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The purpose of this study is to identify the business strategies that entrepreneurs have formulated to establish the business with the intention of scaling up in the information technology (IT) sector in Chile, given that they have managed to scale up sustainably at an average annual rate of 73.3% and an average annual employee growth rate of 37% for four consecutive years after an establishment period of 25 months.

Three methodological steps were used to identify which strategic initiatives are relevant to the establishment of small- and medium-sized enterprises (SMEs) on the path to scaling up. The first part consisted of identifying the literature and defining the research propositions and research questions. The second part was to prepare, collect and analyse the data to conduct the research by applying, transcribing, reviewing and coding the sources of evidence to explore how SMEs are able to develop strategic initiatives for the start-up process. The final stage was to validate the research proposal to identify potential strategic initiatives identified during the multi-case study.

As a result of the data analysis and empirical findings, three deliberate strategic initiatives were identified: staying engaged with customers, delivering successful business solutions and articulating social capital. However, in crisis situations, entrepreneurs readjust their strategies based on their management skills and an emergent strategic initiative was identified as securing the financial structure and revolutionising change. While this research was not designed to identify personal attributes, it did highlight the importance of adaptation and learning as a skill to drive the business model for scaling up during the establishment of their business.

It is clear that the study focused on Chile and cannot be replicated in other regions or sectors due to the characteristics of the sample itself, but it provides empirical evidence that there are cycles prior to scale up that need to be understood. The findings were empirically validated during the establishment phase, but the deliberate and emergent strategic initiatives that consolidated the SME to prepare for its scale-up process are not evident in the theory.

The IT sector will continue to grow and change after the pandemic, and the global economy will use more digital systems, creating new ways of working with the use of IT. This context will impact on SMEs where strategies, whether deliberate or emergent, will need to be part of the new business models, and therefore, caution should be exercised when using the results of this study. Public and private institutions should educate and guide entrepreneurs for the potential scaling up of their SMEs without having to wait 42 months, according to Global Entrepreneurship Monitor 2021-2022 (Hill et al., 2022). Scaling up can begin as early as 25 months after establishment, breaking the paradigm of the theory that the SME must be established in a period of 3.5 years. This period cannot be generalised as business opportunities in the IT sector are faster. The research also contributes by reporting that contingency planning is relevant during the establishment phase.

Educational institutions and the public sector have made efforts to change business cultures regarding the importance of strengthening entrepreneurship, but teaching the emergent strategies that often challenge SME creation is not yet widespread in educational formats. This is a challenge not only for institutions but also for entrepreneurs trying to anticipate the constant changes in the global economy. This research provides an opportunity to create more dynamic business models with more conscious risk planning.

Although the literature has confirmed the findings, this research has provided a pre-scaling picture that links these two important stages on the axis of deliberate and emergent strategies. The findings confirm the importance of correctly embedding five strategic initiatives for the establishment of the SME if it is to continue on its journey towards business scale-up. However, there is a lack of empirical evidence in emerging economies on how entrepreneurs have found the right path to scale-up.

This paper aims to examine the key challenges experienced and lessons learned when organizations undergo large-scale agile transformations and seeks to answer the question of how incumbent firms achieve agility at scale.

Building on a case study of a multinational corporation seeking to scale up agility, the authors combined 36 semistructured interviews with secondary data from the organization to analyze its transformation since the early planning period.

The results show how incumbent firms develop and successfully integrate agility-enhancing capabilities to sense, seize and transform in times of digital transformation and rapid change. The findings highlight how agility can be established initially at the divisional level, namely with a key accelerator in the form of a center of competence, and later prepared to be scaled up across the organization. Moreover, the authors abstract and organize the findings according to the dynamic capabilities framework and offer propositions of how companies can achieve organizational agility by scaling up agility from a divisional to an organizational level.

Along with in-depth insights into agile transformations, this article provides practitioners with guidance for developing agility-enhancing capabilities within incumbent organizations and creating, scaling and managing agility across them.

Examining the case of a multinational corporation's exceptional, pioneering effort to scale agility, this article addresses the strategic importance of agility and explains how organizational agility can serve incumbent firms in industries characterized by uncertainty and intense competition.

This article aims to present the results of an exploratory pilot study that demonstrates the validity of a self-created implementation methodology to develop the students' level of perceived achievement of the social entrepreneurship competency and explain how this is equally valid in developing the perceived achievement of the complex thinking competency.

