Exploring the relationship between big data analytics capability and competitive performance: The mediating roles of dynamic and operational capabilities

A central question for information systems (IS) researchers and practitioners is if, and how, big data can help attain a competitive advantage. To address this question, this study draws on the resource-based view, dynamic capabilities view, and on recent literature on big data analytics, and examines the indirect relationship between a firm’s big data analytics capability (BDAC) and competitive performance. The study extends existing research by proposing that BDACs enable firms to generate insight that can help strengthen their dynamic capabilities, which, in turn, positively impact marketing and technological capabilities. To test our proposed research model, we used survey data from 202 chief information officers and IT managers working in Norwegian firms. By means of partial least squares structural equation modeling, results show that a strong BDAC can help firms build a competitive advantage. This effect is not direct but fully mediated by dynamic capabilities, which exerts a positive and significant effect on two types of operational capabilities: marketing and technological capabilities. The findings suggest that IS researchers should look beyond direct effects of big data investments and shift their attention on how a BDAC can be leveraged to enable and support organizational capabilities.     

Big data analytics in E-commerce: a systematic review and agenda for future research

There has been an increasing emphasis on big data analytics (BDA) in e-commerce in recent years. However, it remains poorly-explored as a concept, which obstructs its theoretical and practical development. This position paper explores BDA in e-commerce by drawing on a systematic review of the literature. The paper presents an interpretive framework that explores the definitional aspects, distinctive characteristics, types, business value and challenges of BDA in the e-commerce landscape. The paper also triggers broader discussions regarding future research challenges and opportunities in theory and practice. Overall, the findings of the study synthesize diverse BDA concepts (e.g., definition of big data, types, nature, business value and relevant theories) that provide deeper insights along the cross-cutting analytics applications in e-commerce.     

Big Data Analytics in Telecommunications: Literature Review and Architecture Recommendations

This paper focuses on facilitating state-of-the-art applications of big data analytics(BDA) architectures and infrastructures to telecommunications(telecom) industrial sector.Telecom companies are dealing with terabytes to petabytes of data on a daily basis. Io T applications in telecom are further contributing to this data deluge. Recent advances in BDA have exposed new opportunities to get actionable insights from telecom big data. These benefits and the fast-changing BDA technology landscape make it important to investigate existing BDA applications to telecom sector. For this, we initially determine published research on BDA applications to telecom through a systematic literature review through which we filter 38 articles and categorize them in frameworks, use cases, literature reviews, white papers and experimental validations. We also discuss the benefits and challenges mentioned in these articles. We find that experiments are all proof of concepts(POC) on a severely limited BDA technology stack(as compared to the available technology stack), i.e.,we did not find any work focusing on full-fledged BDA implementation in an operational telecom environment. To facilitate these applications at research-level, we propose a state-of-the-art lambda architecture for BDA pipeline implementation(called Lambda Tel) based completely on open source BDA technologies and the standard Python language, along with relevant guidelines.We discovered only one research paper which presented a relatively-limited lambda architecture using the proprietary AWS cloud infrastructure. We believe Lambda Tel presents a clear roadmap for telecom industry practitioners to implement and enhance BDA applications in their enterprises.     

Deliberate storytelling in big data analytics adoption

The emergence of big data analytics (BDA) has posed opportunities as well as multiple challenges to business practitioners, who have called for research on the behavioural factors underlying BDA adoption at the individual level. The purpose of this study is to extend the information systems (IS) research on storytelling and to explore the role and characteristics of deliberate storytelling in individual‐level BDA adoption. This case study used the grounded theory approach to extract qualitative data from 24 interviews, field notes, and documentary data. The explicit contributions of the study to the literature include (a) increasing our understanding of the facilitating role of deliberate storytelling in individual‐level BDA adoption, (b) identifying four deliberate storytelling patterns and seven underlying corporate stories disseminated by organizations to influence individual behaviour, and (c) defining the core characteristics of effective deliberate storytelling. This study has multiple implications for business practitioners and demonstrates how deliberate storytelling can be used as a facilitating mechanism in daily business practice.     

Strategic business value from big data analytics: An empirical analysis of the mediating effects of value creation mechanisms

Big data are a prominent source of value capable of generating competitive advantage and superior business performance. This paper represents the first empirical investigation of the theoretical model proposed by Grover et al. (2018), considering the mediating effects of four value creation mechanisms on the relationship between big data analytics capabilities (BDAC) and four value targets. The four value creation mechanisms investigated (the source of the value being pursued) are transparency, access, discovery, and proactive adaptation, while the four value targets (the impacts of the value creation process) are organization performance, business process improvement, customer experience and market enhancement, and product and service innovation. The proposed empirical validation of Grover et al.’s (2018) model adopts an econometric analysis applied to data gathered through a survey involving 256 BDA experts. The results reveal that transparency mediates the relationship for all the value targets, while access and proactive adaptation mediate only in case of some value targets, and discovery does not have any mediating effect. Theoretical and practical implications are discussed at the end of the paper.     

