Using Data and Big Data in Retailing

Fisher, Marshall L.; Raman, Ananth

doi:10.1111/poms.12846

Cited by 58 publications

(26 citation statements)

References 11 publications

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“…To overcome this challenge, it requires each participant in a supply chainincluding distributors, retailers, manufacturers and suppliersto offer the chain with their own competence sets (Greco et al, 2010). For example, the main competence set of distributors is associated with delivery, distributions and the planning of all related logistics tasks (Sander, 2014); the retailers' competence set is to manage the upstream supplier exchanges and downstream customer demands effectively (Wang and Alexander, 2015;Fisher and Raman, 2017); the competence set of manufacturers is related to offer the form utility of products by transforming components and raw materials into goods that are beneficial to end consumers (Sander, 2014;Tan et al, 2015); the suppliers' competence set is focused on providing essential inputs to the production process of supply chain manufacturers (Wang et al, 2016). Moreover, Yu and Chen (2010) states that competence set expansion plays an important role in today's changing business environment.…”

Section: Competence Set For Solving E Tr (E)mentioning

confidence: 99%

“…For example, Walmart is an early adopter of information-driven supply chains and improves its operational efficiency by leveraging big data analytics. As a result, it informs its everyday decision making from sources across its whole supply chains -from automatic purchase to inventory tracking and customer fulfilment (Waller and Fawcett, 2013;Tan and Zhan, 2017;Fisher and Raman, 2017). Moreover, the analysis of big data may have a huge operational and strategic impact on supply chain management, and therefore allow firms that adopt it to achieve competitive advantage by improving effectiveness and efficiency, enhancing decision making, increasing the value-added services and producing higher-quality products (Coussement et al, 2015;Pape, 2016;Kunc and O'Brien, 2018).…”

Section: Introductionmentioning

confidence: 99%

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An analytic infrastructure for harvesting big data to enhance supply chain performance

Zhan

Tan

2020

European Journal of Operational Research

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Big data has already received a tremendous amount of attention from managers in every industry, policy and decision makers in governments, and researchers in many different areas. However, the current big data analytics have conspicuous limitations, especially when dealing with information silos. In this paper, we synthesise existing researches on big data analytics and propose an integrated infrastructure for breaking down the information silos, in order to enhance supply chain performance. The analytic infrastructure effectively leverages rich big data sources (i.e. databases, social media, mobile and sensor data) and quantifies the related information using various big data analytics. The information generated can be used to identify a required competence set (which refers to a collection of skills and knowledge used for specific problem solving) and to provide roadmaps to firms and managers in generating actionable supply chain strategies, facilitating collaboration between departments, and generating factbased operational decisions. We showcase the usefulness of the analytic infrastructure by conducting a case study in a world-leading company that produces sports equipment. The results indicate that it enabled managers: (a) to integrate information silos in big data analytics to serve as inputs for new product ideas; (b) to capture and interrelate different competence sets to provide an integrated perspective of the firm's operations capabilities; and (c) to generate a visual decision path that facilitated decision making regarding how to expand competence sets to support new product development.

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Section: Competence Set For Solving E Tr (E)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An analytic infrastructure for harvesting big data to enhance supply chain performance

Zhan

Tan

2020

European Journal of Operational Research

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“…Understanding the topics that constitute these sub-fields and the problems in these topics as well as developing accurate solutions for these problems would surely improve the productivity and success of retail activities conducted by businesses on the internet. Interdisciplinary studies with omnichannel and big-data in e-tailing applications (Fisher and Raman, 2018) may be suggested topics for future research. Also, the integration of e-tailing systems with emerging smart-city systems may also be worthy of further examination.…”

Section: Limitations and Future Researchmentioning

confidence: 99%

The transformation of the e-tailing field: a bibliometric analysis

Altıntaş

Kılıç

Akhan

2019

IJRDM

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Purpose There is an increased interest in e-tailing research in the literature, along with the development of new technologies and e-tailing platforms based on consumer and industry perspective. The purpose of this paper is to examine the periodic changes within the content of e-tailing literature. Design/methodology/approach The data set of this study includes academic papers cited in the Web of Science, which was published between 2000 and 2017. In this context, a co-word analysis was conducted using SciMat software based on the keywords, including “online retailing,” “e-tailing,” “e-store,” “online store,” “e-tail” and “online retail,” found in the titles of published academic articles. Findings In this study, three different periods of the e-tailing study field were examined, major and emerging themes for each period were determined with a comparative bibliometric analysis. In this regard, consumer acceptance, choice and satisfaction were found as the major themes in the conceptualization of e-tailing research. Originality/value Understanding the transition from traditional marketing channels to online channels is an essential factor for retailers as well as consumers’ use and the acceptance of new technologies. This study contributes to the effective execution of the e-tailing systems.

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“…These decision‐makers may use information from digital sources to either reinforce or “unbelieve” their biases. Leveraging increased information to form more objective decisions may lead to greater welfare on the part of individuals, firms, and society (Fisher and Raman 2018, Tang et al. 2015).…”

Section: Discussionmentioning

confidence: 99%

Decisions Under the Illusion of Objectivity: Digital Embeddedness and B2B Purchasing

Krijestorac

Garg

Konana

2021

Production and Operations Management

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Research suggests that increased reliance on digital sources (e.g., blogs, social media, online forums) exhibited by the emerging millennial workforce may affect procurement decisions. These arguments contend with studies which indicate that organizational buyers rely on decision shortcuts (e.g., brand, loyalty, and peer opinions) to mitigate risk or to simplify purchase decisions. One can argue that buyers gain more information from digital sources, which may attenuate the role of these shortcuts. At the same time, digital sources may be used to strengthen prior beliefs, which can increase bias toward decision proxies. This study draws from information processing theory to understand the role of digital sources of information in decision‐making, in the context of business purchasing. Based on the Accessibility‐Diagnosticity framework, we introduce a Digital Embeddedness construct to conceptualize the extent to which information from digital sources is integral to an individual’s decision‐making. Analysis of survey responses from 196 purchasing managers suggests that more digitally embedded buyers are more willing to adopt innovations, yet interestingly, focus more on vendors from strong brands and peer opinions. We also find that while more digitally embedded buyers feel more cognitively attached to their existing vendors, this attachment does not translate into purchase loyalty. We discuss implications for buyer‐supplier relations and B2B marketing. By elucidating individual‐level differences in the use of digital sources, this research also has implications for future work that examines the interpretation of digital information to inform business decisions.

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Using Data and Big Data in Retailing

Cited by 58 publications

References 11 publications

An analytic infrastructure for harvesting big data to enhance supply chain performance

An analytic infrastructure for harvesting big data to enhance supply chain performance

The transformation of the e-tailing field: a bibliometric analysis

Decisions Under the Illusion of Objectivity: Digital Embeddedness and B2B Purchasing

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