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Fish quarantine is a measure to prevent the entry and spread of quarantine fish pests and diseases abroad and from one area to another within Indonesia's territory. Based on these backgrounds, this study aims to identify the knowledge, knowledge management (KM) processes and knowledge management system (KMS) priority needs for quarantine fish and other fishery products measures (QMFFP) and then develop a classification model and web-based decision support system (DSS) for QMFFP decisions.

This research methodology uses combination approaches, namely, contingency factor analysis (CFA), the cross-industry standard process for data mining (CRISP-DM) and knowledge management system development life cycle (KMSDLC). The CFA for KM solution design is performed by identifying KM processes and KMS priorities. The CRISP-DM for decision classification model is done by using a decision tree algorithm. The KMSDLC is used to develop a web-based DSS.

The highest priority requirements of KM technology for QMFFP are data mining and DSS with predictive features. The main finding of this study is to show that web-based DSS (functions and outputs) can support and accelerate QMFFP decisions by regulations and field practice needs. The DSS was developed using the CTree algorithm model, which has six main attributes and eight rules.

This study proposes a novel comprehensive framework for developing DSS (combination of CFA, CRISP-DM and KMSDLC), a novel classification model resulting from comparing two decision tree algorithms and a novel web-based DSS for QMFFP.

This study aims to investigate the current state of research using deep learning methods for text classification in the tourism and hospitality field and to propose specific guidelines for future research.

This study undertakes a qualitative and critical review of studies that use deep learning methods for text classification in research fields of tourism and hospitality and computer science. The data was collected from the Web of Science database and included studies published until February 2022.

Findings show that current research has mainly focused on text feature classification, text rating classification and text sentiment classification. Most of the deep learning methods used are relatively old, proposed in the 20th century, including feed-forward neural networks and artificial neural networks, among others. Deep learning algorithms proposed in recent years in the field of computer science with better classification performance have not been introduced to tourism and hospitality for large-scale dissemination and use. In addition, most of the data the studies used were from publicly available rating data sets; only two studies manually annotated data collected from online tourism websites.

The applications of deep learning algorithms and data in the tourism and hospitality field are discussed, laying the foundation for future text mining research. The findings also hold implications for managers regarding the use of deep learning in tourism and hospitality. Researchers and practitioners can use methodological frameworks and recommendations proposed in this study to perform more effective classifications such as for quality assessment or service feature extraction purposes.

The paper provides an integrative review of research in text classification using deep learning methods in the tourism and hospitality field, points out newer deep learning methods that are suitable for classification and identifies how to develop different annotated data sets applicable to the field. Furthermore, foundations and directions for future text classification research are set.

In this paper, the authors review the literature on the nature of the small and medium-sized enterprise concept. The review examines the broad diversity of terms and definitions used to describe these kinds of firms in scholarly and practical settings. They relate this examination to the concept of small business for the purpose of comparison, in order to highlight differences and similarities between the concepts.

Relevant literature including articles from academia and defining documents from practical settings was identified through a scope literature review. Field data were subsequently collected via questionnaires sent to editors and authors of articles related to the theme. The data were content analyzed and the resulting codes consolidated into dimensions in accordance with the Gioia method. Chi-squared tests were applied to categorical data.

The use of the composite category “small and medium” was found to be predominant in the labeling of small businesses in scientific articles, including those in journals that specialize in small businesses, with no justifications presented for this, characterizing a widespread and consensual practice between authors and editors. In the defining documents of practical settings, however, the authors observed greater consistency and precision both in the terms used and in the delimiting values for a small business (self-employed, micro business, small business). In the sample of 27 defining documents mentioned in the articles, 25 specifically defined “small business” and 20 defined “micro business,” using indicators such as number of employees and annual turnover. The indicators delimiting values regarding the category of micro business were the same in all the documents analyzed and, regarding the category of small business, many documents used the same delimiting values.

Recognizing the “non-large enterprise” myth will provide a more effective posture for editors and authors to avoid using the term “small and medium,” resulting in greater precision, understanding and knowledge regarding small businesses. A better definition of a small business by academia can help public policymakers and managers of organizations that support small businesses to tailor their actions better according to the different sizes of companies. This will also lead to social gains, given the importance of small businesses in terms of job creation and countries' economies.

