Discovery of factors affecting tourists' fine dining experiences at five-star hotel restaurants in Istanbul

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Semra Aktas-Polat ◽  
Serkan Polat
Keyword(s):  
Text Mining ◽  
Food Quality ◽  
Design Methodology ◽  
Latent Dirichlet Allocation ◽  
Content Type ◽  
Factors Affecting ◽  
Customer Delight ◽  
Dirichlet Allocation

PurposeThe purpose of this study is to discover the factors affecting customer delight, satisfaction and dissatisfaction in fine dining experiences (FDEs).Design/methodology/approachOnline user generated 2,585 reviews on TripAdvisor for 46 five-star hotel restaurants operating in Istanbul were analyzed with the latent Dirichlet allocation (LDA) algorithm.FindingsLDA created nine, eight and seven topics for delight, satisfaction and dissatisfaction, respectively. The most salient topics for customer delight, satisfaction and dissatisfaction in FDEs are staff (17.3%), view (19%), and food quality (23%), respectively.Originality/valueThis study is one of the few studies investigating customer delight and satisfaction together. The study shows that FDEs can be analyzed with text mining techniques. Moreover, the study contributes to the literature on customer delight by adding staff topic as an antecedent.

Examining research topics with a dependency-based noun phrase extraction method: a case in accounting

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Lei Lei ◽  
Yaochen Deng ◽  
Dilin Liu
Keyword(s):  
Design Methodology ◽  
Latent Dirichlet Allocation ◽  
Noun Phrases ◽  
Specific Area ◽  
Large Set ◽  
Important Research ◽  
Research Topics ◽  
Content Type ◽  
Noun Phrase Extraction ◽  
Dirichlet Allocation

PurposeExamining research topics in a specific area such as accounting is important to both novice and veteran researchers. The present study aims to identify the research topics in the area of accounting and to investigate the research trends by finding hot and cold topics from all those identified ones in the field.Design/methodology/approachA new dependency-based method focusing on noun phrases, which efficiently extracts research topics from a large set of library data, was proposed. An AR(1) autoregressive model was used to identify topics that have received significantly more or less attention from the researchers. The data used in the study included a total of 4,182 abstracts published in six leading (or premier) accounting journals from 2000 to May 2019.FindingsThe study identified 48 important research topics across the examined period as well as eight hot topics and one cold topic from the 48 topics.Originality/valueThe research topics identified based on the dependency-based method are similar to those found with the technique of latent Dirichlet allocation latent Dirichlet allocation (LDA) topic modelling. In addition, the method seems highly efficient, and the results are easier to interpret. Last, the research topics and trends found in the study provide reference to the researchers in the area of accounting.

Latent Dirichlet allocation-based temporal summarization

2019 ◽  
Vol 15 (1) ◽  
pp. 83-102 ◽  
Author(s):  
Ahmed Amir Tazibt ◽  
Farida Aoughlis
Keyword(s):  
Topic Modeling ◽  
Design Methodology ◽  
Latent Dirichlet Allocation ◽  
Topic Model ◽  
External Source ◽  
Decision Makers ◽  
Content Type ◽  
Available Information ◽  
Different Sources ◽  
Dirichlet Allocation

Purpose During crises such as accidents or disasters, an enormous volume of information is generated on the Web. Both people and decision-makers often need to identify relevant and timely content that can help in understanding what happens and take right decisions, as soon it appears online. However, relevant content can be disseminated in document streams. The available information can also contain redundant content published by different sources. Therefore, the need of automatic construction of summaries that aggregate important, non-redundant and non-outdated pieces of information is becoming critical. Design/methodology/approach The aim of this paper is to present a new temporal summarization approach based on a popular topic model in the information retrieval field, the Latent Dirichlet Allocation. The approach consists of filtering documents over streams, extracting relevant parts of information and then using topic modeling to reveal their underlying aspects to extract the most relevant and novel pieces of information to be added to the summary. Findings The performance evaluation of the proposed temporal summarization approach based on Latent Dirichlet Allocation, performed on the TREC Temporal Summarization 2014 framework, clearly demonstrates its effectiveness to provide short and precise summaries of events. Originality/value Unlike most of the state of the art approaches, the proposed method determines the importance of the pieces of information to be added to the summaries solely relying on their representation in the topic space provided by Latent Dirichlet Allocation, without the use of any external source of evidence.

