Marketing academia stands at a pivotal moment. The work we do extends far beyond publishing research鈥攊t shapes how we mentor students, influence organizations, and lead within our institutions. Whether you’re preparing for your first faculty position, navigating the tenure process, refining your teaching methods, or stepping into administrative roles, your career path deserves dedicated attention and support. The 2026 Summer Academic Conference recognizes that professional growth happens across multiple dimensions, and every stage of your academic journey matters.

Join us in Denver, CO, to connect with colleagues who share your commitment to excellence in all aspects of academic life. Through curated workshops, panels, and collaborative sessions, you’ll gain practical strategies for advancing your scholarship, teaching effectiveness, and leadership capabilities. This conference creates space for doctoral students seeking job market guidance, clinical faculty exploring new pedagogical approaches, and experienced professors transitioning into administrative positions. Together, we’ll build the knowledge, relationships, and momentum that fuel meaningful careers in marketing academia.

In-Person and Virtual Options

During the 2026 萝莉社官网 Summer Academic Conference in Denver, CO, July 24-26, we will explore how marketing academics can strengthen their impact across scholarship, teaching, and organizational leadership at every career stage.

If you cannot join us in Denver, select virtual programming will be available on Monday, July 20.

Why Attend?

Receive valuable feedback on your research from leading scholars and get guidance to help prepare your work for publication.

Engage in dynamic conversations on the latest marketing innovations and build meaningful connections with researchers, reviewers, and editors鈥攁dvancing your career and gaining fresh perspectives on the field.

Dive deeper into current events and innovative topics in marketing through insightful Expert and Intensive Workshops facilitated by prominent academics in the industry.

Celebrate the achievements of our community at the largest 萝莉社官网 Academic Awards Luncheon, where we鈥檒l honor the 萝莉社官网-EBSCO-RRBM Annual Award for Responsible Research in Marketing winners, the Robert J. Lavidge Global Marketing Research Award recipient, and more.

Maximize Your Time Onsite

Conference Co-Chairs

Kay Peters

UC Davis & University of Hamburg

Kelly Hewett

Colorado State University

Kim Whitler

University of Virginia

Meet the 2026 Conference Track Chairs

Conference Tracks

萝莉社官网 Event Policies

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Join Marc Pritchard, the Chief Brand Officer of Procter & Gamble, and 萝莉社官网’s CEO, Bennie F. Johnson, for a conversation to explore marketing strategies, practices, and learning that will shape the future of the profession.聽

The 萝莉社官网 is bringing together industry leaders and academics to advance marketing education. Join us for a thoughtful dialogue that will:

• Advance marketing education by allowing academics to engage with executives who are shaping the profession.
• Nurture marketing theory and research by giving scholars access to first hand accounts of the challenges facing leading executives.
• Support industry thought leadership and community interplay by bringing together industry leaders and academics to build the future of the profession.

This webinar airs at 12 PM and will be available on-demand for six months after airing.

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Conference Proceedings

Explore the latest research from the 2026 event.

Global Voices, Shared Challenges: Marketing and Policy Beyond Borders

Around the world, marketing scholars, policymakers, and societal decision-makers are grappling with complex, interdependent challenges鈥攆rom climate change and health inequities to data privacy and financial inclusion. These issues cross national, disciplinary, and institutional borders, and addressing them requires a collective, boundary-spanning approach. Recognizing and challenging these boundaries is essential to fostering more inclusive, innovative, and impactful research. By deliberately looking beyond these boundaries鈥攂oth literal and figurative鈥攚e can discover alternative approaches and draw on examples of research, collaborations, and policies that meaningfully improve the well-being of individuals, businesses, societies, and the planet.

The 2026 Marketing & Public Policy Conference convenes in Ottawa, Ontario, marking the first time this conference has been held outside the United States. As the national capital of a country known for its pluralism and progressive social policies, Ottawa offers a compelling context for engaging in global dialogue. Explore research that examines the scope of marketing & public policy scholarship and reflects a diverse range of voices, disciplines, contexts and methodologies to address shared challenges and drive impactful marketing and public policy solutions.

Keynote Speaker

Straight from the Heart: A Fireside Chat with the Honourable Jean Chr茅tien
Former Prime Minister of Canada (1993-2003)

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Why Attend?

Be part of an intimate community focused on advancing marketing鈥檚 role in addressing critical policy issues.

Co-create research-driven solutions that promote resilience and social impact through collaboration with academic and industry leaders.

Explore emerging insights at the intersection of marketing, public policy, and innovation.

