How can SMEs benefit from big data? Challenges and a path forward

Big data is big news, and large companies in all sectors are making significant advances in their customer relations, product selection and development and consequent profitability through using this valuable commodity. Small and medium enterprises (SMEs) have proved themselves to be slow adopters of the new technology of big data analytics and are in danger of being left behind. In Europe, SMEs are a vital part of the economy, and the challenges they encounter need to be addressed as a matter of urgency. This paper identifies barriers to SME uptake of big data analytics and recognises their complex challenge to all stakeholders, including national and international policy makers, IT, business management and data science communities. The paper proposes a big data maturity model for SMEs as a first step towards an SME roadmap to data analytics. It considers the ‘state-of-the-art’ of IT with respect to usability and usefulness for SMEs and discusses how SMEs can overcome the barriers preventing them from adopting existing solutions. The paper then considers management perspectives and the role of maturity models in enhancing and structuring the adoption of data analytics in an organisation. The history of total quality management is reviewed to inform the core aspects of implanting a new paradigm. The paper concludes with recommendations to help SMEs develop their big data capability and enable them to continue as the engines of European industrial and business success. Copyright © 2016 John Wiley & Sons, Ltd.Peer ReviewedPostprint (author's final draft

London Creative and Digital Fusion

date-added: 2015-03-24 04:16:59 +0000 date-modified: 2015-03-24 04:16:59 +0000date-added: 2015-03-24 04:16:59 +0000 date-modified: 2015-03-24 04:16:59 +0000The London Creative and Digital Fusion programme of interactive, tailored and in-depth support was designed to support the UK capital’s creative and digital companies to collaborate, innovate and grow. London is a globally recognised hub for technology, design and creative genius. While many cities around the world can claim to be hubs for technology entrepreneurship, London’s distinctive potential lies in the successful fusion of world-leading technology with world-leading design and creativity. As innovation thrives at the edge, where better to innovate than across the boundaries of these two clusters and cultures? This booklet tells the story of Fusion’s innovation journey, its partners and its unique business support. Most importantly of all it tells stories of companies that, having worked with London Fusion, have innovated and grown. We hope that it will inspire others to follow and build on our beginnings.European Regional Development Fund 2007-13

Carving out new business models in a small company through contextual ambidexterity: the case of a sustainable company

Business model innovation (BMI) and organizational ambidexterity have been pointed out as mechanisms for companies achieving sustainability. However, especially considering small and medium enterprises (SMEs), there is a lack of studies demonstrating how to combine these mechanisms. Tackling such a gap, this study seeks to understand how SMEs can ambidextrously manage BMI. Our aim is to provide a practical artifact, accessible to SMEs, to operationalize BMI through organizational ambidexterity. To this end, we conducted our study under the design science research to, first, build an artifact for operationalizing contextual ambidexterity for business model innovation. Then, we used an in-depth case study with a vegan fashion small e-commerce to evaluate the practical outcomes of the artifact. Our findings show that the company improves its business model while, at the same time, designs a new business model and monetizes it. Thus, our approach was able to take the first steps in the direction of operationalizing contextual ambidexterity for business model innovation in small and medium enterprises, democratizing the concept. We contribute to theory by connecting different literature strands and to practice by creating an artifact to assist managemen

