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This study aims to investigate the synergies between the economic environment and the smart living dimension embedded in the current smart city initiatives, focusing on the localization of female entrepreneurship in contemporary cities. This interaction is under-investigated and controversial as it includes cities' practices enabling users and citizens to develop their potential and build their own lives, affecting entrepreneurial and economic outcomes. Building upon the perspective of the innovation ecosystems, this study focuses on the impact of smart living dimensions and R&D investments on the localization of female entrepreneurial activities.

The study uses a Generalized Method of Moments (GMM) and a panel dataset that considers 30 Italian smart city projects for 12 years to demonstrate the relationship between smart living practices in cities and the localization of female entrepreneurship. The complementary effect of public R&D investment is also included as a driver in the “smart” city transition.

The study found that the advancement of smart living practices in cities drives the localization of female entrepreneurship. The study highlights the empirical results, the interaction over the years and a current overview through choropleth maps. The public R&D investment also affects this relationship.

This study advances the theoretical discussion on (1) female entrepreneurial intentions, (2) smart city advancement (as a context) and (3) smart living dimension (as a driver) and offers valuable insight for governance and policymakers.

This study offers empirical contributions to the preliminary academic debate on enterprise development and smart city trajectories at the intersection between human-based practices and female entrepreneurship.

This study offers empirical contributions to the preliminary academic debate on enterprise development and smart city trajectories at the intersection between human-based practices and female entrepreneurship. The findings provide valuable insights into the localization of female entrepreneurship in the context of smart cities.

The paper examines the recent trends in information technology for combating corruption and its impact on the Indian economy. It further explores how technology is being used to tackle corruption in India and the resulting economic benefits.

The methodology encompasses qualitative analysis to investigate corruption comprehensively. It involves content analysis of corruption-related documents, case studies, and expert interviews. Recent information technological advancements are explored, including blockchain and AI, for their anti-corruption potential.

This study reveals that the negative impacts of corruption on society include reduced economic growth, weakened institutions, and decreased public trust in government. Various technological advancements such as e-governance, blockchain, AI, and big data analytics have been implemented to enhance transparency and accountability in government processes. Special cases and examples of application of such technology tools and techniques adopted by the organizations to control corruption are discussed.

This paper highlights the need for legal reforms, institutional strengthening, and awareness-raising campaigns to complement technological advancements in the fight against corruption.

The purpose of this research is to explore the transformative impact of AI-augmented tools on design pedagogy. It aims to understand how artificial intelligence technologies are being integrated into educational settings, particularly in creative design courses, and to assess the potential advancements these tools can bring to the field.

The research adopts a case-study approach, examining three distinct courses within a creative technology curriculum. This methodology involves an in-depth investigation of the role and impact of AI in each course, focusing on how these technologies are incorporated into different creative disciplines such as production design, fine arts, and digital artistry.

The research findings highlight that the integration of AI with creative disciplines is not just a passing trend but signals the onset of a new era in technological empowerment in creative education. This amalgamation is found to potentially redefine the boundaries of creative education, enhancing various aspects of the learning process. However, the study also emphasizes the irreplaceable value of human mentorship in cultivating creativity and advancing analytical thinking.

The limitations of this research might include the scope of the case studies, which are limited to three courses in a specific curriculum. This limitation could affect the generalizability of the findings. The implications of this research are significant for educational institutions, as it suggests the need for a balanced interaction between AI's computational abilities and the intrinsic qualities of human creativity, ensuring that the core essence of artistry is preserved in the age of AI.

The originality of this paper lies in its specific focus on the intersection of AI and creative education, a relatively unexplored area in design pedagogy. The value of this research is in its contribution to understanding how AI can be harmoniously integrated with traditional creative teaching methods. It offers insights for educational institutions preparing for this technological transformation, highlighting the importance of maintaining a balance between technological advancements and humanistic aspects of creative education.

The study aimed to test the validity and reliability of the Polish version of the identity leadership inventory (ILI) proposed by Steffens, Haslam, Reicher et al. (2014) and to confirm the relationship between identity leadership and various job-related outcomes (i.e., trust in leaders, job satisfaction, work engagement and turnover intentions) among employees from Poland-based organizations. Identity leadership appears to be a universal construct (van Dick, Ciampa, & Liang, 2018) but no one has studied it in Poland so far.

The sample consisted of 1078 employees collected in two independent subsamples from different organizations located in Northern and Central Poland. We evaluated the ILI’s factorial structure using confirmatory factor analysis.

The results confirm that the 15-item Polish version of the ILI has a four-dimensional structure with factors representing prototypicality, advancement, entrepreneurship and impresarioship. It showed satisfactory reliability. The identity leadership inventory-short form (four items) also showed a good fit with the data. As expected, the relationships between identity leadership and important work-related outcomes (general level of job satisfaction, work engagement, trust toward the leader and turnover intentions) were also significant.

