Generative AI is increasingly embedded in B2B selling, yet its implications for salesperson creativity and value co-creation remain poorly understood. Prior sales research largely conceptualizes AI as an automation or decision-support tool, offering limited insight into how generative systems reshape core selling processes. Drawing on socio-technical systems theory and evolutionary creativity theory, this study develops a process model explaining how generative AI augments salesperson creativity across preparation, incubation, and verification stages of the selling process. Using qualitative analysis of five publicly documented B2B cases involving Salesforce, Adobe, SAP, IBM, and Siemens, the findings show that generative AI enhances relative creativity by expanding solution sets and accelerating proposal development, while salespeople remain essential for absolute creativity through contextual judgment and strategic value articulation. Sales performance improvements arise when generative AI is embedded within salesperson workflows and used interactively with customers under managerial governance. The study advances personal selling theory by positioning generative AI as a socio-technical resource that reshapes salesperson creativity and buyer-seller co-creation.

Artificial intelligence has progressed through distinct phases, from early automation to generative and agentic systems capable of autonomous goal pursuit. While agentic artificial intelligence represents an important advance, its organizational impact remains constrained by existing coordination structures and governance arrangements. This article argues that the next surge in artificial intelligence will be defined not by increasingly capable agents, but by the institutionalization of artificial intelligence as an organizing layer that orchestrates coordination, embeds governance, and reallocates decision rights across socio-technical systems. Drawing on organization theory, research on algorithmic decision-making, and emerging scholarship on artificial intelligence governance, a conceptual framework is developed for understanding artificial intelligence–orchestrated organizations. Next, outlined are the theoretical foundations of this shift and a preview of its implications for managerial roles, organizational design, and competitive advantage. The article concludes by positioning artificial intelligence orchestration as a central managerial challenge and a critical domain for future research.

While scarcity often enhances product desire, we introduce and examine synthetic scarcity, a demand-side deception where firms falsely imply excess demand despite sufficient supply. Six studies demonstrate that synthetic (vs. authentic) scarcity elicits greater resentment, but this effect is critically moderated by consumer Machiavellianism. For low Machs, synthetic scarcity triggers feelings of betrayal, driving heightened resentment. High Machs, in contrast, report equally high resentment towards both scarcity types, as their response is driven by a perceived loss of control rather than betrayal. We provide indirect process evidence for this mechanism by identifying structural power as a boundary condition: the interactive effect of scarcity type and Machiavellianism emerges only under high power, where high Machs' need for control is activated; under low power, the interaction dissipates. These findings illuminate the distinct psychological pathways through which consumers respond to deceptive scarcity tactics.

Agentic AI is reshaping how organizations operate. As autonomous systems take over pricing, content, personalization, and supply chain decisions, the human choices that historically built brand identity are progressively displaced. We term the result agentic brand drift: the gradual, unintended divergence between a firm's intended brand identity and the emergent brand character produced by its AI-orchestrated operations. Unlike brand inconsistency or deliberate identity change, agentic brand drift is internally generated, has no triggering event, and co-occurs with improving performance metrics, making it invisible to conventional monitoring. Critically, this failure mode falls outside the scope of existing AI governance frameworks such as NIST AI RMF and ISO/IEC 42001, which govern system behavior rather than meaning coherence. A firm executing those frameworks flawlessly will still experience agentic brand drift. We theorize three mechanisms, Decision Diffusion, Temporal Collapse, and Accountability Dissolution, operating as a causal sequence, and derive two complementary frameworks, CORE and GUARD, that give organizations capabilities existing governance does not provide: identifying which decisions carry identity stakes, supplying agents with organizational reasoning behind past brand choices, and monitoring output patterns for identity coherence over time.

Influencer marketing has emerged as a prominent strategy for brands to engage with consumers in the digital age, with virtual social influencers playing a pivotal role in shaping consumer perceptions and behaviors. To effectively evaluate influencer effectiveness and optimize marketing strategies, there is a need for a standardized measure. We introduce a virtual social influencer scale, which provides a comprehensive framework for assessing their performance across the dimensions of trustworthiness and connectedness. Drawing on insights from socio-technical systems theory, the use of an algorithmic approach in scale development spawns from the psychology literature. This research provides the foundation of its introduction into the marketing discipline. Future research directions include refining the scale, addressing algorithmic limitations, and exploring cross-cultural variations to enhance the scale's validity and applicability in diverse contexts. The virtual social influencer scale represents a significant advancement in influencer marketing research, offering valuable insights to marketers, researchers, and practitioners in the digital marketing landscape.