Retrospective and prospective approaches of coronavirus publications in the last half-century: a Latent Dirichlet allocation analysis

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Farshid Danesh ◽  
Meisam Dastani ◽  
Mohammad Ghorbani
Keyword(s):  
Immune Response ◽  
Text Mining ◽  
Topic Modeling ◽  
Latent Dirichlet Allocation ◽  
Statistical Population ◽  
Content Type ◽  
Number Of Publications ◽  
Python Programming ◽  
Mining Algorithms ◽  
Dirichlet Allocation

PurposeThe present article's primary purpose is the topic modeling of the global coronavirus publications in the last 50 years.Design/methodology/approachThe present study is applied research that has been conducted using text mining. The statistical population is the coronavirus publications that have been collected from the Web of Science Core Collection (1970–2020). The main keywords were extracted from the Medical Subject Heading browser to design the search strategy. Latent Dirichlet allocation and Python programming language were applied to analyze the data and implement the text mining algorithms of topic modeling.FindingsThe findings indicated that the SARS, science, protein, MERS, veterinary, cell, human, RNA, medicine and virology are the most important keywords in the global coronavirus publications. Also, eight important topics were identified in the global coronavirus publications by implementing the topic modeling algorithm. The highest number of publications were respectively on the following topics: “structure and proteomics,” “Cell signaling and immune response,” “clinical presentation and detection,” “Gene sequence and genomics,” “Diagnosis tests,” “vaccine and immune response and outbreak,” “Epidemiology and Transmission” and “gastrointestinal tissue.”Originality/valueThe originality of this article can be considered in three ways. First, text mining and Latent Dirichlet allocation were applied to analyzing coronavirus literature for the first time. Second, coronavirus is mentioned as a hot topic of research. Finally, in addition to the retrospective approaches to 50 years of data collection and analysis, the results can be exploited with prospective approaches to strategic planning and macro-policymaking.

Blockchain as supply chain technology: considering transparency and security

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Pei Xu ◽  
Joonghee Lee ◽  
James R. Barth ◽  
Robert Glenn Richey
Keyword(s):  
Supply Chain ◽  
Text Mining ◽  
Information Security ◽  
Design Methodology ◽  
Use Cases ◽  
Future Research ◽  
Content Type ◽  
Blockchain Technology ◽  
Twitter Data ◽  
The Way

PurposeThis paper discusses how the features of blockchain technology impact supply chain transparency through the lens of the information security triad (confidentiality, integrity and availability). Ultimately, propositions are developed to encourage future research in supply chain applications of blockchain technology.Design/methodology/approachPropositions are developed based on a synthesis of the information security and supply chain transparency literature. Findings from text mining of Twitter data and a discussion of three major blockchain use cases support the development of the propositions.FindingsThe authors note that confidentiality limits supply chain transparency, which causes tension between transparency and security. Integrity and availability promote supply chain transparency. Blockchain features can preserve security and increase transparency at the same time, despite the tension between confidentiality and transparency.Research limitations/implicationsThe research was conducted at a time when most blockchain applications were still in pilot stages. The propositions developed should therefore be revisited as blockchain applications become more widely adopted and mature.Originality/valueThis study is among the first to examine the way blockchain technology eases the tension between supply chain transparency and security. Unlike other studies that have suggested only positive impacts of blockchain technology on transparency, this study demonstrates that blockchain features can influence transparency both positively and negatively.

Mining numerical measure of consumers’ product evaluation expressed in words based on latent Dirichlet allocation

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ziang Wang ◽  
Feng Yang
Keyword(s):  
Latent Dirichlet Allocation ◽  
Product Evaluation ◽  
Online Reviews ◽  
Product Evaluations ◽  
Product Attributes ◽  
Data Set ◽  
Content Type ◽  
Face To Face ◽  
Online Retailers ◽  
Dirichlet Allocation

