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This study intends to analyze the relationship between the digital maturity of SMEs and intellectual capital, investigating the determining factors. Starting from the endowment in terms of intellectual capital and evaluating Management Style, Decision-Making Competences, and Business Network, a model is proposed aiming to provide a comprehensive measure of SMEs’ digital maturity and thus to improve understanding and, consequently, effectiveness. The empirical analysis allows assessing the validity and applicability of the suggested model, providing valuable insights for the improvement of digital strategy and competitiveness of SMEs in the Amalfi Coast Tourist District (Italy), with evident implications also for policymakers and the community.

A mixed-methods research strategy was utilized to confirm research hypotheses that were derived from literature review. The field study was organized into two separate phases: the first phase, which is qualitative, employed focus groups comprising key stakeholders (managers and entrepreneurs) from various companies within the Amalfi Coast Tourist District. This phase adhered to the principles of homogeneity (to facilitate deeper discussions) and heterogeneity (to allow for a broader range of viewpoints among participants). The insights gathered from these preliminary focus groups informed the subsequent quantitative phase. In this second phase, structured interviews were conducted using a questionnaire to probe the participants’ views on digital maturity. This analysis involved 94 companies, all part of the Amalfi Coast Tourist District, assessing their digitalization levels and highlighting key management attributes. Logistic regression was applied to quantitatively analyze the data, effectively assessing the impact of various independent variables (such as Management Style, Decision-Making Competencies and Business Network) on the dependent variable, digital maturity. Employing both qualitative and quantitative methods provides a thorough and nuanced understanding of the digital maturity landscape within the specified context.

The main results suggest the existence of a correlation between the analyzed variables and digital maturity. Innovation, indeed, increases by applying a data-driven leadership style. Intellectual capital (measured in its three components of human capital: decision-making competences; structural capital: management style; and relational capital: business network) influences digital maturity, although some of the variables used are not equally weighted.

The main contribution of this article is to provide an in-depth understanding of the company components that favor digital maturity, to support strategic choices oriented towards a conscious digital transition. The results enrich the existing literature on intellectual capital in terms of its contribution to the digitalization of organizations, which can be a critical success factor in the context of SMEs.

This paper aims to explore the intersection of artificial intelligence (AI) and marketing within the context of knowledge management (KM). It investigates how AI technologies facilitate data-driven decision-making, enhance business communication, improve customer personalization, optimize marketing campaigns and boost overall marketing effectiveness.

This study uses a quantitative and systematic approach, integrating citation analysis, text mining and co-citation analysis to examine foundational research areas and the evolution of AI in marketing. This comprehensive analysis addresses the current gap in empirical investigations of AI’s influence on marketing and its future developments.

This study identifies three main perspectives that have shaped the foundation of AI in marketing: proxy, tool and ensemble views. It develops a managerially relevant conceptual framework that outlines future research directions and expands the boundaries of AI and marketing literature within the KM landscape.

This research proposes a conceptual model that integrates AI and marketing within the KM context, offering new research trajectories. This study provides a holistic view of how AI can enhance knowledge sharing, strategic planning and decision-making in marketing.

The paper aims at addressing the following research questions: does institutional quality improve countries' environmental efficiency? And which pillars of institutional quality improve countries' environmental efficiency?

By specifying a directional distance function in the context of stochastic frontier method where GHG emissions are considered as the bad output and the GDP is referred as the desirable one, the work computes the environmental efficiency into the appraisal of a production function for the European countries over three decades.

According to the countries' performance, the findings confirm that high and upper middle-income countries have higher environmental efficiency compared to low middle-income countries. In this environmental context, the role of institutional quality turns out to be really important in improving the environmental efficiency for high income countries.

This article attempts to analyze the role of different dimensions of institutional quality in different European countries' performance – in terms of mitigating GHGs (undesirable output) – while trying to raise their economic performance through their GDP (desirable output).

1. The paper aims at addressing the following research question: does institutional quality improve countries' environmental efficiency?

2. We adopt a directional distance function in the context of stochastic frontier method, considering 40 European economies over a 30-year time interval.

3. The findings confirm that high and upper middle-income countries have higher environmental efficiency compared to low middle-income countries.

4. The role of institutional quality turns out to be really important in improving the environmental efficiency for high income countries, while the performance decreases for the low middle-income countries.

The paper aims at addressing the following research question: does institutional quality improve countries' environmental efficiency?

We adopt a directional distance function in the context of stochastic frontier method, considering 40 European economies over a 30-year time interval.