The impact of big data analytics on firms’ high value business performance

Big Data Analytics (BDA) is an emerging phenomenon with the reported potential to transform how firms manage and enhance high value businesses performance. The purpose of our study is to investigate the impact of BDA on operations management in the manufacturing sector, which is an acknowledged infrequently researched context. Using an interpretive qualitative approach, this empirical study leverages a comparative case study of three manufacturing companies with varying levels of BDA usage (experimental, moderate and heavy). The information technology (IT) business value literature and a resource based view informed the development of our research propositions and the conceptual framework that illuminated the relationships between BDA capability and organizational readiness and design. Our findings indicate that BDA capability (in terms of data sourcing, access, integration, and delivery, analytical capabilities, and people’s expertise) along with organizational readiness and design factors (such as BDA strategy, top management support, financial resources, and employee engagement) facilitated better utilization of BDA in manufacturing decision making, and thus enhanced high value business performance. Our results also highlight important managerial implications related to the impact of BDA on empowerment of employees, and how BDA can be integrated into organizations to augment rather than replace management capabilities. Our research will be of benefit to academics and practitioners in further aiding our understanding of BDA utilization in transforming operations and production management. It adds to the body of limited empirically based knowledge by highlighting the real business value resulting from applying BDA in manufacturing firms and thus encouraging beneficial economic societal changes.     

Big Data Analytics in Healthcare — A Systematic Literature Review and Roadmap for Practical Implementation

The advent of healthcare information management systems (HIMSs) continues to produce large volumes of healthcare data for patient care and compliance and regulatory requirements at a global scale. Analysis of this big data allows for boundless potential outcomes for discovering knowledge. Big data analytics (BDA) in healthcare can, for instance, help determine causes of diseases, generate effective diagnoses, enhance QoS guarantees by increasing efficiency of the healthcare delivery and effectiveness and viability of treatments, generate accurate predictions of readmissions, enhance clinical care, and pinpoint opportunities for cost savings. However, BDA implementations in any domain are generally complicated and resource-intensive with a high failure rate and no roadmap or success strategies to guide the practitioners. In this paper, we present a comprehensive roadmap to derive insights from BDA in the healthcare (patient care) domain, based on the results of a systematic literature review. We initially determine big data characteristics for healthcare and then review BDA applications to healthcare in academic research focusing particularly on NoSQL databases. We also identify the limitations and challenges of these applications and justify the potential of NoSQL databases to address these challenges and further enhance BDA healthcare research. We then propose and describe a state-of-the-art BDA architecture called Med-BDA for healthcare domain which solves all current BDA challenges and is based on the latest zeta big data paradigm. We also present success strategies to ensure the working of Med-BDA along with outlining the major benefits of BDA applications to healthcare. Finally, we compare our work with other related literature reviews across twelve hallmark features to justify the novelty and importance of our work. The aforementioned contributions of our work are collectively unique and clearly present a roadmap for clinical administrators, practitioners and professionals to successfully implement BDA initiatives in their organizations.      

Leveraging internet of things and big data analytics initiatives in European and American firms: Is data quality a way to extract business value?

Big data analytics (BDA) and the Internet of Things (IoT) tools are considered crucial investments for firms to distinguish themselves among competitors. Drawing on a strategic management perspective, this study proposes that BDA and IoT capabilities can create significant value in business processes if supported by a good level of data quality, which will lead to a better competitive advantage. Responses are collected from 618 European and American firms that use IoT and BDA applications. Partial least squares results reveal that better data quality is needed to unlock the value of IoT and BDA capabilities.     

基于SQL Server 2000的数据挖掘实现

大型数据库中的可升级数据挖掘是当今数据库研究领域的难题之一。SQL Server 是当今最流行的数据库管理软件之一,所以很容易想到在SQL Server 2000上研究在数据挖掘实现方面的主要方法。讨论了通过使用像SQL Server Analysis Service这样的典型工具来如何实现数据挖掘,且为商业组织的决定挖掘出必要的数据。     

Actualizing big data analytics affordances: A revelatory case study

Drawing on a revelatory case study, we identify four big data analytics (BDA) actualization mechanisms: (1) enhancing, (2) constructing, (3) coordinating, and (4) integrating, which manifest in actions on three socio-technical system levels, i.e., the structure, actor, and technology levels. We investigate the actualization of four BDA affordances at an automotive manufacturing company, i.e., establishing customer-centric marketing, provisioning vehicle-data-driven services, data-driven vehicle developing, and optimizing production processes. This study introduces a theoretical perspective to BDA research that explains how organizational actions contribute to actualizing BDA affordances. We further provide practical implications that can help guide practitioners in BDA adoption.     