Purpose This study investigates how integrating non-fungible tokens (NFTs) into product offerings influences consumers' monetary valuations across pre-purchase, purchase and post-purchase stages. Design/methodology/approach Three pre-registered experiments and a single-paper meta-analysis test NFT effects on willingness-to-pay (WTP), product valuation and willingness-to-accept (WTA).Findings NFTs amplify valuations exclusively post-purchase (higher WTA), mediated by product attachment. Effects emerge only among incremental theorists (believing traits are malleable), not entity theorists (believing traits are fixed). Practical implications Marketers should leverage NFTs for retention (versus acquisition) and target incremental theorists. These insights offer actionable guidance for enhancing customer-to-customer (C2C) interactions in banking, particularly in peer-to-peer markets for tokenized assets where NFT-driven attachment influences pricing and trust dynamics. Originality/value This is the first study to (1) pinpoint NFTs' post-purchase valuation effects, (2) identify attachment as the mechanism and (3) reveal implicit theories as a boundary condition.

Explicit content consumption has the potential to lead to maladaptive reliance on such arousing imagery as a coping mechanism and has been found to profoundly impact intimate relationships and society at large. We examine the impact of arousing imagery consumption on an intimate partner’s perceived relationship and intimacy satisfaction. Two experiments, with a total of 489 online participants, document a crossover interaction effect such that individuals who believe that their sex life is fixed respond negatively toward explicit content consumption by their partner, whereas individuals who believe their sex life is malleable respond more positively (Studies 1 and 2). This effect is mediated by situationally induced desire for closeness (Study 2). The paper concludes with theoretical contributions and a research agenda for future inquiries.

Reality-enhancing technologies influence consumer decisions, yet our understanding of how consumers respond to marketing content using such technologies is nascent. We develop a scale to measure consumers’ simulated responses in reality-enhancing technology experiences, resulting in the creation of a 12-item simulated responses scale with two dimensions, simulated satiation (6 items) and simulated immersion (6 items). The analyses revealed that simulated responses is a multidimensional construct, with each dimension considered an independent scale. Consequently, simulated satiation lowers evaluations of augmented reality tools and purchase intent, yet simulated immersion raises both responses. The results reveal how content variety and exposure duration interact with simulated satiation and simulated immersion to influence consumers’ evaluations and decisions. By employing the simulated responses scale, marketing practitioners can assess the effectiveness of their reality-enhancing technology strategies. The goal is to optimize content variety and exposure duration to achieve better outcomes prior to launching a marketing campaign.

The emergence of AI agents as digital intermediaries marks a fundamental transformation in how consumers discover, evaluate, and select products and services. As consumers increasingly delegate their decisions to these algorithmic gatekeepers, it compels firms to expand beyond traditional human-centered search engine optimization and user experience design to compete for algorithmic selection. Firms face a new competitive imperative: achieving what we term Algorithmic Fidelity, the capacity to be accurately discovered, interpreted, and executed by AI systems acting on behalf of users. We trace the theoretical foundations of this shift through resource dependence, bounded rationality and institutional convergence to reveal how agent-based intermediation compresses choice sets, reshapes market access, and redefines brand visibility. Drawing on early evidence from major platforms’ AI implementations, we develop a Delegation Readiness Audit positioning firms in the Intermediation Vulnerability Matrix to identify four distinct strategic archetypes and prescribe tailored pathways for achieving algorithmic competitiveness. Our analysis reveals that success in this emerging economy requires fundamental reimagination of business models, from optimizing for human attention to engineering for machine selection. We offer a forward-looking framework for senior executives seeking to maintain relevance as AI agents become the new gatekeepers of demand, with implications that extend beyond individual firms’ strategy to reshape market structure, competitive dynamics, and the nature of consumer agency itself.

As AI technologies become increasingly integral to diverse service industries, understanding how to effectively address and recover from AI failures is paramount. Drawing upon an extensive review of the literature and empirical evidence, this chapter elucidates the multifaceted challenges inherent in service recovery when AI malfunctions. It explores various dimensions of AI malfunction scenarios, encompassing technical failures and misinterpretation of data. Moreover, a 2×2 matrix is provided for plotting service malfunctions in terms of severity and impact as well as providing practical strategies for orchestrating robust service recovery processes in such circumstances. By integrating insights from AI ethics, crisis management and service management literature, this scholarly inquiry offers valuable guidance to practitioners, managers, and policymakers in formulating proactive and responsible approaches to mitigate the adverse impacts of AI failures on service quality, customer satisfaction, and organisational reputation.