Purpose It has always been a hot topic for online retailers to obtain consumers’ product evaluations from massive online reviews. In the process of online shopping, there is no face-to-face interaction between online retailers and customers. After collecting online reviews left by customers, online retailers are eager to acquire answers to some questions. For example, which product attributes will attract consumers? Or which step brings a better experience to consumers during the process of shopping? This paper aims to associate the latent Dirichlet allocation (LDA) model with the consumers’ attitude and provides a method to calculate the numerical measure of consumers’ product evaluation expressed in each word. Design/methodology/approach First, all possible pairs of reviews are organized as a document to build the corpus. After that, latent topics of the traditional LDA model noted as the standard LDA model, are separated into shared and differential topics. Then, the authors associate the model with consumers’ attitudes toward each review which is distinguished as positive review and non-positive review. The product evaluation reflected in consumers’ binary attitude is expanded to each word that appeared in the corpus. Finally, a variational optimization is introduced to calculate parameters mentioned in the expanded LDA model. Findings The experiment’s result illustrates that the LDA model in the research noted as an expanded LDA model, can successfully assign sufficient probability with words related to products attributes or consumers’ product evaluation. Compared with the standard LDA model, the expanded model intended to assign higher probability with words, which have a higher ranking within each topic. Besides, the expanded model also has higher precision on the prediction set, which shows that breaking down the topics into two categories fits better on the data set than the standard LDA model. The product evaluation of each word is calculated by the expanded model and depicted at the end of the experiment. Originality/value This research provides a new method to calculate consumers’ product evaluation from reviews in the level of words. Words may be used to describe product attributes or consumers’ experiences in reviews. Assigning words with numerical measures can analyze consumers’ products evaluation quantitatively. Besides, words are labeled themselves, they can also be ranked if a numerical measure is given. Online retailers can benefit from the result for label choosing, advertising or product recommendation.

Thomas Stewart: what leaders need to know about the new design revolution in services

2017 ◽  
Vol 45 (3) ◽  
pp. 16-22
Author(s):  
Brian Leavy
Keyword(s):  
Design Methodology ◽  
Service Design ◽  
Design Strategy ◽  
Harvard Business Review ◽  
Service Firms ◽  
Content Type ◽  
Innovation Activities ◽  
Customer Delight ◽  
Modern Manufacturing ◽  
Practical Implications

Purpose With the growing importance of services in the overall economy, it is surprising that the notion of service firms investing in systematic and dedicated innovation activities has taken so long to materialize. This is now set to change as service firms undertake the kind of research, design and development disciplines which for more than a century have been mainstays of modern manufacturing. Design/methodology/approach S&L interviews the well-known former editor of Harvard Business Review Thomas A. Stewart and his co-author, former BloombergBusinessweek.com editor Patricia O’Connell, in their latest book, Woo, Wow and Win: Service Design, Strategy and the Art of Customer Delight (Harper Business, 2016). They believe we are on the cusp of a “design revolution” in services. Findings The central thesis of their book is that services “should be designed with as much care as products are” and they include service “delivery” in that premise. Practical implications Service design principles offer powerful new ways to address the three basic strategy questions: What do we sell? To whom? And how do we win? Originality/value Service design helps you understand how to configure a set of activities, behaviors and touchpoints–a journey–that allows you to serve that customer well.

Organizing sports events: the promoters’ perspective

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ana Brochado ◽  
Pedro Dionísio ◽  
Maria do Carmo Leal ◽  
Adrien Bouchet ◽  
Henrique Conceição
Keyword(s):  
Design Methodology ◽  
Stakeholder Management ◽  
Business Plan ◽  
Measurement Tool ◽  
Sports Facilities ◽  
City Image ◽  
Event Management ◽  
Content Type ◽  
Factors Affecting ◽  
Sports Events

Purpose This study aims to develop a battery of items that assess the factors affecting sports events’ success from the promoters’ perspective and a measurement tool that identifies these stakeholders’ main priorities based on the importance-performance analysis (IPA) framework. Design/methodology/approach The research was conducted using mixed methods. In the first qualitative step, sports event management’s main dimensions were identified based on the existing literature and a comprehensive battery of corresponding items were developed via content analysis of interviews with experts. The second quantitative step focused on Lisbon, the 2021 European City of Sport. Promoters of 21 different medium or large sports events (number = 41 respondents) were asked to fill out a survey ranking each dimension’s items by performance and room for improvement. The IPA’s results are presented both for the overall sample and by promoter type (i.e. events with or without sports facilities). Findings The 46 items identified fall into the following three categories: sports infrastructure, city image and hospitality and event management (i.e. pre-event, event and post-event). Pre-event includes stakeholder management, organigram and responsibilities, business plan, marketing mix, risk assessment and sponsorship management. Sponsorship management stands out among the areas considered a priority by event promotors. Originality/value This study adds to the literature by offering a comprehensive approach to assessing empirically all stages of the event management process.

scholarly journals Text mining analysis roadmap (TMAR) for service research

2020 ◽  
Vol 34 (1) ◽  
pp. 30-47 ◽  
Author(s):  
Mohamed Zaki ◽  
Janet R. McColl-Kennedy
Keyword(s):  
Machine Learning ◽  
Text Mining ◽  
Design Methodology ◽  
Data Sources ◽  
Theory Testing ◽  
Service Research ◽  
Data Types ◽  
Content Type ◽  
Research And Practice ◽  
Mining Methods

