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Open Access
Article
Publication date: 15 August 2023

Doreen Nkirote Bundi

The purpose of this study is to examine the state of research into adoption of machine learning systems within the health sector, to identify themes that have been studied and…

Abstract

Purpose

The purpose of this study is to examine the state of research into adoption of machine learning systems within the health sector, to identify themes that have been studied and observe the important gaps in the literature that can inform a research agenda going forward.

Design/methodology/approach

A systematic literature strategy was utilized to identify and analyze scientific papers between 2012 and 2022. A total of 28 articles were identified and reviewed.

Findings

The outcomes reveal that while advances in machine learning have the potential to improve service access and delivery, there have been sporadic growth of literature in this area which is perhaps surprising given the immense potential of machine learning within the health sector. The findings further reveal that themes such as recordkeeping, drugs development and streamlining of treatment have primarily been focused on by the majority of authors in this area.

Research limitations/implications

The search was limited to journal articles published in English, resulting in the exclusion of studies disseminated through alternative channels, such as conferences, and those published in languages other than English. Considering that scholars in developing nations may encounter less difficulty in disseminating their work through alternative channels and that numerous emerging nations employ languages other than English, it is plausible that certain research has been overlooked in the present investigation.

Originality/value

This review provides insights into future research avenues for theory, content and context on adoption of machine learning within the health sector.

Details

Digital Transformation and Society, vol. 3 no. 1
Type: Research Article
ISSN: 2755-0761

Keywords

Open Access
Article
Publication date: 16 November 2023

Bahareh Farhoudinia, Selcen Ozturkcan and Nihat Kasap

This paper aims to conduct an interdisciplinary systematic literature review (SLR) of fake news research and to advance the socio-technical understanding of digital information…

1107

Abstract

Purpose

This paper aims to conduct an interdisciplinary systematic literature review (SLR) of fake news research and to advance the socio-technical understanding of digital information practices and platforms in business and management studies.

Design/methodology/approach

The paper applies a focused, SLR method to analyze articles on fake news in business and management journals from 2010 to 2020.

Findings

The paper analyzes the definition, theoretical frameworks, methods and research gaps of fake news in the business and management domains. It also identifies some promising research opportunities for future scholars.

Practical implications

The paper offers practical implications for various stakeholders who are affected by or involved in fake news dissemination, such as brands, consumers and policymakers. It provides recommendations to cope with the challenges and risks of fake news.

Social implications

The paper discusses the social consequences and future threats of fake news, especially in relation to social networking and social media. It calls for more awareness and responsibility from online communities to prevent and combat fake news.

Originality/value

The paper contributes to the literature on information management by showing the importance and consequences of fake news sharing for societies. It is among the frontier systematic reviews in the field that covers studies from different disciplines and focuses on business and management studies.

Details

Aslib Journal of Information Management, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2050-3806

Keywords

Article
Publication date: 25 October 2023

Magdalena Maria Popowska and Monika Sady

This study aims to identify a sustainable university’s key features. It is an essential step in tracing the topics discussed in the context of a sustainable university and their…

Abstract

Purpose

This study aims to identify a sustainable university’s key features. It is an essential step in tracing the topics discussed in the context of a sustainable university and their evolution in the scientific discourse.

Design/methodology/approach

This paper relies on a systematic literature review (SLR) conducted using two scholarly databases: Emerald and Scopus. The timeframe selected by the authors for reviewing the available sources spans from 2001 to 2021.

Findings

The analysis distinguished seven sustainable university categories, each revealing critical features of sustainable higher education. Each of these categories represents an intriguing area for in-depth analysis. The SLR reveals gaps requiring further scientific exploration.

Research limitations/implications

The performed literature review was determined by the choice of entries (keywords) to identify the scientific papers in the selected databases. Moreover, as the authors aimed to focus on peer-reviewed sources, this SLR did not include books and doctoral dissertations dealing with the studied issues.

Practical implications

The results of the analysis can be used practically by both researchers and practitioners in the field of sustainable development (SD). Identified scientific gaps become a potential research field, and practitioners interested in the transition toward SD may contribute by accompanying universities in this journey. Collaboration and networking with business stakeholders are critical vectors for spreading the idea of SD.

Social implications

Society’s growing concern for climate change requires accurate and specific actions from institutions. As entities educating future generations, universities have a unique role in transforming toward SD. The findings allow us to get acquainted with the existing main activities undertaken by higher education institutions in this field and understand the importance of this topic for researchers.