Although uncertainty is traditionally associated with risk aversion and unfavorable consumer responses, such as hesitation or avoidance in decision-making, marketing practitioners are witnessing a surge in consumers' appreciation for mysterious consumption (i.e. blind boxes). Two registered experimental studies and one single-paper meta-analysis show that higher (vs. lower) outcome uncertainty leads to significantly higher purchase intent. This positive effect of outcome uncertainty is contingent upon the probability uncertainty; the effect of outcome uncertainty is present only when the probability uncertainty is high (vs. low). Furthermore, when probability uncertainty is high, greater outcome uncertainty is more prone to enhance the level of sensation-seeking, which in turn increases consumers' purchase intent. Theoretically, this research showcases sensation-seeking as the underlying causal mechanism bounded by probability uncertainty. Practically, when marketers attempt to capitalize on uncertainty to boost consumer responses, it seems optimal to maximize both outcome and probability uncertainty. Additionally, marketers ought to use sensation-provoking techniques and strategies to promote greater patronage behaviors.

The incorporation of Generative Artificial Intelligence (GenAI) technologies into service delivery processes has surfaced as a transformative trend with serious implications for several industries and sectors. In response to the growing need for comprehensive frameworks to examine the impact of AI-driven service delivery systems, we propose the development of a GenAI service delivery scale. Grounded in socio-technical systems theory, the scale aims to measure consumer perceptions of GenAI in service delivery. The development of the scale holds significant implications for researchers, practitioners, and policymakers, providing a standardized measure of consumer perceptions of GenAI in service delivery. Through collaborative efforts and ongoing refinement, the GenAI service delivery scale aims to advance our understanding of consumer perceptions of AI-driven service delivery and contribute to the progress of best practices in the field.

Purpose: This chapter examines the link between AI adoption in HR and misinformation, discussing its impacts on organizational growth and recommending mitigating strategies. It aims to advance both theoretical understanding and practical guidance for organizations navigating these challenges. Need for the study: AI plays a critical role in promoting organizational growth via data-driven decision-making. However, AI-related misinformation poses significant challenges, such as resource misallocation and erosion of employee trust, which need to be addressed to fully harness AI’s potential in HR. Methodology: A systematic analysis of academic literature, industry practices, and real-world cases. It integrates theoretical perspectives such as socio-technical systems theory and risk management to develop a novel 2 × 2 framework categorizing AI-related misinformation by potential harm and prevalence. Findings: The 2 × 2 framework identifies four categories of AI-related misinformation challenges: (1) Critical risks: High harm, high prevalence, (2) Widespread challenges: Low harm, high prevalence, (3) Emerging threats: High harm, low prevalence, and (4) Minor concerns: Low harm, low prevalence. The analysis highlights how misinformation can lead to resource misallocation and undermine employee trust. It underscores the importance of AI literacy programs and transparent AI policies in combating these challenges. Practical implications: Organizations that proactively address AI-related misinformation through systematic assessment and management will achieve long-term growth and competitive advantage. The chapter provides real-world examples and emphasizes the implementation of AI literacy initiatives and transparent policies to mitigate risks effectively.

Customer citizenship behaviours (CCBs), such as advocacy and helping, are critical in self-service environments where peer assistance substitutes for employee support. Yet, gaps persist in understanding how perceptions of peer helpers’ competence, post-interaction comfort and trust drive further CCBs. Integrating the cognitive-affective-behavioural (CAB) framework and social exchange theory, this study examines these dynamics through a survey of 264 South African fast-food app users. Results reveal that interaction competence (approachability and accommodation) and comfort are primary drivers of post-assistance CCBs, with trust amplifying the link between competence and outcomes. Task competence and informational support exert weaker effects. Theoretically, the study positions peer helpers as quasi-employees whose social skills and emotional rapport sustain reciprocity. Practically, findings advocate for self-service designs that prioritise interpersonal fluency – such as gamified recognition for empathetic contributors – and trust-building mechanisms like verified user badges. By bridging post-interaction gaps, this work equips firms to harness peer driven advocacy, enhancing customer-driven advocacy networks.

