City-level institutions and perceived entrepreneurial ecosystem’s growth orientation

This study uses both secondary and primary data on perceptions of 1789 ecosystem actors from 17 cities in Europe to perform an empirical analysis of three institutional dimensions: regulatory, cultural values and socio-cultural practices – and tests their association with the entrepreneurial ecosystem’s growth orientation. As a result, we develop a framework for the entrepreneurial ecosystem’s factors and provide policy recommendations for those interested in supporting the entrepreneurial ecosystem’s growth orientation in cities. Among other conclusions, the findings suggest a positive association between the socio-cultural practices of environmental sustainability behaviour in businesses with entrepreneurial ecosystem’s growth orientation

A review of the empirical literature on ‘engagement’ within the context of entrepreneurship and small business management

Previous reviews on (work/job/employee) ‘engagement’ have neglected research within the context of entrepreneurship and small and medium-sized enterprises (SMEs). We present a systematic narrative review of the empirical literature on ‘engagement’ within this industrial and employment context. Our aims are to a) determine the scope of research conducted, b) identify key insights, and c) uncover key gaps and problems. We searched four databases for material published between 2010 and 2023. From systematic sifting of 1155 items, 40 articles met our quality and relevance criteria. These articles derive from various disciplines, yet mainly adopt a psychological focus. However, the literature lacks methodological pluralism and cultural contextualization, and an underplaying of institutional/market factors. We find two distinct streams: i) studies focusing on employee engagement with SMEs, and ii) studies focusing on entrepreneurial engagement. There are opportunities to connect these streams in a more interdisciplinary way as well as to develop each in more meaningful ways

Impact of terminal group on temperature-dependent excited state relaxation in cationic dyes

Cationic organic dyes carry a positive charge distributed along the molecule, and the localization of this charge significantly affects their symmetry and optical properties. Depending on the different factors (topology of the terminal groups, the polarity of the solvent, and the temperature) the polyene, polymethine, or donor-acceptor structure form in such dyes, and excited state relaxation for such systems is not fully explored, particularly at low temperatures. At room temperature, the studied cationic dyes, regardless of symmetry in the ground state, are mostly symmetrical in the excited state. At low temperatures, charge localization effects become evident, leading to symmetry breaking in both ground and excited states. In this paper, we distinguish how terminal groups at the end of the cationic dyes impact the relaxation of excited states by analyzing experimental low-temperature time-resolved spectra combined with quantum-chemical calculations. Distinctive emission (690 nm) in the anti-Stokes range of polymethine band (700–730 nm) features polyene structures forming depending on the temperature, solvent polarity, and charge-donating properties of the dye's terminal groups. Furthermore, in low-temperature time-resolved photoluminescence, a 760 nm band is distinguished and associated with intramolecular charge transfer. Our calculations revealed unequal distribution of total positive charge in different molecular fragments (polymethine chain and terminal groups) and formation of negative charge on polymethine chain. We propose a model of excited state relaxation transitions for linear cationic molecular systems that enable donor-acceptor features. This model offers valuable insights for designing new functional materials with tunable properties or efficient energy transfer systems for artificial photosynthesis

Impact of loyal and new customer segments on product upgrades: The role of quality differentiation through online reviews

Firms often strive to expand their market share beyond their established customer base by launching quality upgrades in their products. They recognize that customers often gauge product quality through online reviews. We develop an analytical model to examine the quality upgrade strategies of two competing firms, revealing two potential market equilibria. In the unilateral upgrading equilibrium where only one firm upgrades, the upgrading firm sees an initial increase in loyal demand, leading to higher prices. This price adjustment, however, may deter potential new customers who turn to the more affordable non-upgrading competitor, referred to as the substitution effect. Despite attracting more loyal customers, the upgrading firm may experience a net loss in broader market share due to the substitution effect. In the bilateral upgrading equilibrium where both firms upgrade and engage in quality competition, the situation becomes akin to a prisoner’s dilemma if loyal customers show indifference to quality improvements. The gains from loyal customers are outweighed by fierce competition for new customers, ultimately disadvantaging both firms. Furthermore, our findings indicate that review-revealed quality difference between the two products leads to a higher degree of quality improvement effort by the high-quality firm, while reducing that of the low-quality firm. Intriguingly, in the unilateral equilibrium, the high-quality firm may not benefit from its review-revealed superior quality, while the low-quality firm may not be disadvantaged, depending on the substitution effect relatively

New Frontiers and The Impact of Artificial Intelligence and the Digital Revolution on the Future of Intellectual Property Laws

Before the Covid pandemic hit, artificial intelligence (AI) had already embedded itself into our everyday lives. AI as an assistive tool adequately responds to humans’ needs, such as virtual digital assistance, almost everyone will have Apple Siri, Alexa and or Google Home, as voice recognition systems. Relying on artificial intelligence systems such as Spotify to provide a recommended list of music based on your existing music choices and preferences is commonplace, and likewise, for producing works of art. Most notably, a project team behind The Next Rembrandt designed algorithms that allowed a computer to create a painting in the style of the 17th century Dutch artist and is known as the Rembrandt 2.0 . Artificial intelligence can produce works which could be considered as copyright works however international law has yet to acknowledge AI as a copyright owner . Humans working in creative, innovative and legal sectors are discussing the consequence of AI systems when it comes to who will own the intellectual property, more importantly, who will the economic rights belong to. Artificial intelligence systems are developing at a significant pace and as a result, reshaping the whole creative and innovative sectors that are protected in the existing intellectual property systems. Therefore, it is necessary to identify the AI systems at present, defining and distinguishing between the concepts of “AI-assisted” and “AI-generated”, to outline the direction of AI development in the context of intellectual property law

Automated Vehicles: Are Cities Ready to Adopt AVs as the Sustainable Transport Solution?

