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Drawing on the technology-organization-environment (TOE) framework, this study aims to investigate determinants of performance of artificial intelligence (AI) adoption in hospitality industry during COVID-19 and identifies the relative importance of each determinant.

A two-stage approach that integrates partial least squares structural equation modeling (PLS-SEM) with artificial neural network (ANN) is used to analyze survey data from 290 managers in the hospitality industry.

The empirical results reveal that perceived AI risk, management support, innovativeness, competitive pressure and regulatory support significantly influence the performance of AI adoption. Additionally, the ANN results show that competitive pressure and management support are two of the strongest determinants.

This research offers guidelines for hospitality managers to enhance the performance of AI adoption and presents policy-making insights to promote and support organizations to benefit from the adoption of AI technology.

This study conceptualizes the performance of AI adoption from both process and firm levels and examines its determinants based on the TOE framework. By adopting an innovative approach combining PLS-SEM and ANN, the authors not only identify the essential performance determinants of AI adoption but also determine their relative importance.

To subscribe: All commands must be sent to LISTSERV@EMAIL.RUTGERS.EDULots of enthusiasm is emerging among Comurbanists for urban biking, so I have to pass on a new table I am refining on amenities in cities (county data shown here). It shows that NYC, LA and some other urban locations rank very high nationally, and above many suburban and smaller population counties, even in bike events (for mountain and road bikes).

Who's Who in America (WWiA), released in its forty‐first edition in May 1980, is one of the few reference books that enjoys two types of renown.

The growth of the web and the increasing number of documents electronically available has been paralleled by the emergence of harmful web pages content such as pornography, violence, racism, etc. This emergence involved the necessity of providing filtering systems designed to secure the internet access. Most of them process mainly the adult content and focus on blocking pornography, marginalizing violence. The purpose of this paper is to propose a violent web content detection and filtering system, which uses textual and structural content‐based analysis.

The violent web content detection and filtering system uses textual and structural content‐based analysis based on a violent keyword dictionary. The paper focuses on the keyword dictionary preparation, and presents a comparative study of different data mining techniques to block violent content web pages.

The solution presented in this paper showed its effectiveness by scoring a 89 per cent classification accuracy rate on its test data set.

Many future work directions can be considered. This paper analyzed only the web page, and an additional analysis of the visual content can be one of the directions of future work. Future research is underway to develop effective filtering tools for other types of harmful web pages, such as racist, etc.

The paper's major contributions are first, the study and comparison of several decision tree building algorithms to build a violent web classifier based on a textual and structural content‐based analysis for improving web filtering. Second, showing laborious dictionary building by finding automatically discriminative indicative keywords.

In this article, we consider the evolution of the US stock market from the 1770s through the early 20th century. Adopting an institutional lens, we conceive of the stock market as an institutional field constituted by socially constructed cultural logics and myths. We focus on the role of the US government as an actor embedded in the stock market field and sharing in the prevailing field logics. Tracking the dominant logics of the stock market field at different historical periods, we examine how these logics impacted government regulatory action upon the stock market, and how those government regulations affected the subsequent logics of the stock market field. Our research included both quantitative content analysis of articles in historical newspapers and qualitative historical analysis of multiple primary and secondary accounts of stock market problems and solutions across more than 150 years. We document how government regulatory action both reflects and shapes the logics of the stock market field.

THE ENGINES The two engines are B.M.W.801 type, each 14‐cylinder air‐cooled radials in two staggered rows, the top cylinder of the rear row being vertical (fig 9). A card in the cockpit gave the following information:

This article tackles the intersection of mothering and labor through the author's own experience as a feminist mother/manager from Istanbul, Turkey. It aims to revisit the first years of motherhood, exploring the struggle to invent a peculiar maternal subjectivity in opposition and negotiation with the patriarchal institution of motherhood, the new definition of maternal labor in a highly digital, neoliberal context and the issue of marital fairness in a dual-income heterosexual marriage.

The article presents an autoethnographic, retrospective and introspective inquiry into the first seven years of the author's mothering experience in order to offer an in-depth exploration of the various aspects of contemporary maternal labor.

The article shows how maternal labor has shifted in nature and expanded in scope in a contemporary non-Western context. It investigates the dissolution of the spatial, temporal and sensorial boundaries between the managerial labor dedicated to the workplace, and to the family. Highlighting the similarities of the two forms of labor, the article manifests the materiality, tangibility and visibility of maternal labor.

Further intersectional studies shall be beneficial to redefine maternal labor in different contexts.

Departing and diverting from the terms “invisible labor” and “mental load”, the article suggests a shift in terminology to stress the multifaceted medley of managerial tasks mothers undertake today.

The article provides an original take on maternal labor through the first-hand experience of a middle-class, professional mother from Istanbul, Turkey.

This study demonstrates two visual methodologies to support analysts using artificial neural networks (ANNs) in data mining operations. The first part of the paper illustrates the differences and similarities between various learning rules that might be employed by ANN data miners. Since different learning rules lead to different connection weights and stability coefficients, a graphical representation of the data that provides a novel visual means of discerning these similarities and differences is demonstrated. The second part of this research demonstrates a methodology for ANN model variable interpretation that uses network connection weights. It uses empirical marketing data to optimize an ANN and response elasticity graphs are built for each ANN model variable by plotting the derivative of the network output with respect to each variable, while changing network input in equal increments across the range of inputs for each variable. Finally, this paper concludes that such an approach to ANN model interpretation can provide data miners with a rich interpretation of variable importance.

Lean manufacturing (LM) is prevalent in the manufacturing industry; thus, focusing on fast and accurate lean tool implementation is the new paradigm in manufacturing. Value stream mapping (VSM) is one of the many LM tools. It is understood that combining LM implementation with VSM tools can generate better outcomes. This paper aims to develop an expert system for optimal sequencing of VSM tools for lean implementation.

A proposed artificial neural network (ANN) model is based on the analytic network process (ANP) devised for this study. It will facilitate the selection of VSM tools in an optimal sequence.

Considering different types of wastes and their level of occurrence, organizations need a set of specific tools that will be effective in the elimination of these wastes. The developed ANP model computes a level of interrelation between wastes and VSM tools. The ANN is designed and trained by data obtained from numerous case studies, so it can predict the accurate sequence of VSM tools for any new case data set.

The design and use of the ANN model provide an integrated result of both empirical and practical cases, which is more accurate because all viable aspects are then considered. The proposed modeling approach is validated through implementation in an automobile manufacturing company. It has resulted in benefits, namely, reduction in bias, time required, effort required and complexity of the decision process. More importantly, according to all performance criteria and subcriteria, the main goal of this research was satisfied by increasing the accuracy of selecting the appropriate VSM tools and their optimal sequence for lean implementation.