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Available. Open Access. Open Access
Article
Publication date: 18 November 2024

Luna Leoni, Ginetta Gueli, Marco Ardolino, Mateus Panizzon and Shivam Gupta

This paper aims to provide empirical evidence on adopting artificial intelligence (AI), including generative AI, in knowledge management (KM) processes and its impact on…

1003

Abstract

Purpose

This paper aims to provide empirical evidence on adopting artificial intelligence (AI), including generative AI, in knowledge management (KM) processes and its impact on organisational decision-making. Specifically, the study addresses three key research questions: RQ1: How is (generative) AI adopted within KM processes in organisations? RQ2: What factors influence the adoption of AI in these processes, either facilitating or inhibiting it? RQ3: How does AI adoption in KM processes affect organisational decision-making?

Design/methodology/approach

An explorative investigation has been conducted through semi-structured interviews with KM and AI experts from a worldwide sample of 52 mostly private, large and for-profit organisations. Interviews have been analysed through a mixed thematic analysis.

Findings

The study provides an original framework in which the three investigated concepts are interconnected according to a dual relationship: linear and retroactive and 20 factors affecting AI adoption within KM processes.

Practical implications

The provided model guides managers in improving their organisational decision-making through AI adoption in KM processes. Moreover, according to the rational decision-making model, the authors propose a six-step systematic procedure for managers.

Originality/value

To the best of the authors’ knowledge, this is the first study that simultaneously addresses AI, KM and decision-making and provides an integrated framework showing the relationships between them, allowing organisations to better and practically understand how to ameliorate their decision-making through AI adoption in KM processes.

Details

Journal of Knowledge Management, vol. 28 no. 11
Type: Research Article
ISSN: 1367-3270

Keywords

Available. Open Access. Open Access
Article
Publication date: 16 April 2024

Luna Leoni

This paper aims to develop a conceptual framework that jointly considers Environmental, Social and Governance (ESG) factors and organisational resilience (OR) components to…

408

Abstract

Purpose

This paper aims to develop a conceptual framework that jointly considers Environmental, Social and Governance (ESG) factors and organisational resilience (OR) components to ameliorate organisations' understanding of sustainability’s overall requirements and related decision-making processes.

Design/methodology/approach

This paper combines ESG and OR through a 3x3 conceptual matrix, where ESG factors are listed along the vertical axis and OR components along the horizontal axis. This results in nine quadrants, which have been read according to two arrangements: (1) static, looking at the specific characteristics of each single quadrant, and (2) dynamic, investigating the relationships between the different quadrants according to the system theory (ST) lens.

Findings

The integration between ESG and OR results in nine organisational typologies, each characterised by a specific focus: (1) green visioning, (2) eco ethos, (3) climate guard, (4) inclusive strategy, (5) empathy ethos, (6) community shield, (7) ethical blueprint, (8) integrity ethos and (9) compliance guard. These typologies and related focuses determine the different strategic options of organisations, the decision-making emphasis concerning ESG factors and OR components and the organisation’s behaviour concerning its internal and external environment. According to ST, the nine typologies interact with each other, emphasising the existence of interconnectedness, interdependence and cascading effects between ESG and OR.

Originality/value

The paper represents a unique attempt to interrelate ESG factors and OR components according to a ST lens, emphasising the dynamic nature of their interactions and organisations’ need for continuous adaptation and learning to make decisions that create sustainable long-term value.

Details

Management Decision, vol. 63 no. 2
Type: Research Article
ISSN: 0025-1747

Keywords

Available. Open Access. Open Access
Article
Publication date: 24 August 2023

Silvia Baiocco, Luna Leoni and Paola Maria Anna Paniccia

This paper aims to enhance understanding of how sustainable entrepreneurship (SE) contributes to sustainable development in the tourism sector. To do so, specific factors that act…

2294

Abstract

Purpose

This paper aims to enhance understanding of how sustainable entrepreneurship (SE) contributes to sustainable development in the tourism sector. To do so, specific factors that act as enablers or inhibitors of SE are identified according to a co-evolutionary lens.

Design/methodology/approach

A co-evolutionary explanation of the firm? Environment relationship is adopted to undertake a qualitative empirical study of the Castelli Romani tourism destination (Italy), via 23 semi-structured interviews according to a narrative approach.

