Search results

1 – 10 of 13
Article
Publication date: 6 December 2023

Ananya Hadadi Raghavendra, Siddharth Gaurav Majhi, Arindam Mukherjee and Pradip Kumar Bala

This study aims to examine the current state of academic research pertaining to the role played by artificial intelligence (AI) in the achievement of a critical sustainable…

Abstract

Purpose

This study aims to examine the current state of academic research pertaining to the role played by artificial intelligence (AI) in the achievement of a critical sustainable development goal (SDG) – poverty alleviation and describe the field’s development by identifying themes, trends, roadblocks and promising areas for the future.

Design/methodology/approach

The authors analysed a corpus of 253 studies collected from the Scopus database to examine the current state of the academic literature using bibliometric methods.

Findings

This paper identifies and analyses key trends in the evolution of this domain. Further, the paper distils the extant literature to unpack the intermediary mechanisms through which AI and related technologies help tackle the critical global issue of poverty.

Research limitations/implications

The corpus of literature used for the analysis is limited to English language studies from the Scopus database. The paper contributes to the extant research on AI for social good, and more broadly to the research on the value of emerging technologies such as AI.

Practical implications

Policymakers and government agencies will get an understanding of how technological interventions such as AI can help achieve critical SDGs such as poverty alleviation (SDG-1).

Social implications

The primary focus of this paper is on the role of AI-related technological interventions to achieve a significant social objective – poverty alleviation.

Originality/value

To the best of the authors’ knowledge, this is the first study to conduct a comprehensive bibliometric analysis of a critical research domain such as AI and poverty alleviation.

Details

VINE Journal of Information and Knowledge Management Systems, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2059-5891

Keywords

Article
Publication date: 17 October 2023

Aaron Tham, Yulin Liu and Poh Theng Loo

This study aims to survey the existing body of knowledge about digital innovation within museums. Academic and practitioner interest in digital innovation has been piqued as…

Abstract

Purpose

This study aims to survey the existing body of knowledge about digital innovation within museums. Academic and practitioner interest in digital innovation has been piqued as technological advances that enable the rapid personalisation of information to cater towards increasingly sophisticated end-user expectations. Yet, the literature on digital innovation in the museum environment appears fragmented and lacks theoretical grounding.

Design/methodology/approach

Drawing on a systematic scoping review of 199 articles published during 2010–2021, this paper provides an initial indication of the size and scope of current research literature on digital innovation of museums.

Findings

This literature review elucidates the status quo and future directions of digital innovation in museum space. An integral conceptual framework is proposed to provide a comprehensive lens to steer future research and practice in this area in a theoretically grounded and systemic manner.

Originality/value

This study mixes both quantitative and qualitative analyses of the literature to produce an up-to-date understanding of extant research by illuminating inspiring processes, foregrounding commonly encountered challenges, framing theoretical and practical implications and proposing avenues for future research.

目的

本文旨在考察有关博物馆数字创新的既有知识。当前学界与业界对于数字创新均抱有浓厚的兴趣, 因为技术进步使信息能够快速个性化, 以满足日益复杂的终端用户需求。然而, 关于博物馆数字创新的文献显得零散且缺乏理论基础。

设计/方法

基于对2010至2021年期间发表的199篇文章的系统性范围性综述, 本文初步描绘了有关博物馆数字创新的既有文献的规模和研究范围。

发现

本文献综述阐明了博物馆数字创新的现状和未来方向, 提出了一个整合的概念框架, 从而期望以一个全面的视角引导该领域今后开展基于理论和系统化的研究与实践。

原创性

本研究综合了文献的定量和定性分析, 以呈现对既有研究的最新理解:包括阐明激发过程, 突出常见挑战, 框定理论和实践影响, 并提出未来研究的途径。

Objetivo

Este estudio tiene como objetivo examinar el conocimiento existente acerca de la innovación digital en los museos. El interés del mundo académico y de los profesionales por la innovación digital se ha visto avivado por los avances tecnológicos que permiten una rápida personalización de la información para satisfacer las expectativas cada vez más sofisticadas de los usuarios finales. Sin embargo, la bibliografía sobre la innovación digital en el entorno museístico parece fragmentada y carece de base teórica.

Diseño/metodología/enfoque

A partir de una revisión sistemática del alcance de 199 artículos publicados entre 2010 y 2021, este documento proporciona una indicación inicial del tamaño y el alcance de la literatura de investigación actual sobre la innovación digital de los museos.

Resultados

Esta revisión bibliográfica dilucida el statu quo y las direcciones futuras de la innovación digital en el espacio museístico. Se propone un marco conceptual integral que proporcione una lente comprensiva para dirigir la investigación y la práctica futuras en este ámbito de una manera teóricamente fundamentada y sistémica.

