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Through the reconstruction of the events of some foreign entrepreneurs who worked in the territory of the Italian city of Bari in the first half of the 19th century, this paper aims to analyze the role of entrepreneurial migration in the economic development of Apulia land in this period.

This study adopts a theoretical framework that combines the concept of mixed embeddedness in a multifocal perspective, with the model of the diffusion of innovation focusing on the role of the so-called agency of actors, and of the network, in the dissemination of innovation. The theoretical framework is applied to multiple case studies to compare the evidence that emerged from the simultaneous analysis of several situations.

By analyzing how innovations have spread within the network of entrepreneurs of that time, it is possible to identify some relevant aspects related to the mechanisms of dissemination of innovations in the context of entrepreneurial migration. Specifically, the opportunity structure is intended in an even broader sense than indicated in the classic approach to mixed embeddedness: it is considered as the result of the joint interaction of the political, institutional and economic context of several places, and the behavioral dynamics of several groups.

Due to the specific method chosen, the outcomes of the research might apply to a narrow context. Therefore, the results need to be tested and confirmed in further empirical studies, and by applying multiple research methods.

Findings are useful and significant in the analysis of the link that exists between the diffusion of innovations and migrant entrepreneurship, and then the conclusions can be applied and extended to the current phenomenon of migration-related innovations, with specific reference to developing countries.

Findings can be applied and extended to the current phenomenon of migration-related innovations and highly skilled migration, with specific reference to developing countries.

This paper contributes to shed new light on the contextual and multifocal factors that influence the development of innovations in the networks of migrant entrepreneurship, in a specific historical period and a specific context. Combining social, human and financial capital with the wider opportunity structure, this study also provides a comprehensive understanding of the modalities through which migrant and high-skilled entrepreneurs could innovate.

The development of green manufacturing has become essential to achieve sustainable development and modernize the nation’s manufacturing and production capacity without increasing nonrenewable resource consumption and pollution. This study investigates the effect of green industrial practices on technical efficiency for Thai manufacturers.

The study uses stochastic frontier analysis (SFA) to estimate the stochastic frontier production function (SFPF) and inefficiency effects model, as pioneered by Battese and Coelli (1995).

This study shows that, on average, Thai manufacturing firms have experienced declining returns-to-scale production and relatively low technical efficiency. However, it is estimated that Thai manufacturing firms with a green commitment obtained the highest technical efficiency, followed by those with green activity, green systems and green culture levels, compared to those without any commitment to green manufacturing practices. Finally, internationalization and skill development can significantly improve technical efficiency.

Green industry policy mixes will be vital for driving structural reforms toward a more environmentally friendly and sustainable economic system. Furthermore, circular economy processes can promote firms' production efficiency and resource use.

To the best of the authors' knowledge, this study is the first to investigate the effect of green industry practices on the technical efficiency of Thai manufacturing enterprises. This study also encompasses analyses of the roles of internationalization, innovation and skill development.

The purpose of this paper is to discuss how enterprises can effectively perceive and use the digital opportunities brought about by digital technologies and dynamic environments and how they can enhance their capabilities to realize digital transformation and adapt to the development of the digital economy era.

Based on the windows of opportunity theory and strategic cognition theory, this paper conducts an empirical analysis of the questionnaire data of 268 enterprises and discusses the influence of external windows of opportunity and internal windows of opportunity on the digital transformation of enterprises, as well as the action mechanism of strategic cognition and entrepreneurship.

The results show that both the external windows of opportunity and the internal windows of opportunity have significant positive effects on the digital transformation of enterprises. Strategic cognition plays a partial mediating role in the external windows of opportunity and the internal windows of opportunity influencing the enterprise digital transformation process. Entrepreneurship plays a positive regulatory role in the process of external windows of opportunity and internal windows of opportunity influencing strategic cognition.

This paper deepens the relationship between internal and external windows of opportunity and enterprise digital transformation and contributes a new theoretical cognition. This paper integrates the strategic cognition theory to clarify the complex process mechanism of digital transformation using external situational opportunities and internal capabilities. This paper introduces entrepreneurship into the path mechanism of digital transformation and expands the characteristics of the study of digital transformation antecedents to the individual level within the enterprise.

The main purpose of this paper is to examine the adoption of big data analytics (BDA) in the Tanzania banking industry by investigating the influence of technological, environmental and organizational (TOE) factors while exploring the moderating role of perceived risk (PR).

