Search results

1 – 10 of 108
Open Access
Article
Publication date: 19 June 2020

Jeffrey D. Kushkowski, Charles B. Shrader, Marc H. Anderson and Robert E. White

Multiple disciplines such as finance, management and economics have contributed to governance research over time. However, the full intellectual structure of the governance…

5169

Abstract

Purpose

Multiple disciplines such as finance, management and economics have contributed to governance research over time. However, the full intellectual structure of the governance “field” including the exchange of knowledge across disciplines and the large variety of governance topics remains to be uncovered. To appreciate the breadth of corporate governance research, it is necessary to understand the disciplinary sources from which the research stems. This manuscript focuses on the interdisciplinary underpinnings of corporate governance research.

Design/methodology/approach

This paper employs bibliometric analysis to trace the evolution of corporate governance using articles included in the ISI Web of Science database between 1990 and 2015. Journals included in these categories encompass a full range of business disciplines and provide evidence of the multi-disciplinary nature of corporate governance. It also uncovers the topics treated by disciplines under the governance umbrella using a machine learning method called latent Dirichtlet allocation (LDA).

Findings

Corporate governance research deals with a number of strategy-related topics. Unlike strategy topics that reside in a single discipline, corporate governance crosses disciplinary boundaries and includes contributions from accounting, finance, economics, law and management. Our analysis shows that over 80% of corporate governance articles come from outside the field of management. Our LDA solution indicates that the major topics in governance research include corporate governance theory, control of family firms, executive compensation and audit committees.

Originality/value

The results illustrate that corporate governance is far more interdisciplinary than previously thought. This is an important insight for corporate governance academics and may lead to collaborative research. More importantly, this research illustrates the usefulness of LDA for investigating interdisciplinary fields. This method is easily transferable to other interdisciplinary fields and it provides a powerful alternative to existing bibliometric methods. We suggest a number of topic areas within library and information science where this method may be applied, including collection development, support for interdisciplinary faculty and basic research into emerging interdisciplinary areas.

Details

Journal of Documentation, vol. 76 no. 6
Type: Research Article
ISSN: 0022-0418

Keywords

Open Access
Article
Publication date: 5 June 2024

Abderahman Rejeb, Karim Rejeb, Andrea Appolloni, Suhaiza Zailani and Mohammad Iranmanesh

Given the growing significance of contemporary socio-economic and infrastructural conversations of Public-Private Partnerships (PPP), this research seeks to provide a general…

Abstract

Purpose

Given the growing significance of contemporary socio-economic and infrastructural conversations of Public-Private Partnerships (PPP), this research seeks to provide a general overview of the academic landscape concerning PPP.

Design/methodology/approach

To offer a nuanced perspective, the study adopts the Latent Dirichlet Allocation (LDA) methodology to meticulously analyse 3,057 journal articles, mapping out the thematic contours within the PPP domain.

Findings

The analysis highlights PPP's pivotal role in harmonising public policy goals with private sector agility, notably in areas like disaster-ready sustainable infrastructure and addressing rapid urbanisation challenges. The emphasis within the literature on financial, risk, and performance aspects accentuates the complexities inherent in financing PPP and the critical need for practical evaluation tools. An emerging focus on healthcare within PPP indicates potential for more insightful research, especially amid ongoing global health crises.

Originality/value

This study pioneers the application of LDA for an all-encompassing examination of PPP-related academic works, presenting unique theoretical and practical insights into the diverse facets of PPP.

Details

International Journal of Public Sector Management, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0951-3558

Keywords

Open Access
Article
Publication date: 27 March 2023

Peter Madzík, Lukáš Falát, Lukáš Copuš and Marco Valeri

This bibliometric study provides an overview of research related to digital transformation (DT) in the tourism industry from 2013 to 2022. The goals of the research are as…

6022

Abstract

Purpose

This bibliometric study provides an overview of research related to digital transformation (DT) in the tourism industry from 2013 to 2022. The goals of the research are as follows: (1) to identify the development of academic papers related to DT in the tourism industry, (2) to analyze dominant research topics and the development of research interest and research impact over time and (3) to analyze the change in research topics during the pandemic.

Design/methodology/approach

In this study, the authors processed 3,683 papers retrieved from the Web of Science and Scopus. The authors performed different types of bibliometric analyses to identify the development of papers related to DT in the tourism industry. To reveal latent topics, the authors implemented topic modeling based on latent Dirichlet allocation with Gibbs sampling.