Celebrities have long been utilized by brands to enhance their products and services, with the underlying assumption that the celebrity's positive attributes and public image would transfer a halo effect to the endorsed products. However, recent years have witnessed a paradigm shift in this phenomenon, marked by a growing number of negative marketing events involving celebrities. We identify several key themes, including consumer attributions, psychological processes, social media amplification, consumer empowerment, and cultural shifts. Each contributes to a holistic understanding of why and how negative events involving celebrities can lead to a diminishing halo effect. Depending on whether consumers attribute the event internally or externally, their response can vary and, subsequently, affect the celebrity's halo effect. Overlaying attribution theory provides deeper insights into the dynamics of consumer perception and response in the context of negative events in marketing. Understanding how consumers attribute causality to negative events and the psychological mechanisms that underpin their responses provides valuable insights for celebrities, brands, and marketers navigating the challenges of the digital age. (c) 2024 Kelley School of Business, Indiana University. Published by Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

Unequal distribution of child rearing and domestic responsibilities between parents contributes to gender inequity, a wicked problem in Australia. Inequitable parental leave policies at Australian public Universities place the burden of care squarely on the mother, diminishing or absenting the father. We examine how the gendered nature of the existing policies are constructed in ways that create inequities and discourage their uptake. A post-structural feminist lens provides us with a theoretical vantage point from which this wicked problem can be problematized. We present three recommendations for enabling more equitable outcomes for parents. The first is to eradicate the punitive approach and support flexibility; second, the policies must be parental leave in name, provision and practice; and finally we recommend a minimum parental leave standard for Australian universities nationally. These findings have policy-level significance for redressing parental leave inequity within the Australian university context. The paper concludes with theoretical contributions, practical implications, and suggestions for future research.

Advances in technology have brought about transformative changes in the wine industry, leading to a convergence of multisensory perception and technological innovation. This special issue explores the intersection of multisensory experiences and technology within the context of the wine industry, examining the impact that various digital technologies have had on sensory engagement, consumer behavior, and the vinicultural landscape. From augmented reality (AR) wine label experiences to smart decanters and aroma simulators, technology is reshaping the way in which consumers perceive, interact with, and appreciate wine. The research curated in this special issue investigates how multisensory technologies influence consumers' wine consumption experiences by enhancing their visual, olfactory, gustatory, tactile, and auditory sensations. Additionally, it explores some of the implications of multisensory digital technology on wine production, marketing strategies, and brand storytelling. By examining the dynamic interplay between sensory perception and technological innovation, this special issue offers insights into the evolving nature of the wine industry and the potential for multisensory digital technologies to redefine the future of wine consumption.

Generative artificial intelligence––defined as AI-enabled technology that analyzes and learns from existing data and generates novel human-like content—has emerged as a revolutionary technology for firms seeking sustainable competitive advantage. We highlight the evolution of generative AI from generic, domain tailored and collaborative systems, that are democratized and only offering demand-driven insights, to the next frontier of alternative perceptual systems. Managers who integrate these current large language models into building their brand persona will empower their firms to experiment along the evolutionary journey. By embedding alternative perceptual systems into generative AI platforms, firms can achieve novel, interactive, and personalized insights that may be difficult for their competitors to replicate.

Purpose The recent pandemic disrupted the way in which businesses transact with each other. In response to maintaining cleanliness in business-to-business (B2B) settings, artificial intelligence (AI)-enabled robots have been adopted as substitutes for cleaning personnel, yet their implications remain largely underexamined. This study aims to examine B2B buyer responses to cleaning information notices (human vs AI-enabled agent) placed at either the entry to the premises or the sales counter, thereby adding to the nascent literature in this line of inquiry. Design/methodology/approach Three field experiments were conducted across diverse B2B businesses (wholesalers in Studies 1–2 and a commercial business in Study 3). To achieve greater empirical rigor and generalizability, this research used diverse stimuli across different B2B settings. In addition, the results ruled out alternate explanations and shed light upon political ideology as a boundary condition. Finally, a single-paper meta-analysis confirmed H1 , consolidating the established effect. Findings Featuring over 1,000 B2B buyers, the results show that politically liberal B2B buyers express greater preference for human over AI-performed cleaning while labor-orientated buyers are indifferent. Importantly, this effect is driven by greater relaxation associated with humans, which in turn, increases their future patronage and referral intent. Originality/value The results enrich the collective knowledge of the adoption of AI-enabled robots, reinforcing for marketing practitioners and businesses that the reliance on human-based outcomes remains a preferred touchpoint in B2B settings, particularly for liberals.