Based on a multivariate descriptive statistical analysis, this article offers the results of an educational intervention carried out on a sample group of students from a Mexican university before and after a training program in social entrepreneurship.

The favorable results showed that the proposed methodology is valid for scaling social entrepreneurship and complex thinking competencies and their subcompetencies.

These results are not only academically valuable, as they highlight the need to delve into the relationship between these two competencies, but they also allow us to appreciate the ample opportunities for practical implementation of entrepreneurship programs by universities and other institutions to work directly with social entrepreneurs and seek alternatives to develop skills through devising, proposing and developing social entrepreneurship projects.

This study aims to examine the enablers of productivity of enterprise-level Agile development process using modified total interpretative structural modeling (TISM). The two main objectives of the current study are to determine the variables influencing enterprise-level agile development productivity and to develop modified TISM for the corresponding components.

To identify enablers of the productivity of enterprise-level agile software development process a literature review and opinions of domain experts were collected. A hierarchical relationship among variables that show direct and indirect influence is created using the modified TISM (M-TISM) technique with Cross Impact Matrix-Multiplication Applied to Classification analysis. This study examined and analyzed the relationships between the determinants within the enterprise using a M-TISM technique.

With the literature review, the study could identify ten enabling factors of the productivity of Agile development process at the enterprise level. Results depict that program increment (PI) planning and scalable backlog management, continuous integration and continuous delivery (CI/CD), agile release trains (ART), agile work culture, delivery excellence, lean and DevOps practices, value stream mapping (VMS), team skills and expertise, collaborative culture, agile coaching, customer engagement have an impact on the productivity of enterprise-level Agile development process. The results show that team collaboration, agile ways of working and customer engagement have a greater impact on productivity improvement for enterprise-level Agile development process.

The developed model is useful for organizations employing scaled Agile development processes in software development. This study provides a recommended listing of key enablers, that may enable productivity improvements in the Agile development process at the enterprise level. Strategists should focus on team collaboration and Agile project management. This study offers a modified TISM model to academicians to help them understand the effects of numerous variables on maintaining the productivity of an enterprise-level Agile. The identified characteristics and their hierarchical structure can help project managers during the execution of Agile projects at the enterprise level, more effectively, increasing their success and productivity.

The study addresses the gap in the literature by interpretative relationships between the identified enabling factors. The model validation is carried out by a panel of nine experts from several information technology organizations deploying Agile software development at the enterprise level. This unique method broadens the knowledge base in Agile software development at scale and provides project managers and practitioners with a practical foundation.

The purpose of this study was a longitudinal study on knowledge organization (KO) realm structure and cluster concepts and emerging KO events based on co-occurrence analysis.

This longitudinal study uses the co-occurrence analysis. This research population includes keywords of articles indexed in the Web of Science Core Collection 1975–1999 and 2000–2018. Hierarchical clustering, multidimensional scaling and co-occurrence analysis were used to conduct the present research. SPSS, UCINET, VOSviewer and NetDraw were used to analyze and visualize data.

The “Information Technology” in 1975–1999 and the “Information Literacy” in 2000–2018, with the highest frequency, were identified as the most widely used keywords of KO in the world. In the first period, the cluster “Knowledge Management” had the highest centrality, the cluster “Strategic Planning” had the highest density in 2000–2018 and the cluster “Information Retrieval” had the highest centrality and density. The two-dimensional map of KO’s thematic and clustering of KO topics by cluster analysis method indicates that in the periods examined in this study, thematic clusters had much overlap in terms of concept and content.

The present article uses a longitudinal study to examine the KO’s publications in the past half-century. This paper also uses hierarchical clustering and multidimensional scaling methods. Studying the concepts and thematic trends in KO can impact organizing information as the core of libraries, museums and archives. Also, it can scheme information organizing and promote knowledge management. Because the results obtained from this article can help KO policymakers determine and design the roadmap, research planning, and micro and macro budgeting processes.

The aim of this paper is to understand how systemic change agents influence the twin digital and green transitions. The authors build on agency-based theories to argue that transition pathways are influenced by a combination of place-based characteristics, the mobilisation and preferences of systemic change agents (such as local clusters), and the institutional and economic context. The conceptual framework defines the different steps of the twin transition, and it identifies how systemic change agents and geographic characteristics determine the direction and speed of the transition pathway.

This paper starts with a literature review to identify the different schools of thoughts on transition pathways and the twin transition, before developing a conceptual framework and deriving policy implications.