Findings

The authors identified eight topics related to DT in the tourism industry: City and urban planning, Social media, Data analytics, Sustainable and economic development, Technology-based experience and interaction, Cultural heritage, Digital destination marketing and Smart tourism management. The authors also identified seven topics related to DT in the tourism industry during the Covid-19 pandemic; the largest ones are smart analytics, marketing strategies and sustainability.

Originality/value

To identify research topics and their development over time, the authors applied a novel methodological approach – a smart literature review. This machine learning approach is able to analyze a huge amount of documents. At the same time, it can also identify topics that would remain unrevealed by a standard bibliometric analysis.

Details

European Journal of Innovation Management, vol. 26 no. 7
Type: Research Article
ISSN: 1460-1060

Keywords

Open Access
Article
Publication date: 26 July 2021

Jeremy St John, Karen St John and Bo Han

This study furthers one’s understanding of the motivations of the crowdfunding crowd by empirically examining critical factors that influence the crowd's decision to support a…

2613

Abstract

Purpose

This study furthers one’s understanding of the motivations of the crowdfunding crowd by empirically examining critical factors that influence the crowd's decision to support a crowdfunding project.

Design/methodology/approach

Backer's comments from a sample of the top 100 most funded technology product projects on KickStarter were collected. A latent Dirichlet allocation (LDA) analysis strategy was adopted to investigate critical motivational factors. Three experts mapped those factors to the known theoretical constructs of social exchange theory (SET).

Findings

Although backers are motivated by value, they are also motivated by far less tangible social factors including trust and a feeling of psychological ownership. Findings suggest that the crowd is far more than a passive group of investors or customers and should be viewed as participatory stakeholders. This study serves as guidance for project owners hoping to motivate the crowd and for future investigators examining backer motivations in other types of crowdsourcing projects.

Research limitations/implications

Online chatter in the form of user-generated comments is an excellent data source for researchers to mine for value and meaning.

Practical implications

Given strong feelings of psychological ownership, project owners should actively engage the crowd and solicit the crowd for advice and help in order to motivate them.

Originality/value

The study presents the first empirical exploration of backer motivations using LDA guided by theory and the knowledge of experts. A framework of latent motivational factors is proposed.

Details

European Journal of Innovation Management, vol. 25 no. 6
Type: Research Article
ISSN: 1460-1060

Keywords

Open Access
Article
Publication date: 28 August 2024

Ya-Fei Liu, Yu-Bo Zhu, Hou-Han Wu and Fangxuan (Sam) Li

This study aims to explore the differences in the tourists’ perceived destination image on travel e-commerce platforms (e.g. Ctrip and Fliggy) and social media platforms (e.g…

Abstract

Purpose

This study aims to explore the differences in the tourists’ perceived destination image on travel e-commerce platforms (e.g. Ctrip and Fliggy) and social media platforms (e.g. Xiaohongshu and Weibo).

Details

Tourism Critiques: Practice and Theory, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2633-1225

Keywords

Open Access
Article
Publication date: 10 June 2024

Lua Thi Trinh

The purpose of this paper is to compare nine different models to evaluate consumer credit risk, which are the following: Logistic Regression (LR), Naive Bayes (NB), Linear…

Abstract

Purpose

The purpose of this paper is to compare nine different models to evaluate consumer credit risk, which are the following: Logistic Regression (LR), Naive Bayes (NB), Linear Discriminant Analysis (LDA), k-Nearest Neighbor (k-NN), Support Vector Machine (SVM), Classification and Regression Tree (CART), Artificial Neural Network (ANN), Random Forest (RF) and Gradient Boosting Decision Tree (GBDT) in Peer-to-Peer (P2P) Lending.

Design/methodology/approach

The author uses data from P2P Lending Club (LC) to assess the efficiency of a variety of classification models across different economic scenarios and to compare the ranking results of credit risk models in P2P lending through three families of evaluation metrics.

Findings

The results from this research indicate that the risk classification models in the 2013–2019 economic period show greater measurement efficiency than for the difficult 2007–2012 period. Besides, the results of ranking models for predicting default risk show that GBDT is the best model for most of the metrics or metric families included in the study. The findings of this study also support the results of Tsai et al. (2014) and Teplý and Polena (2019) that LR, ANN and LDA models classify loan applications quite stably and accurately, while CART, k-NN and NB show the worst performance when predicting borrower default risk on P2P loan data.

