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The perks and perils of artificial intelligence use in lateral exchange markets
... This theme focuses on the transformative socio-technical potential of AI, emphasizing its capacity to reshape interactions among businesses, users, and societal structures within the SE (Reshetilo, 2017(Reshetilo, , 2018. AI is credited with enabling organizations to create large networks and reap the benefits of network effects (Satornino et al., 2023;Chen et al., 2022;Reshetilo, 2018). It offers the possibility to match users more effectively at a large scale and diffuse knowledge in online marketplaces (Yu, 2022), thereby enhancing user experiences and expanding platform networks. ...
... Cloud-based platforms play a crucial role in scalable business models, utilizing data from user behavior to enhance their offerings (Narayan, 2022). Success in lateral exchange markets depends on the value delivered to users (Satornino et al., 2023). Society experiences benefits from SE, which promotes resource utilization and green logistics (Liyanage et al., 2019;Lim et al., 2020). ...
... Business model design requires understanding an organization's structure, innovation drivers, and customer preferences (Pisano, 2015). AI enhances platform efficiency and personalization (Satornino et al., 2023) and evaluates competitiveness in B2B e-commerce . Algorithms utilizing big data from the IoT can effectively classify vast amounts of data and predict the developmental trajectory of SE and new business models (Yin, 2022). ...
Emerging technologies, including artificial intelligence (AI), blockchain, machine learning, big data, and the Internet of Things, play a pivotal role in sharing economy (SE) services. Empirical studies on the roles of these emerging technologies in SE are increasing. However, the existing studies are highly fragmented, and the current literature fails to provide a comprehensive understanding of the role these technologies play in SE. Employing AI-based machine-learning topic modeling techniques, this systematic literature review identifies dominant research areas and maps the research related to the role of emerging technologies in SE. Our content analysis identified nine themes organized into four broad categories. Based on these findings, we have developed an integrated framework and provided insights into future research avenues. Furthermore, based on the findings, we provide theoretical and practical implications.
... Noting such trends, scholars also have begun to take stock of the advantages of AI deployment in B2B and consumer domains (e.g., Satornino, Grewal, Guha, Schweiger, & Goodstein, 2023;Saura, Ribeiro-Soriano, & Palacios-Marqués, 2021), revealing some unprecedented opportunities, as well as possibilities for sustainable development efforts (Voola, Bandyopadhyay, Voola, Ray, & Carlson, 2022). Yet AI-enabled tools also present challenges regarding how B2B firms should deploy them optimally. ...
... The dark sides of AI in various domains (e.g., Du & Xie, 2021;Satornino et al., 2023), including B2B markets (Grewal et al., 2021), also raise concerns about privacy, bias perpetuation, neglect of individual uniqueness, opportunism, and manipulation. These challenges are particularly manifest in contexts marked by asymmetries in power and information. ...
... It also highlights how AI technologies work with employees and management to improve shop productivity (Warnick 2020). Furthermore, Nike's use of AI to design new sneakers ahead of the 2024 Olympics (Marcus, 2024), Coca-Cola's use of AI to analyze customer data to inform personalized marketing campaigns (Rogers, 2024), and eBay's reliance on AI to optimize seller listings (Satornino et al., 2023) demonstrate the wide range of applications of AI in modern marketing. These examples demonstrate how AI can improve marketing strategy, increase consumer engagement, and boost operational efficiency. ...
The integration of Artificial Intelligence (AI) has transformed the digital marketing landscape, with marketers increasingly adopting AI technologies to enhance customer engagement, deliver personalized experiences, optimize campaign performance, and predict consumer behavior. This study employs bibliometric analysis using the Bibliometrix R tool to explore the evolving relationship between AI and digital marketing. Through a systematic literature review, we identify key research themes, emerging trends, and future research opportunities in this domain. The study maps influential authors, journals, and countries shaping AI applications in digital marketing research. Our comprehensive overview provides valuable insights into current marketing practices and establishes a roadmap for future research in this rapidly evolving field. The findings benefit both academics seeking to advance theoretical frameworks and practitioners aiming to implement AI-driven digital marketing strategies in an increasingly technology-driven marketplace.
... Accordingly, AI-driven predictions power millions of business decisions, including "whom to call, mail, approve, test, diagnose, warn, investigate, incarcerate, set up on a date or medicate" (Abdullahi, 2024). Such widespread applications in domains spanning advertising, marketing, customer service, sales, healthcare, peer-topeer marketplaces, and fraud detection (Abdullahi, 2024;Satornino et al., 2023), together with illustrative use cases and illustrations presented in prior research (Davenport et al., 2020;Guha et al., 2021;Shankar, 2018), emphasize the capacity for substantial value creation. Such practices also have facilitated reductions in prediction costs (Agrawal et al., 2019). ...
Generative AI (Gen AI) is shaping the future of marketing. In the next decade, Gen AI will influence how marketers interact and communicate with customers, help create and deliver marketing content (text, images, and video), and inform methods for researching and developing new products and services. In both service and sales settings, Gen AI will affect customers directly and significantly. Therefore, marketers, researchers, and public policy makers require a clear understanding of Gen AI and its potential, as well as its limitations. To assist marketers in thinking through the adoption and implementation of Gen AI, the current article presents a four-quadrant organizing framework that highlights trade-offs in both the nature of Gen AI inputs and the extent of human augmentation needed to deliver Gen AI–generated outputs. This framework provides guidance for the selection and implementation of Gen AI tools, as well as recommendations for further research.
