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Digital business models are often designed for rapid growth, and some relatively young companies have indeed achieved global scale. However, despite the visibility and importance of this phenomenon, analysis of scale and scalability remains underdeveloped in management literature. When it is addressed, analysis of this phenomenon is often over-influenced by arguments about economies of scale in production and distribution. To redress this omission, this paper draws on economic, organization, and technology management literature to provide a detailed examination of the sources of scaling in digital businesses. We propose three mechanisms by which digital business models attempt to gain scale: engaging both non-paying users and paying customers; organizing customer engagement to allow self-customization; and orchestrating networked value chains, such as platforms or multi-sided business models. Scaling conditions are discussed, and propositions developed and illustrated with examples of big data entrepreneurial firms.

Social innovation is a recent theme, and the practices related to this area are characterized by punctual actions and projects restricted by time and space that make it difficult to develop strategies that can be sustained in this field. Therefore, one point that deserves to be highlighted in studies on social innovation is a matter of scalability. This paper aims to deal with a bibliometry whose objective was to map the existing studies about scalability of social innovation carried out in the Capes and EBSCOHost portals.

This paper deals with a bibliometry. The topic researched in this bibliometry is scalability of social innovation. The databases chosen for this research were Portal Periódico Capes and EBSCOHost because they are the leading providers of search databases.

A total of 42 papers were considered, distributed between 2002 and 2017. The analysis criteria for the study were origin (composed by year, author, country of origin, periodical and impact factor), focus of the investigations, justification, method and main techniques of research, contributions and theoretical advances and challenges and paths.

Among the main results found, one of them is that scalability is a topic that began to be researched recently, so that the USA and Brazil lead the research. Most of the studies focused on the scalability process and justified the importance of studies on the subject as a way to explore the potential of expanding the social impacts of a social innovation. Several studies have emphasized the role of networks as being quite positive for the scalability process and have been concerned with identifying factors that contribute to the scalability process. The challenge that most stood out among the papers was the financial sustainability of a social innovation. At the end, a research agenda was proposed.

The study aimsto analyze the main elements associated with the evolution of Brazilian agtechs from the initial conception of the business model to becoming companies in the scale-up stage.

The exploratory research was conducted based on data collected through in-depth interviews. The answers were analyzed quantitatively using descending hierarchical classification (DHC) and correspondence factor analysis (CFA) and qualitatively using content analysis.

Five main elements were identified as responsible for the evolution of the companies up to their entering the scale-up phase: (1) governance, (2) decisions inherent to resource allocation, (3) monitoring of strategic, tactical and operational activities, (4) fostering human capital development and (5) business model validation. Each element presents a set of performance indicators that show the scalability of these companies.

The model developed can help companies that have not yet advanced from the conception of the business model to the scalability of different sectors, in addition to agribusiness.

Proposal of a model that presents the main elements that impact on scalability and respective indicators that contributed to the scalability process of Brazilian agtechs.

This study contributed to advancing the knowledge on the organizational life cycle (OLC) of agricultural startups, particularly regarding the factors responsible for their scalability.

This study aims to propose and assess a model with the main elements influencing Brazilian agtech scalability with the aim of supporting the scalability process of startups that find themselves in the stage of initial conception of the business model.

The research was conducted using the design science research (DSR) method. The data for assessing the proposed model were collected through in-depth interviews. The answers were analyzed quantitatively, using descending hierarchical classification (DHC), correspondence factor analysis (CFA), and level of agreement; and qualitatively, using content analysis.

Considering the Brazilian context, the objective, environmental, structural, and evaluative dimensions presented positive characteristics for the main criteria analyzed (operational viability, generality, clarity, adaptation to the reality studied, completeness, consistency, comprehensibility, and structural simplicity). Specific improvements were proposed in all the criteria analyzed.

The artifact can be considered a strategic guide for agtechs that have yet to overcome the barrier of initial conception of the business model. The model enables the identification of the main problems that agtechs can encounter in their life cycle, as well as seeking solutions in advance.

Despite increased attention to, and investment in, scaling up of disaster risk reduction (DRR), there has been little detailed discussion of scalability. The purpose of this paper is to respond to this critical gap by proposing a definition of scaling up for DRR, what effective scaling up entails, and how to measure and plan for scalability.