Originality/value

The main contributions of the research to the empirical literature review include: comparing nine prediction models of consumer loan application risk through statistical and machine learning algorithms evaluated by the performance measures according to three separate families of metrics (threshold, ranking and probabilistic metrics) that are consistent with the existing data characteristics of the LC lending platform through two periods of reviewing the current economic situation and platform development.

Details

Journal of Economics, Finance and Administrative Science, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2077-1886

Keywords

Open Access
Article
Publication date: 7 August 2023

Tiziano Volpentesta, Esli Spahiu and Pietro De Giovanni

Digital transformation (DT) is a major challenge for incumbent organisations, as research on this phenomenon has revealed a high failure rate. Given this consideration, this paper…

3025

Abstract

Purpose

Digital transformation (DT) is a major challenge for incumbent organisations, as research on this phenomenon has revealed a high failure rate. Given this consideration, this paper reviews the literature on DT in incumbent organisations to identify the main themes and research directions to be undertaken.

Design/methodology/approach

The authors adopt a systematic literature review (SLR) and computational literature review (CLR) employing a machine learning algorithm for topic modelling (LDA) to surface the themes discussed in 103 peer-reviewed studies published between 2010 and 2022 in a multidisciplinary article sample.

Findings

The authors identify and discuss the five main themes emerging from the studies, offering the state-of-the-art of DT in established firms' literature. The authors find that the most discussed topics revolve around the DT of healthcare, the process of renewal and change, the project management, the changes in value performances and capabilities and the consequences on the products of DT. Accordingly, the authors identify the topics overlooked by literature that future studies could tackle, which concern sustainability and contextualisation of the DT phenomenon.

Practical implications

The authors further propose managerial insights which equip managers with a revolutionary mindset that is not constraining but, rather, integration-seeking. DT is not only about technology (Tabrizi B et al., 2019). Successful DT initiatives require managerial capabilities that foster a sustainable departure from the current organising logic (Markus, 2004). This study pinpoints and prioritises the role that paradox-informed thinking can have to sustain an effective digital mindset (Eden et al., 2018) that allows for the building of momentum in DT initiatives and facilitates the renewal process. Indeed, managers lagging behind DT could shift from an “either-or” solutions mindset where one pole is preferred over the other (e.g. digital or physical) to embracing a “both-and-with” thinking balancing between poles (e.g. digital and physical) to successfully fuse the digital and the legacy (Lewis and Smith, 2022b; Smith, Lewis and Edmondson, 2022), enact the renewal, and build and maintain momentum for DTs. The outcomes of adopting a paradox mindset in managerial practice are enabling learning and creativity, fostering flexibility and resilience and, finally, unleashing human potential (Lewis and Smith, 2014).

Social implications

The authors propose insight that will equip managers with a mindset that will allow DT to fail less often than current reported rates, which failure may imply potential organisational collapse, financial bankrupt and social crisis.

Originality/value

The authors offer a multidisciplinary review of the DT complementing existing reviews due to the focus on the organisational context of established organisations. Moreover, the authors advance paradoxical thinking as a novel lens through which to study DT in incumbent organisations by proposing an array of potential research questions and new avenues for research. Finally, the authors offer insights for managers to help them thrive in DT by adopting a paradoxical mindset.

Details

European Journal of Innovation Management, vol. 26 no. 7
Type: Research Article
ISSN: 1460-1060

Keywords

Open Access
Article
Publication date: 17 May 2022

M'hamed Bilal Abidine, Mourad Oussalah, Belkacem Fergani and Hakim Lounis

Mobile phone-based human activity recognition (HAR) consists of inferring user’s activity type from the analysis of the inertial mobile sensor data. This paper aims to mainly…

Abstract

Purpose

Mobile phone-based human activity recognition (HAR) consists of inferring user’s activity type from the analysis of the inertial mobile sensor data. This paper aims to mainly introduce a new classification approach called adaptive k-nearest neighbors (AKNN) for intelligent HAR using smartphone inertial sensors with a potential real-time implementation on smartphone platform.

Design/methodology/approach

The proposed method puts forward several modification on AKNN baseline by using kernel discriminant analysis for feature reduction and hybridizing weighted support vector machines and KNN to tackle imbalanced class data set.

