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The purpose of this paper is to investigate whether some motives for the choice of an accounting career, disproportionately stronger among women than among men, explain disproportionately more women (60 percent) than men (40 percent) in the accounting profession.

The ordered probit model is used to analyze online survey data of approximately 580 responses collected from members of the American Institute of Certified Public Accountants.

This study finds three reasons why more women (than men) enter the accounting profession: locational freedom, social status, and income stability. Women who choose accounting as a career value these three offered by accounting more than do men who choose accounting as a career. These findings represent mainly those of older CPAs (who are older than 50). The finding related to social status is reversed in the case of younger CPAs.

The paper's findings may be limited to some extent because the authors investigate only three motives for the choice of an accounting career. Also, the online survey data may not be generalized to the entire CPA population.

The hypothesis that relates motives for the choice of an accounting career to more women in the accounting profession is carefully derived using Bayes’ theorem. This hypothesis is tested by the ordered probit method.

The purpose of this paper is to determine consumer characteristics, buying behaviour, and the factors that influence the Chinese wine consumer.

The study applies qualitative and quantitative methodology, together with a literature review and a strategic environmental scan of the Chinese wine market and consumer behavior.

Consumer education, wine‐related activities, channels of communication, taste, country of origin, quality, and price rank are found to be important factors influencing the buying and consumption behavior of Chinese consumers.

Results suggest that there is a need for stakeholders to develop and implement informational and educational marketing strategies to educate and inform consumers in ways that reflect their needs and expectations according to demographic characteristics.

The challenge for the stakeholders will be to: penetrate this emerging market to establish presence and capture market share; strive for long‐term growth and profit sustainability; create competitive advantage through core competencies; promote and sell quality products applying the principles of yield management “to charge the right price, to the ideal consumer, at the right time, in the right place”; and build brand loyalty.

The paper offers useful findings for stakeholders in the wine supply chain. Special attention should be given to the alcoholic beverage retailer and hospitality operators for whom wine revenue is the core of aggregate beverage revenue.

The paper contributes to the body of knowledge of consumer behavior in relation to wine consumption in an emerging market. The results benefit players in the wine supply chain; especially retail and hospitality operations.

The purpose of this paper is to identify the wine consumption preferences and behavior of Gen Z wine consumers in the USA and to determine if and how Gen Z differ from other major generational cohorts in the USA. This study applies the concepts of generational cohort theory to the US wine market to examine similarities and differences between age cohorts and their potential impact on future wine sales.

A quantitative survey was conducted with a quota sample of 1,136 US wine consumers located in all 50 states. Data analysis included one-way ANOVA analysis to test the null hypothesis that the generational cohort means are equal. If the test detected at least one mean difference across cohorts, then pairwise comparisons were performed to identify, which groups differed. The Tukey–Kramer method was used for all post hoc tests. Basic descriptive statistics were also calculated.

The results show some parallels in terms of similar consumption levels and a higher preference for red wine across all cohorts. However, on the majority of other common wine consumer research topics, Gen Z shows significant differences. Of specific interest, Gen Z consumers report higher levels of preference for sparkling wine than other cohorts; prefers to drink in social situations; are much more interested in labels and package; make decisions based on varietal and alcohol level and are much more engaged on Instagram and Snapchat social media platforms – all pointed to new marketing tactics needed to reach this new consumer segment.

This is the first empirical wine research study to explore the wine preferences and behaviors of Gen Z in the US market. This is valuable because Gen Z is a very large population of consumers, comprising 32% of the world population (Miller and Wei, 2018) and already represent more than $143bn in buying power (Dill, 2015). They are expected to have a huge impact on consumer products, not only in the USA but also on a global basis. Given that the USA is currently the largest wine market in the world in both volume and value (Wine Institute, 2019; VinExpo, 2018), it is important that research is conducted on this new and powerful generation.

