Sustainable tourism research towards twenty-five years of the journal of sustainable tourism

© 2018 Advances in Hospitality and Tourism Research. All Rights Reserved. The Journal of Sustainable Tourism (JOST) is a main journal in 'Geography, Planning and Development'. The concept of sustainable tourism has gained importance over time. This paper presents a general overview of the journal over its lifetime by using bibliometric indicators. Moreover, in order to establish the position of sustainable tourism research, this paper identifies the trends in research through bibliometric studies. It uses the Scopus database to analyse the bibliometric data. This analysis includes key issues such as the publication and citation structure of the journal; the most cited articles; the leading authors, institutions, and countries in the journal; and the keywords that are most often used. This paper also uses the visualization of similarities to graphically map the bibliographic material. This analysis provides further insights into how JOST links to other journals and how it links researchers across the globe. These results indicate that JOST is one of the leading journals in the areas where the journal is indexed, with a wide range of authors from institutions and countries from all over the world publishing in it. The results of the current study can provide insights into topics related to sustainable tourism that can be researched in the future

Integer programming modeling on group decision making with incomplete hesitant fuzzy linguistic preference relations

© 2013 IEEE. Complementing missing information and priority vector are of significance important aspects in group decision making (GDM) with incomplete hesitant fuzzy linguistic preference relations (HFLPRs). In this paper, an integer programming model is developed based on additive consistency to estimate missing values of incomplete HFLPRs by using additive consistency. Once the missing values are complemented, a mixed 0-1 programming model is established to derive the priority vectors from complete HFLPRs, in which the underlying idea of the mixed 0-1 programming model is the probability sampling in statistics and minimum deviation between the priority vector and HFLPR. In addition, we also propose a new GDM approach for incomplete HFLPRs by integrating the integer programming model and the mixed 0-1 programming model. Finally, two case studies and comparative analysis detail the application of the proposed models

Fuzzy indicators for customer retention

It is widely known that market orientation (MO) and customer value help companies achieve sustainable sales growth over time. Nevertheless, one cannot ignore the existence of a gap on how to measure this relationship. Following this idea, this study proposes six fuzzy key performance indicators that aims to measure customer retention and loyalty of the portfolio. The work uses 300 sales executives. This exploratory study concludes that indicators such as MO, customer orientation (CO), degree of CO value of sales force, innovation capability, lifetime value, and customer service quality positively influence customer retention and loyalty portfolio

Bonferroni distances with OWA operators

© 2016 IEEE. The aim of the paper is to develop new aggregation operators using Bonferroni means, ordered weighted averaging (OWA) operators and some distance measures. We introduce the Bonferroni-Hamming weighted distance, Bonferroni OWA distance, and Bonferroni distances with OWA operators and weighted averages. The main advantages of using these operators are that they allow considering different aggregations contexts, multiple-comparison between each argument and distance measures in the same formulation

Outlining new product development research through bibliometrics: Analyzing journals, articles and researchers

© 2018, Emerald Publishing Limited. Purpose: New product development (NPD) is a noteworthy field that has attracted the attention of scholars for its relevance for firm success. Based on bibliometric indicators and spatial distance network analysis, the authors outline the general structure overview of NPD research through the last 40 years of scientific production; identify and categorize key articles, authors, journals, institutions, and countries related to NPD research; identify and map the research subareas that have mostly contributed to the construction of NPD intellectual structure. The paper aims to discuss these issues. Design/methodology/approach: The work uses the Web of Science Core Collection and the visualization of similarities viewer software. The analysis searches for all the documents connected to NPD available in the database. The graphical visualization maps the bibliographic data in terms of bibliographic coupling and co-citation. Findings: The general NPD citation pattern evidences a construction of knowledge and learning, as evidenced in different subjects, such as biology or physics. Relevant contributions and contributors are highlighted as journals, articles, researchers, countries and institutions in overall NPD research and in its constituent subfields. Five subareas related to the NPD field based on journals and authors network are identified: marketing; operations and production; strategy; industrial engineering and operations; and management. Originality/value: This paper contributes to the NPD literature by offering a global perspective on the field by using bibliometric data graphical networks, providing insights about the influence of individual actors and its contributions to build bridges between the different subfields of research in NPD

Business and management research in Latin America: A country-level bibliometric analysis

Bibliometrics is a scientific discipline that studies quantitatively the bibliographic material of a particular topic. This study analyzes management research published by Latin American countries between 1990 and 2019. The work uses the Web of Science database and provides several country-level bibliometric indicators including the total number of publications and citations, and the h-index. The results indicate that Brazil, Chile and Mexico have constantly led the region's scientific publications. The temporal evolution shows a significant increase on the number of publications during the last years that seems to continue in the future. The results also show that operations research and finance are the most significant topics in the region

Cloud Sentiment Accuracy Comparison using RNN, LSTM and GRU

Cloud computing has become a de facto choice of many individuals and enterprises for computing solutions. In the last few years, many cloud providers appear in the market that offers the same services. It is a trivial job to choose an optimal service best suited for organisations in such a massive arms race of service providers. Existing consumer experience could help significantly build a holistic perception of their experiences that ultimately influence service adoption decisions. Sentiment analysis is an effective tool to understand consumer experience about the product or service. The sophisticated sentiment analysis could help businesses to gain a better insight and respond proactively to consumer issues. There are various methods for sentiment analysis that produces ideal results under different conditions. Therefore, it is very important to choose the right method to predict consumer's sentiment for a greatest result. In this paper we analyse the sentiment prediction accuracy of widely used neural network methods - recurrent neural network (RNN), long short-term memory (LSTM) and gated recurrent network (GRU). We use software as a service (SaaS) dataset having 6258 reviews. From analysis results we find that GRU outperforms the LSTM and RNN methods

Long Short-Term Memory-based Sentiment Classification of Cloud Dataset

Text Sentiment Classification is a crucial task for various decision-making processes in many organizations. It identifies the polarity of texts positively and negatively and highlights the opinions and views hidden within the comments or reviews of a product or service. Performing it on big data from social media and related sources is quite tricky and time-consuming. Nowadays, Deep Learning (DL) is widely used for sentiment analysis due to its high performance. In this paper, Recurrent Neural Network (RNN) based Long Short-Term Memory (LSTM) approach is applied to perform sentiment analysis of a cloud review dataset. The cloud review dataset contains cloud consumer reviews regarding the services provided by different cloud service providers and the dataset is achieved as a result of the Harvesting-as-a-Service (HaaS) framework. The study focuses on observing the behaviour of the deep learning RNN-LSTM approach on a cloud dataset. Results of the experiment are evaluated using various evaluation and performance metrics. The approach tends to achieve 95 % accuracy