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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.

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.

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.

While scaling is a viable approach to respond to growing demand, service providers in contact-intensive services (CIS) – such as education, healthcare and social services – struggle to innovate their offerings. The reason is that the scaling of CIS – unlike purely digital settings – has resource limitations. To help ease the situation, the purpose of this paper is to identify and describe the practices used in scaling CIS to support ICT-enabled service innovation.

The research draws on an in-depth analysis of three CIS to examine service innovation practices. The analysis informs model development for service scaling.

The analysis uncovers three practices for service scaling – service interaction analysis, service pivoting and service validation – and their related activities that are applied in a cyclic and iterative logic.

While the findings reveal that the scalability of CIS is limited and determined by the formative characteristic of personal interaction, this study and its findings describe how to leverage scalability in CIS.

The insights into the practices enable service providers of CIS to iteratively revise their service offerings and the logic of creating value with the service.

This research identifies and describes for the first time the practices for the scaling of CIS as an operationalisation of ICT-enabled service innovation.

To demonstrate the scalability of the genetic hybrid algorithm (GHA) in monitoring a local neural network algorithm for difficult non‐linear/chaotic time series problems.

GHA is a general‐purpose algorithm, spanning several areas of mathematical problem solving. If needed, GHA invokes an accelerator function at key stages of the solution process, providing it with the current population of solution vectors in the argument list of the function. The user has control over the computational stage (generation of a new population, crossover, mutation etc) and can modify the population of solution vectors, e.g. by invoking special purpose algorithms through the accelerator channel. If needed, the steps of GHA can be partly or completely superseded by the special purpose mathematical/artificial intelligence‐based algorithm. The system can be used as a package for classical mathematical programming with the genetic sub‐block deactivated. On the other hand, the algorithm can be turned into a machinery for stochastic analysis (e.g. for Monte Carlo simulation, time series modelling or neural networks), where the mathematical programming and genetic computing facilities are deactivated or appropropriately adjusted. Finally, pure evolutionary computation may be activated for studying genetic phenomena. GHA contains a flexible generic multi‐computer framework based on MPI, allowing implementations of a wide range of parallel models.

The results indicate that GHA is scalable, yet due to the inherent stochasticity of neural networks and the genetic algorithm, the scalability evidence put forth in this paper is only indicative. The scalability of GHA follows from maximal node intelligence allowing minimal internodal communication in problems with independent computational blocks.

The paper shows that GHA can be effectively run on both sequential and parallel platforms. The multicomputer layout is based on maximizing the intelligence of the nodes – all nodes are provided with the same program and the available computational support libraries – and minimizing internodal communication, hence GHA does not limit the size of the mesh in problems with independent computational tasks.

The purpose of this paper is to explore how big-picture sustainability strategies are translated into tangible product development efforts. The authors assert that most sustainable products currently remain confined to niche markets and do not permeate the mainstream. The authors propose that there is a missing link between strategic sustainability goals and operational product development initiatives. The authors establish a path to bridging this gap.

The manuscript is based on a qualitative research design with a sample of 32 companies. Data were collected from semi-structured interviews with product developers as well as secondary data analysis.

The authors delineate three empirically derived approaches firms from the sample pursue to develop sustainable products. The authors identify a phenomenon that the authors’ call the fallacy of trickle-down product sustainability. The authors find that only one of the three approaches – codification – is equipped to successfully turn strategic sustainability targets into authentic sustainable products.

This study provides an actionable guide to executives and product developers with respect to bridging the gap between often elusive sustainability aspirations and tangible product improvements via the process of rigorous codification.

This study provides a novel and unique perspective into strategy, sustainability and product development. The authors synthesize the extant literature on sustainable product development, juxtapose the emergent structure with primary interview data, and elaborate the resource-based view (RBV) to provide theoretical and practical implications. The authors establish scalability as the missing RBV capability of many attempts toward mass–market compatibility of more sustainable products.