A literature review of debates, case studies and good practices in DRR and parallel sectors (i.e. education, health and the wider development field) unveiled and enabled the weighting of key concepts that inform scalability. The mixed methods research then developed, validated and employed a scalability assessment framework to examine 20 DRR and five non-DRR initiatives for which a minimum set of evidence was accessible.

Support from national, regional and/or local authorities strongly influenced the scalability of all initiatives assessed. Currently, insufficient to support effective scaling up, monitoring and evaluation were also found to be critical to both identify potential for and measure scalability.

The paper ends with a scalability assessment and planning tool to measure and monitor the scalability potential of DRR initiatives, highlighting areas for corrective action that can improve the quality and effectiveness of DRR interventions.

This paper develops a framework exploring the question, “How does service affect the economics of e‐commerce?” Development of the framework requires an understanding of the different forms service takes in e‐commerce. These are described as “virtual” (either pure information or automated) and “physical” (requiring some degree of human intervention). The framework suggests that because the nature and quantity of physical service necessary to deliver value to customers influences the quantity of human intervention required, it also influences a firm’s ratio of variable to fixed costs, which alters its “scalability”. The paradox comes in that while reduced scalability is viewed negatively by many venture capitalists and proponents of e‐commerce, the cause of that reduction in scalability, human intervention, may help a firm to differentiate its offering to customers, thus providing a source of competitive advantage.

Improving the performance of recommender systems (RSs) has always been a major challenge in the area of e-commerce because the systems face issues such as cold start, sparsity, scalability and interest drift that affect their performance. Despite the efforts made to solve these problems, there is still no RS that can solve or reduce all the problems simultaneously. Therefore, the purpose of this study is to provide an effective and comprehensive RS to solve or reduce all of the above issues, which uses a combination of basic customer information as well as big data techniques.

The most important steps in the proposed RS are: (1) collecting demographic and behavioral data of customers from an e-clothing store; (2) assessing customer personality traits; (3) creating a new user-item matrix based on customer/user interest; (4) calculating the similarity between customers with efficient k-nearest neighbor (EKNN) algorithm based on locality-sensitive hashing (LSH) approach and (5) defining a new similarity function based on a combination of personality traits, demographic characteristics and time-based purchasing behavior that are the key incentives for customers' purchases.

The proposed method was compared with different baselines (matrix factorization and ensemble). The results showed that the proposed method in terms of all evaluation measures led to a significant improvement in traditional collaborative filtering (CF) performance, and with a significant difference (more than 40%), performed better than all baselines. According to the results, we find that our proposed method, which uses a combination of personality information and demographics, as well as tracking the recent interests and needs of the customer with the LSH approach, helps to improve the effectiveness of the recommendations more than the baselines. This is due to the fact that this method, which uses the above information in conjunction with the LSH technique, is more effective and more accurate in solving problems of cold start, scalability, sparsity and interest drift.

The research data were limited to only one e-clothing store.

In order to achieve an accurate and real-time RS in e-commerce, it is essential to use a combination of customer information with efficient techniques. In this regard, according to the results of the research, the use of personality traits and demographic characteristics lead to a more accurate knowledge of customers' interests and thus better identification of similar customers. Therefore, this information should be considered as a solution to reduce the problems of cold start and sparsity. Also, a better judgment can be made about customers' interests by considering their recent purchases; therefore, in order to solve the problems of interest drifts, different weights should be assigned to purchases and launch time of products/items at different times (the more recent, the more weight). Finally, the LSH technique is used to increase the RS scalability in e-commerce. In total, a combination of personality traits, demographics and customer purchasing behavior over time with the LSH technique should be used to achieve an ideal RS. Using the RS proposed in this research, it is possible to create a comfortable and enjoyable shopping experience for customers by providing real-time recommendations that match customers' preferences and can result in an increase in the profitability of e-shops.