Build meaningful relationships with experts and peers who share your commitment to impactful, interdisciplinary work.

Junior Scholars Workshops

Jump-start your conference experience by arriving early to connect with fellow scholars and receive mentorship on your work.

LEARN MORE

Conference Co-Chairs

Monica LaBarge

Queen鈥檚 University

Jacob Brower

Queen鈥檚 University

Michael Mulvey

University of Ottawa

萝莉社官网 Event Policies

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Conference Proceedings

Explore the latest research from the 2026 event.

Bridging at the Frontiers: Marketing for a World in Transition

As the 萝莉社官网’s first conference outside the U.S., Bridging at the Frontiers highlights marketing’s unique role in connecting diverse ideas, geographies and priorities while exploring the boundaries of innovation and practice. In today’s world of transition鈥攎arked by geopolitical tensions, within-country polarization and ideological contests, environmental challenges and the transformative impact of AI鈥攎arketers must navigate complex trade-offs and seize opportunities to create meaningful value.

The 2026 theme emphasizes the dual challenge of bridging divides鈥攍ocal and global, technological and human, ideological and practical鈥攚hile advancing the frontiers of what marketing can achieve. How can marketing leaders respond to shifting societal norms and skepticism toward policies of sustainability and inclusion while fostering trust across diverse audiences? What role can AI play in transforming customer journeys while driving ethical innovation and equitable growth, such as balancing personalization with privacy and fairness? How can firms manage the dynamic tension between global ambitions and local relevance, ensuring their strategies resonate across distinct cultural and economic contexts?

Join us to explore these pressing questions and opportunities, from rethinking how marketing helps to balance resilience and efficiency in rapidly evolving markets to leveraging emerging technologies to address societal challenges. Together, we’ll bridge the gap between academia and practice, connect global trends with local realities, and chart new frontiers for marketing in a dynamic and interconnected world in transition.

Why Attend?

Hear new perspectives from colleagues across the discipline at this premier marketing research event, where top-tier scholars will present their compelling research. 

Engage in conversations about the latest research topics with researchers, reviewers and editors using our inclusive community and contribute to a more comprehensive approach to marketing.

Connect with like-minded scholars through various learning and networking opportunities.

Celebrate milestones within the community as 萝莉社官网 honors the 2026 萝莉社官网 Fellows cohort, the 2026 萝莉社官网-Irwin-McGraw-Hill Distinguished Marketing Educator Award recipient, and other distinguished winners.

Maximize Your Time Onsite

Conference Co-Chairs

Michael Haenlein

ESCP Business School

K. Sudhir

Yale University

Ela Veresiu

York University

Meet the 2026 Conference Track Chairs

萝莉社官网 Event Policies

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The mobile app market has been witnessing record levels of growth and valuation in the last decade. Apps emerged as a significant engine of business value via engagement-driven revenue models, such as in-app advertising or purchases, that highly depend on continued use. However, engagement typically diminishes quickly over time, pushing businesses to develop new strategies to maintain user activity. To keep users engaged, many companies implement gamification by incorporating features like levels, badges, points, etc., alongside traditional value rewards to enhance an app’s appeal. Still, it is hard to exactly determine how gamification serves to maintain user engagement and business value.

A investigates whether gamification can truly help keep users engaged in ways that also generate tangible business value. Specifically, the authors examine how chasing game rewards (points, levels, etc.) and value rewards (discounts, credits, etc.) interact to influence user engagement with the app and whether this interaction enhances the quality and quantity of such engagement. The findings suggest that game-based rewards are more effective than regular rewards at getting users involved, which in turn increases business value. They find that this effect is especially strong when users are closer to the rewards. At the same time, the study points out a drawback of gamification: When users become deeply absorbed in the game, their engagement with the game is less likely to translate into actions that add real value.

Game-based rewards are more effective than regular rewards at getting users involved, which in turn increases business value.

Managers can use these findings to design smarter reward systems in their apps. Adding simple game elements like points or levels is a low-cost way to keep users engaged, but these should be carefully linked to value-creating actions such as watching ads, making purchases, or completing tasks. Using both game rewards and real rewards works best when they are spaced out over time rather than given at the same moment. Managers should also be careful not to make the game so immersive that users ignore value-creating activities; showing ads or prompts earlier, before users become deeply absorbed, can help. Overall, the main idea is to balance fun and business goals so that engagement leads to real value rather than distraction.