... In addition to criticising the reproduction of negative gender stereotypes in virtual personal assistants, these authors explore the possible legal consequences that could arise from these practices, which go against the work of the Committee against Discrimination against Women and, in terms of human rights, against the UN Guiding Principles on Business and Human Rights (Adams & Loideáin, 2019). Therefore, it is essential to monitor and mitigate bias in AI algorithms (Satornino et al., 2023). ...
This chapter explores the use of artificial intelligence (AI) in market research and its potential impact on the field. Discuss how AI can be used for data collection, filtering, analysis, and prediction, and how it can help companies develop more accurate predictive models and personalized marketing strategies. Highlight the drawbacks of AI, such as the need to ensure diverse and unbiased data and the importance of monitoring and interpreting results and covers various AI techniques used in market research, including machine learning, natural language processing, computer vision, deep learning, and rule-based systems. The applications of AI in marketing research are also discussed, including sentiment analysis, market segmentation, predictive analytics, and adaptive recommendation engines and personalization systems. The chapter concludes that while AI presents many benefits, it also presents several challenges related to data quality and accuracy, algorithmic biases and fairness issues, as well as ethical considerations that need to be carefully considered.
... The possibility of AI producing new inefficiencies rather than eliminating is non-trivial. In fact, for lateral (peer-to-peer) exchange markets, AI has shown to amplify price vulnerability and bias concerns in buyers, while sellers incur platform opportunism and manipulation risks (Satornino et al.;, 2023) The speed of execution adds a temporal dimension to the market efficiency discourse. Fast network infrastructure and model efficiency coordinate to achieve near-instant data input, training, and trade execution. ...
Artificial intelligence’s (AI) ingress into the investment landscape warrants an urgent rethink of traditional economic theories and governance frameworks. AI’s superior information processing prowess, ability to analyze alternative datasets, and speed of execution defy the utility of traditional theories like the efficient market hypothesis. This paper focuses specifically on AI’s impacts in retail investing, where opacity of algorithms and information asymmetry disproportionately disadvantage non-institutional investors. Firstly, I formulate an algorithm-conscious spectral efficiency model spanning beyond the rigid efficient–anomalous binary. Secondly, I recommend an ethical oversight blueprint fusing agency theory with stewardship principles to align investor-algorithm incentives via a contractual solution. The proposal encompasses regulatory sandboxes for controlled AI testing, retail investor representation in external audits of algorithms, plain language disclosure standards, and specialized financial literacy campaigns. In addition, I sketch five potential insurance products to mitigate AI-induced risks in investment. I conclude by underscoring early ethical interventions in governing financial AI to avoid entrenching negative externalities. Together, these institutional solutions offer updated theories and pragmatic frameworks for firms and regulators to integrate AI in investment pro-socially.
Employers expect university graduates seeking entry-level marketing jobs to be well-versed in contemporary topics, such as sustainable development, digital marketing, big data, analytics, and the role of artificial intelligence (AI) in both traditional and contemporary marketing domains. Because many of today’s cutting-edge technological advances are deeply relevant to marketing, marketing educators must reconsider how they prepare marketing students to enter the technology-enabled world and workforce. The authors propose that marketing educators adapt their teaching of foundational marketing concepts to reflect the technology-augmented marketing era. Such reconsiderations span multiple arenas, including how classes are conducted, which topics are covered, how assignments are crafted, and how technology—and AI and generative artificial intelligence (gen AI) in particular—will transform future marketing roles. The authors also suggest ways educators can modify and reimagine existing marketing courses to prepare students for a successful entry into technology-enabled marketing jobs, as exemplified with some sample class assignments.
Engaging customers is critical for sharing economy platforms (SEPs) to sustain and grow their user base. Although the interactions between users who consume the service (customers) and those who provide it (peer service providers) are the primary source of SEPs’ economic value, little is known about the role of interactivity in driving customer engagement. This research links these two important concepts and tests the influence of interactivity (two-way communication, participation, and joint problem-solving) on customer engagement. The findings from two surveys with customers of 10 leading SEPs (total N = 1,654) indicate that two-way communication and participation, two key dimensions of interactivity, enhance engagement while joint problem-solving remains non consequential. However, additional analyses reveal that the impact of these interactivity dimensions varies as a function of user (platform familiarity) and platform (service type) characteristics. The findings offer guidance to SEP managers seeking to improve user engagement.
This paper pioneers an interdisciplinary framework integrating behavioral finance, ethics, and cutting-edge machine learning to elucidate the promise and perils as AI proliferates in finance. Novel taxonomies are proposed classifying biases as cognitive, social, or data-driven. The paper reconceptualizes market efficiency as aspectrum using adaptive systems modeling. It constructs innovative optimization frameworks synthesizing prospect theory and principal-agent constructs to model human-AI alignments and misalignments. Multi-agent simulations offer pathways to understand emergent volatility from AI ecosystems. The paper presses for retrofitting orthodox theories and developing novel theoretical paradigms to address challenges like black box governance, synchronized algorithmic behavior, andhuman-AI value misalignments. It offers proposals like regulatory sandboxes, retail representation inoversight boards, and AI-centric financial literacy programs. Mathematical modeling of bias interactions and amplification provides tools to audit algorithms.