The authors identified and described the myth of the “non-large enterprise” among academics, characterized by the dichotomous view of the business universe, composed of “large enterprises” and “non-large enterprises,” the latter group being characterized by the widespread use of the term “small and medium.”

The purpose of this study is twofold. On the one hand, it studies the impact of IFRS 9 adoption on the firm value; and on the other hand, it investigates the impact of the classification of financial assets on the firm value.

The study covers a sample of 55 listed banks in the Middle Eastern and North African (MENA) region. Data is collected for three years (2017–2019).

The findings show that banks’ value is not impacted by IFRS 9 adoption but by financial assets’ classification. Firm value is positively affected by fair value through other comprehensive income assets, while it is negatively affected by amortized cost and fair value through profit or loss assets. The results of the additional analysis show consistent outcomes.

This research reveals important managerial implications. Priority should be given to the financial assets’ classification strategy following the adoption of IFRS 9 to boost the market valuation of banks. It may be useful for investors, managers and regulators in their decision-making.

This study enriches previous research as IFRS 9 is a new standard, and its adoption consequences need to be investigated. A few recent studies have focused on IFRS 9 as a whole or on other parts of IFRS 9, namely, the impairment regime and hedge accounting and concern developed contexts. However, this research adds to the knowledge of capital market studies by investigating the application of IFRS 9 in terms of classification in the MENA region.

The purpose of this study is to explain the opinions of the Muslim jurists available in the fiqh books so that they may be compared with the approaches adopted by modern scholars for defining the concept of riba. It is argued that the method of jurists was different from the one adopted by the modern Muslim jurists and Islamic economists. The new method dichotomizes riba into those of the Quran and Sunnah. On the contrary, jurists of four Sunni schools considered the Quran and Sunnah in this regard as a single whole, and they saw Sunnah as the elaboration of riba. By explaining the similarities shared by different fiqh schools, it is explained that there is no need for a definition of riba.

The paper uses the method of content analysis. The authors have consulted the authentic fiqh manuals of the four Sunni fiqh schools to substantiate the objectives.

One of the major findings of this paper is that interest charged in loan transactions, including bank loans, is riba according to the four Sunni fiqh schools. Moreover, the paper also shows that the similarities among the four Sunni fiqh schools are far more significant than the often-highlighted disagreements among them regarding the concept of riba. The methodology adopted by modern Muslim scholars seems to add confusions around the concept of riba.

The paper discusses views of only four Sunni fiqh jurists.

The paper explains the common methodology followed by the jurists for understanding riba, the significant similarities resulting from their common method, the link between the concept of riba and different types of financial transactions within the framework of the jurists and that combining several fiqh schools at a time is a contradiction-ridden methodology.

In order to estimate the value of semi-automated subject indexing in operative library catalogues, the study aimed to investigate five different automated implementations of an open source software package on a large set of Swedish union catalogue metadata records, with Dewey Decimal Classification (DDC) as the target classification system. It also aimed to contribute to the body of research on aboutness and related challenges in automated subject indexing and evaluation.

On a sample of over 230,000 records with close to 12,000 distinct DDC classes, an open source tool Annif, developed by the National Library of Finland, was applied in the following implementations: lexical algorithm, support vector classifier, fastText, Omikuji Bonsai and an ensemble approach combing the former four. A qualitative study involving two senior catalogue librarians and three students of library and information studies was also conducted to investigate the value and inter-rater agreement of automatically assigned classes, on a sample of 60 records.

The best results were achieved using the ensemble approach that achieved 66.82% accuracy on the three-digit DDC classification task. The qualitative study confirmed earlier studies reporting low inter-rater agreement but also pointed to the potential value of automatically assigned classes as additional access points in information retrieval.

The paper presents an extensive study of automated classification in an operative library catalogue, accompanied by a qualitative study of automated classes. It demonstrates the value of applying semi-automated indexing in operative information retrieval systems.

