BigdataanalyticsinE-commerce:asystematicreviewandagenda ...

1 POSITION PAPERBig data analytics in E- commerce : a systematic review and agendafor future researchShahriar Akter1&Samuel Fosso Wamba2 Received: 1 September 2015 /Accepted: 26 February 2016 /Published online: 16 March 2016#Institute of Applied Informatics at University of Leipzig 2016 AbstractThere has been an increasing emphasis on big dataanalytics (BDA) in e- commerce in recent years. However, itremains poorly-explored as a concept, which obstructs its the-oretical and practical development. This position paper ex-plores BDA in e- commerce by drawing on a systematic re-view of the literature. The paper presents an interpretiveframework that explores the definitional aspects, distinctivecharacteristics, types, business value and challenges of BDAin the e- commerce landscape.

2 The paper also triggers broaderdiscussions regarding future research challenges and opportu-nities in theory and practice. Overall, the findings of the studysynthesize diverse BDA concepts ( , definition of big data,types, nature, business value and relevant theories) that pro-vide deeper insights along the cross-cutting analytics applica-tions in data valueJEL the past few years, an explosion of interest in big data hasoccurred from both academia and the e- commerce explosion is driven by the fact that e- commerce firms thatinject big data analytics (BDA) into their value chain experi-ence 5 6 % higher productivity than their competitors(McAfee and Brynjolfsson2012). A recent study by BSAS oftware Alliance in the United States (USA) indicates thatBDA contributes to 10 % or more of the growth for 56 % offirms (Columbus2014).

3 Therefore, 91 % of Fortune 1000companies are investing in BDA projects, an 85 % increasefrom the previous year (Kiron et ). While the use ofemerging internet-based technologies provides e-commercefirms with transformative benefits ( , real-time customerservice, dynamic pricing, personalized offers or improved in-teraction) (Riggins1999 ), BDA can further solidify these im-pacts by enabling informed decisions based on critical insights(Jao 2013 ). Specifically, in the e- commerce context, big dataenables merchants to track each user s behavior and connectthe dots to determine the most effective ways to convert one-time customers into repeat buyers (Jao 2013 , ). Big dataanalytics (BDA) enables e- commerce firms to use data moreefficiently, drive a higher conversion rate, improve decisionmaking and empower customers (Miller2013 ).

4 From the per-spective of transaction cost theory in e- commerce (Devarajet ; Williamson1981 ), BDA can benefit online firmsby improving market transaction cost efficiency ( , buyer-seller interaction online), managerial transaction cost efficien-cy ( , process efficiency- recommendation algorithms byAmazon) and time cost efficiency ( , searching, bargainingand after sale monitoring). Drawing on the resource-basedview (RBV)(Barney1991 ), we argued that BDA is a distinc-tive competence of the high-performance business process tosupport business needs, such as identifying loyal and profit-able customers, determining the optimal price, detecting qual-ity problems, or deciding the lowest possible level of inven-tory (Davenport and Harris2007a).

5 In addition to the RBV,this research views BDA from the relational ontology ofsociomaterialism perspective, which puts forward theResponsible Editor: Rainer Alt*Shahriar of Wollongong, NSW 2500, Australia2 NEOMA Business School, Rouen 76825, FranceElectron Markets (2016) 26:173 194 DOI that different organizational capabilities ( , man-agement, technology and talent) are constitutively entangled(Orlikowski2007 ) and mutually supportive (Barton and Court2012 ) in achieving firm performance. Finally, service market-ing offers the perspective of improving service innovationmodels, which has been reflected by firms such as RollsRoyce (Barrett et ), Amazon, Google and Netflix(Davenport and Harris2007a). As such, the extant literatureidentifies BDA as the platform for growth of employment,increased productivity, and increased consumer surplus (Loebbecke and Picot2015, ), the next big thing ininnovation (Gobble2013, ); the fourth paradigm ofscience (Strawn (2012); the next frontier for innovation,competition, and productivity (p.))

6 1) and the next manage-ment revolution (p. 3) (McAfee and Brynjolfsson2012 ); orthat BDA is bringing a revolution in science and technology (Ann Keller et ); etc. Due to the high impact in e- commerce , notably in generating business value, BDA hasrecently become the focus of academic and industry investi-gation (Fosso Wamba et c). As shown on Table1,there is a steady growth in the BDA market, and in the numberof global e- commerce customers and their per capita an increasing amount of published materials hasfocused on practitioners in this domain, the literature remainslargely anecdotal and fragmented. There is a paucity of re-search that provides a general taxonomy from which to ex-plore the dimensions and applications of big data in e-com-merce.

7 The purpose of this research therefore is to identifydifferent conceptual dimensions of big data in e-commerceand their relevance to business value. This paper focuses one- commerce firms that capture business value through usingbig data analytics (BDA). The extant literature shows thatBDA could allow an e- commerce firm to achieve a range ofbenefits, such as: enhanced pricing strategies for products andservices (Christian2013 ); targeted advertising; better commu-nication between research and development (R&D) and prod-uct development; improved customer service; improvedmulti-channel integration and coordination; enhanced globalsourcing from multiple business units and locations, and,overall, suggesting models and ways to capture greater busi-ness value (Beath et ; Fosso Wamba et ;Sharma et ).

8 The research question that has driven thisstudy focuses on: how is big data analytics different fromtraditional analytics in the e- commerce environment in creat-ing business value? To answer this research question, the pa-per aims to provide a general taxonomy to broaden the under-standing of BDA and its role in creating business value. Morespecifically, the aims of this paper are:&To identify definitional perspectives of big data analytics&To distinguish the characteristics of big data within e- commerce &To explore the types of big data within e- commerce &To illustrate the business value of big data in e- commerce &To provide guidelines for tackling the challenges of bigdata application within , this paper intends to provide a thorough represen-tation of the meaning of big data in the e- commerce have organized this paper into five main parts.

9 Firstly, insection 2, we explain the methodological gestalt and presentthe results of our systematic review. By collating this informa-tion, in section 3, we then define the role of big data in e- commerce and identify alternative definitional , in section 4, we analyze the distinctive attributesand types of big data within e- commerce . Thirdly, in section5, we recommend different types of business value that can bederived using BDA in the e- commerce domain. Finally, weidentify the challenges and provide solutions to tackle them inorder to foster the growth of BDA in approachThe study was grounded in a literature review to identify andappraise the current knowledge on the definitional aspects,attributes, types and business value of BDA in defining e- commerce , Kalakota and Whinston (1997 )fo-cused on four perspectives: online buying and selling, tech-nology driven business process, communication of informa-tion and customer service.

10 However, this definition does notprovide adequate focus on transaction cost and other aspectsof e- commerce ( , B2B, B2G, C2C etc.) Thus, illuminatingthese critical aspects, Frost and Strauss (2013) extends thedefinition focusing on buying and selling online, digital valuecreation, virtual market places and storefronts and new distri-bution intermediaries. However, this definition heavily focus-es on e-marketing and fails to integrate other important e-business processes. As such, this study puts forward a moreholistic definition of e- commerce in big data environment,Table 1 Global growth in e- commerce and big data analytics (BDA)Year Growth in the numberof e-commercecustomers worldwide(in millions)Growth in e- commerce salesper customerworldwide (inUS$)Growth in big dataanalytics (BDA)market worldwide(in billions)2011 : Adapted from emarketer (2013 ) and (Piatetsky2014 )174 Akter S.