Findings

The paper demonstrates that entrepreneurs play a crucial role in sustainable development but cannot act in isolation. In fact, according to the co-evolutionary approach, they influence and are influenced by 20 factors. Accordingly, SE can be conceptualised as resulting from effective co-evolutionary interactions between micro (i.e. entrepreneurs and their firm), meso (i.e. the destination where tourism firms are based) and macro (i.e. the wider socio-economic and natural system) levels.

Practical implications

Several actions are suggested to entrepreneurs and policymakers to help achieve specific sustainable development goals. These actions focus on: (1) training courses, (2) investments in technologies, (3) creation of innovative business models, (4) exploitation of cultural and natural resources, (5) community involvement and (6) multi-level partnerships.

Originality/value

This is the first study that adopts a co-evolutionary lens to investigate the influencing factors of SE in tourism, shedding light on the effects of their dynamic interdependence. Thus, it provides a more nuanced SE conceptualisation that takes a holistic and dynamic view of sustainability.

Details

Journal of Small Business and Enterprise Development, vol. 30 no. 7
Type: Research Article
ISSN: 1462-6004

Keywords

Available. Content available

Abstract

Details

Journal of Manufacturing Technology Management, vol. 33 no. 4
Type: Research Article
ISSN: 1741-038X

Available. Open Access. Open Access
Article
Publication date: 7 October 2022

Luna Leoni, Marco Ardolino, Jamal El Baz, Ginetta Gueli and Andrea Bacchetti

This paper aims to provide and empirically test a conceptual model in which artificial intelligence (AI), knowledge management processes (KMPs) and supply chain resilience (SCR…

6749

Abstract

Purpose

This paper aims to provide and empirically test a conceptual model in which artificial intelligence (AI), knowledge management processes (KMPs) and supply chain resilience (SCR) are simultaneously considered in terms of their reciprocal relationships and impact on manufacturing firm performance (MFP).

Design/methodology/approach

In the study, six hypotheses have been developed and tested through an empirical survey administered to 120 senior executives of Italian manufacturing firms. The data analysis has been carried out via the partial least squares structural equation modelling approach, using the Advanced Analysis for Composites 2.0 variance-based software program.

Findings

Using a conceptual model validated using an empirical survey, the study sheds light on the relationships between AI, KMPs and SCR, as well as their impacts on MFP. In particular, the authors show the positive effects of the adoption of AI on KMPs, as well as the influence of KMPs on SCR and MFP. Finally, the authors demonstrate that KMPs act as a mediator through which AI affects SCR and MFP.

Practical implications

This study highlights the critical role of KMPs for manufacturing firms that can deploy AI to stimulate KMPs and through attaining a high level of the latter might succeed in enhancing both their SCR and MFP.

Originality/value

This study demonstrates that manufacturing firms interested in properly applying AI to ameliorate their performance and resilience must carefully consider KMPs as a mediator mechanism.

Details

International Journal of Operations & Production Management, vol. 42 no. 13
Type: Research Article
ISSN: 0144-3577

Keywords

Available. Open Access. Open Access
Article
Publication date: 12 February 2024

Ivo Hristov, Matteo Cristofaro, Riccardo Camilli and Luna Leoni

This paper aims to (1) identify the different performance drivers (lead indicators) and outcome measures (lag indicators) investigated in the literature concerning the four…

2412

Abstract

Purpose

This paper aims to (1) identify the different performance drivers (lead indicators) and outcome measures (lag indicators) investigated in the literature concerning the four balanced scorecard (BSC) perspectives in operations management (OM) contexts and (2) understand how performance drivers and outcome measures (and substantiated perspectives) are related.

Design/methodology/approach

We undertake a systematic literature review of the BSC literature in OM journals. From the final sample of 40 articles, performance drivers and outcome measures have been identified, and the relationships amongst them have been synthesised according to the system dynamics approach.

Findings

Findings show (1) the most relevant performance drivers and outcome measures within each BSC perspective, (2) their relationships, (3) how the perspectives are linked through the performance drivers and outcome measures and (4) how the different measures relate systemically. Accordingly, four causal loops amongst identified measures have been built, which – jointly considered – allowed for the creation of a dynamic strategy map for OM.

Originality/value

This study is the first one that provides a comprehensive and holistic view of how the different performance drivers and outcome measures within and between the four BSC perspectives in OM relate systemically, increasing the knowledge and understanding of scholars and practitioners.