Cities are looking for an approach to affordable, integrated and sustainable transport systems across all transport modes and services. Automated vehicle (AV) technologies use emerging technologies to integrate multimodal transport systems and ensure sustainable mobility in a city. Vehicle automation has entered the public conscious with several auto companies leading recent developments in legislation and affordable cars. Governments support AVs through policies and legal frameworks, and it is the responsibility of AV dealers to comply with legal and policy provisions so that the benefits of this new and promising industry can be felt. Despite the growing interest in AVs as a potential solution for sustainable transportation, several research gaps remain in relation to technology and infrastructure readiness, policy and regulation, equity and accessibility concerns, public acceptance and behaviour, and integration with public transport. This paper discusses the challenges and dilemmas of adopting AVs within the existing urban transportation system and within existing design standards in the United Kingdom and explores the progress and opportunities related to policies of transportation that may stem from the emergence of AV technologies in the UK. The potential of AVs is still limited by cyber insecurity, incompetent infrastructure, social acceptance, and public awareness. However, AVs are crucial to a city’s efficiency and prosperity and will become essential components for the provision of more flexible, convenient, integrated and sustainable travel options

Achieving Biodiesel Standards Through Saturation Level Optimisation

Biodiesels made from waste feedstock are viable sustainable fuels for compression ignition engine use. However, biodiesel produced from single waste source do not always comply with the European biodiesel standard. This study investigated the fuel quality and engine performance when two biodiesels with different characteristics were blended at various proportions. Waste cooking oil biodiesel was blended with sheep fat biodiesel which has a lower unsaturated fatty acid content. The engine performance, combustion and exhaust emission characteristics of the neat biodiesels and their blends (at 60/40, 50/50, and 30/70 ratios) were analysed. The results showed that 60/40 and 50/50 blends met the core parameters of BS EN 14214 biodiesel standard and improved the combustion and emission characteristics as compared to their neat biodiesels and diesel. The 50/50 blends gave up to 5% and 14% improvements in the in-cylinder pressure and maximum heat release rate respectively, when compared to the same results for neat biodiesel operation. Reduction of up to 73% in CO, 96% in smoke and 3% in CO2 emissions were observed. However, NOx emission was 2.5% higher than diesel. The results revealed that carefully selected biodiesel-biodiesel blending could meet fuel standards, improve engine performance and reduce exhaust emissions

Circular Economy in Small and Medium-Sized Enterprises—Current Trends, Practical Challenges and Future Research Agenda

The Circular Economy (CE) has evolved as a philosophy to transform industrial supply chains to become greener to combat climate change issues. Countries’ target of achieving Net Zero will never be fulfilled unless, along with larger organizations, small and medium-sized enterprises (SMEs) are decarbonized, as more than 90% of the world’s businesses are SMEs. Although, recently, there have been many studies on SMEs’ sustainability practices and performance covering drivers, bottlenecks, and opportunities, the holistic approach for embedding circular economy and sustainability covering design, planning, implementation, and operations is missing. This research bridges this knowledge gap by revealing trends and theories of circular economy adoption in SMEs. Additionally, this research derives the drivers/enablers, issues, and challenges and determines strategies, resources, and competencies for CE adoption in SMEs. This study concludes with a consolidated framework comprising factors and methods for CE implementation in SMEs. This entire piece of research has been undertaken using the secondary data analysis method through the content analysis of 188 published articles in highly ranked peer-reviewed journals

Inferring structure of cortical neuronal networks from activity data: A statistical physics approach

Understanding the relation between cortical neuronal network structure and neuronal activity is a fundamental unresolved question in neuroscience, with implications to our understanding of the mechanism by which neuronal networks evolve over time, spontaneously or under stimulation. It requires a method for inferring the structure and composition of a network from neuronal activities. Tracking the evolution of networks and their changing functionality will provide invaluable insight into the occurrence of plasticity and the underlying learning process. We devise a probabilistic method for inferring the effective network structure by integrating techniques from Bayesian statistics, statistical physics, and principled machine learning. The method and resulting algorithm allow one to infer the effective network structure, identify the excitatory and inhibitory type of its constituents, and predict neuronal spiking activity by employing the inferred structure. We validate the method and algorithm's performance using synthetic data, spontaneous activity of an in silico emulator, and realistic in vitro neuronal networks of modular and homogeneous connectivity, demonstrating excellent structure inference and activity prediction. We also show that our method outperforms commonly used existing methods for inferring neuronal network structure. Inferring the evolving effective structure of neuronal networks will provide new insight into the learning process due to stimulation in general and will facilitate the development of neuron-based circuits with computing capabilities