We recently had the opportunity to contact all the authors of this research to gain deeper insight into their motivations, managerial implications of this research, and additional insights of interest:

Q: What was your motivation behind this research? Was there something you observed in real-world app usage that made you curious about how gamified rewards shape user behavior, and what prompted you to take a closer look at their impact on engagement and business outcomes?

A: This research was directly motivated by a collaboration with an app provider who approached us for advice on how to make their app more engaging. The focal app was a market research app, and their request immediately posed a challenge: the app鈥檚 core value-adding activity鈥攖hat is, answering client survey questions鈥攊s not inherently engaging for most users. Moreover, these client surveys only arrive intermittently, meaning that users often simply could not do anything in the app that would have earned them rewards.

The provider had introduced an additional activity that was non-value-added from the firm鈥檚 perspective but potentially engaging for users: so-called “fun questions.” These questions were designed purely to keep users active in the app and to motivate repeated usage, even when no client surveys were available.

Importantly, the app’s incentive structure clearly separated these two types of activity. Specifically, users received (traditional) monetary rewards (in-app coins that could be redeemed for vouchers, for example) for answering client surveys (value-added activities). Answering fun questions (non-value-added activities) was rewarded with experience points and with climbing up levels by collecting experience points (gamified rewards). This design ensured that every activity contributed to one of the two reward systems鈥攖hus maintaining engagement鈥攚hile only the value-added activities generated actual costs for the provider.

When we looked beyond this specific case and considered the mobile app market from a broader perspective, we realized that this dual reward system extends far beyond market research apps. Many apps combine value-added activities and non-value-added activities. A fitness app, for example, may create (monetary) value for the provider through subscriptions, while users primarily engage in workouts, challenges, or tracking features that are available for free, and revenue comes from a subset of users upgrading to premium. Similarly, social media and content apps rely on high levels of user engagement that only translate into revenue indirectly, for example, by increasing advertising exposure. This observation led to a broader conceptual insight: Many apps operate with dual or hybrid reward structures that simultaneously reward different types of activities through different psychological mechanisms. While marketing research has studied reward-pursuit effects extensively, this work has almost exclusively focused on a single reward engine, typically monetary rewards tied to value-added activities. However, once an additional gamified reward engine is introduced, the psychology of reward pursuit changes fundamentally. Understanding how these dual reward engines interact with each other, reinforce each other, or sometimes undermine each other became the central motivation for this research.

Q: Marketers often prize immersion (flow) as the gold standard of engagement. In fact, previous studies show that it can elevate customer experience, boosting satisfaction and loyalty. However, your work highlights an important dark side. Could you please elaborate on why a highly engaged user in a deep flow state might ultimately contribute less marginal value, even while spending more time in the app?

A: You are absolutely right that immersion and flow are often seen as the gold standard of engagement. From the user鈥檚 perspective, flow typically enhances enjoyment, satisfaction, and the overall experience when using an app. Our findings do not contradict this view, but they show that flow is not unambiguously beneficial from a firm鈥檚 value-creation perspective.

The key issue is that time spent in an app is not the same as the value created for the firm. In many engagement-based business models, value for the firm is generated only when users engage in specific activities, such as providing data, viewing ads attentively, or making purchases. In our context, these are the value-added activities that can interrupt the gameplay experience.

When users enter a deep flow state during gameplay, their attention becomes narrowly focused on the game-like activity itself. Psychologically, flow is characterized by intense concentration, reduced awareness of external stimuli, and a strong desire to maintain uninterrupted progress. In such a state, any activity that pulls users away from the game feels more like a disruption than an opportunity. As a result, even though highly immersed users spend more time in the app, they become less responsive to value-adding tasks because they want to avoid gameplay interruption.

Overall, users in a high flow state may engage in a lot of activity and time spent in the app, but the incremental value of the additional engagement for the firm decreases. These users鈥� attention is increasingly focused on maintaining the flow experience and less on performing value-added activities. In extreme cases, users may then rush through the value-added activities or avoid them altogether, reducing both the quantity and quality of value created for the firm. From a managerial perspective, this finding implies that maximizing flow is not always optimal.

Q: For companies that solely rely on traditional value-added programs (coupons, discounts etc.), how can marketing managers effectively introduce 鈥済ame rewards鈥� to complement (without cannibalizing) their existing strategy? Could you please share your thoughts on how to strike this balance?

A: For firms that already rely on traditional value-added reward programs, the key is not to replace these mechanisms but to use game rewards as a complementary motivational layer. Our findings suggest that the greatest challenge lies not in introducing game rewards per se but in introducing them in such a way that they do not compete with or distract from value-added activities.