The findings confirm that high and upper middle-income countries have higher environmental efficiency compared to low middle-income countries.

The role of institutional quality turns out to be really important in improving the environmental efficiency for high income countries, while the performance decreases for the low middle-income countries.

Given the workplace’s reinvention to accommodate the global pandemic’s novel conditions, knowledge hiding (KH) behaviour in knowledge-intensive organisations must be examined from a fresh perspective. In this context, the relationship between workplace ostracism (WO) as KH’s antecedent and quiet quitting (QQ) as its consequence is undertaken via the mediating role of KH behaviour among knowledge workers (KWs).

Through stratified sampling, data from 649 KWs is obtained to test the model. Partial least squares structural equation modelling (PLS-SEM) using SMART-PLS 4.0. software establishes a significant influence of WO on KH and QQ. KH significantly mediates the relationship between WO and QQ, highlighting its critical intermediary role PLSPredict evaluates the model’s predictiveness. WO and KH’s effects on QQ are examined using necessity logic by collectively applying PLS-SEM and necessary condition analysis (NCA).

The model wherein WO plays a significant role in increasing KH and QQ, with KH as a partial mediator in the relationship, has high predictive relevance. Moreover, NCA confirms WO as the key predictor variable that provides variance in QQ, followed by KH. The Importance-performance map analysis technique supports the study’s managerial implications.

This study enriches QQ’s emerging literature by empirically identifying its antecedents-WO and KH. Methodologically, this paper gives a model for using PLS-SEM and NCA together in relation to QQ by identifying WO as its necessary condition. Evidence of selected constructs’ interrelationships may help organisations draft leadership programmes to curtail KH and QQ behaviour.

The function of banking development in reducing income inequality is critical because financial institutions can grant loans, stimulating prospective productive investments. Based on this promise, the aim of this study is to fill the vacuum by particularly evaluating the influence of banking development, as proxied by bank cost efficiency estimated using a parametric approach, on income inequality.

To evaluate the impact of banking development on income inequality, the authors use data from 20 Italian regions from 2004 to 2017. Particular attention will be made to the consequences that the varied composition of the Italian banking structure, namely, the presence of cooperative and non-cooperative banks, may have on income inequality. To do this, the authors use a generalized method of moments (GMM) regression on panel data to address the endogeneity problem that exists between banking development and income inequality.

Evidence reveals that increasing bank development plays an important impact in reducing income inequality, with cooperative banks faring best. A set of robustness tests generally validates our empirical findings and brings relevant policy implications.

A “qualitative” measure, such as cost efficiency, which is computed using a parametric technique, has been used as a proxy for banking development to analyse the relationship between banking development and income inequality. The contribution, in particular, focuses on how bank diversity influences the nexus between banking development and income inequality in a homogenous context.

This study aims to fully assess the readiness for Lean Six Sigma (LSS) and Quality Performance Improvement (QPI) in an Italian Public Healthcare ecosystem.

Drawing from previously established survey development and adaptation protocols, a replication study was carried out; Lean, Six Sigma and QPI were extracted and validated through confirmatory factor analysis in an Italian Public Healthcare setting, with a sample of health professionals from the Campania region.

This study reports the adaptation of an existing scale for measuring LSS and QPI in an Italian public healthcare organisation. This analysis extracts six conceptual domains and constitutes an original adaptation of an existing scale to assess the readiness to adopt Lean, Six Sigma and Quality Performance in Italian Public Health Organizations. The constructs show strong levels of internal consistency, as demonstrated by each item factor loading and each subscale reliability.

Managers, policymakers and academics can employ the proposed tool to assess the public healthcare ecosystem’s capability to implement LSS initiatives and strategies to improve quality performance.

This is one of the first studies to assess cross-regional organisational readiness for LSS and QPI in an Italian Public Healthcare environment at this scope and level.

Given the evolution that knowledge management (KM) has undergone since the advent of the digital transition, the purpose of this paper is to evaluate how KM processes have changed as a result of agile organisations’ adoption of digital technologies.

Years have passed since the onset of the fourth industrial revolution, and the technologies unique to this revolution have permeated every organisation to varying degrees. Whether organisations have been at the forefront of technological innovation or have had to adapt to much more advanced digitised processes, they have had to change how they manage operations internally and with the remainder of the supply chain they serve. These changes have been much more significant for agile organisations, which rely heavily on digital systems and have strong supplier and customer interactions. Due to the large amount of data generated, these organisations are referred to as knowledge-intensive businesses, and as a result, their KM processes are of the utmost importance. For this reason, a multiple case study with a grounded theory approach has been implemented to carry out a field analysis.

