule space and the mapping between parameter space and the resulting global structure formed by the agents.