First, functional separation is key to adding game rewards effectively. Game rewards should primarily incentivize non-value-added activities, such as exploration, learning, habit formation, or repeated app access, while value rewards should remain tightly linked to behaviors that directly generate value for the app provider. This separation preserves the economic logic of the reward system and avoids teaching customers that rewards can be earned 鈥渃heaply鈥� through gameplay.

Second, managers should carefully manage timing and proximity. Our results show that proximity to rewards can be highly motivating, especially when users are close to both a game reward and a value reward at the same time. In terms of designing the reward system, this means that game rewards should prepare users for value-added activities by keeping them active and emotionally engaging them in using the app without triggering reward attainment in both reward systems at the same time. Avoiding simultaneous reward resets is important, as double reward attainment can temporarily dampen engagement.

Third, firms should actively manage flow levels. While some degree of gamified engagement is beneficial for sustaining usage, deep flow states can reduce responsiveness to value-added activities. Firms should thus insert value-added activities at natural breakpoints in the game experience, such as between levels, after milestones, or during cool-down phases, rather than during moments of peak flow.

Q: Your study highlights 鈥渄ouble post-reward resetting鈥� as a critical user retention risk. Why does receiving simultaneous rewards lead to a sharper decline in engagement compared to receiving just one reward? What specific strategies can marketing managers employ to prevent that dip and maintain user momentum?

A: Double post-reward resetting occurs when users attain a game reward and a value reward at approximately the same time. Each reward on its own can trigger a short-term decline in motivation and engagement, as users have achieved the immediate goal and the next goal suddenly seems further away. When both reward engines reset simultaneously, these declines in motivation compound, leading to a sharper and more persistent decline in engagement than after a single reward attainment.

Psychologically, this double reset happens because reward pursuit is driven by a sense of progress and momentum. When users are close to a reward, effort feels meaningful and directed. Once a reward is attained, that sense of progress collapses and has to be rebuilt. If two reward engines reset at the same time, users experience a double loss of momentum. There is no nearby goal in either system that pulls them back into activity, which makes disengagement more likely.

From a managerial perspective, this risk is particularly relevant in hybrid reward systems. While multiple reward types increase engagement during pursuit, they also increase the likelihood that users will reach multiple endpoints simultaneously.

One effective strategy is temporal separation of rewards. Managers can design reward thresholds so that game rewards and value rewards are unlikely to be achieved on the same day or within the same session. For example, game rewards can operate on shorter cycles, with frequent small milestones, while value rewards are spaced out over longer horizons.

A second strategy is staggered goal visibility. When a user attains a reward in one reward engine, the system should immediately make progress toward the next reward in the other engine highly salient. By highlighting that another attainable goal is already within reach, the system maintains a sense of forward momentum and prevents users from feeling as though they are starting from zero across the entire reward structure.

Third, firms can use post-reward bridging mechanisms. After a reward is attained, users can be offered a light follow-up task that quickly restores progress, such as a bonus challenge, a progress boost, or a limited-time multiplier. The goal is not to add more rewards but to shorten the psychological distance to the next meaningful milestone.

Overall, double post-reward resetting is not a reason to avoid hybrid reward systems. It is a reminder that firms need to use strategies that preserve user momentum and fully capture the engagement benefits of combining game and value reward.

Q: You propose using algorithms to detect users’ engagement state in real-time. Practically speaking, how do you envision AI-driven personalization and adaptive interfaces to shape the long-term effects of gamification on customer engagement, and do you believe AI can help mitigate the flow-state issue by detecting immersion and adjusting value-added prompts accordingly?

A: We do not propose AI as a way to make gamification more intense but as a way to make it more situationally intelligent. The central idea is that engagement is not static. Users move between different states over time, ranging from low engagement to goal-oriented reward pursuit to deep flow. The long-term effectiveness of gamification depends on responding to these states dynamically rather than applying the same interface logic and reward activities to everyone at all times.

In practical terms, AI-driven personalization can infer a user鈥檚 current engagement state from behavioral signals that are already available in most apps. These include interaction speed, session length, task switching, error rates, response times, and progression velocity. In our study, flow manifests itself in unusually fast progress and highly concentrated activity. It is precisely these patterns that machine learning models are well-suited to detect in real time.