Despite the cultural specifics of Polish organizations, the research results were generally very similar to those in other countries, confirming the universality of the ILI as shown in the Global Identity Leadership Development project (GILD, see van Dick, Ciampa, & Liang, 2018; van Dick et al., 2021).

This study aims to investigate the impact of brain drain on government AI readiness in EU member countries, considering the distinctive governance characteristics, macroeconomic conditions and varying levels of ICT specialists.

The research employs a dynamic panel data model using the System Generalized Method of Moments (GMM) to analyze the relationship between brain drain and government AI readiness from 2018 to 2022. The study incorporates various control variables such as GDP per capita growth, government expenditure growth, employed ICT specialists and several governance indicators.

The results indicate that brain drain negatively affects government AI readiness. Additionally, the presence of ICT specialists, robust governance structures and positive macroeconomic indicators such as GDP per capita growth and government expenditure growth positively influence AI readiness.

Major limitations include the focus on a specific region of countries and the relatively short period analyzed. Future research could extend the analysis with more comprehensive datasets and consider additional variables that might influence AI readiness, such as the integration of AI with emerging quantum computing technologies and the impact of governance reforms and international collaborations on AI readiness.

The theoretical value of this study lies in providing a nuanced understanding of how brain drain impacts government AI readiness, emphasizing the critical roles of skilled human capital, effective governance and macroeconomic factors in enhancing AI capabilities, thereby filling a significant gap in the existing literature.

This research fills a significant gap in the existing literature by providing a comprehensive analysis of the interaction between brain drain and government AI readiness. It uses control variables such as ICT specialists, governance structures and macroeconomic factors within the context of the European Union. It offers novel insights for policymakers to enhance AI readiness through targeted interventions addressing brain drain and fostering a supportive environment for AI innovation.

Despite its devastating nature, the COVID-19 pandemic has also catalyzed a substantial surge in the adoption and integration of technological tools within economies, exerting a profound influence on the dissemination of information among participants in stock markets. Consequently, this present study delves into the ramifications of post-pandemic dynamics on stock market behavior. It also examines the relationship between investors' sentiments, underlying behavioral drivers and their collective impact on global stock markets.

Drawing upon data spanning from 2012 to 2023 and encompassing major world indices classified by Morgan Stanley Capital International’s (MSCI) market and regional taxonomy, this study employs a threshold regression model. This model effectively distinguishes the thresholds within these influential factors. To evaluate the statistical significance of variances across these thresholds, a Wald coefficient analysis was applied.

The empirical results highlighted the substantive role that investors' sentiments and behavioral determinants play in shaping the predictability of returns on a global scale. However, their influence on developed economies and the continents of America appears comparatively lower compared with the Asia–Pacific markets. Similarly, the regions characterized by a more pronounced influence of behavioral factors seem to reduce their reliance on these factors in the post-pandemic landscape and vice versa. Interestingly, the post COVID-19 technological advancements also appear to exert a lesser impact on developed nations.

This study pioneers the investigation of these contextual dissimilarities, thereby charting new avenues for subsequent research studies. These insights shed valuable light on the contextualized nexus between technology, societal dynamics, behavioral biases and their collective impact on stock markets. Furthermore, the study's revelations offer a unique vantage point for addressing market inefficiencies by pinpointing the pivotal factors driving such behavioral patterns.

In the context of the journey toward digital transformation and the realization of a fully connected factory, concepts such as data science, artificial intelligence (AI), machine learning (ML) and even predictive models emerge as indispensable pillars. Given the relevance of these topics, the present study focused on the analysis of customer complaint data, employing ML techniques to anticipate complaint accountability. The primary objective was to enhance data accessibility, harnessing the potential of ML models to optimize the complaint handling process and thereby positively contribute to data-driven decision-making. This approach aimed not only to reduce the number of units to be analyzed and customer response time but also to underscore the pressing need for a paradigm shift in quality management. The application of AI techniques sought to enhance not only the efficiency of the complaint handling process and data accessibility but also to demonstrate how the integration of these innovative approaches could profoundly transform the way quality is conceived and managed within organizations.

To conduct this study, real customer complaint data from an automotive company was utilized. Our main objective was to highlight the importance of artificial intelligence (AI) techniques in the context of quality. To achieve this, we adopted a methodology consisting of 10 distinct phases: business analysis and understanding; project plan definition; sample definition; data exploration; data processing and pre-processing; feature selection; acquisition of predictive models; evaluation of the models; presentation of the results; and implementation. This methodology was adapted from data mining methodologies referenced in the literature, taking into account the specific reality of the company under study. This ensured that the obtained results were applicable and replicable across different fields, thereby strengthening the relevance and generalizability of our research findings.