Artificial intelligence (AI) will substantially impact retailing. Building on past research and from interviews with senior managers, we examine how senior retailing managers should think about adopting AI, involving factors such as the extent to which an AI application is customer-facing, the amount of value creation, whether the AI application is online, and extent of ethics concerns. In addition, we highlight that the near-term impact of AI on retailing may not be as pronounced as the popular press might suggest, and also that AI is likely to be more effective if it focuses on augmenting (rather than replacing) managers’ judgments. Finally, while press coverage typically involves customer-facing AI applications, we highlight that a lot of value can be obtained by adopting non-customer-facing applications. Overall, we remain very optimistic as regards the impact of AI on retailing. Finally, we lay out a research agenda and also outline implications for practice.
The authors develop a three-stage framework for strategic marketing planning, incorporating multiple artificial intelligence (AI) benefits: mechanical AI for automating repetitive marketing functions and activities, thinking AI for processing data to arrive at decisions, and feeling AI for analyzing interactions and human emotions. This framework lays out the ways that AI can be used for marketing research, strategy (segmentation, targeting, and positioning, STP), and actions. At the marketing research stage, mechanical AI can be used for data collection, thinking AI for market analysis, and feeling AI for customer understanding. At the marketing strategy (STP) stage, mechanical AI can be used for segmentation (segment recognition), thinking AI for targeting (segment recommendation), and feeling AI for positioning (segment resonance). At the marketing action stage, mechanical AI can be used for standardization, thinking AI for personalization, and feeling AI for relationalization. We apply this framework to various areas of marketing, organized by marketing 4Ps/4Cs, to illustrate the strategic use of AI.
Artificial intelligence (AI) helps companies offer important benefits to consumers, such as health monitoring with wearable devices, advice with recommender systems, peace of mind with smart household products, and convenience with voice-activated virtual assistants. However, although AI can be seen as a neutral tool to be evaluated on efficiency and accuracy, this approach does not consider the social and individual challenges that can occur when AI is deployed. This research aims to bridge these two perspectives: on one side, the authors acknowledge the value that embedding AI technology into products and services can provide to consumers. On the other side, the authors build on and integrate sociological and psychological scholarship to examine some of the costs consumers experience in their interactions with AI. In doing so, the authors identify four types of consumer experiences with AI: (1) data capture, (2) classification, (3) delegation, and (4) social. This approach allows the authors to discuss policy and managerial avenues to address the ways in which consumers may fail to experience value in organizations’ investments into AI and to lay out an agenda for future research.
The sharing economy (SE) has become a prominent theme in a broad variety of research domains in the last decade. With conceptions from an increasing range of theoretical perspectives, SE literature is disperse and disconnected, with a great proliferation of definitions and related terms which hinder organized and harmonious research. This study carries out a systematic literature review from 1978 to September 2020, uncovering 50 definitions as units of analysis. The authors, through a qualitative–interpretative analysis, review definitions, identify perspectives, and critically assess their conceptual nature on an evolutionary basis. Findings show that despite the SE has been extending its routes and approaches, it is far from a stock of conceptual grounds. The paper makes three contributions. First, we portray SE within a common evolutionary framework by developing it as a life cycle model. Second, we clarify the definitional and terminological jungle. And third, we suggest a new definition that can enrich the discussion.
In the future, artificial intelligence (AI) is likely to substantially change both marketing strategies and customer behaviors. Building from not only extant research but also extensive interactions with practice, the authors propose a multidimensional framework for understanding the impact of AI involving intelligence levels, task types, and whether AI is embedded in a robot. Prior research typically addresses a subset of these dimensions; this paper integrates all three into a single framework. Next, the authors propose a research agenda that addresses not only how marketing strategies and customer behaviors will change in the future, but also highlights important policy questions relating to privacy, bias and ethics. Finally, the authors suggest AI will be more effective if it augments (rather than replaces) human managers.
The last decade has seen the emergence of the sharing economy as well as the rise of a diverse array of research on this topic both inside and outside the marketing discipline. However, the sharing economy’s implications for marketing thought and practice remain unclear. This article defines the sharing economy as a technologically enabled scoioeconomic system with five key characteristics (i.e., temporary access, transfer of economic value, platform mediation, expanded consumer role, and crowdsourced supply). It also examines the sharing economy’s impact on marketing’s traditional beliefs and practices in terms of how it challenges three key foundations of marketing: institutions (e.g., consumers, firms and channels, regulators), processes (e.g., innovation, branding, customer experience, value appropriation), and value creation (e.g., value for consumers, value for firms, value for society) and offers future research directions designed to push the boundaries of marketing thought. The article concludes with a set of forward-looking guideposts that highlight the implications of the sharing economy’s paradoxes, maturation, and technological development for marketing research. Collectively, this article aims to help marketing scholars not only keep pace with the sharing economy but also shape its future direction.
Online digital platforms organize and mediate an ever-increasing share of economic and societal activities. Moreover, the opportunities that platform-mediated markets offer not only attract enormous numbers of entrepreneurs, but also support the growth of entire ecosystems of producers, sellers, and specialized service providers. The increased economic and business significance of digital platforms has attracted an outpouring of studies exploring their power dynamics and general impact. And yet, to date, this research has overlooked the power imbalance that entrepreneurs experience as members of the platform ecosystem and provided little guidance on how these far more numerous firms should compete. Drawing upon Emerson’s power-dependence theory, we show that the power asymmetry at the heart of the relationship between the platform and its ecosystem members is intrinsic to the economics of digital platforms, platform design, technological architecture, and contractual agreements between the parties. We undertake a conceptual analysis of the sources of this power, and we unravel the novel component of risks that emanate from this imbalance. Our analysis suggests that the conditions of engagement for platform entrepreneurs are so different from traditional entrepreneurship that these entrepreneurs are more usefully termed “platform-dependent entrepreneurs (PDEs). Further, we explore the strategies that PDEs are developing to mitigate their dependence. Finally, our study provides a framework for policy makers that are considering regulating platform-organized markets.