The paper offers a survey of major Italian accounting scholars and their work for the period from 1900 to 1950. Apart from the late works of Rossi and Besta, the main focus is on the contributions by Zappa, who undoubtedly dominated the scene. In this period, as well as later, most Italian accountants and “aziendalisti” adopted the so‐called “income system”. Although its premises originated with Fabio Besta, master of the so‐called “patrimonial or proprietorship system”, the Italian School under Zappa gave this system a new theoretical basis that differed fundamentally from that of Besta. Zappa also developed the dynamic aspect of accounting and business economics that still prevails in Italy. The paper also devotes attention to other Italian scholars, less well‐known abroad. In the area of cost accounting it concentrates on the views of De Minico and his disciple Amodeo, but also mentions other contributors. The final Section deals with Italian contributions to accounting history during this period

The founders of modern economic thought in Italy are Francesco Ferrara (1810–1900), Luigi Cossa (1831–1896) and Angelo Messedaglia (1820–1901).

The present study aims to identify the antecedents and consequences of blockchain adoption in the wine industry from the absorptive capacity perspective through a multiple case study approach.

A multiple case study approach was utilized, wherein data was gathered through semi-structured interviews and secondary sources of information from the five Italian innovative wineries.

The results show that the dynamic capabilities of wineries and the creation of strategic partnerships are common conditions for the adoption of blockchain technology (BCT). In addition, findings reveal that creating value for all supply chain stakeholders and preventing counterfeit products are the most important consequences of BCT adoption.

This study fills a gap in the literature by investigating the drivers of BCT technology adoption under two theoretical lenses, Absorptive capacity and Dynamic capability theories, confirming the usefulness of these two theories in explaining the process of innovation adoption.

The originality of our research study lies in its investigation of the antecedents and consequences of BCT adoption in the wine supply chain, focusing specifically on the role of dynamic capabilities, knowledge acquisition and assimilation, strategic partnerships and open interactions with external actors in driving technological innovation and the successful implementation of BCT in the wine industry. By offering empirical data on the drivers and impacts of BCT adoption, as well as theoretical implications for technological innovation and value creation in the wine sector, our study contributes to filling gaps in the existing literature and provides practical insights for wineries and AgTechs seeking to integrate BCT into their business models.

This is a study of Attilio da Empoli’s reception in English. Describes the search to find his works or references to him. Gives details of the search process. There are only a few references to his work in English. There is nothing about his life in English. The first biography in English, “Attilio da Empoli’s Life” is given. Describes and discusses his reception in the English language, including comments on the historical context in which his writing occurred. Contains observations about his only book in English and the theory it contains. Concludes that he deserves more recognition than he has received. Contains suggestions about the kind of research program that is needed to put him on the record in English.

Gene selection is considered as the fundamental process in the bioinformatics field. The existing methodologies pertain to cancer classification are mostly clinical basis, and its diagnosis capability is limited. Nowadays, the significant problems of cancer diagnosis are solved by the utilization of gene expression data. The researchers have been introducing many possibilities to diagnose cancer appropriately and effectively. This paper aims to develop the cancer data classification using gene expression data.

The proposed classification model involves three main phases: “(1) Feature extraction, (2) Optimal Feature Selection and (3) Classification”. Initially, five benchmark gene expression datasets are collected. From the collected gene expression data, the feature extraction is performed. To diminish the length of the feature vectors, optimal feature selection is performed, for which a new meta-heuristic algorithm termed as quantum-inspired immune clone optimization algorithm (QICO) is used. Once the relevant features are selected, the classification is performed by a deep learning model called recurrent neural network (RNN). Finally, the experimental analysis reveals that the proposed QICO-based feature selection model outperforms the other heuristic-based feature selection and optimized RNN outperforms the other machine learning methods.

The proposed QICO-RNN is acquiring the best outcomes at any learning percentage. On considering the learning percentage 85, the accuracy of the proposed QICO-RNN was 3.2% excellent than RNN, 4.3% excellent than RF, 3.8% excellent than NB and 2.1% excellent than KNN for Dataset 1. For Dataset 2, at learning percentage 35, the accuracy of the proposed QICO-RNN was 13.3% exclusive than RNN, 8.9% exclusive than RF and 14.8% exclusive than NB and KNN. Hence, the developed QICO algorithm is performing well in classifying the cancer data using gene expression data accurately.

This paper introduces a new optimal feature selection model using QICO and QICO-based RNN for effective classification of cancer data using gene expression data. This is the first work that utilizes an optimal feature selection model using QICO and QICO-RNN for effective classification of cancer data using gene expression data.