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A few decades ago, most research works on internationalisation were aligned to studies in developed economies. In recent times, business entrepreneurs in developing and emerging economies have shown their potential to permeate international markets. The current capability of business entrepreneurs in developing and emerging economies, which drives their ability to overcome the numerous barriers to internationalisation, particularly within clusters, requires a critical examination. As a result, the study situates the discussion on internationalisation within the theory of agglomeration in developing and emerging economies and argues that the gains enjoyed by business entrepreneurs from operating in close proximity in clusters are critical for overcoming the barriers of internationalisation. This research adopts a systematic review of secondary data to tease out the unique attributes of clusters in developing and emerging economies, which supports the internationalisation drive. The findings show that most emerging economy clusters are engaged in exports but there is minimal work on international entrepreneurs operating within clusters. The unique features that drive exporting clusters are the presence of multinational companies, public agencies and collaborative relationships. These unique features have the capacity to minimise the constraints to internationalisation and determine the export performance of businesses in the cluster.

Introduction: Today the ability of nations to develop and implement innovations is core for their international competitiveness. Ukraine is striving for innovation progress; however, its innovation performance is relatively low. The research problem is to find the bottlenecks, affecting Ukraine’s innovation capability.

Purpose: This study aims to research the national innovation capability profiles, based on cluster analysis, to develop an understanding of drivers and threats for the innovation capability of Ukraine.

Need of the study: The knowledge-based economy, which had already turned into one of the most efficient developmental models of the 21st century, became a key driver of international competitiveness for the leading developed countries due to their progressive structural shifts towards the growth of high-technology manufacturing and knowledge-intensive sectors. These trends are significant to capture for the sake of increasing the innovation capability of the economy of Ukraine.

Methodology: The study is based on the K-means clustering method, which is employed for identifying 10 country clusters based on the indicators of their R&D and innovation activities, which allowed us to assess the innovation capability of Ukraine in comparison with 140 countries of the world. Data selection and normalisation were based on the 2019 Global Competitiveness Report indicators.

Findings: The study showed that Ukraine’s innovation capability problems are typical for most developing countries and are prevalently connected to low R&D expenditures, patent applications, and international co-invention activities. Most countries, except for the technologically developed ones, follow the so-called ‘passive technological learning’ strategies, which usually result in low economic productivity.

Practical implications: Several innovation policy implications have been developed for the government of Ukraine based on the cluster analysis results and accounting for the problems of the national innovation system (NIS).

This study is intended to investigate the volatility patterns in Bombay Stock Exchange Limited Sensitivity Index (BSE Sensex) based on time series data collected for 10 years period of time. To reach out the predefined objectives of the study, the authors have employed generalized autoregressive conditional heteroscedastic models. The study revealed that the presence of heteroscedasticiy is found in BSE Sensex. Further, the model produced highly accurate results when the researchers compared the estimated results from actual. Furthermore, the volatility of BSE Sensex has shown the features of clustering and significant time varying. Moreover, the model has indicated that there is a positive correlation between daily stock returns and the BSE Sensex volatility.

The efficient distribution of bank notes is a first-order responsibility of central banks. The authors study the distribution patterns of bank notes with an administrative dataset from the Bank of Canada’s Currency Inventory Management Strategy. The single note inspection procedure generates a sample of 900 million bank notes in which the authors can trace the length of the stay of a bank note in the market. The authors define the duration of the bank note circulation cycle as beginning on the date the bank note is first shipped by the Bank of Canada to a financial institution and ending when it is returned to the Bank of Canada. In addition, the authors provide information regarding where the bank note is shipped and later received, as well as the physical fitness of the bank note upon return to the Bank of Canada’s distribution centers. K–prototype clustering classifies bank notes into types. A hazard model estimates the duration of bank note circulation cycles based on their clusters and characteristics. An adaptive elastic net provides an algorithm for dimension reduction. It is found that while the distribution of the duration is affected by fitness measures, their effects are negligible when compared with the influence exerted by the clusters related to bank note denominations.

This paper explores the emergence and coordination of synchrony in networked groups like those that develop integrated product platforms in collaborative ecosystems. While synchronized actions are an important objective for many groups, interorganizational network theory has yet to explore synchrony in depth perhaps because it does not fit the typical diffusion models this research relies upon. By adding organizationally realistic features – sparse network structure and intentional coordination – to the firefly model from theoretical biology, I take some first steps in understanding synchrony in organizational groups. Like diffusion, synchrony is more effective in denser networks, but unlike diffusion clustering decelerates synchrony’s emergence. Coordination by a few group members accelerates group-wide synchrony, and benefits the coordinating organizations with a higher likelihood that it converges to the coordinating organization’s preferred rhythm. This likelihood of convergence to an organization’s preferred rhythm – what I term synchrony performance – increases in denser networks, but is not dependent on tie strength and clustering.