In this study, by considering a combination of personality traits, demographic characteristics and time-based purchasing behavior of customers along with the LSH technique, we were able for the first time to simultaneously solve the basic problems of CF, namely cold start, scalability, sparsity and interest drift, which led to a decrease in significant errors of recommendations and an increase in the accuracy of CF. The average errors of the recommendations provided to users based on the proposed model is only about 13%, and the accuracy and compliance of these recommendations with the interests of customers is about 92%. In addition, a 40% difference between the accuracy of the proposed method and the traditional CF method has been observed. This level of accuracy in RSs is very significant and special, which is certainly welcomed by e-business owners. This is also a new scientific finding that is very useful for programmers, users and researchers. In general, the main contributions of this research are: 1) proposing an accurate RS using personality traits, demographic characteristics and time-based purchasing behavior; 2) proposing an effective and comprehensive RS for a “clothing” online store; 3) improving the RS performance by solving the cold start issue using personality traits and demographic characteristics; 4) improving the scalability issue in RS through efficient k-nearest neighbors; 5) Mitigating the sparsity issue by using personality traits and demographic characteristics and also by densifying the user-item matrix and 6) improving the RS accuracy by solving the interest drift issue through developing a time-based user-item matrix.

The primary purpose of this paper is to present a comprehensive strategy for performance, reliability and scalability (PSR) testing of multi‐tier web applications.

The strategy for PSR testing is presented primarily through examination of the intangible knowledge base in the PSR testing field. The paper also draws on relevant recent work conducted in the area of software performance evaluation.

The study revealed that appropriate testing procedures are critical for the success of web‐based multi‐tier applications. However, there was little academic work that collectively focused on PSR testing issues. This paper provides step‐by‐step testing procedures to ensure that web‐based applications are functioning well to meet user demands.

Given the rapid changes in technology and business environments, more applied research will be needed in the area of PSR testing to ensure the successful functioning of web‐based applications. For future studies, structured interviews or case‐study methods could be employed to present the views of online companies.

This paper provides a comprehensive strategy and the suggested steps for managers and technical personnel to ensure that the multi‐tier, web‐based applications are effective, scalable and reliable.

This chapter reviews the underlying technologies of cryptoassets, including fundamental cryptographic primitives used in distributed ledger technologies and permissionless blockchain technologies and their consensus protocols such as proof-of-work and proof-of-stake. It discusses the pros and cons of existing approaches to improve blockchain scalability and considers the requirements for security and decentralization. The chapter also examines the following techniques: layer 1 tuning, layer 1 sharding, and layer 2 solutions. It concludes with an overview of technologies to swap cryptoassets off-chain, technical requirements for cross-chain transactions, and reviews cross-chain atomic swap implementation using hashed time lock contracts.

The study aimed to examine the robotic process automation (RPA) contextual (center of excellence and scalability) and the multidisciplinary (TOE) determinants of RPA adoption in service industries in the emerging economy.

Ten factors were identified through literature surveys and popular studies grounded in technology, organization and environment. SPSS AMOS SEM is used for scale measurement and hypotheses testing. A sample of 313 respondents was collected from middle to above middle management executives of service industries from India. The authors tested the hidden layers and non-linear relationships using artificial neural network (ANN) analysis.

The low complexity, center of excellence (CoE), and industry/business partner pressure were significant to the RPA adoption in service industries in emerging economies. Counterintuitively, the scalability showed a negative influence on the RPA adoption, and the process capability did not show influence. The results of SEM and ANN were consistent.

This research can unfold the RPA adoption scholarly debate to multiple services industries beyond the telecom sector in emerging economies.

RPA is a disruptive technology on the artificial intelligence (AI) continuum. It has the potential to change the ways of working and enable technology-driven transformation. However, despite having thriving service industries that can benefit from RPA, emerging economies lag in adoption compared to the developed nations.

The RPA and automation can bring transformation to human society. Large economies such as India and China have large-scale demand for services, and the waiting lines are a common issue struggled by society. RPA can address the scalability issues of several services.

This study is among the first to examine technology-organization-environment (TOE) with RPA, including RPA contextual variables such as the CoE and scalability. Literature reports TOE applications on several emerging technologies of Industry 4.0 such as cloud, blockchain, big data and 3 Dimensional Printing (3DP), but no or little reported studies around RPA in services industries in emerging markets.