Risk preferences play a critical role in almost every facet of economic activity. Experimental economists have sought to infer the risk preferences of subjects from choice behavior over lotteries. To help mitigate the influence of observable, and unobservable, heterogeneity in their samples, risk preferences have been estimated at the level of the individual subject. Recent work has detailed the lack of statistical power in descriptively classifying individual subjects as conforming to Expected Utility Theory (EUT) or Rank Dependent Utility (RDU). I discuss the normative consequences of this lack of power and provide some suggestions to improve the accuracy of normative inferences about individual-level choice behavior.

Unstructured data such as images have defied usage in property valuation for a long time. Instead, structured data in tabular format are commonly employed to estimate property prices. This study attempts to quantify the shape of land lots and uses the resultant output as an input variable for subsequent land valuation models.

Imagery data containing land lot shapes are fed into a convolutional neural network, and the shape of land lots is classified into two categories, regular and irregular-shaped. Then, the intermediate output (regularity score) is utilized in four downstream models to estimate land prices: random forest, gradient boosting, support vector machine and regression models.

Quantification of the land lot shapes and their exploitation in valuation led to an improvement in the predictive accuracy for all subsequent models.

The study findings are expected to promote the adoption of elusive price determinants such as the shape of a land lot, appearance of a house and the landscape of a neighborhood in property appraisal practices.

Today, individuals use social media to express their opinions and feelings, which offers a living laboratory to researchers in various fields, such as management, innovation, technology development, environment and marketing. It is therefore necessary to understand how the language used in user-generated content and the emotions conveyed by the content affect responses from other social media users.

In this study, almost 700,000 posts from Twitter (as well as Facebook, Instagram and forums in the appendix) are used to test a conceptual model grounded in signaling theory to explain how the language of user-generated content on social media influences how other users respond to that communication.

Extending developments in linguistics, this study shows that users react negatively to content that uses self-inclusive language. This study also shows how emotional content characteristics moderate this relationship. The additional information provided indicates that while most of the findings are replicated, some results differ across social media platforms, which deserves users' attention.

This article extends research on Internet behavior and social media use by providing insights into how the relationship between self-inclusive language and emotions affects user responses to user-generated content. Furthermore, this study provides actionable guidance for researchers interested in capturing phenomena through the social media landscape.

Consequence prediction is an emerging topic in safety management concerning the severity outcome of accidents. In practical applications, it is usually implemented through supervised learning methods; however, the evaluation of classification results remains a challenge. The previous studies mostly adopted simplex evaluation based on empirical and quantitative assessment strategies. This paper aims to shed new light on the comprehensive evaluation and comparison of diverse classification methods through visualization, clustering and ranking techniques.

An empirical study is conducted using 9 state-of-the-art classification methods on a real-world data set of 653 construction accidents in China for predicting the consequence with respect to 39 carefully featured factors and accident type. The proposed comprehensive evaluation enriches the interpretation of classification results from different perspectives. Furthermore, the critical factors leading to severe construction accidents are identified by analyzing the coefficients of a logistic regression model.

This paper identifies the critical factors that significantly influence the consequence of construction accidents, which include accident type (particularly collapse), improper accident reporting and handling (E21), inadequate supervision engineers (O41), no special safety department (O11), delayed or low-quality drawings (T11), unqualified contractor (C21), schedule pressure (C11), multi-level subcontracting (C22), lacking safety examination (S22), improper operation of mechanical equipment (R11) and improper construction procedure arrangement (T21). The prediction models and findings of critical factors help make safety intervention measures in a targeted way and enhance the experience of safety professionals in the construction industry.

The empirical study using some well-known classification methods for forecasting the consequences of construction accidents provides some evidence for the comprehensive evaluation of multiple classifiers. These techniques can be used jointly with other evaluation approaches for a comprehensive understanding of the classification algorithms. Despite the limitation of specific methods used in the study, the presented methodology can be configured with other classification methods and performance metrics and even applied to other decision-making problems such as clustering.

This study sheds new light on the comprehensive comparison and evaluation of classification results through visualization, clustering and ranking techniques using an empirical study of consequence prediction of construction accidents. The relevance of construction accident type is discussed with the severity of accidents. The critical factors influencing the accident consequence are identified for the sake of taking prevention measures for risk reduction. The proposed method can be applied to other decision-making tasks where the evaluation is involved as an important component.