First, this paper argues that each transition involves three steps: framing, piloting and scaling. Each of these steps is driven by systemic change agents who engage local actors in trust-based collaboration, pool resources, create network effects and exchange information to source solutions for industry-level challenges. Second, the combination of place-based characteristics and the actions of local systemic change agents define the path of the transition and the new (post-transition) equilibrium. Finally, this paper sets out implications for policymakers who are interested in using systemic change agents to shape transition pathways in their local area.

Further research is needed to provide robust empirical evidence from a range of territorial realities for the hypotheses in this paper. Specifically, the role of systemic change agents, such as trade associations, regional organisations, clusters or research groupings, needs to be investigated more closely. These agents can play a key role in progressing the transition because they already focus on sourcing solutions to joint challenges and opportunities by exchanging information, engaging local actors in trust-based collaboration, pooling resources and fostering network effects and critical mass. Future research should investigate how policymakers can best leverage on these crucial actors to progress or steer transitions and how this varies depending on place-based characteristics. This could include, for instance, training activities, networking and collaboration (e.g. through the European Cluster Collaboration Platform) or clearer sign-posting the key next steps required for the transition.

This paper identifies specific ways in which local actors can influence the direction and speed of transitions at each stage of the transition: at the framing stage, political entrepreneurship can be fostered through collaboration and smooth information flows between different levels of governance, at the piloting stage, commercial and social entrepreneurship require effective knowledge sharing and a wide and open search for solutions which, in turn, may require capacity building at the local level and coordination across stakeholder groups and levels of governance and effective scaling up can be fostered through network effects, joint commitment from a broad range of stakeholders and pooling of resources to achieve economies of scale.

An important implication of the framework is that, if several places are undergoing a parallel or joint transition, the result may not be convergence between these places. Instead, different places may choose different end points and they may proceed at different speeds. For instance, in the context of the European Union’s green and digital transitions, it is unlikely that every region will transition to a similar level of digitisation or make steps in the same direction when it comes to sustainability.

This paper plugs a gap in understanding how systemic transitions unfold and how their speed and direction are influenced by different stakeholder groups. This paper develops a conceptual framework to define twin transition pathways and it analyses prominent place-based factors affecting these pathways.

Major public cloud providers, such as AWS, Azure or Google, offer seamless experiences for infrastructure as a service (IaaS), platform as a service (PaaS) and software as a service (SaaS). With the emergence of the public cloud's vast usage, administrators must be able to have a reliable method to provide the seamless experience that a public cloud offers on a smaller scale, such as a private cloud. When a smaller deployment or a private cloud is needed, OpenStack can meet the goals without increasing cost or sacrificing data control.

To demonstrate these enablement goals of resiliency and elasticity in IaaS and PaaS, the authors design a private distributed system cloud platform using OpenStack and its core services of Nova, Swift, Cinder, Neutron, Keystone, Horizon and Glance on a five-node deployment.

Through the demonstration of dynamically adding an IaaS node, pushing the deployment to its physical and logical limits, and eventually crashing the deployment, this paper shows how the PackStack utility facilitates the provisioning of an elastic and resilient OpenStack-based IaaS platform that can be used in production if the deployment is kept within designated boundaries.

The authors adopt the multinode-capable PackStack utility in favor of an all-in-one OpenStack build for a true demonstration of resiliency, elasticity and scalability in a small-scale IaaS. An all-in-one deployment is generally used for proof-of-concept deployments and is not easily scaled in production across multiple nodes. The authors demonstrate that combining PackStack with the multi-node design is suitable for smaller-scale production IaaS and PaaS deployments.

Depression is a mental health problem characterized by a persistent sense of sadness and loss of interest. EEG signals are regarded as the most appropriate instruments for diagnosing depression because they reflect the operating status of the human brain. The purpose of this study is the early detection of depression among people using EEG signals.

(i) Artifacts are removed by filtering and linear and non-linear features are extracted; (ii) feature scaling is done using a standard scalar while principal component analysis (PCA) is used for feature reduction; (iii) the linear, non-linear and combination of both (only for those whose accuracy is highest) are taken for further analysis where some ML and DL classifiers are applied for the classification of depression; and (iv) in this study, total 15 distinct ML and DL methods, including KNN, SVM, bagging SVM, RF, GB, Extreme Gradient Boosting, MNB, Adaboost, Bagging RF, BootAgg, Gaussian NB, RNN, 1DCNN, RBFNN and LSTM, that have been effectively utilized as classifiers to handle a variety of real-world issues.