This study groups mutual funds using k-means clustering analysis and compares the k-means clustering process with existing clustering techniques using mutual fund data for equity funds, general fixed-income funds, and balanced open-end mutual funds rated by the Association of Investment Management Companies. Data are from January 2016 to December 2020 for 60 months and includes information on prices, risks, and investment policies. The sample for this study comprises 173 funds from 10 asset management companies with the highest net assets. The tool used for analysis is the k-means technique using a statistical package set for k = 3. The funds can be divided into three groups: Group 1 has 5 mutual funds (2.89%), Group 2 has 24 mutual funds (13.87%), and Group 3 has a total of 144 mutual funds (83.24%). In Group 1, four of the five mutual funds are equity funds with a track record of beating the market, and fund managers have good market timing skills. Moreover, the efficiency of fund grouping using the k-means technique was compared with the existing grouping with close results at 57.23%. This work provides a methodology to obtain a better categorization of mutual funds by using k-means clustering, allowing the investors to know how mutual funds are. This categorization is very useful for improving the formulation of mutual funds, with the goal of further optimizing investment.

The chapter pays specific attention to the organizing and reorganizing process of the embedding of new technology. The aim is to increase the understanding of how a focal technology is incrementally aligned into a customer’s different business settings. Embedding becomes subject to intense organizing efforts. It becomes a struggle with activating different features of the focal technology by forging and modifying the resource interfaces between the focal technology and customer resources.

The organizing efforts are about seeking, in an explorative mood, for resource interfaces between the focal technology and the customer resources. This organizing process enables the identification of new adaptation opportunities for technology embedding processes, whereby the focal technology obtains certain feature and values.

A systematically developed knowledge of resource interfaces is a key for activating different features of the focal technology and thereby facilitating its embedding into the customer’s various business settings. This is described in a single case study in the chapter. This case and the analysis show how a supplier and a customer struggle with developing resource interface knowledge to activate the different features of the focal technology, thereby facilitating its embedding process. The first part of the chapter establishes a theoretical framework, followed in the second part by the case study and analysis. The concluding discussion emphasizes the importance of understanding and managing various interfaces as part of the organizing processes.

Recent developments in the competitive landscape of the management support software industry sub-sector — characterised by saturation, consolidation and defragmentation — have also attracted attention to the phenomenon of cluster formation. Layers of SMEs forming satellite systems centred on large players in this industry fill in structural holes in the competitive fabric of the market which are left open — at least temporarily — by the large ‘incubator’ company, while at the same time also acting as a type of capacity buffer or technology spearhead for the larger player which does not need to invest its own capacities into certain niches. This paper discusses the significance of clustering in the management support software sector and aims to offer explanations predicated on intangible constructs which may retrofit traditional agglomeration economies approaches. The findings of a pilot study corroborate the implications of clustering in relation to knowledge spillover effects, labour mobility, network formation, the generation of social capital, reputation building and effects of the urban ambience as factors potentially moderating the competitive environment within clusters vis-à-vis outside. Moreover, effects of the density of cluster labour pools and spin-off are considered. Simultaneously, the unique efficacy of these effects by means of mitigation of the idiosyncratic growth constraints affecting SMEs in this industry context — such as high levels of customer dependence, lack of legitimacy and the importance of reputation as well as customer references — is proposed.

In various settings (e.g., political elections, marketing campaigns), competing groups attempt to disseminate messages that promote different viewpoints. Moreover, these groups often differ substantially in the resources they have available for promotion, which may generate inequities in the reach of their messages. With a series of computer simulations, the author investigates the role that social network structure plays in perpetuating or mitigating the inequalities in reach brought about by asymmetric access to resources. The author models such asymmetric access by varying the number of "seeds” available to disseminators (i.e., places in the social network from where their messages begin spreading). The author finds that long ties – links that connect otherwise distant regions of a social network – help to decrease the disparities in dissemination brought about by asymmetric access to seeds. The author shows that this finding generalizes to different assumptions about the proportion of long ties present, the seeding asymmetry, and the rate that messages spread, and argues that information and communication technologies like Facebook and Twitter can foster dissemination equality by prioritizing interactions across long ties.