Toward the development of a big data analytics capability

The era of big data has begun such that organizations in all industries have been heavily investing in big data initiatives. We know from prior studies that investments alone do not generate competitive advantage; instead, firms need to create capabilities that rival firms find hard to match. Drawing on the resource-based theory of the firm and recent work in big data, this study (1) identifies various resources that in combination build a big data analytics (BDA) capability, (2) creates an instrument to measure BDA capability of the firm, and (3) tests the relationship between BDA capability and firm performance. Results empirically validate the proposed theoretical framework of this study and provide evidence that BDA capability leads to superior firm performance.     

A Model to Create Organizational Value with Big Data Analytics

Value creation is a major factor not only in the sustainability of organizations but also in the maximization of profit, customer retention, business goals fulfillment, and revenue. When the value is intended to be created from Big Data scenarios, value creation entails being understood over a broader range of complexity. A question that arises here is how organizations can use this massive quantity of data and create business value? The present study seeks to provide a model for creating organizational value using Big Data Analytics (BDA). To this end, after reviewing the related literature and interviewing experts, the BDA-based organizational value creation model is developed. Accordingly, five hypotheses are formulated, and a questionnaire is prepared. Then, the respective questionnaire is given to the research statistical population (i.e., IT managers and experts, particularly those specializing in data analysis) to test the research hypotheses. In next phase, connections between model variables are scrutinized using the structural equation modeling (measurement and structural models). The results of the study indicate that investigating the infrastructures of the Big Data Analytics, as well as the capabilities of the organization and those of Big Data Analytics is the initial requirement to create organizational value using BDA. Thereby, the Big Data Analytics strategy is formulated, and ultimately, the organizational value is created as well.     

An application of the Delta Model and BPR in transforming electronic business – the case of a food ingredients company in UK

Abstract. Companies are leveraging existing resources and the internet to come up with new or modified business models to respond to the demands of e‐business. Companies in the food processing industry are also waking to the reality that e‐business technology can make a big difference to the bottom line. This paper presents the result of an action research project which applied a new approach labelled as the Delta Model developed by Hax and Wilde (2001) of Sloan School of Management to strategy development for brick‐n‐mortar companies launching e‐business initiatives. The case company is called Whetstone Food Ingredients (WFI), located in UK. The company is firmly embedded in the agri‐product industry and operates in the ‘egg by‐product’ subsector. This paper is based upon action research supplemented by a programme of in‐depth interviews with managers at WFI for various aspects of their supply chain and e‐business initiatives. On the basis of these interviews and the companies also made a range of documents available throughout. These included internal memos, strategy plans, operational control documents and minutes of meetings. The Delta Model and Davenport's methodology of business process reengineering were adopted to structure this analysis. Used in conjunction, they helped to develop a vision, analyse the business processes, identify critical business processes, benchmark the critical processes, and then develop the information technology infrastructure. The infrastructure thus supported the critical business processes and leveraged the e‐business supply chain to enable the company to gain competitive edge.     

国外电力企业大数据需求侧应用分析

针对大数据技术的研究和实际应用,总结了国外企业大数据技术的实际应用现状。在国外企业大数据需求侧管理应用中,介绍了法国电力公司、美国巴尔的摩燃气电力公司、美国南加州爱迪生电力公司和德国意昂电力公司四大能源企业大数据技术的应用主题和管理模式。通过上述介绍,在数据平台建设、数据管理和数据分析应用三个方面总结了各类大数据应用的启示,即集中建设统一大数据平台,并采用云部署方式是企业目前大数据平台建设的主流方式;统一数据标准规范有利于数据共享与数据管控,实现数据资源价值最大化;大数据分析应用有两个特点：一是大数据分析应用应紧紧围绕业务需求,以专业级应用为重心,配备充足的人力资源,准确聚焦业务痛点,快速解决业务实际问题。二是企业应结合业务特点和发展要求开展基础技术和基础应用方面的研究,为专业级分析应用提供服务和支撑。     

Exploring the inbound and outbound strategies enabled by user generated big data: Evidence from leading smartphone applications