The artificial intelligence models with machine learning that exhibit the best predictive accuracy, and therefore, the most powerful ones, are, paradoxically, those with the most opaque black-box architectures. At the same time, the unstoppable computerization of advanced industrial societies demands the use of these machines in a growing number of domains. The conjunction of both phenomena gives rise to a control problem on AI that in this paper we analyze by dividing the issue into two. First, we carry out an epistemological examination of the AI’s opacity in light of the latest techniques to remedy it. And second, we evaluate the rationality of delegating tasks in opaque agents.
Lateral exchange markets (LEMs) are sites of technologically intermediated exchange between actors occupying equivalent network positions. To develop an enriched understanding of these markets, the authors develop a more broad-based and differentiated understanding of peer-to-peer, sharing, and access-based markets. They focus on two key axes: the extent of (1) consociality and (2) platform intermediation. Drawing on these attributes, the authors theoretically deduce four ideal types-Forums, Enablers, Matchmakers, and Hubs. Each type provides value in a different way: Forums connect actors, Enablers equip actors, Matchmakers pair actors, and Hubs centralize exchange. Twenty organizational cases reveal insights into the failure, adaptation, and success of LEMs. Lateral exchange markets shift responsibility for personal and exchange security to relevant personal actors, to institutions, or to the governing algorithms of technology platforms. Extending the general proposition that sociality increasingly infuses market logics, the findings suggest a new frontier in which social resources and software platform algorithms interact as operand resources whose negative consequences (e.g., opportunism) require careful management through assurances and institutional arrangements matched to the type of LEM operation.
Collaborative consumption (CC) is an increasingly prevalent form of exchange. CC occurs within a triangle of actors: a platform provider (e.g., Uber), a peer service provider (e.g., an Uber driver) and a customer. The platform provider’s main role is matchmaking, so that a customer can access assets of a peer service provider. This paper has three objectives. First, this article identifies three criteria to delineate CC from related constructs such as access-based consumption, sharing or renting. Second, it introduces a literature-based framework explicating the roles of the actors in the CC triangle along three dimensions: motives, activities and resources and capabilities. Third, it highlights areas for further research, such as the dynamics of CC, context-dependent motives and the emergence of professional (peer) service providers.
Collaborative consumption (CC) is an increasingly prevalent form of exchange. CC occurs within a triangle of actors: a platform provider (e.g., Uber), a peer service provider (e.g., an Uber driver) and a customer. The platform provider's main role is matchmaking, so that a customer can access assets of a peer service provider. This paper has three objectives. First, this article identifies three criteria to delineate CC from related constructs such as access-based consumption, sharing or renting. Second, it introduces a literature-based framework explicating the roles of the actors in the CC triangle along three dimensions: motives, activities and resources and capabilities. Third, it highlights areas for further research, such as the dynamics of CC, context-dependent motives and the emergence of professional (peer) service providers.
The use of the term platform has proliferated in management research. However, theoretical work on the concept has lagged behind. We present a systematic review of the platform literature, identifying four distinct streams: organizational platforms, product family platforms, market intermediary platforms, and platform ecosystems. Each of these streams is characterized by a distinctive, although usually implied, theoretical logic. We elaborate on the theoretical logics of leverage and architectural openness, both of which underpin all four streams of platform research. We further discuss three distinctive leverage rationales exhibited in different platform variants-production, innovation, and transaction-and illustrate how platform ecosystems combine aspects of all three. We explain the metalogic of architectural leverage to facilitate the purposive manipulation of platforms, providing a link between platform design features and sources of leverage. This provides a model that allows the different platform types to be placed into context with others. Finally, we outline how the concept of architectural leverage can be used to understand platform evolution.
This paper brings together the recent literature on industry platforms and shows how it relates to managing innovation within and outside the firm as well as to dealing with technological and market disruptions and change over time. First, we identify distinct types of platforms. Our analysis of a wide range of industry examples suggests that there are two predominant types of platforms: internal or company-specific platforms, and external or industry-wide platforms. We define internal (company or product) platforms as a set of assets organized in a common structure from which a company can efficiently develop and produce a stream of derivative products. We define external (industry) platforms as products, services, or technologies that act as a foundation upon which external innovators, organized as an innovative business ecosystem, can develop their own complementary products, technologies, or services. Second, we summarize from the literature general propositions on the design, economics, and strategic management of platforms. Third, we review the case of Intel and other examples to illustrate the range of technological, strategic, and business challenges that platform leaders and their competitors face as markets and technologies evolve. Finally, we identify practices associated with effective platform leadership and avenues for future research to deepen our understanding of this important phenomenon and what firms can do to manage platform-related competition and innovation.