The achieved results not only demonstrated the ability of ML models to predict complaint accountability with an accuracy of 64%, but also underscored the significance of the adopted approach within the context of Quality 4.0 (Q4.0). This study served as a proof of concept in complaint analysis, enabling process automation and the development of a guide applicable across various areas of the company. The successful integration of AI techniques and Q4.0 principles highlighted the pressing need to apply concepts of digitization and artificial intelligence in quality management. Furthermore, it emphasized the critical importance of data, its organization, analysis and availability in driving digital transformation and enhancing operational efficiency across all company domains. In summary, this work not only showcased the advancements achieved through ML application but also emphasized the pivotal role of data and digitization in the ongoing evolution of Quality 4.0.

This study presents a significant contribution by exploring complaint data within the organization, an area lacking investigation in real-world contexts, particularly focusing on practical applications. The development of standardized processes for data handling and the application of predictions for classification models not only demonstrated the viability of this approach but also provided a valuable proof of concept for the company. Most importantly, this work was designed to be replicable in other areas of the factory, serving as a fundamental basis for the company’s data scientists. Until then, limited data access and lack of automation in its treatment and analysis represented significant challenges. In the context of Quality 4.0, this study highlights not only the immediate advantages for decision-making and predicting complaint outcomes but also the long-term benefits, including clearer and standardized processes, data-driven decision-making and improved analysis time. Thus, this study not only underscores the importance of data and the application of AI techniques in the era of quality but also fills a knowledge gap by providing an innovative and replicable approach to complaint analysis within the organization. In terms of originality, this article stands out for addressing an underexplored area and providing a tangible and applicable solution for the company, highlighting the intrinsic value of aligning quality with AI and digitization.

This paper aims to discuss recent criticism related to partial least squares structural equation modeling (PLS-SEM).

Using a combination of literature reviews, empirical examples, and simulation evidence, this research demonstrates that critical accounts of PLS-SEM paint an overly negative picture of PLS-SEM’s capabilities.

Criticisms of PLS-SEM often generalize from boundary conditions with little practical relevance to the method’s general performance, and disregard the metrics and analyses (e.g., Type I error assessment) that are important when assessing the method’s efficacy.

We believe the alleged “fallacies” and “untold facts” have already been addressed in prior research and that the discussion should shift toward constructive avenues by exploring future research areas that are relevant to PLS-SEM applications.

All statistical methods, including PLS-SEM, have strengths and weaknesses. Researchers need to consider established guidelines and recent advancements when using the method, especially given the fast pace of developments in the field.

This research addresses criticisms of PLS-SEM and offers researchers, reviewers, and journal editors a more constructive view of its capabilities.

Organizations increasingly use knowledge arbitrage to stimulate innovation and achieve competitive advantage. However, in knowledge management its use in startups is yet unexplored. This study aims to examine the utilization of knowledge arbitrage by startups, specifically during COVID-19.

This study employed an open-ended essay methodology to explore the drivers and barriers that startups face in utilizing knowledge arbitrage. We collected data from 40 participants to understand the role of knowledge arbitrage in startups’ knowledge management practices.

This study’s findings highlight the significance of knowledge arbitrage for startups. The benefits identified include organizational benefits such as building networks, innovating new products and achieving competitive advantage and financial benefits such as cost reduction and sales growth. The study also identifies several technological and organizational drivers and barriers that startups confront during knowledge arbitrage.

This study contributes to the existing literature on knowledge management by extending our understanding of knowledge arbitrage’s role in startups. Additionally, it sheds light on the importance of knowledge arbitrage for startups and the challenges they face, particularly in a disrupted environment reared by COVID-19. The study provides insights for the scholars and practitioners interested in effective knowledge management in startups.

In recent years we have seen major technological advancements including the launch of large language models such as ChatGPT and the popularity of the digital transformation topic among professionals and academics. Despite this, the pace of digital transformation is surprisingly slow. We aimed to identify behavioral antecedents of an organization’s sluggish digital transformation.

We adopted the organizational level of analysis, which differs from prior analyses of technological revolutions that looked at the phenomenon from an aggregate labor market or society level of analysis.

We identified dehumanization as a key construct useful in examining the behavioral impediments to digital transformation. We indicated that the traditionally dual understanding of dehumanization needs to incorporate the actual involvement of non-human agents in operational and decision-making processes in organizations.

We complemented the predominant approach of digital transformation, which focuses on technology and related business model development, with a behavioral approach. We considered digital transformation as an extreme degree of change, similar to the Industrial Revolution. We paved the way for the conceptual development of dehumanization in the digital world and for developing managerial practices useful in alleviating concerns that impede the pace of digital transformation.