The Case of Mongolia

학위논문(석사) -- 서울대학교대학원 : 공과대학 협동과정 기술경영·경제·정책전공, 2021.8. Jorn Altmann.Small and medium enterprises (SMEs) are considered key players in any country's social and economic development. Adopting innovative technologies such as Big Data Analytics (BDA) can bring better performance and competitive advantage for SMEs, which is important for a country's economic growth. This study aims to assess the main challenges and potentials of BDA adoptions in SMEs and examine the impacts of its adoption into business performance for SMEs in developing countries aspect. To achieve the study's goal, a systematic literature review (SLR) is conducted regarding the adoption of BDA in SMEs. The most common SLR method among the researchers in information system research, which was initiated by Kitchencham et al. (Kitchenham, Budgen, & Brereton, 2015) and Okoli et al.(Okoli & Schabram, 2010), is adapted in the study. In doing so, the SLR is focused on defining SMEs within various aspects and is directed to determine the most common influencing factors in BDA adoption in SMEs. In the result of the SLR, widely discussed 34 distinct influencing factors are identified in the adoption of BDA in SMEs from the previous literature. In addition, the hypotheses are developed based on the influencing factors, which show consensus among the researchers. After that, a conceptual framework is developed for developing the country aspect and control variables, and the moderating variables’ effect is also estimated. To evaluate hypotheses and the conceptual framework, an online questionnaire is conducted among Mongolia SMEs which run businesses in various industries. The online questionnaire is distributed to decision-makers and information technology specialists in the firm. In total, 170 respondents participated in the online survey. Based on the survey result, hypotheses are tested. As a consequence, the collected data and proposed framework are analyzed by using Partial Least Squares (PLS). This is a method of Structure Equation Modeling (SEM) that allows investigating the inter-relationship between the latent and observed variables. In terms of statistical software tools, Smart PLS v3.3.3 was employed, which is one of the useriv friendly tools for data analysis. Finally, policies and recommendations are deployed based on the findings.중소기업 (SME)은 모든 국가의 사회 및 경제 개발에서 핵심적인 역할을 하고 있는 것으로 간주된다. 빅 데이터 분석 (BDA)과 같은 혁신적인 기술의 채택은 국가 경제 성장에 중요한 역할을 하는 있는 중소기업에 더 나은 경영 성과와 경쟁력을 가져올 수 있다. 본 연구는 중소기업에서 BDA 채택하는 데에 있는 주요 과제와 잠재력을 평가하고 개발 도상국 측면에서 BDA 채택은 중소기업의 경영 성과에 대한 영향을 조사하는 것을 목표로 한다. 본 연구의 목표를 이루기 위해 우선 SME에서 BDA 채택과 관련한 문헌검토(systematic literature review (SLR))를 하였다. 정보 시스템 연구자들 중에 Kitchencham et al [1]과 Okoli et al. [2]에 의해 시작된 정보 시스템 연구는 가장 일반적인 SLR 방법이라고 할 수 있다. 이 방법은 본 연구에 적용됩니다. 본 연구는 문헌 검토를 통해서 다양한 측면에서 SME를 정의하는 데 초점을 맞추고 있으며 SME에서 BDA 채택의 가장 일반적인 영향 요인을 밝혔다 . 문헌 검토한 결과를 보면, 선행 연구에서 SME의 BDA 채택에 있어서 34 개의 뚜렷한 영향 요인을 논의했다는 것을 확인되었다. 본 연구의 가설은 연구자들의 일치한 관점을 보여주는 영향 요인을 기반으로 설정하었다. 그 다음에 개발 도상국을 위한 개념의 체계를 세우고 통제 변인과 조절 변인의 영향도 추정하였다. 가설과 개념 체계를 평가하기 위해 본 연구는 몽골의 다양한 사업을 운영하고 있는 중소기업을 대상으로 온라인 설문조사를 실시하였다. 온라인 141 설문조사의 참여자는 회사의 주요 의사 결정자 및 정보 기술 전문가였다. 이를 통해 수집 된 데이터와 제안 된 체계를 PLS (Partial Least Squire)를 사용하여 분석하였다. 이 방법은 잠재 변수와 관찰 변수 간의 상호 관계를 조사 할 수있는 구조 방정식 모형 (SEM) 방법이다. 통계 소프트웨어 도구 측면에서는 접하기가 쉬운 데이터 분석 도구 중 하나인 SmartPLS v3.3.3 을 이용하였다. 마지막으로, 본 연구는 분석한 결과를 기반하여 정책 및 제안을 제시하였다.Chapter 1. Introduction 1 Chapter 2. Background on Big Data Analytics Adoption 6 2.1 Defination of Big Data 6 2.2 Defination of Small and Medium enterprises 9 2.3 Role of Big Data 10 2.4 Charateristics of developing countries 11 Chapter 3. Methodology and Model Design 13 3.1 Methdogology fused for analyzing Big Data Analytics in Small and Medium Enterprises in Developing countries 13 3.2. Model design 26 3.2.1 Factors 26 3.2.2. Theories 28 3.2.3. Classification of factors into categories 36 3.2.4. Impact on developing country 46 3.2.5. Impact on different industries 50 3.2.6. Theoritical background and hypothesis development 51 3.2.7. Technological context 54 3.2.8. Organizational context 58 3.2.9. Environmental context 61 3.2.10. Moderating variables 63 3.2.11. Control variables 65 Chapter 4. Framework for Mongolian case 67 4.1. Mongolia 67 4.2. Data collection 68 4.3. Basic understanding on moderating effect 70 4.4. Data analysis 71 4.5. Results 74 4.5.1. Reliability and validity 74 4.5.2. Structual model analysis 78 4.5.3. Moderating variables 82 Chapter 5. Conclusion 85 5.1. Discussion 85 5.1.1. Technological context 85 5.1.2. Organizational context 88 5.1.3. Environmental context 88 5.2. Contrubitions 89 5.3. Policy implication 90 5.4. Limitation and outlok 91 Appendix.1 93 Appendix.2 110 Bibliography 115 Abstract in Korean 140석