Access-based consumption, defined as transactions that can be market mediated but where no transfer of ownership takes place, is becoming increasingly popular, yet it is not well theorized. This study examines the nature of access as it contrasts to ownership and sharing, specifically the consumer-object, consumer-consumer, and consumer-marketer relationships. Six dimensions are identified to distinguish among the range of access-based consumptionscapes: temporality, anonymity, market mediation, consumer involvement, the type of accessed object, and political consumerism. Access-based consumption is examined in the context of car sharing via an interpretive study of Zipcar consumers. Four outcomes of these dimensions in the context of car sharing are identified: lack of identification, varying significance of use and sign value, negative reciprocity resulting in a big-brother model of governance, and a deterrence of brand community. The implications of our findings for understanding the nature of exchange, consumption, and brand community are discussed.
We contribute to multiple agency theory by examining cases in which ventures making initial public offerings (IPOs) have managerial agents on their boards whose goals conflict with those of the investment bank agents hired to underwrite the stock. Underwriters have an incentive to underprice IPOs to maintain strong ties with institutional investors. We develop theory on agent differences based on time horizons and risk taking by managerial agents as framed through behavioral agency theory. Examining governance mechanisms, we find both monitoring by board insiders and board experience decrease underpricing. Furthermore, underpricing increases when venture capitalists have prior ties with underwriters.
Much of the recent literature on interfirm relationships has focused on strategies for controlling opportunism. Somewhat surprisingly, little attention has been paid in this literature to the opportunism construct itself. Specifically, prior research has failed to recognize the different types of behavior that are hidden behind the general opportunism label. As a consequence, the knowledge of strategies for managing opportunism remains incomplete. The authors review the original and emergent conceptualizations of opportunism and illustrate them using actual industry cases. The authors also develop a conceptual framework of governance strategies that can be used to manage different forms of opportunism.
Of the offensive yet non-pathological personalities in the literature, three are especially prominent: Machiavellianism, subclinical narcissism, and subclinical psychopathy. We evaluated the recent contention that, in normal samples, this ‘Dark Triad’ of constructs are one and the same. In a sample of 245 students, we measured the three constructs with standard measures and examined a variety of laboratory and self-report correlates. The measures were moderately inter-correlated, but certainly were not equivalent. Their only common Big Five correlate was disagreeableness. Subclinical psychopaths were distinguished by low neuroticism; Machiavellians, and psychopaths were low in conscientiousness; narcissism showed small positive associations with cognitive ability. Narcissists and, to a lesser extent, psychopaths exhibited self-enhancement on two objectively scored indexes. We conclude that the Dark Triad of personalities, as currently measured, are overlapping but distinct constructs.
The sociology of markets has been one of the most vibrant fields in sociology in the past 25 years. There is a great deal of agreement that markets are social structures characterized by extensive social relationships between firms, workers, suppliers, customers, and governments. But, like in many sociological literatures, the theory camps that have formed often seem to speak by each other. We show that some of the disagreement between theory camps is due to differences in conceptual language, and other disagreements stem from the fact that theory camps ignore the concepts in other theory camps, thereby making their theories less complete. We end by considering deeper controversies in the literature that seem open both to new conceptualization and further empirical research.
This paper provides a basic conceptual framework for interpreting non-price instruments used by multi-sided platforms (MSPs) by analogizing MSPs as "private regulators" who regulate access to and interactions around the platform. We present evidence on Facebook, TopCoder, Roppongi Hills and Harvard Business School to document the "regulatory" role played by MSPs. We find MSPs use nuanced combinations of legal, technological, informational and other instruments (including price-setting) to implement desired outcomes. Non-price instruments were very much at the core of MSP strategies.
People with the personality traits of Machiavellianism, narcissism, and psychopathy (i.e., the dark triad) are prevalent in the sales profession. Yet research into sales performance drivers tends to focus on positive personality traits. The authors examine the dark triad’s effect on salesperson performance and reveal some conditioning factors that influence these links. A longitudinal study that pairs surveys and objective performance data from a national insurance agency reveals the complex effects of dark triad traits on performance over time. Another study, based on data from a direct sales organization and employing social network analysis, relies on partial least squares structural equation modeling to determine the role that the social structure plays in conditioning the relationship between dark triad traits and performance. The results yield insights into both the challenges and the opportunities that dark traits present for managers and scholars.
That artificial intelligence (AI) has the potential to provide significant benefits is generally accepted by both practitioners and scholars. However, the dark side of AI is less discussed, and less understood. In this paper, the authors first classify the wellspring of AI benefits in both B2C and B2B settings. In B2C settings AI benefits are primarily via customized experiences, while B2B AI benefits are manifested via business efficiencies. Next, guided by the relationship marketing literature, the authors identify the drivers of the dark side of AI - lack of trust and power asymmetries, with lack of trust being a stronger factor in B2C settings and power asymmetries being a stronger factor in B2B settings. Finally, the authors provide an organizing framework for understanding both the bright side and the dark side of AI, in both B2C settings and B2B settings. This paper is differentiated from prior work by its focus on B2B settings (most focus on B2C settings), and by its focus on the dark side of AI (most focus on the bright side of AI).
Data-driven innovation (DDI) gains its prominence due to its potential to transform innovation in the age of AI. Digital giants Amazon, Alibaba, Google, Apple, and Facebook, enjoy sustainable competitive advantages from DDI. However, little is known about algorithmic biases that may present in the DDI process, and result in unjust, unfair, or prejudicial data product developments. Thus, this guest editorial aims to explore the sources of algorithmic biases across the DDI process using a systematic literature review, thematic analysis and a case study on the Robo-Debt scheme in Australia. The findings show that there are three major sources of algorithmic bias: data bias, method bias and societal bias. Theoretically, the findings of our study illuminate the role of the dynamic managerial capability to address various biases. Practically, we provide guidelines on addressing algorithmic biases focusing on data, method and managerial capabilities.