1. Among all, alpha, alpha asymmetry, gamma and gamma asymmetry give the best results in linear features, while RWE, DFA, CD and AE give the best results in non-linear feature. 2. In the linear features, gamma and alpha asymmetry have given 99.98% accuracy for Bagging RF, while gamma asymmetry has given 99.98% accuracy for BootAgg. 3. For non-linear features, it has been shown 99.84% of accuracy for RWE and DFA in RF, 99.97% accuracy for DFA in XGBoost and 99.94% accuracy for RWE in BootAgg. 4. By using DL, in linear features, gamma asymmetry has given more than 96% accuracy in RNN and 91% accuracy in LSTM and for non-linear features, 89% accuracy has been achieved for CD and AE in LSTM. 5. By combining linear and non-linear features, the highest accuracy was achieved in Bagging RF (98.50%) gamma asymmetry + RWE. In DL, Alpha + RWE, Gamma asymmetry + CD and gamma asymmetry + RWE have achieved 98% accuracy in LSTM.

A novel dataset was collected from the Central Institute of Psychiatry (CIP), Ranchi which was recorded using a 128-channels whereas major previous studies used fewer channels; the details of the study participants are summarized and a model is developed for statistical analysis using N-way ANOVA; artifacts are removed by high and low pass filtering of epoch data followed by re-referencing and independent component analysis for noise removal; linear features, namely, band power and interhemispheric asymmetry and non-linear features, namely, relative wavelet energy, wavelet entropy, Approximate entropy, sample entropy, detrended fluctuation analysis and correlation dimension are extracted; this model utilizes Epoch (213,072) for 5 s EEG data, which allows the model to train for longer, thereby increasing the efficiency of classifiers. Features scaling is done using a standard scalar rather than normalization because it helps increase the accuracy of the models (especially for deep learning algorithms) while PCA is used for feature reduction; the linear, non-linear and combination of both features are taken for extensive analysis in conjunction with ML and DL classifiers for the classification of depression. The combination of linear and non-linear features (only for those whose accuracy is highest) is used for the best detection results.

Along their journey to achieve exponential growth, startups must process a vast amount of information and make quick decisions, reevaluate and adjust strategies and simultaneously redesign their organization along with the venture lifecycle. This paper delineates the evolution of startups' organizational design and identifies the influencing factors in every phase of the lifecycle.

This study adopts an explorative qualitative approach using a multiple case study methodology for six Indonesian startups. Indonesia is chosen as an emerging country in Southeast Asia with tremendous growth in digital startup businesses.

The research findings suggest that, as they experience exponential growth, startups strive to manage the tension between being structured and being flexible and hence remain innovative by combining management-centric and employee-centric approaches. In particular, this study identified three main factors that potentially influence the evolution of startups' organizational design: founders, investors and the characteristics of business and market.

The present study focuses mainly on Indonesian digital startups and does not fully explain how the influencing factors work in each phase of the venture journey.

This study offers practical contributions for startups pursuing business growth by focusing on the importance of balancing the tension between structured and flexible organizational design and placing more attention on founders, investors and business-market characteristics.

This empirical study is among the first to delineate nuances of organizational design evolution during the startup lifecycle by adopting an explorative qualitative method.

Cloud computing originated in central data centers that are connected to the backbone of the Internet. The network transport to and from a distant data center incurs long latencies that hinder modern low-latency applications. In order to flexibly support the computing demands of users, cloud computing is evolving toward a continuum of cloud computing resources that are distributed between the end users and a distant data center. The purpose of this review paper is to concisely summarize the state-of-the-art in the evolving cloud computing field and to outline research imperatives.

The authors identify two main dimensions (or axes) of development of cloud computing: the trend toward flexibility of scaling computing resources, which the authors denote as Flex-Cloud, and the trend toward ubiquitous cloud computing, which the authors denote as Ubi-Cloud. Along these two axes of Flex-Cloud and Ubi-Cloud, the authors review the existing research and development and identify pressing open problems.

The authors find that extensive research and development efforts have addressed some Ubi-Cloud and Flex-Cloud challenges resulting in exciting advances to date. However, a wide array of research challenges remains open, thus providing a fertile field for future research and development.

This review paper is the first to define the concept of the Ubi-Flex-Cloud as the two-dimensional research and design space for cloud computing research and development. The Ubi-Flex-Cloud concept can serve as a foundation and reference framework for planning and positioning future cloud computing research and development efforts.