Adoption of supply chain analytics in SMEs: an exploratory study

Objective Given the extant knowledge in the literature of the intersection among big data, analytics, and supply chain management, this thesis is aimed to explore the adoption of supply chain analytics in the SMEs. More specifically, the thesis’ main objectives are to investigate under what situations the SMEs adopt supply chain analytics and provide the recommendations for SMEs in adopting supply chain analytics. Summary Based on the content analysis of interviews with solution providers from different countries, the thesis has explored the main motivations behind the adoptions from SMEs, and the necessary existing resources and the challenges for SMEs to adopt supply chain analytics. Given such findings, a framework for future research on the factors that affect the adoption of supply chain analytics in SMEs is proposed and detailed recommendations for such companies are also discussed. Conclusions In conclusion, the adoption of supply chain analytics in SMEs is still in modest rate due to certain barriers and complex required resources for SMEs in adopting such practices. The decisions to adopt supply chain analytics in SMEs depends on factors such as perceived benefits, dynamic environment, data-driven culture, necessary resources, and challenges of the adoptions. The thesis recommends that SMEs should firstly build basic awareness of analytics, and technical capability related to data management before adopting supply chain analytics. Then, SMEs also need to emphasize on change management and adopt alignment strategy to optimize the benefits gained from analytics adoptions

Industry 4.0 for SME

Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Business AnalyticsIndustry 4.0 has been growing within companies and impacting the economy and society, but this has been a more complex challenge for some types of companies. Due to the costs and complexity associated with Industry 4.0 technologies, small and medium enterprises face difficulties in adopting them. This thesis proposes to create a model that gives guidance and simplifies how to implement Industry 4.0 in SMEs with a low-cost perspective. It is intended that this model can be used as a blueprint to design and implement an Industry 4.0 project within a manufactory SME. To create the model, a literature review of the different fields regarding Industry 4.0 were conducted to understand the most suited technologies to leverage within the manufacturing industry and the different use cases where these would be applicable. After the model was built, expert interviews were conducted, and based on the received feedback, the model was tweaked, improved, and validated

The Data-Driven Business Value Matrix - A Classification Scheme for Data-Driven Business Models

Increasing digitization is generating more and more data in all areas of business. Modern analytical methods open up these large amounts of data for business value creation. Expected business value ranges from process optimization such as reduction of maintenance work and strategic decision support to business model innovation. In the development of a data-driven business model, it is useful to conceptualise elements of data-driven business models in order to differentiate and compare between examples of a data-driven business model and to think of opportunities for using data to innovate an existing or design a new business model. The goal of this paper is to identify a conceptual tool that supports data-driven business model innovation in a similar manner: We applied three existing classification schemes to differentiate between data-driven business models based on 30 examples for data-driven business model innovations. Subsequently, we present the strength and weaknesses of every scheme to identify possible blind spots for gaining business value out of data-driven activities. Following this discussion, we outline a new classification scheme. The newly developed scheme combines all positive aspects from the three analysed classification models and resolves the identified weaknesses

Business Intelligence and Analytics in Small and Medium-Sized Enterprises

This thesis presents a study of Business Intelligence and Analytics (BI&A) adoption in small and medium-sized enterprises (SMEs). Although the importance of BI&A is widely accepted, empirical research shows SMEs still lag in BI&A proliferation. Thus, it is crucial to understand the phenomenon of BI&A adoption in SMEs. This thesis will investigate and explore BI&A adoption in SMEs, addressing the main research question: How can we understand the phenomenon of BI&A adoption in SMEs? The adoption term in this thesis refers to all the IS adoption stages, including investment, implementation, utilization, and value creation. This research uses a combination of a literature review, a qualitive exploratory approach, and a ranking-type Delphi study with a grounded Delphi approach. The empirical part includes interviews with 38 experts and Delphi surveys with 39 experts from various Norwegian industries. The research strategy investigates the factors influencing BI&A adoption in SMEs. The study examined the investment, implementation, utilization, and value creation of BI&A technologies in SMEs. A thematic analysis was adopted to collate the qualitative expert interview data and search for potential themes. The Delphi survey findings were further examined using the grounded Delphi method. To better understand the study’s findings, three theoretical perspectives were applied: resource-based view theory, dynamic capabilities, and IS value process models. The thesis’ research findings are presented in five articles published in international conference proceedings and journals. This thesis summary will coherently integrate and discuss these results.publishedVersio