Purpose The purpose of this paper is to offer a step-by-step text mining analysis roadmap (TMAR) for service researchers. The paper provides guidance on how to choose between alternative tools, using illustrative examples from a range of business contexts. Design/methodology/approach The authors provide a six-stage TMAR on how to use text mining methods in practice. At each stage, the authors provide a guiding question, articulate the aim, identify a range of methods and demonstrate how machine learning and linguistic techniques can be used in practice with illustrative examples drawn from business, from an array of data types, services and contexts. Findings At each of the six stages, this paper demonstrates useful insights that result from the text mining techniques to provide an in-depth understanding of the phenomenon and actionable insights for research and practice. Originality/value There is little research to guide scholars and practitioners on how to gain insights from the extensive “big data” that arises from the different data sources. In a first, this paper addresses this important gap highlighting the advantages of using text mining to gain useful insights for theory testing and practice in different service contexts.

Identification of operational demand in law enforcement agencies

2019 ◽  
Vol 53 (3) ◽  
pp. 333-372 ◽  
Author(s):  
Marcio Pereira Basilio ◽  
Valdecy Pereira ◽  
Gabrielle Brum
Keyword(s):  
Law Enforcement ◽  
Text Mining ◽  
Knowledge Discovery ◽  
Emergency Response ◽  
Latent Dirichlet Allocation ◽  
Social Dynamics ◽  
Law Enforcement Agencies ◽  
Content Type ◽  
The Government ◽  
The City

Purpose The purpose of this paper is to develop a methodology for knowledge discovery in emergency response service databases based on police occurrence reports, generating information to help law enforcement agencies plan actions to investigate and combat criminal activities. Design/methodology/approach The developed model employs a methodology for knowledge discovery involving text mining techniques and uses latent Dirichlet allocation (LDA) with collapsed Gibbs sampling to obtain topics related to crime. Findings The method used in this study enabled identification of the most common crimes that occurred in the period from 1 January to 31 December of 2016. An analysis of the identified topics reaffirmed that crimes do not occur in a linear manner in a given locality. In this study, 40 per cent of the crimes identified in integrated public safety area 5, or AISP 5 (the historic centre of the city of RJ), had no correlation with AISP 19 (Copacabana – RJ), and 33 per cent of the crimes in AISP 19 were not identified in AISP 5. Research limitations/implications The collected data represent the social dynamics of neighbourhoods in the central and southern zones of the city of Rio de Janeiro during the specific period from January 2013 to December 2016. This limitation implies that the results cannot be generalised to areas with different characteristics. Practical implications The developed methodology contributes in a complementary manner to the identification of criminal practices and their characteristics based on police occurrence reports stored in emergency response databases. The generated knowledge enables law enforcement experts to assess, reformulate and construct differentiated strategies for combating crimes in a given locality. Social implications The production of knowledge from the emergency service database contributes to the government integrating information with other databases, thus enabling the improvement of strategies to combat local crime. The proposed model contributes to research on big data, on the innovation aspect and on decision support, for it breaks with a paradigm of analysis of criminal information. Originality/value The originality of the study lies in the integration of text mining techniques and LDA to detect crimes in a given locality on the basis of the criminal occurrence reports stored in emergency response service databases.

Automatic recommendation of prognosis measures for mechanical components based on massive text mining

2018 ◽  
Vol 14 (4) ◽  
pp. 480-494 ◽  
Author(s):  
Jorge Martinez-Gil ◽  
Bernhard Freudenthaler ◽  
Thomas Natschläger
Keyword(s):  
Fault Diagnosis ◽  
Text Mining ◽  
Design Methodology ◽  
Content Type ◽  
The Past ◽  
Mechanical Component ◽  
The World ◽  
Fault Prognosis ◽  
Mechanical Components ◽  
Unstructured Information

Purpose The purpose of this study is to automatically provide suggestions for predicting the likely status of a mechanical component is a key challenge in a wide variety of industrial domains. Design/methodology/approach Existing solutions based on ontological models have proven to be appropriate for fault diagnosis, but they fail when suggesting activities leading to a successful prognosis of mechanical components. The major reason is that fault prognosis is an activity that, unlike fault diagnosis, involves a lot of uncertainty and it is not always possible to envision a model for predicting possible faults. Findings This work proposes a solution based on massive text mining for automatically suggesting prognosis activities concerning mechanical components. Originality/value The great advantage of text mining is that makes possible to automatically analyze vast amounts of unstructured information to find corrective strategies that have been successfully exploited, and formally or informally documented, in the past in any part of the world.

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