The proliferating gig economy relies on online freelance marketplaces, which support relatively anonymous interactions by text-based messages. Informational asymmetries thus arise that can lead to exchange uncertainties between buyers and freelancers. Conventional marketing thought recommends reducing such uncertainty. However, uncertainty reduction and uncertainty management theories indicate that buyers and freelancers might benefit more from balancing, rather than reducing, uncertainty, such as by strategically adhering to or deviating from common communication principles. With dyadic analyses of calls for bids and bids from a leading online freelance marketplace, this study reveals that buyers attract more bids from freelancers when they provide moderate degrees of task information and concreteness, avoid sharing personal information, and limit the affective intensity of their communication. Freelancers’ bid success and price premiums increase when they mimic the degree of task information and affective intensity exhibited by buyers. However, mimicking a lack of personal information and concreteness reduces freelancers’ success, so freelancers should always be more concrete and offer more personal information than buyers do. These contingent perspectives offer insights into buyer–seller communication in two-sided online marketplaces; they clarify that despite, or sometimes due to, communication uncertainty, both sides can achieve success in the online gig economy.
Digital communication, the electronic transmission of information, reflects and influences consumers’ perceptions, attitudes, behaviors, and shopping journeys. Thus, data stemming from digital communication is an important source of insights for retailers, manufacturers, and service firms alike. This article discusses emerging trends and recent advances in digital communication research, as well as its future opportunities for retail practice and research. The authors outline four consumer–retailer domains relevant to digital communication, which in turn frame their discussion of the properties of communication dynamics (e.g., trends, variations) within messages, communicators, and their interaction, as well as communication multimodality (i.e., numeric heuristics, text, audio, image, and video). These factors are critical for understanding and predicting consumers’ behaviors and market developments. Furthermore, this article delineates conceptual and methodological challenges for researchers working in contexts that feature dynamics and multimodality. Finally, this article proposes an agenda for continued research, with the goal of stimulating further efforts to unlock the “black boxes” of digital communication and gain insights into both consumers and markets.
When should B2B firms encourage their salespeople to advocate for the customer in pricing negotiations? This research extends dual agency theory to the sales domain to address this question. In Study 1, the authors examine discount negotiations with secondary data from a major U.S. industrial distributor. They find that the customer and seller both experience the most favorable outcomes when the salesperson engages in both customer advocacy toward the seller and seller advocacy toward the customer; either type of advocacy alone is counterproductive. Study 2 confirms these results using matched survey, pricing, and profit data and demonstrates a key boundary condition: broad customer–seller ties enable the synergy between customer advocacy and seller advocacy by enhancing the firms’ abilities to monitor the salesperson. In Study 3, experiments with B2B buyers replicate key findings and provide evidence for theorized mechanisms. This research emphasizes the interdependence between the salesperson’s dual roles and demonstrates how the salesperson can serve as an effective agent of both the customer and seller, thereby mitigating challenges associated with role conflict.
Artificial intelligence (AI) is revolutionising the way customers interact with brands. There is a lack of empirical research into AI-enabled customer experiences. Hence, this study aims to analyse how the integration of AI in shopping can lead to an improved AI-enabled customer experience. We propose a theoretical model drawing on the trust-commitment theory and service quality model. An online survey was distributed to customers who have used an AI- enabled service offered by a beauty brand. A total of 434 responses were analysed using partial least squares-structural equation modelling. The findings indicate the significant role of trust and perceived sacrifice as factors mediating the effects of perceived convenience, personalisation and AI-enabled service quality. The findings also reveal the significant effect of relationship commitment on AI-enabled customer experience. This study contributes to the existing literature by revealing the mediating effects of trust and perceived sacrifice and the direct effect of relationship commitment on AI-enabled customer experience. In addition, the study has practical implications for retailers deploying AI in services offered to their customers.
Relationship marketing-establishing, developing, and maintaining successful relational exchanges-constitutes a major shift in marketing theory and practice. After conceptualizing relationship marketing and discussing its ten forms, the authors (1) theorize that successful relationship marketing requires relationship commitment and trust, (2) model relationship commitment and trust as key mediating variables, (3) test this key mediating variable model using data from automobile tire retailers, and (4) compare their model with a rival that does not allow relationship commitment and trust to function as mediating variables. Given the favorable test results for the key mediating variable model, suggestions for further explicating and testing it are offered.
Unprecedented competition and emergent technologies have posed a challenge to many traditional retailers in recent years. Yet within this competitive environment, emerging innovative business models have thrived and successfully disrupted the industry. We analyze the nature of disruptive business-model innovations and the ways they disrupt the fashion retail industry. To that end, we examine three disruptors in the industry: born-digital brands, AI-enabled demand forecasting and product design, and collaborative consumption. After introducing the concept of disruptive business-model innovation, we discuss the three disruptors’ effects on the fashion industry. We find that all of these models keenly answer fundamental needs unmet by current business models, such as offering quality products at a competitive price, curated services, and sustainable consumption. At the same time, all three disruptors suggest effective operation models for handling demand uncertainty, inventory management, and timely responses to the market, all of which are inherent issues for current push supply chains and forecast-based, inventory-driven systems. Based on this analysis, we discuss important implications for both academics and industry practitioners.
