Insight is power: Understanding the terms of the consumer-firm data exchange

Krafft, Manfred; Kumar, Vikas; Harmeling, Colleen M.; Singh, Siddharth S.; Zhu, Ting; Chen, Jialie; Duncan, Tom; Fortin, Whitney; Rosa, Erin

doi:10.1016/j.jretai.2020.11.001

Cited by 49 publications

(31 citation statements)

References 90 publications

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“…S. Gavrila Gavrila, A. de Lucas Ancillo (2021) analyzed potential Ereceipt applications to the offline retail market. Other discussed topics include the benefits of the Internet of Things for retailers (KHAN; TARIQ, 2021), predicting remaining time on site for e-commerce users (KAO et al, 2021), information standards in retail (HÄNNINEN; LUOMA; MITRONEN, 2021), the terms of the consumer-firm data exchange (KRAFFT et al, 2021).…”

Section: Resultsmentioning

confidence: 99%

Competitiveness of retailers in the context of digitalization: a literature review

Mayorova

Panasenko

Ramazanov

et al. 2021

Laplage

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This article aims to provide a review of published academic research on the competitiveness of retailers in the context of digitalization. The input was sourced from published works indexed in the Scopus base for 2000-2020. The analysis was conducted in four stages: topical trends; keyword research; citation analysis; content analysis of the most-cited published works. The findings helped to track the evolution of research in the analyzed domain and identify four stages (2000-2003, 2004-2010, 2011-2016, 2017-present time) by distinct variation of research activity and the scope of covered problems. The findings of this review can substantiate further development of methodological approaches to studying the competitiveness of retailers in the context of digitalization and can help researchers to make more justified choices of research problems and practitioners to navigate the complicated transformations of managing competitiveness in the wake of digitalization.

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Section: Resultsmentioning

confidence: 99%

Competitiveness of retailers in the context of digitalization: a literature review

Mayorova

Panasenko

Ramazanov

et al. 2021

Laplage

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“…While important in the B2C context, they are less relevant for a B2B company and therefore outside the scope of this research. For a recent discussion on privacy in consumer marketing, we refer the interested reader to the research of Krafft et al (2021), who developed a framework to understand how both individuals and firms derive value from the data exchange. Algorithmic bias has been covered recently by Lambrecht and Tucker (2020).…”

Section: Ai Risks and How To Mitigate Them?mentioning

confidence: 99%

Smart Business and the Social Value of AI

Leszkiewicz¹,

Hormann²,

Krafft³

2022

Advanced Series in Management

Self Cite

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Organizations across industries are increasingly using Artificial Intelligence (AI) systems to support their innovation processes, supply chains, marketing and sales and other business functions. Implementing AI, firms report efficiency gains from automation and enhanced decision-making thanks to more relevant, accurate and timely predictions. By exposing the benefits of digitizing everything, COVID-19 has only accelerated these processes. Recognizing the growing importance of AI and its pervasive impact, this chapter defines the "social value of AI" as the combined value derived from AI adoption by multiple stakeholders of an organization. To this end, we discuss the benefits and costs of AI for a business-to-business (B2B) firm and its internal, external and societal stakeholders. Being mindful of legal and ethical concerns, we expect the social value of AI to increase over time as the barriers for adoption go down, technology costs decrease, and more stakeholders capture the value from AI. We identify the contributions to the social value of AI, by highlighting the benefits of AI for different actors in the organization, business consumers, supply chain partners and society at large. This chapter also offers future research opportunities, as well as practical implications of the AI adoption by a variety of stakeholders.

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“…First, since in the context of ZPD disclosures consumers know what information they are sharing and why, they can decide how much to reveal, which constitutes an ideal condition to empower and foster trust (Martin and Palmatier, 2020 ; Schmidt et al, 2020 ; Hall, 2021 ; Krafft et al, 2021 ; Mishra, 2021 ; Quach et al, 2022 ). Specifically, trust exists when one party has confidence in the other party's reliability and integrity, as may be assumed when the consumer is giving permission for an organization to personalize experiences and recommendations.…”

Section: Zero Party Data: Definition Use Cases and Rationalementioning

confidence: 99%

“…Recent attempts to navigate the tensions between relevance, technology and privacy have produced a substantial body of literature and continue to motivate scholars and practitioners working in this space (Elizabeth et al, 2016 ; Karwatzki et al, 2017 ). Interestingly, a proposal that was recently made is to reimagine consumer information exchanges by using so-called zero-party data (ZPD) disclosures (Martin and Palmatier, 2020 ; Schmidt et al, 2020 ; Hall, 2021 ; Krafft et al, 2021 ; Mishra, 2021 ; Quach et al, 2022 ). ZPD refers to information that consumers knowingly and willingly provide to retailers in exchange for more meaningful personalization.…”

Section: Introductionmentioning

confidence: 99%

Zero party data between hype and hope

Polonioli¹

2022

Front. Big Data

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Zero Party Data (ZPD) is a hot topic in the context of privacy-aware personalization, as the exponential growth of consumer data collected by retailers has made safeguarding data privacy a key priority. Articles arguing for the value of ZPD to improve personalization and engender consumer trust have appeared in the popular press, in business magazines as well as in academic journals. Advocates of ZDP argue that instead of inferring what customers want, retailers can simply ask them. Provided that the value exchange is clear, customers will willingly share data such as purchase intentions and preferences to improve personalization and help retailers create a picture of who they are. While the rise of ZPD is a welcome development, this paper takes issue with the claim that ZPD is necessarily accurate as it comes directly from the customer. This view is at odds with established conclusions from decades of research in the social and cognitive sciences, showing that self reports can be influenced by the instrument and that people have limited insight into the factors underlying their behavior. This paper argues that while ZDP disclosures are an important tool for retailers, it is critical to carefully understand their limitations as well. The paper also provides a catalog of biases for identifying potential problems in survey design to help practitioners collect more accurate data.

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Insight is power: Understanding the terms of the consumer-firm data exchange

Cited by 49 publications

References 90 publications

Competitiveness of retailers in the context of digitalization: a literature review

Competitiveness of retailers in the context of digitalization: a literature review

Smart Business and the Social Value of AI

Zero party data between hype and hope

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