Originalidad

Este estudio mezcla análisis cuantitativos y cualitativos de la bibliografía para producir una comprensión actualizada de la investigación existente iluminando los procesos inspiradores, poniendo en primer plano los retos comúnmente encontrados, enmarcando las implicaciones teóricas y prácticas y proponiendo vías para futuras investigaciones.

Article
Publication date: 27 March 2024

Jyoti Mudkanna Gavhane and Reena Pagare

The purpose of this study was to analyze importance of artificial intelligence (AI) in education and its emphasis on assessment and adversity quotient (AQ).

Abstract

Purpose

The purpose of this study was to analyze importance of artificial intelligence (AI) in education and its emphasis on assessment and adversity quotient (AQ).

Design/methodology/approach

The study utilizes a systematic literature review of over 141 journal papers and psychometric tests to evaluate AQ. Thematic analysis of quantitative and qualitative studies explores domains of AI in education.

Findings

Results suggest that assessing the AQ of students with the help of AI techniques is necessary. Education is a vital tool to develop and improve natural intelligence, and this survey presents the discourse use of AI techniques and behavioral strategies in the education sector of the recent era. The study proposes a conceptual framework of AQ with the help of assessment style for higher education undergraduates.

Originality/value

Research on AQ evaluation in the Indian context is still emerging, presenting a potential avenue for future research. Investigating the relationship between AQ and academic performance among Indian students is a crucial area of research. This can provide insights into the role of AQ in academic motivation, persistence and success in different academic disciplines and levels of education. AQ evaluation offers valuable insights into how individuals deal with and overcome challenges. The findings of this study have implications for higher education institutions to prepare for future challenges and better equip students with necessary skills for success. The papers reviewed related to AI for education opens research opportunities in the field of psychometrics, educational assessment and the evaluation of AQ.

Details

Education + Training, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0040-0912

Keywords

Article
Publication date: 27 November 2023

Gopal Krushna Gouda and Binita Tiwari

Smart HR 4.0 is a new concept characterized by adopting innovative technologies of Industry 4.0 (I4.0) in the HR domain. This study attempts to identify the key factors of Smart…

Abstract

Purpose

Smart HR 4.0 is a new concept characterized by adopting innovative technologies of Industry 4.0 (I4.0) in the HR domain. This study attempts to identify the key factors of Smart HR 4.0 to foster organizational innovation ambidexterity.

Design/methodology/approach

Based on review of literature and survey from expert opinions by using the Delphi method, 12 factors were found most suitable for this study. Further, the fuzzy-TISM technique was used to establish contextual relationships and develop a hierarchical model on the identified factors. Subsequently, the MICMAC analysis was applied to classify these factors according to their driving and dependence power.

Findings

This study framed a conceptual hierarchical model of Smart HR 4.0 and established contextual relationships among identified factors. Result shows that smart organic structure, industry–institute interface, IT-enabled system and ambidextrous leadership are important factors as they have the highest driving power. Further, knowledge management, learning culture and psychological empowerment are the linkage factors having both driving as well as dependency power in the whole system.

Practical implications

This study can guide the managers in smoothly implementing these practices to manage their human capital amidst digital disruption, ensuring innovation competitiveness of the firm. The structural hierarchical framework of Smart HR 4.0 may serve as a blueprint for HR professionals and business leaders to attain organizational innovation ambidexterity in the current wave of digital disruptions (Industry 4.0).

Originality/value

This study provides a holistic model of smart HR 4.0 integrating innovation ambidexterity in I4.0.

Details

Journal of Organizational Effectiveness: People and Performance, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2051-6614

Keywords

Article
Publication date: 1 January 2024

Youngjoon Yu, Jae-Hyeon Ahn, Dongyeon Kim and Kyuhong Park

While prior studies have explored the relationship between visual appeal and purchasing decisions, the role of bookmarking has largely been underemphasized. This research aims to…

Abstract

Purpose

While prior studies have explored the relationship between visual appeal and purchasing decisions, the role of bookmarking has largely been underemphasized. This research aims to address this gap by focusing on the impact of bookmarking on consumer behavior, guided by the cognitive load theory and dual-system theory.

Design/methodology/approach

The authors executed a controlled experiment and analyzed the results using a two-stage regression method that linked visual appeal, bookmarking and purchase intent. Further empirical analysis was conducted to authenticate the authors' proposed model, utilizing real-world mobile commerce data from a clothing company.

Findings

This study's findings suggest that visual appeal influences purchase intent primarily through the full mediation of bookmarking, rather than exerting a direct influence. Furthermore, an increase in colorfulness corresponds positively with visual appeal, while visual complexity exhibits an inverted U-shaped relationship with it.

Originality/value

This study provides novel insights into the choice-set formation process through the theoretical lens of dual-system theory. Additionally, the authors employed an image processing technique to quantify a product's visual appeal as depicted in a photograph. This study also incorporates a comprehensive econometric analysis to connect the objective aspects of visual appeal with subjective responses.