As far back as the industrial revolution, significant development in technical innovation has succeeded in transforming numerous manual tasks and processes that had been in existence for decades where humans had reached the limits of physical capacity. Artificial Intelligence (AI) offers this same transformative potential for the augmentation and potential replacement of human tasks and activities within a wide range of industrial, intellectual and social applications. The pace of change for this new AI technological age is staggering, with new breakthroughs in algorithmic machine learning and autonomous decision-making, engendering new opportunities for continued innovation. The impact of AI could be significant, with industries ranging from: finance, healthcare, manufacturing, retail, supply chain, logistics and utilities, all potentially disrupted by the onset of AI technologies. The study brings together the collective insight from a number of leading expert contributors to highlight the significant opportunities, realistic assessment of impact, challenges and potential research agenda posed by the rapid emergence of AI within a number of domains: business and management, government, public sector, and science and technology. This research offers significant and timely insight to AI technology and its impact on the future of industry and society in general, whilst recognising the societal and industrial influence on pace and direction of AI development.
Cambridge Core - Artificial Intelligence and Natural Language Processing - Artificial Intelligence - by David L. Poole
Purpose
The purpose of this study is to examine the impact of LEM participation on moral identity. Lateral exchange markets (LEMs) enable ordinary people to monetize idle personal resources such as cars, homes, gadgets and skills. Despite its champions portraying actors in these exchange as moral citizens of society, recent findings suggest that egoistic motives drive participation. A salient moral identity motivates behaviors that show social sensitivity to others and enable cooperative actions. Given that platform-providing firms rely on users’ cooperative behaviors to facilitate lateral exchange, understanding factors that affect moral identity can have important implications for the success of such business models.
Design/methodology/approach
In this research, the authors move away from the ideological discourse behind actors’ motivations, to provide a pragmatic explanation of how participation erodes moral identity. The authors apply a social cognitive framework to examine how the environment in LEMs impacts behaviors and personal factors in a recursive fashion.
Findings
Across two studies, findings reveal that prolonged participation in lateral exchange diminishes the centrality of moral identity to the working self-concept. Moreover, the results show that keeping puritan peers moral has positive business outcomes. This research also discerns a boundary condition that determines when peers remain consistent with their moral compasses. Specifically, when engagement is perceived as effortful, the behavior becomes an informative input in the inference of one’s moral disposition reinforcing moral identity.
Originality/value
Marketers can use this research to design business models in ways that mitigate the decay of moral identity.
Relationship marketing—establishing, developing, and maintaining successful relational exchanges—constitutes a major shift in marketing theory and practice. After conceptualizing relationship marketing and discussing its ten forms, the authors (1) theorize that successful relationship marketing requires relationship commitment and trust, (2) model relationship commitment and trust as key mediating variables, (3) test this key mediating variable model using data from automobile tire retailers, and (4) compare their model with a rival that does not allow relationship commitment and trust to function as mediating variables. Given the favorable test results for the key mediating variable model, suggestions for further explicating and testing it are offered.
Research on buyer-supplier relationships (BSRs) has often focused on only one side of the relationship and, thus, has tended to overlook asymmetries. Yet, a buyer (supplier) may often deal with a bigger supplier (buyer) or one that has higher levels of trust, respect, and reciprocity. Therefore, we examined how two types of asymmetries-size and relational capital-affect perceived opportunism and performance. We used dyadic data from 106 buyers and their matched suppliers gathered from a survey and an archival database. The results demonstrate that the degree and direction of both asymmetries affect the BSR. Our results also reveal that an imbalance of relational capital in a firm's favor may have the opposite effect from that intended. In other words, the firm's counterpart perceives more, rather than less, firm opportunism. The results also suggest that a buyer observes lower benefits in the presence of size asymmetry, whereas the supplier's perception of benefits is unaffected. Thus, our research represents a significant step forward in understanding BSRs and asymmetries by (i) bringing attention to two key asymmetries inherent in BSRs and (ii) showing that these asymmetries are not unidirectional in their influence on perceived opportunism and performance.
Online marketplaces have become ubiquitous, as sites such as eBay, Taobao, Uber, and Airbnb are frequented by billions of users. The success of these marketplaces is attributed to not only the ease in which buyers can find sellers, but also the trust that these marketplaces help facilitate through reputation and feedback systems. I begin by briefly describing the basic ideas surrounding the role of reputation in facilitating trust and trade, and offer an overview of how feedback and reputation systems work in online marketplaces. I then describe the literature that explores the effects of reputation and feedback systems on online marketplaces and highlight some of the problems of bias in feedback and reputation systems as they appear today. I discuss ways to address these problems to improve the practical design of online marketplaces and suggest some directions for future research. Expected final online publication date for the Annual Review of Economics Volume 8 is September 06, 2016. Please see http://www.annualreviews.org/catalog/pubdates.aspx for revised estimates.
