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The objective of this chapter is to identify the key characteristics of Global Services businesses that will thrive and achieve success in the future. These factors are integrated into three main pillars, which we refer to as the Triple-Win. The first and most obvious pillar is technology as a tool. The second pillar is the design and sustainability of the business model, without which the previous factor would be merely a cost and not an investment. And last but not the least, there is the purpose which gives meaning to the proposal, focusing on the human being and their environment. The DIDPAGA business model sits at the intersection of these three elements.

Contemporary frameworks in the resource-based view (RBV) of the firm observe that the proprietary firm-specific assets of multinational companies (MNCs) from developed economies give them competitive advantage in international markets. However, the question “how do emerging market firms (EMFs) achieve accelerated internationalisation in knowledge based industries – despite not possessing proprietary assets and lacking critical elements of innovation eco-systems, institutions and infrastructure” has yet to be addressed. This paper aims to adopt a knowledge-based view (KBV) of the firm, identifying knowledge, both inside and outside of the firm, as a critical element for the internationalization of EMFs.

This research entailed deductive econometric analyses using panel data analysis from 925 firm-year observations, which tested our predictions on capabilities that contribute to the internationalization of EMFs from the IT industry in India.

The findings of the authors’ panel data analysis reveal that the capacity to internationalize is predicated by knowledge leverage in three principal domains: absorptive capacity, tacit knowledge and knowledge-codification. This study shows internationalization is driven by higher-order capabilities of EMFs that draw on the absorptive capacity of individuals and collectives as a dynamic capability to serve international clients worldwide.

This study highlights that the process by which EMFs gain competence is different to the Western MNCs insofar as the extent to which EMFs have to stretch their efforts of learning from clients.

For practitioners, the findings of this research are a useful guide to understand that EMFs need to make strategic investments to understand the idiosyncrasies of a variety of clients’ needs and operating environments to dynamically adapt, document the learning and leverage the tacit knowledge.

This study captures the innate ability of entrepreneurs in emerging economies to unlock their potential in human capital for globalizing operations and targeting new market segments in the industry. This can directly benefit in uplifting the income level of millions in relatively underdeveloped countries and bringing about much-needed equity in the level of income in the society.

The value of this study lies in its novel and contemporary insight on how EMFs leapfrog in a fast-changing technology space. What distinguishes the work from the static framework in literature is that EMFs learning is dynamic, and happens in an interactive mode, alongside clients in close proximity. This study captures the innate ability of entrepreneurs to unlock the potential of human capital in emerging economies for globalizing operations.

The purpose of this study is to solve the problem of an unknown initial position in a multi-robot raster map fusion. The method includes two-dimensional (2D) raster maps and three-dimensional (3D) point cloud maps.

A fusion method using multiple algorithms was proposed. For 2D raster maps, this method uses accelerated robust feature detection to extract feature points of multi-raster maps, and then feature points are matched using a two-step algorithm of minimum Euclidean distance and adjacent feature relation. Finally, the random sample consensus algorithm was used for redundant feature fusion. On the basis of 2D raster map fusion, the method of coordinate alignment is used for 3D point cloud map fusion.

To verify the effectiveness of the algorithm, the segmentation mapping method (2D raster map) and the actual robot mapping method (2D raster map and 3D point cloud map) were used for experimental verification. The experiments demonstrated the stability and reliability of the proposed algorithm.

This algorithm uses a new visual method with coordinate alignment to process the raster map, which can effectively solve the problem of the demand for the initial relative position of robots in traditional methods and be more adaptable to the fusion of 3D maps. In addition, the original data of the map can come from different types of robots, which greatly improves the universality of the algorithm.

This paper aims to evaluate and compare the performance of different computer vision algorithms in the context of visual servoing for augmented robot perception and autonomy.

The authors evaluated and compared three different approaches: a feature-based approach, a hybrid approach and a machine-learning-based approach. To evaluate the performance of the approaches, experiments were conducted in a simulated environment using the PyBullet physics simulator. The experiments included different levels of complexity, including different numbers of distractors, varying lighting conditions and highly varied object geometry.

The experimental results showed that the machine-learning-based approach outperformed the other two approaches in terms of accuracy and robustness. The approach could detect and locate objects in complex scenes with high accuracy, even in the presence of distractors and varying lighting conditions. The hybrid approach showed promising results but was less robust to changes in lighting and object appearance. The feature-based approach performed well in simple scenes but struggled in more complex ones.

This paper sheds light on the superiority of a hybrid algorithm that incorporates a deep neural network in a feature detector for image-based visual servoing, which demonstrates stronger robustness in object detection and location against distractors and lighting conditions.

The purpose of this study is to explore the exogenous and endogenous drivers of the high-growth of Unicorn start-ups along their life cycle, with a particular focus on Unicorns in the fintech industry.

The study employs an explorative longitudinal analysis with a matched-pair of two cases of Unicorns start-ups with similar antecedent features to understand holistically drivers over the longer term.

High-growth patterns over the longer term are the result of a combined industry- and company-life cycle perspective. Drivers and growth patterns vary significantly according to the time of entry in the industry and its development status. The findings are systematised within a set of propositions to be tested in future research.