Details

Industrial Management & Data Systems, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0263-5577

Keywords

Article
Publication date: 4 July 2023

Jantanee Dumrak and Seyed Ashkan Zarghami

The purpose of this article is to analyze the existing studies on the application of artificial intelligence (AI) in lean construction management (LCM). Further, this study offers…

Abstract

Purpose

The purpose of this article is to analyze the existing studies on the application of artificial intelligence (AI) in lean construction management (LCM). Further, this study offers a classification scheme that specifies different categories of AI tools, as applied to the field of LCM to support various principles of LCM.

Design/methodology/approach

This research adopts the systematic literature review (SLR) process, which consists of five consecutive steps: planning, searching, screening, extraction and synthesis and reporting. As a supplement to SLR, a bibliometric analysis is performed to examine the quantity and citation impact of the reviewed papers.

Findings

In this paper, seven key areas related to the principles of LCM for which AI tools have been used are identified. The findings of this research clarify how AI can assist in bolstering the practice of LCM. Further, this article presents directions for the future evolution of AI tools in LCM based on the current emerging trends.

Practical implications

This paper advances the LCM systems by offering a lens through which construction managers can better understand key concepts in the linkage of AI to LCM.

Originality/value

This research offers a new classification scheme that allows researchers to properly recall, identify and group various applications of AI categories in the construction industry based on various principles of LCM. In addition, this study provides a source of references for researchers in the LCM discipline, which advances knowledge and facilitates theory development in the field.

Details

Engineering, Construction and Architectural Management, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0969-9988

Keywords

Open Access
Article
Publication date: 29 March 2024

Haihan Li, Per Hilletofth, David Eriksson and Wendy Tate

This study aims to investigate the manufacturing reshoring decision-making content from an Eclectic Paradigm perspective.

Abstract

Purpose

This study aims to investigate the manufacturing reshoring decision-making content from an Eclectic Paradigm perspective.

Design/methodology/approach

Data were collected through a six-step systematic literature review on factors influencing manufacturing reshoring decision-making. The review is based on 100 peer-reviewed journal papers discussing reshoring decision-making contents published from 2009 to 2022.

Findings

In total, 80 decision factors were extracted and then categorized into resource-seeking (8%), market-seeking (11%), efficiency-seeking (41%) and strategic asset-seeking (16%) advantages. Additionally, 24% of these were identified as hybrid, which means that they were classified into multiple categories. Some decision factors were further identified as reshoring influencing factors (i.e. drivers, enablers and barriers).

Research limitations/implications

Scholars need to consider what other theories can be used or developed to identify and evaluate the decision factors (determinants) of manufacturing reshoring as well as how currently adopted theory can be further advanced to create clearer and comprehensive theoretical frameworks.

Practical implications

This research underscores the importance of developing clearer and more comprehensive theoretical frameworks. For practitioners, understanding the multifaceted nature of decision factors could enhance strategic decision-making regarding reshoring initiatives.

Originality/value

To the best of the authors’ knowledge, this is the first study to investigate the value and practicality of the Eclectic Paradigm in categorizing factors in manufacturing reshoring decision-making content and presents in-depth theoretical classifications. In addition, it bridges the gap between decision factors and influencing factors in the decision-making content research realm.

Details

European Business Review, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0955-534X

Keywords

Article
Publication date: 8 August 2023

Smita Abhijit Ganjare, Sunil M. Satao and Vaibhav Narwane

In today's fast developing era, the volume of data is increasing day by day. The traditional methods are lagging for efficiently managing the huge amount of data. The adoption of…

Abstract

Purpose

In today's fast developing era, the volume of data is increasing day by day. The traditional methods are lagging for efficiently managing the huge amount of data. The adoption of machine learning techniques helps in efficient management of data and draws relevant patterns from that data. The main aim of this research paper is to provide brief information about the proposed adoption of machine learning techniques in different sectors of manufacturing supply chain.

Design/methodology/approach

This research paper has done rigorous systematic literature review of adoption of machine learning techniques in manufacturing supply chain from year 2015 to 2023. Out of 511 papers, 74 papers are shortlisted for detailed analysis.

Findings

The papers are subcategorised into 8 sections which helps in scrutinizing the work done in manufacturing supply chain. This paper helps in finding out the contribution of application of machine learning techniques in manufacturing field mostly in automotive sector.

Practical implications

The research is limited to papers published from year 2015 to year 2023. The limitation of the current research that book chapters, unpublished work, white papers and conference papers are not considered for study. Only English language articles and review papers are studied in brief. This study helps in adoption of machine learning techniques in manufacturing supply chain.