The study employed a qualitative research design, and the research instrument was developed using per-defined measurement items adopted from prior studies; the items were slightly adjusted to fit the current context. The questionnaires were distributed to top and middle managers in selected banks in Tanzania using the snowball sampling technique. Out of 360 received responses, 302 were considered complete and valid for data analysis. The study employed partial least squares structural equation modeling (PLS-SEM) to examine the developed conceptual framework.

Top management support and financial resources emerged as influential organizational factors, as did competition intensity for the environmental factors. Notably, bank size and perceived trends showed no significant impacts on BDA adoption. The study's novelty lies in revealing PR as a moderating factor, weakening the link between technological readiness, perceived usefulness and the intent to adopt BDA.

This study extends literature by extending the TOE model, through examining the moderating roles of PR on technological factors. Furthermore, the study provides useful managerial support for the adoption of BDA in banking in emerging economies.

Using the theoretical framework of the substantive economy, this study aims to point out the main aspects of the substantive mode of operation that help the integration of disadvantaged people while at the same time shedding light on the barriers that hinder economically efficient functioning in a market economy.

Research focuses on Hungarian rural work integration social cooperatives, which are engaged in producing activity by the employment of disadvantaged people. In the research, mixed methods were applied: results of a questionnaire survey covering 102 cooperatives, as well as 20 semi-structured interviews and experiences from the field. A total of 17 indicators were used to explore the substantive operational features, promoting mechanisms and problems in the following areas: organisational goals and outcomes; integrating roles and functions; productive functions; and the embeddedness of cooperatives.

As for results, substantive operational mechanisms and tools that support the integration of disadvantaged people have been identified such as mentoring, social incentives, the ability to create local value or the expansion of local community services. At the same time, several barriers have been detected that make it difficult to operate economically, such as cooperatives being a stepping stone for workers, excessive product heterogeneity or the lack of vertically structured bridging relationships.

The value of the study is to counterpoint the mechanisms promoting social purposes of work-integration social cooperatives and the obstacles to their long-term sustainability within the framework of the substantive economy, to better understand their functioning and the less quantifiable factors of their performance.

This study aims to explore how the absorptive capacity of emerging market multinationals (EMNEs) facilitates increased acquirer performance in industry exploration and technology exploration cross-border acquisitions (CBAs).

The research context for this study is Brazilian EMNEs and their CBAs. The final database contains 101 CBAs.

The authors find that industry exploration strategies negatively affect financial performance, but technology exploration strategies have a positive effect. The acquirer’s absorptive capacity can exacerbate the negative effects, except in instances of technology exploration strategies, where there is a demonstrable benefit from the acquirer’s absorptive capacity.

The study contributes first by providing a more nuanced understanding of the effects of absorptive capacity on postacquisition performance, depending on the type of knowledge explored. Second, by drawing on EMNE learning perspectives, the authors demonstrate the versatility of absorptive capacity in emerging markets.

This paper provides practical advice for archaeologists and heritage specialists wishing to use ML approaches to identify archaeological features in high-resolution satellite imagery (or other remotely sensed data sources). We seek to balance the disproportionately optimistic literature related to the application of ML to archaeological prospection through a discussion of limitations, challenges and other difficulties. We further seek to raise awareness among researchers of the time, effort, expertise and resources necessary to implement ML successfully, so that they can make an informed choice between ML and manual inspection approaches.

Automated object detection has been the holy grail of archaeological remote sensing for the last two decades. Machine learning (ML) models have proven able to detect uniform features across a consistent background, but more variegated imagery remains a challenge. We set out to detect burial mounds in satellite imagery from a diverse landscape in Central Bulgaria using a pre-trained Convolutional Neural Network (CNN) plus additional but low-touch training to improve performance. Training was accomplished using MOUND/NOT MOUND cutouts, and the model assessed arbitrary tiles of the same size from the image. Results were assessed using field data.

Validation of results against field data showed that self-reported success rates were misleadingly high, and that the model was misidentifying most features. Setting an identification threshold at 60% probability, and noting that we used an approach where the CNN assessed tiles of a fixed size, tile-based false negative rates were 95–96%, false positive rates were 87–95% of tagged tiles, while true positives were only 5–13%. Counterintuitively, the model provided with training data selected for highly visible mounds (rather than all mounds) performed worse. Development of the model, meanwhile, required approximately 135 person-hours of work.

Our attempt to deploy a pre-trained CNN demonstrates the limitations of this approach when it is used to detect varied features of different sizes within a heterogeneous landscape that contains confounding natural and modern features, such as roads, forests and field boundaries. The model has detected incidental features rather than the mounds themselves, making external validation with field data an essential part of CNN workflows. Correcting the model would require refining the training data as well as adopting different approaches to model choice and execution, raising the computational requirements beyond the level of most cultural heritage practitioners.