The limitations lie in empirical evidence, as the analysis is limited to one-matched-pair. The revealed Unicorns' drivers for long-term growth might encourage future research to further investigate these drivers on a larger scale.

The study offers practical recommendations for start-ups with high-growth ambitions and advice to policy makers regarding the development of tailor-made support programs.

The study significantly extends extant work on growth and high-growth by examining endogenous and exogenous triggers over time and by linking the Unicorn-life cycle to the industry life cycle, an approach which has, to the best of the authors’ knowledge, not yet been applied.

Shape memory materials are functional materials having a good number of applications due to their unique features of programmable material technology such as self-stretching, self-assembly and self-tightening. Advancements in today’s technology led to the easy fabrication of such novel materials using 3D printing techniques. When an external stimulus causes a 3D printed specimen to change shape on its own, this process is known as 4D printing. This study aims to investigate the effect of graphene nano platelet (GNPs) on the shape memory behaviour of shape memory photo polymer composites (SMPPCs) and to optimize the shape-changing response by using the Taguchi method.

SMPPCs are synthesized by blending different weight fractions (Wt.%) of flexible or soft photopolymer (FPP) resin with hard photopolymer (HPP) resin, then reinforced with GNPs at various Wt.% to the blended PP resin, and then fabricated using masked stereolithography (MSLA) apparatus. The shape memory test is conducted to assess the shape recovery time (T), shape fixity ratio (R_f), shape recovery ratio (R_r) and shape recovery rate (V_r) using Taguchi analysis by constructing an L9 orthogonal array with parameters such as Wt.% of a blend of FPP and HPP resin, Wt.% of GNPs and holding time.

SMPPCs with A3, B3 and C2 result in a faster T with 2 s, whereas SMPPCs with A1, B1 and C3 result in a longer T with 21 s. The factors A and B are ranked as the most significant in the Pareto charts that were obtained, whereas C is not significant. It can be seen from the heatmap plot that when factors A and B increase, T is decreasing and V_r is increasing. The optimum parameters for T and V_r are A3, B3 and C2 at the same time for R_f and R_r are A1, B3 and C1.

Faster shape recovery results from a higher Wt.% of FPP resin in a blend than over a true HPP resin. This is because the flexible polymer links in FPP resin activate more quickly over time. However, a minimum amount of HPP resin also needs to be maintained because it plays a role in producing higher R_f and V_r. The use of GNPs as reinforcement accelerates the T because nanographene conducts heat more quickly, releasing the temporary shape of the specimen more quickly.

The use of FPP and HPP resin blends, fabricating the 4D-printed SMPPCs specimens with MSLA technology, investigating the effect of GNPs and optimizing the process parameters using Taguchi and the work was validated using confirmation tests and regression analysis, which increases the originality and novelty.

The authors analysed the operations of two synchronised channels by focusing on “buy online and return in store” (BORS) strategies in fast-fashion retail by investigating internal and external factors affecting this omnichannel strategy.

The authors apply a combination of techniques to identify the BORS factors. Firstly, a strengths, weaknesses, opportunities and threats (SWOT) analysis was used to define the operational factors of BORS adoption. The authors then apply analytic hierarchy process (AHP) to evaluate the factors under four SWOT categories for kids, male and female consumer groups. The factors of BORS were then ranked using the fuzzy VlseKriterijumska Optimizacija I Kompromisno Resenje (fuzzy VIKOR) approach.

Combining the SWOT, AHP and fuzzy VIKOR techniques, the authors identified 21 factors in this study. The opportunity that BORS provides for trying in the fitting room for a better convenient shopping experience was ranked as the most important factor, followed by the opportunity to create a loyal customer profile with an easy and well-organised return process. Furthermore, the results reveal that the child consumer group is the most critical of the stated operations factors, followed by male and female consumers.

The authors described the operational factors and supported the decision-making system of BORS for each consumer group with a priority ranking to realise effective managerial management for fast-fashion retailers and practitioners.

The study contributes to the growing literature on the BORS omnichannel strategy, specifically for fast-fashion retail based on consumer needs.

This study aims to estimate the quality-adjusted changes in residential mobile consumer prices by controlling for the changes in the relevant service characteristics and quality, in a case study on Hungary between 2015 and 2021; compare the results with changes measured by the traditionally calculated official telecommunications price index of the Statistical Office; and discuss separating the hedonic price changes from the effect of a specific government intervention that occurred in Hungary, namely, the significant reduction in the value added tax rate (VAT) levied on internet services.

Since the price of commercial mobile offers does not directly reflect the continuous improvements in service characteristics and functionalities over time, the price changes need to be adjusted for changes in quality. The authors use hedonic regression analysis to address this issue.

The results show significant hedonic price changes over the observed seven-year period of over 30%, which turns out to be primarily driven by the significant developments in the comprising service characteristics and not the VAT policy change.

This paper contributes to the literature on hedonic price analyses on complex telecommunications service plans and enhances this methodology by using weights and analysing the content-related features of the mobile packages.