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Book part

Joanne Jin Zhang, Yossi Lichtenstein and Jonathan Gander

Digital business models are often designed for rapid growth, and some relatively young companies have indeed achieved global scale. However, despite the visibility and…

Abstract

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.

Details

Business Models and Modelling
Type: Book
ISBN: 978-1-78560-462-1

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Article

Larissa Medianeira Bolzan, Claudia Cristina Bitencourt and Bibiana Volkmer Martins

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…

Abstract

Purpose

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.

Design/methodology/approach

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.

Findings

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.

Originality/value

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.

Details

Innovation & Management Review, vol. 16 no. 3
Type: Research Article
ISSN: 2515-8961

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Article

Hannah Vaughan-Lee, Lezlie Caro Moriniere, Isabelle Bremaud and Marilise Turnbull

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…

Abstract

Purpose

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.

Design/methodology/approach

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.

Findings

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.

Originality/value

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.

Details

Disaster Prevention and Management: An International Journal, vol. 27 no. 4
Type: Research Article
ISSN: 0965-3562

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Article

Roger Hallowell

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…

Abstract

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.

Details

International Journal of Service Industry Management, vol. 12 no. 1
Type: Research Article
ISSN: 0956-4233

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Article

Samira Khodabandehlou, S. Alireza Hashemi Golpayegani and Mahmoud Zivari Rahman

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…

Abstract

Purpose

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.

Design/methodology/approach

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.

Findings

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.

Research limitations/implications

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

Practical implications

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.

Originality/value

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.

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Article

Lakshmi S. Iyer, Babita Gupta and Nakul Johri

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

Abstract

Purpose

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

Design/methodology/approach

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.

Findings

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.

Research limitations/implications

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.

Originality/value

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.

Details

Industrial Management & Data Systems, vol. 105 no. 5
Type: Research Article
ISSN: 0263-5577

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Article

Stefan Kleinschmidt, Christoph Peters and Jan Marco Leimeister

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…

Abstract

Purpose

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.

Design/methodology/approach

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

Findings

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.

Research limitations/implications

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.

Practical implications

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.

Originality/value

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

Details

Journal of Service Management, vol. 31 no. 4
Type: Research Article
ISSN: 1757-5818

Keywords

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Article

Sebastian Brockhaus, Moritz Petersen and A. Michael Knemeyer

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…

Abstract

Purpose

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.

Design/methodology/approach

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.

Findings

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.

Practical implications

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.

Originality/value

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.

Details

International Journal of Operations & Production Management, vol. 39 no. 9/10
Type: Research Article
ISSN: 0144-3577

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Article

Ralf Östermark

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.

Abstract

Purpose

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.

Design/methodology/approach

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.

Findings

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.

Originality/value

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.

Details

Kybernetes, vol. 37 no. 9/10
Type: Research Article
ISSN: 0368-492X

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Article

Hui Shi, Dazhi Chong and Gongjun Yan

Semantic Web is an extension of the World Wide Web by tagging content with “meaning”. In general, question answering systems based on semantic Web face a number of…

Abstract

Purpose

Semantic Web is an extension of the World Wide Web by tagging content with “meaning”. In general, question answering systems based on semantic Web face a number of difficult issues. This paper aims to design an experimental environment with custom rules and scalable data sets and evaluate the performance of a proposed optimized backward chaining ontology reasoning system. This study also compares the experimental results with other ontology reasoning systems to show the performance and scalability of this ontology reasoning system.

Design/methodology/approach

The authors proposed a semantic question answering system. This system has been built using ontological knowledge base including optimized backward chaining ontology reasoning system and custom rules. With custom rules, the proposed semantic question answering system will be able to answer questions that contain qualitative descriptors such as “groundbreaking” resesarch and “tenurable at university x”. Scalability has been one of the difficult issues faced by an optimized backward chaining ontology reasoning system and semantic question answering system. To evaluate the proposed ontology reasoning system, first, the authors design a number of innovative custom rule sets and corresponding query sets. The innovative custom rule sets and query sets will contribute to the future research on evaluating ontology reasoning systems as well. Then they design an experimental environment including ontologies and scalable data sets and metrics. Furthermore, they evaluate the performance of the proposed optimized backward chaining reasoning system on supporting custom rules. The evaluation results have been compared with other ontology reasoning systems as well.

Findings

The proposed innovative custom rules and query sets can be effectively employed for evaluating ontology reasoning systems. The evaluation results show that the scalability of the proposed backward chaining ontology reasoning system is better than in-memory reasoning systems. The proposed semantic question answering system can be integrated in sematic Web applications to solve scalability issues. For light weight applications, such as mobile applications, in-memory reasoning systems will be a better choice.

Originality/value

This paper fulfils an identified need for a study on evaluating an ontology reasoning system on supporting custom rules with and without external storage.

Details

Information Discovery and Delivery, vol. 46 no. 1
Type: Research Article
ISSN: 2398-6247

Keywords

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