Findings

Extensive experiments on a five large scale daily activity recognition data set have been performed to demonstrate the effectiveness of the method in terms of error rate, recall, precision, F1-score and computational/memory resources, with several comparison with state-of-the art methods and other hybridization modes. The results showed that the proposed method can achieve more than 50% improvement in error rate metric and up to 5.6% in F1-score. The training phase is also shown to be reduced by a factor of six compared to baseline, which provides solid assets for smartphone implementation.

Practical implications

This work builds a bridge to already growing work in machine learning related to learning with small data set. Besides, the availability of systems that are able to perform on flight activity recognition on smartphone will have a significant impact in the field of pervasive health care, supporting a variety of practical applications such as elderly care, ambient assisted living and remote monitoring.

Originality/value

The purpose of this study is to build and test an accurate offline model by using only a compact training data that can reduce the computational and memory complexity of the system. This provides grounds for developing new innovative hybridization modes in the context of daily activity recognition and smartphone-based implementation. This study demonstrates that the new AKNN is able to classify the data without any training step because it does not use any model for fitting and only uses memory resources to store the corresponding support vectors.

Details

Sensor Review, vol. 42 no. 4
Type: Research Article
ISSN: 0260-2288

Keywords

Open Access
Article
Publication date: 9 December 2022

Xuwei Pan, Xuemei Zeng and Ling Ding

With the continuous increase of users, resources and tags, social tagging systems gradually present the characteristics of “big data” such as large number, fast growth, complexity…

Abstract

Purpose

With the continuous increase of users, resources and tags, social tagging systems gradually present the characteristics of “big data” such as large number, fast growth, complexity and unreliable quality, which greatly increases the complexity of recommendation. The contradiction between the efficiency and effectiveness of recommendation service in social tagging is increasingly becoming prominent. The purpose of this study is to incorporate topic optimization into collaborative filtering to enhance both the effectiveness and the efficiency of personalized recommendations for social tagging.

Design/methodology/approach

Combining the idea of optimization before service, this paper presents an approach that incorporates topic optimization into collaborative recommendations for social tagging. In the proposed approach, the recommendation process is divided into two phases of offline topic optimization and online recommendation service to achieve high-quality and efficient personalized recommendation services. In the offline phase, the tags' topic model is constructed and then used to optimize the latent preference of users and the latent affiliation of resources on topics.

Findings

Experimental evaluation shows that the proposed approach improves both precision and recall of recommendations, as well as enhances the efficiency of online recommendations compared with the three baseline approaches. The proposed topic optimization–incorporated collaborative recommendation approach can achieve the improvement of both effectiveness and efficiency for the recommendation in social tagging.

Originality/value

With the support of the proposed approach, personalized recommendation in social tagging with high quality and efficiency can be achieved.

Details

Data Technologies and Applications, vol. 58 no. 3
Type: Research Article
ISSN: 2514-9288

Keywords

Open Access
Article
Publication date: 30 September 2019

Jun Yeop Lee and Juhyeon Lee

Using the methodologies of text mining, this paper examines the implications of US and Chinese policies on bilateral trade. Official speeches by political leaders of the U.S. and…

Abstract

Using the methodologies of text mining, this paper examines the implications of US and Chinese policies on bilateral trade. Official speeches by political leaders of the U.S. and China on the issues of trade were collected and analytically examined for US-China gaps in major foreign policies, such as bilateral trade and the Belt and Road Initiative. In this paper, a term frequency-inverse document frequency word cloud, a network similarities index, machine learning-processed latent Dirichlet allocation (LDA), and structural equivalence are applied to examine the meanings of the speeches. The main arguments in this paper are as follows. First, the document similarity between the speeches of Chinese and US leaders appears to be completely different. Also, while the documents from Chinese leaders are considerably similar, the documents from US leaders differ by far. Secondly, LDA topic analysis indicates that China concentrates more on international and collaborative relationships, while the U.S. has more focus on domestic and economic interests. Third, from a word hierarchy analysis, the basic words used by American and Chinese leaders are also completely different. Agriculture, farmers, automobiles, and negotiations are the basic words for American leaders, but for Chinese leaders, the basic words are planning, markets, and education.

Details

Journal of International Logistics and Trade, vol. 17 no. 3
Type: Research Article
ISSN: 1738-2122

Keywords

1 – 10 of 108