Originality/value

SLR is a cornerstone of research synthesis and helps integrate scientific evidence from qualitative and quantitative published studies. Conducted research presents knowledge about university sustainability and can help scientists find research gaps.

Details

International Journal of Sustainability in Higher Education, vol. 25 no. 3
Type: Research Article
ISSN: 1467-6370

Keywords

Article
Publication date: 10 March 2022

Jayaram Boga and Dhilip Kumar V.

For achieving the profitable human activity recognition (HAR) method, this paper solves the HAR problem under wireless body area network (WBAN) using a developed ensemble learning…

94

Abstract

Purpose

For achieving the profitable human activity recognition (HAR) method, this paper solves the HAR problem under wireless body area network (WBAN) using a developed ensemble learning approach. The purpose of this study is,to solve the HAR problem under WBAN using a developed ensemble learning approach for achieving the profitable HAR method. There are three data sets used for this HAR in WBAN, namely, human activity recognition using smartphones, wireless sensor data mining and Kaggle. The proposed model undergoes four phases, namely, “pre-processing, feature extraction, feature selection and classification.” Here, the data can be preprocessed by artifacts removal and median filtering techniques. Then, the features are extracted by techniques such as “t-Distributed Stochastic Neighbor Embedding”, “Short-time Fourier transform” and statistical approaches. The weighted optimal feature selection is considered as the next step for selecting the important features based on computing the data variance of each class. This new feature selection is achieved by the hybrid coyote Jaya optimization (HCJO). Finally, the meta-heuristic-based ensemble learning approach is used as a new recognition approach with three classifiers, namely, “support vector machine (SVM), deep neural network (DNN) and fuzzy classifiers.” Experimental analysis is performed.

Design/methodology/approach

The proposed HCJO algorithm was developed for optimizing the membership function of fuzzy, iteration limit of SVM and hidden neuron count of DNN for getting superior classified outcomes and to enhance the performance of ensemble classification.

Findings

The accuracy for enhanced HAR model was pretty high in comparison to conventional models, i.e. higher than 6.66% to fuzzy, 4.34% to DNN, 4.34% to SVM, 7.86% to ensemble and 6.66% to Improved Sealion optimization algorithm-Attention Pyramid-Convolutional Neural Network-AP-CNN, respectively.

Originality/value

The suggested HAR model with WBAN using HCJO algorithm is accurate and improves the effectiveness of the recognition.

Details

International Journal of Pervasive Computing and Communications, vol. 19 no. 4
Type: Research Article
ISSN: 1742-7371

Keywords

Article
Publication date: 16 December 2022

Fatemeh Mozaffari, Marzieh Rahimi, Hamidreza Yazdani and Babak Sohrabi

This research intends to develop a model for predicting employees at a high-risk attrition and identify the most important factors affecting them.

Abstract

Purpose

This research intends to develop a model for predicting employees at a high-risk attrition and identify the most important factors affecting them.

Design/methodology/approach

In this study, using the triangulation technique of a mixed research method, the employee attrition problem is investigated by identifying its affecting factors. For that matter, data related to the human resources department of a pharmaceutical company in Iran are used. And to achieve the intended goal, advanced data mining algorithms and interviews with human resource managers are applied.

Findings

A model for predicting employees at a high-risk attrition is presented based on the gradient boosting machine algorithm with 89% accuracy. The use of the mixed research approach shows that qualitative and quantitative methods can be more effective in identifying the factors affecting employee churn or loss of staff. The results also contain a new situation arising out of the COVID-19 pandemic and remote working scenarios having impact on employee attrition. Finally, human resource policies are presented based on variables related to each of the identified factors.

Originality/value

The novel contributions of this study include real data related to a leading pharmaceutical company as well as a combination of two quantitative and qualitative methods. The hybrid approach can identify the reasons for attrition and, consequently, retention policies to benefit from the advantage of both approaches. Data mining can be useful to identify the factors, which are usually not mentioned in termination interviews, such as direct managers. On the other hand, the results obtained from termination interviews can also include features that the authors cannot identify through data mining, which are specifically related to the characteristics of the pharmaceutical industry such as building a more professional career path. From a practical perspective, since this company specializes in pharmaceutical marketing in a new way and is primarily comprised graduates, it is important to note that the churn of specialized people disperses organizational and technological know-how. On the other hand, the pharmacist community in Iran is small, and their attrition might adversely affect not only the reputation of an organization but the employer's brand as well. So, this research would help other similar firms in retaining their valuable human capital.

Details

Benchmarking: An International Journal, vol. 30 no. 10
Type: Research Article
ISSN: 1463-5771

Keywords

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