Details

Journal of Manufacturing Technology Management, vol. 35 no. 4
Type: Research Article
ISSN: 1741-038X

Keywords

Available. Content available
Article
Publication date: 1 February 2016

19

Abstract

Details

Journal of Manufacturing Technology Management, vol. 27 no. 1
Type: Research Article
ISSN: 1741-038X

Available. Content available

Abstract

Details

Journal of Manufacturing Technology Management, vol. 35 no. 4
Type: Research Article
ISSN: 1741-038X

Available. Open Access. Open Access
Article
Publication date: 1 March 2023

Francesco Leoni, Martina Carraro, Erin McAuliffe and Stefano Maffei

The purpose of this paper is three-fold. Firstly, through selected case studies, to provide an overview of how non-traditional data from digital public services were used as a…

1558

Abstract

Purpose

The purpose of this paper is three-fold. Firstly, through selected case studies, to provide an overview of how non-traditional data from digital public services were used as a source of knowledge for policymaking. Secondly, to argue for a design for policy approach to support the successful integration of non-traditional data into policymaking practice, thus supporting data-driven innovation for policymaking. Thirdly, to encourage a vision of the relation between data-driven innovation and public policy that considers policymaking outside the authoritative instrumental logic perspective.

Design/methodology/approach

A qualitative small-N case study analysis based on desk research data was developed to provide an overview of how data-centric public services could become a source of knowledge for policymaking. The analysis was based on an original theoretical-conceptual framework that merges the policy cycle model and the policy capacity framework.

Findings

This paper identifies three potential areas of contribution of a design for policy approach in a scenario of data-driven innovation for policymaking practice: the development of sensemaking and prefiguring activities to shape a shared rationale behind intra-/inter-organisational data sharing and data collaboratives; the realisation of collaborative experimentations for enhancing the systemic policy analytical capacity of a governing body, e.g. by integrating non-traditional data into new and trusted indicators for policy evaluation; and service design as approach for data-centric public services that connects policy decisions to the socio-technical context in which data are collected.

Research limitations/implications

The small-N sample (four cases) selected is not representative of a broader population but isolates exemplary initiatives. Moreover, the analysis was based on secondary sources, limiting the assessment quality of the real use of non-traditional data for policymaking. This level of empirical understanding is considered sufficient for an explorative analysis that supports the original perspective proposed here. Future research will need to collect primary data about the potential and dynamics of how data from data-centric public services can inform policymaking and substantiate the proposed areas of a design for policy contribution with practical experimentations and cases.

Originality/value

This paper proposes a convergence, yet largely underexplored, between the two emerging perspectives on innovation in policymaking: data for policy and design for policy. This convergence helps to address the designing of data-driven innovations for policymaking, while considering pragmatic indications of socially acceptable practices in this space for practitioners.

Details

Transforming Government: People, Process and Policy, vol. 17 no. 3
Type: Research Article
ISSN: 1750-6166

Keywords

Available. Open Access. Open Access
Article
Publication date: 25 October 2019

Ning Yan and Oliver Tat-Sheung Au

The purpose of this paper is to make a correlation analysis between students’ online learning behavior features and course grade, and to attempt to build some effective prediction…

8348

Abstract

Purpose

The purpose of this paper is to make a correlation analysis between students’ online learning behavior features and course grade, and to attempt to build some effective prediction model based on limited data.

Design/methodology/approach

The prediction label in this paper is the course grade of students, and the eigenvalues available are student age, student gender, connection time, hits count and days of access. The machine learning model used in this paper is the classical three-layer feedforward neural networks, and the scaled conjugate gradient algorithm is adopted. Pearson correlation analysis method is used to find the relationships between course grade and the student eigenvalues.

Findings

Days of access has the highest correlation with course grade, followed by hits count, and connection time is less relevant to students’ course grade. Student age and gender have the lowest correlation with course grade. Binary classification models have much higher prediction accuracy than multi-class classification models. Data normalization and data discretization can effectively improve the prediction accuracy of machine learning models, such as ANN model in this paper.

Originality/value

This paper may help teachers to find some clue to identify students with learning difficulties in advance and give timely help through the online learning behavior data. It shows that acceptable prediction models based on machine learning can be built using a small and limited data set. However, introducing external data into machine learning models to improve its prediction accuracy is still a valuable and hard issue.

Details

Asian Association of Open Universities Journal, vol. 14 no. 2
Type: Research Article
ISSN: 2414-6994

Keywords

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