The Open Innovation paradigm has been increasingly considered as a relevant approach to innovation. Among the different sources, end users are particularly meaningful. Scholars have highlighted several methods and strategies to involve them in the innovation process by asking, observing, and giving them the chance to actually co‐create. Digital technologies are expanding the span of opportunities in this direction, gathering a huge amount and variety of data, while the end user enjoys a digital product; these data can be called “user generated big data” (UGBD). The aim of this research is to understand whether UGBD can contribute in user innovation and to highlight the enabled strategies to create value through them. Leveraging on a multiple case study (Twitter, Spotify, Strava, and Deliveroo), the paper first classifies UGBD among the methods to foster user centered innovation, and then it defines two strategies to create value relying on UGBD. First, companies can leverage on a “using data” strategy—addressing both the end user or other player in the ecosystem—fostering service innovation through an inbound approach. Second, a “selling data” strategy can be pursued, addressing new clients and fostering business model innovation, enlarging the company's value chain in an outbound perspective.     

Utilizing big data analytics for information systems research: challenges,promises and guidelines

This essay discusses the use of big data analytics (BDA) as a strategy of enquiry for advancing information systems (IS) research. In broad terms, we understand BDA as the statistical modelling of large, diverse, and dynamic data sets of user-generated content and digital traces. BDA, as a new paradigm for utilising big data sources and advanced analytics, has already found its way into some social science disciplines. Sociology and economics are two examples that have successfully harnessed BDA for scientific enquiry. Often, BDA draws on methodologies and tools that are unfamiliar for some IS researchers (e.g., predictive modelling, natural language processing). Following the phases of a typical research process, this article is set out to dissect BDA’s challenges and promises for IS research, and illustrates them by means of an exemplary study about predicting the helpfulness of 1.3 million online customer reviews. In order to assist IS researchers in planning, executing, and interpreting their own studies, and evaluating the studies of others, we propose an initial set of guidelines for conducting rigorous BDA studies in IS.     

基于SQL Server 2000下数据挖掘算法的研究

微软的SQL Server2000是当今最流行的数据库管理软件之一,研究了在SQL Server 2000上数据挖掘实现方面的决策树算法.决策树算法通过构造精度高、小规模的决策树采掘训练集中的分类知识.SQL Server 2000/Analysis Service两层结构决策树,采用了以类记数表及深度优先策略生成,在建树算法和数据库间设立数据挖掘中间件.并讨论了通过使用像SQL Server 2000 Analysis Service这样的典型工具来如何实现数据挖掘模型的创建,且为商业组织的决定挖掘出必要的数据.     

Understanding the Factors Affecting the Organizational Adoption of Big Data

Big data is rapidly becoming a major driver for firms seeking to gain a competitive advantage. Grounded in the Diffusion of Innovation theory (DOI), the institutional theory, and the Tech-nology–Organization–Environment (TOE) framework, this study applies the results of a content analysis to develop a framework to identify the main factors affecting the organizational adoption of big data. The content analysis is based on the retrieval and review of relevant papers in the business intelligence & analytics (BI&A) literature published during the period 2009–2015. The 26 factors identified by this review are then integrated into a TOE framework. The findings of this research enrich the current big data literature and enhance practitioners’ understanding of the decision-making processes involved in a firm’s adoption of big data.     

An evaluation method of outsourcing services for developing an elastic cloud platform

To gain and retain competitive advantages in a competitive business arena, a business cloud-computing platform should continuously strive to offer new services and remain competitive. Unfortunately, it becomes more and more recognized by the industry that a cloud-computing platform could not cover all aspects of IT layers engaged in infrastructure, platform and application. In practice, end users’ requests are nearly unlimited; while the services held by a cloud-computing platform is relatively limited, no matter in service category or in service capacity. In view of this challenge, an elastic cloud platform is investigated by recruited outside services that are absent from the cloud platform. Concretely, through dynamically hiring a qualified service on Internet to replace the absent service inside a cloud platform, an elastic cloud platform could nearly provide unlimited capabilities in an outsourcing service way, e.g., computing power, storage, application functions, etc. At last, the validity of the method is evaluated by a case study.     

Does model consistency in business model innovation matter? A contingency‐based approach

Successful business model innovation requires managers to come up not only with new, but also with viable business models. To this end, it has been argued that business model consistency plays a vital role, as the internal fit of business model elements can generate reinforcing effects, thereby influencing performance and competitive advantage. Little research has been conducted to measure consistency and confirm these effects, especially within business model innovation. We tackle this issue by developing and testing a measurement of business model consistency, and investigate its relationship with business model innovation and its performance. We find evidence supporting the positive effect of consistency on innovation performance. We contribute to extant research by developing a concept and measurement for business model consistency based on contingency theory and empirically verifying it. Our findings underline that managers should pay close attention to the consistency of their business model designs during business model innovation.