This study investigates the use of private rules in exercising power in asymmetric business relationships. In asymmetric business relationships, the stronger party is likely to be able to dominate and exercise power over the conclusion of contracts and, thereby determine the processes and outcomes of the relationship. The study demonstrates how companies exercise their power in asymmetric relationships through private rules. Private rules are typically expressed in the General Terms and Conditions of Trade (GTCT) of the more powerful actor in a business relationship and are continually adapted to changing business and market requirements. Drawing on an empirical investigation in the German grocery retail business conducted in the years between 2011 and 2013, the present study demonstrates that power is exercised by the stronger parties through intervention–enforcement–sanctioning practices that are codified in private rules. Private rules frame, standardize and legitimize the terms and conditions under which exchanges among counterparts may take place thus institutionalizing the inherent power asymmetry.
Retailers gather data about customers’ online behavior to develop personalized service offers. Greater personalization typically increases service relevance and customer adoption, but paradoxically, it also may increase customers’ sense of vulnerability and lower adoption rates. To demonstrate this contradiction, an exploratory field study on Facebook and secondary data about a personalized advertising campaign indicate sharp drops in click-through rates when customers realize their personal information has been collected without their consent. To investigate the personalization paradox, this study uses three experiments that confirm a firm's strategy for collecting information from social media websites is a crucial determinant of how customers react to online personalized advertising. When firms engage in overt information collection, participants exhibit greater click-through intentions in response to more personalized advertisements, in contrast with their reactions when firms collect information covertly. This effect reflects the feelings of vulnerability that consumers experience when firms undertake covert information collection strategies. Trust-building marketing strategies that transfer trust from another website or signal trust with informational cues can offset this negative effect. These studies help unravel the personalization paradox by explicating the role of information collection and its impact on vulnerability and click-through rates.
Because the literature on platform competition emphasizes the role of network effects, it prescribes rapidly expanding a network of platform users and complementary applications to capture entire markets. We challenge the unconditional logic of a winner‐take‐all (WTA) approach by empirically analyzing the dominant strategies used to build and position platform systems in the U.S. video game industry. We show that when platform firms pursue two popular WTA strategies concurrently and with equal intensity (growing the number and variety of applications while also securing a larger fraction of those applications with exclusivity agreements), it diminishes the benefits of each strategy to the point that it lowers platform performance. We also show that a differentiation strategy based on distinctive positioning improves a platform's performance only when a platform system is highly distinctive relative to its rivals. Our results suggest that platform competition is shaped by important strategic trade‐offs and that the WTA approach will not be universally successful. Copyright © 2013 John Wiley & Sons, Ltd.
The central role of "platform" products and services in mediating the activities of disaggregated "clusters" or "ecosystems" of firms has been widely recognized. But platforms and the systems in which they are embedded are very diverse. In particular, platforms may exist within firms as product lines, across firms as multi-product systems, and in the form of multi-sided markets. In this paper we argue that there is a fundamental unity in the architecture of platforms. Platform architectures are modularizations of complex systems in which certain components (the platform itself) remain stable, while others (the complements) are encouraged to vary in crosssection or over time. Among the most stable elements in a platform architecture are the modular interfaces that mediate between the platform and its complements. These interfaces are even more stable than the interior core of the platform, thus control over the interfaces amounts to control over the platform and its evolution. We describe three ways of representing platform architectures: network graphs, design structure matrices and layer maps. We conclude by addressing a number of fundamental strategic questions suggested by a unified view of platforms.
Sharing systems are increasingly challenging sole ownership as the dominant means of obtaining product benefits, making up a market estimated at over $100 B annually in 2010. Consumer options include cell phone minute sharing plans, frequent flyer mile pools, bike sharing programs, and automobile sharing systems, among many others. However, marketing research has yet to provide a framework for understanding and managing these emergent systems. The present paper conceptualizes commercial sharing systems within a typology of shared goods. Three studies then demonstrate that beyond cost-related benefits of sharing, the perceived risk of scarcity related to sharing is a central determinant of its attractiveness. Results suggest that managers can use perceptions of personal and sharing partners’ usage patterns to affect risk perceptions and subsequent propensity to participate in a commercial sharing system.
In the context of platform competition in a two-sided market, we study how uncertainty and asymmetric information concerning the success of a new technology affects the strategies of the platforms and the market outcome. We find that the incumbent dominates the market by setting the welfare-maximizing quantity when the difference in the degree of asymmetric information between buyers and sellers is significant. However, if this difference is below a certain threshold, then even the incumbent platform will distort its quantity downward. Since a monopoly incumbent would set the welfare-maximizing quantity, this result indicates that platform competition may lead in a market failure: Competition results in a lower quantity and lower welfare than a monopoly. We consider two applications of the model. First, the model provides a compelling argument why it is usually entrants, not incumbents, that bring major technological innovations to the market. Second, we consider multi-homing. We find that the incumbent dominates the market and earns higher profit under multi-homing than under single-homing. Multi-homing solves the market failure resulting from asymmetric information in that the incumbent can motivate the two sides to trade for the first-best quantity even if the difference in the degree of asymmetric information between the two sides is narrow.
Why do you want that? (And who's it for?). Amazon Science
- A Boteanu
Boteanu, A. (2020, March 12). Why do you want that? (And who's it for?). Amazon
Science. Retrieved from https://www.amazon.science/blog/why-do-you-want-thatand-whos-it-for. Accessed July, 2022.
Sharin''s not just for start-ups
- R Botsman
Botsman, R. (2014). Sharin''s not just for start-ups. Harvard Business Review, 92(9),
23-25.
Platform market power
- K A Bamberger
- O Lobel
Bamberger, K. A., & Lobel, O. (2017). Platform market power. Berkeley Technology Law
Journal, 32, 1051.