Improving the pre-trained model’s performance would require considerable time and resources, on top of the time already invested. The degree of manual intervention required – particularly around the subsetting and annotation of training data – is so significant that it raises the question of whether it would be more efficient to identify all of the mounds manually, either through brute-force inspection by experts or by crowdsourcing the analysis to trained – or even untrained – volunteers. Researchers and heritage specialists seeking efficient methods for extracting features from remotely sensed data should weigh the costs and benefits of ML versus manual approaches carefully.

Our literature review indicates that use of artificial intelligence (AI) and ML approaches to archaeological prospection have grown exponentially in the past decade, approaching adoption levels associated with “crossing the chasm” from innovators and early adopters to the majority of researchers. The literature itself, however, is overwhelmingly positive, reflecting some combination of publication bias and a rhetoric of unconditional success. This paper presents the failure of a good-faith attempt to utilise these approaches as a counterbalance and cautionary tale to potential adopters of the technology. Early-majority adopters may find ML difficult to implement effectively in real-life scenarios.

Unlike many high-profile reports from well-funded projects, our paper represents a serious but modestly resourced attempt to apply an ML approach to archaeological remote sensing, using techniques like transfer learning that are promoted as solutions to time and cost problems associated with, e.g. annotating and manipulating training data. While the majority of articles uncritically promote ML, or only discuss how challenges were overcome, our paper investigates how – despite reasonable self-reported scores – the model failed to locate the target features when compared to field data. We also present time, expertise and resourcing requirements, a rarity in ML-for-archaeology publications.

This study investigates the optimum size for manufacturing firms and the impact of subcontracting on firms' likelihood of achieving their optimal scale in Vietnam.

Using data from the enterprise census in 2017 and 2021, the paper first estimates the production function to identify the optimum firm size for manufacturing firms and then, applies the logit model to investigate factors associated with the optimal firm size.

The study reveals that medium-sized firms exhibit the highest level of productivity. Nevertheless, a consistent trend emerges, indicating that nearly 90% of manufacturing firms in Vietnam operated below their optimal scale in both 2017 and 2021. An analysis of the impact of subcontracting on firms' likelihood to achieve their optimal scale emphasizes its crucial role, especially for foreign firms, exerting an influence nearly five times greater than that of the judiciary system.

The paper's findings offer crucial policy implications, suggesting that initiatives aimed at enhancing the overall productivity of the manufacturing sector should prioritise facilitating contract arrangements to encourage firms to reach their optimal size. These insights are also valuable for other countries with comparable firm size distributions.

This paper provides the first empirical evidence on the relationship between firm size and productivity as well as the role of subcontracting in firms' ability to reach their optimal scale in a country with a right-skewed distribution of firm sizes.

This paper explores the collaborative dynamics and dimensions within a virtual multi-cultural and interdisciplinary workplace. The study focusses on the use of online communication technologies to enhance social inclusion and networking within academia.

This study uses an autoethnographic approach to draw on the personal experiences of a team of four scholars, including three early-career researchers and a senior scholar. Their reflections on their academic positionality and the institutional constraints reveal both the strengths and vulnerabilities of collaborating in a virtual workplace.

The findings offer insights into the complexities of navigating social dynamics, such as delegating responsibilities, organising meetings across various time zones and encouraging continuous collaboration, inclusivity and effective communication during an extensive timeline. As a result, their experiences revealed that a virtual workplace culture with similar and different attributes to a “normal” workplace emerged.

The paper demonstrates how to create an effective and inclusive virtual workplace by exemplifying best practices in academia and providing practical guidance for individuals and institutions based on honest, co-produced autoethnographic reflections of the authors’ lived experiences.

The purpose of this study is to examine the propagation of consumer and business confidence in the euro area with a particular focus on the global financial crisis (GFC), the European sovereign debt crisis (ESDC) and the COVID-19-induced Great Lockdown.

The authors apply Diebold and Yilmaz’s connectedness framework and the improved method based on the time-varying parameter vector autoregressive model.

The authors find that although the evolution of business confidence marked the GFC and the ESDC the role of consumer confidence (mainly in those countries with stricter containment and closure measures) increased in the COVID-19-induced crisis.

The findings are related to the different origins of the examined crisis periods, and the analysis of their interrelationship is a very relevant topic for future research.