Originality/value

This study is one of the few studies which investigate machine learning techniques in manufacturing sector and supply chain through systematic literature survey.

Highlights

  1. A comprehensive understanding of Machine Learning techniques is presented.

  2. The state of art of adoption of Machine Learning techniques are investigated.

  3. The methodology of (SLR) is proposed.

  4. An innovative study of Machine Learning techniques in manufacturing supply chain.

A comprehensive understanding of Machine Learning techniques is presented.

The state of art of adoption of Machine Learning techniques are investigated.

The methodology of (SLR) is proposed.

An innovative study of Machine Learning techniques in manufacturing supply chain.

Details

The TQM Journal, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1754-2731

Keywords

Open Access
Article
Publication date: 18 October 2023

Ivan Soukal, Jan Mačí, Gabriela Trnková, Libuse Svobodova, Martina Hedvičáková, Eva Hamplova, Petra Maresova and Frank Lefley

The primary purpose of this paper is to identify the so-called core authors and their publications according to pre-defined criteria and thereby direct the users to the fastest…

Abstract

Purpose

The primary purpose of this paper is to identify the so-called core authors and their publications according to pre-defined criteria and thereby direct the users to the fastest and easiest way to get a picture of the otherwise pervasive field of bankruptcy prediction models. The authors aim to present state-of-the-art bankruptcy prediction models assembled by the field's core authors and critically examine the approaches and methods adopted.

Design/methodology/approach

The authors conducted a literature search in November 2022 through scientific databases Scopus, ScienceDirect and the Web of Science, focussing on a publication period from 2010 to 2022. The database search query was formulated as “Bankruptcy Prediction” and “Model or Tool”. However, the authors intentionally did not specify any model or tool to make the search non-discriminatory. The authors reviewed over 7,300 articles.

Findings

This paper has addressed the research questions: (1) What are the most important publications of the core authors in terms of the target country, size of the sample, sector of the economy and specialization in SME? (2) What are the most used methods for deriving or adjusting models appearing in the articles of the core authors? (3) To what extent do the core authors include accounting-based variables, non-financial or macroeconomic indicators, in their prediction models? Despite the advantages of new-age methods, based on the information in the articles analyzed, it can be deduced that conventional methods will continue to be beneficial, mainly due to the higher degree of ease of use and the transferability of the derived model.

Research limitations/implications

The authors identify several gaps in the literature which this research does not address but could be the focus of future research.

Practical implications

The authors provide practitioners and academics with an extract from a wide range of studies, available in scientific databases, on bankruptcy prediction models or tools, resulting in a large number of records being reviewed. This research will interest shareholders, corporations, and financial institutions interested in models of financial distress prediction or bankruptcy prediction to help identify troubled firms in the early stages of distress.

Social implications

Bankruptcy is a major concern for society in general, especially in today's economic environment. Therefore, being able to predict possible business failure at an early stage will give an organization time to address the issue and maybe avoid bankruptcy.

Originality/value

To the authors' knowledge, this is the first paper to identify the core authors in the bankruptcy prediction model and methods field. The primary value of the study is the current overview and analysis of the theoretical and practical development of knowledge in this field in the form of the construction of new models using classical or new-age methods. Also, the paper adds value by critically examining existing models and their modifications, including a discussion of the benefits of non-accounting variables usage.

Details

Central European Management Journal, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2658-0845

Keywords

Article
Publication date: 13 February 2024

Amer Jazairy, Emil Persson, Mazen Brho, Robin von Haartman and Per Hilletofth

This study presents a systematic literature review (SLR) of the interdisciplinary literature on drones in last-mile delivery (LMD) to extrapolate pertinent insights from and into…

Abstract

Purpose

This study presents a systematic literature review (SLR) of the interdisciplinary literature on drones in last-mile delivery (LMD) to extrapolate pertinent insights from and into the logistics management field.

Design/methodology/approach

Rooting their analytical categories in the LMD literature, the authors performed a deductive, theory refinement SLR on 307 interdisciplinary journal articles published during 2015–2022 to integrate this emergent phenomenon into the field.

Findings

The authors derived the potentials, challenges and solutions of drone deliveries in relation to 12 LMD criteria dispersed across four stakeholder groups: senders, receivers, regulators and societies. Relationships between these criteria were also identified.

Research limitations/implications

This review contributes to logistics management by offering a current, nuanced and multifaceted discussion of drones' potential to improve the LMD process together with the challenges and solutions involved.

Practical implications

The authors provide logistics managers with a holistic roadmap to help them make informed decisions about adopting drones in their delivery systems. Regulators and society members also gain insights into the prospects, requirements and repercussions of drone deliveries.

Originality/value

This is one of the first SLRs on drone applications in LMD from a logistics management perspective.

Details

The International Journal of Logistics Management, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0957-4093

Keywords

1 – 10 of 13