The results show that Industry 4.0 technological advances can be included in the scientific debate on KM and agile innovation, given the effects that such technologies have on organisations.

In today’s increasingly connected world, these findings have the potential to generate significant economic value by improving coordination and collaboration in KM processes.

As social networks have developed to be a ubiquitous platform of public opinion spreading, it becomes more and more crucial for maintaining social security and stability by accurately predicting various trends of public opinion dissemination in social networks. Considering the fact that the dissemination of online public opinion is a dynamic process full of uncertainty and complexity, this study establishes a novel conformable fractional discrete grey model with linear time-varying parameters, namely the CFTDGM(1,1) model, for more accurate prediction of online public opinion trends.

First, the conformable fractional accumulation and difference operators are employed to build the CFTDGM(1,1) model for enhancing the traditional integer-order discrete grey model with linear time-varying parameters. Then, to improve forecasting accuracy, a base value correction term is introduced to optimize the iterative base value of the CFTDGM(1,1) model. Next, the differential evolution algorithm is selected to determine the optimal order of the proposed model through a comparison with the whale optimization algorithm and the particle swarm optimization algorithm. The least squares method is utilized to estimate the parameter values of the CFTDGM(1,1) model. In addition, the effectiveness of the CFTDGM(1,1) model is tested through a public opinion event about “IG team winning the championship”. Finally, we conduct empirical analysis on two hot online public opinion events regarding “Chengdu toddler mauled by Rottweiler” and “Mayday band suspected of lip-syncing,” to further assess the prediction ability and applicability of the CFTDGM(1,1) model by comparison with seven other existing grey models.

The test case and empirical analysis on two recent hot events reveal that the CFTDGM(1,1) model outperforms most of the existing grey models in terms of prediction performance. Therefore, the CFTDGM(1,1) model is chosen to forecast the development trends of these two hot events. The prediction results indicate that public attention to both events will decline slowly over the next three days.

A conformable fractional discrete grey model is proposed with the help of conformable fractional operators and a base value correction term to improve the traditional discrete grey model. The test case and empirical analysis on two recent hot events indicate that this novel model has higher accuracy and feasibility in online public opinion trend prediction.

This paper aims to explore the integration of artificial intelligence (AI) in smart cities (SC) as a key aspect of enhancing urban governance and achieving the UN's Sustainable Development Goals (SDGs). This integration presents significant opportunities and certain risks that require careful and proportionate management.

Adopting a multidisciplinary approach, and using a hierarchical impact assessment method, this study suggests how to measure the impact of AI-enhanced SC projects on ethical principles throughout respective projects’ life cycle. Having outlined a typology of SC applications, and having matched them with specific AI models, this paper develops and applies an analytical framework that serves as a valuable tool for monitoring ethical aspects of AI-based projects implemented in SC.

The study presents a descriptive model and a single visual scheme that synthesize the analytical framework. These tools provide support to public and private stakeholders, including citizens, NGOs and academic and research institutes by offering a unified method to assess and understand the ethical implications of complex SC systems. Through a hierarchical approach, the study aggregates the impacts of child nodes at each layer.

The study's contribution consists in the introduction of a comprehensive analytical framework that enables a structured assessment of ethical implications in AI-enhanced SC projects. By providing a clear approach for monitoring ethical impacts, the research fills a gap in literature as well as in practice as regards responsible and ethical use of AI in urban governance.

Industry 4.0 defines the application of digital technologies on business infrastructure and processes. With the increasing need to take into account the social and environmental impact of technologies, the concept of Society 5.0 has been proposed to restore the centrality of humans in the proper utilization of technology for the exploitation of innovation opportunities. Despite the identification of humans, resilience and sustainability as the key dimensions of Society 5.0, the definition of the key factors that can enable Innovation in the light of 5.0 principles has not been yet assessed.

An SLR, followed by a content analysis of results and a clustering of the main topics, is performed to (1) identify the key domains and dimensions of the Industry 5.0 paradigm; (2) understand their impact on Innovation 5.0; (3) discuss and reflect on the resulting implications for research, managerial practices and the policy-making process.

The findings allow the elaboration of a multileveled framework to redefine Innovation through the 5.0 paradigm by advancing the need to integrate ICT and technology (Industry 5.0) with the human-centric, social and knowledge-based dimensions (Society 5.0).

The study detects guidelines for managers, entrepreneurs and policy-makers in the adoption of effective strategies to promote human resources and knowledge management for the attainment of multiple innovation outcomes (from technological to data-driven and societal innovation).