Once engagement states can be inferred, adaptive interfaces can adjust how and when value-added prompts are presented. Here, AI can directly mitigate the flow-state issue. Instead of interrupting users indiscriminately, the system can learn when a user is likely to be more receptive to value-added activities versus when the user would perceive such prompts as disruptive. For example, during periods of deep flow, the interface might temporarily suppress value-added activities or defer them to moments when the user naturally slows down, completes a level, or exits a task sequence.

Over the long run, this kind of adaptation helps align user experience and firm value creation. Instead of maximizing flow at any cost or pushing value-added activities too aggressively, AI enables a balance between sustained engagement through gamification and economically meaningful behaviors. Importantly, this setup also reduces heterogeneity issues. Some users thrive in flow-heavy environments, while others respond better to extrinsic incentives, which are often provided for value-added activities. AI enables firms to learn these patterns at the individual level and adjust trajectories accordingly.

Q: Could you please tell us whether any unexpected findings or emergent patterns arose during your analysis that surprised you or challenged your initial assumptions about how gamification drives engagement?

A: Yes, several findings genuinely surprised us and led us to rethink some common assumptions about gamification and engagement.

One unexpected result was how strong and robust the effects of reward proximity were for both types of rewards. We anticipated that getting close to a game reward would primarily increase game engagement, and that proximity to value rewards would mainly affect value-added behavior. Instead, we found strong cross-effects between the two reward systems. Being close to a value reward also increased game engagement, and being close to a game reward increased value-added engagement. This finding suggests that reward proximity creates a general motivational state rather than activating narrowly targeted behaviors. That was something we had not expected at the beginning.

Another unexpected insight was that more engagement was not always better. Before running the analysis, we expected that higher game engagement would monotonically translate into higher value-added engagement. While this was true on average, the moderation by flow revealed a clear nonlinearity. Once users entered a deep flow state, additional engagement led to diminishing and even negative marginal returns for the firm. This finding forced us to move away from a simple 鈥渕aximize engagement鈥� narrative and to arrive at a more nuanced view where the quality and direction of engagement matter as much as its intensity.

Finally, we were struck by how systematically these patterns emerged in a very large and granular dataset. The effects were not driven by a small subgroup of users or by short-lived novelty effects. Instead, they reflected stable behavioral regularities over time. This consistency increased our confidence that these dynamics are not idiosyncratic to a single app but point to broader mechanisms at work in gamified digital environments.

Source: Jens W. Paschmann, Hern谩n A. Bruno, Harald J. van Heerde, Franziska V枚lckner, and Kristina Klein (2024), 鈥�,鈥� Journal of Marketing Research, 62 (2), 249鈥�73. doi:

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Online marketplaces rely on simple signals to guide complex decisions. On eBay, one of the most noticeable signals is the Top-Rated Seller badge, or eTRS. For buyers, the badge appears directly in search results and provides a quick indication of seller quality. For sellers, it can lead to visibility, credibility, and sales. Xiang Hui, Ginger Zhe Jin, and Meng Liu when eBay redesigned this badge and, in doing so, altered how quality was defined on the platform.

The redesign emerged from a practical concern. eBay believed that the original version of the badge relied too heavily on consumer feedback that could reflect issues beyond a seller鈥檚 control. A delayed package caused by a courier strike or a misunderstanding with a buyer could threaten certification status, even when the seller followed platform rules. In response, eBay shifted the badge criteria toward administrative performance measures, such as shipping timeliness based on tracking data and the handling of unresolved buyer claims. These measures were intended to reflect actions sellers could directly manage, giving sellers clearer control over how their efforts translated into certification.

How Sellers and Buyers Reacted to the Change

This change created a clear shift in the problem sellers were solving. Under the new system, maintaining certification depended less on how buyers reported their experience and more on meeting specific operational thresholds. The authors show that sellers responded accordingly. Performance improved on the dimensions tied to certification, particularly near the cutoff points that determined badge eligibility. Sellers learned the certification cutoff and adjusted their effort to remain just above the threshold, reflecting strategic behavior in response to the redesigned incentive system.

Buyers, meanwhile, were not explicitly made aware of the new criteria. Instead, they inferred what the badge meant on the basis of how well it aligned with their own experience. The study finds that the certificate carries more weight in markets where the administrative measures used by eBay are more closely related to consumer-reported satisfaction. In these categories, buyers saw the badge as more valuable, were more likely to purchase listings displaying it, and were more likely to return to the platform within six months. Where that alignment was weaker, the badge had less influence, even though the badge remained equally salient across categories.

Further Effects of Redesigned Trust Badges

The study also highlights how attention shapes the effectiveness of trust systems. Detailed seller ratings were already available on profile pages, yet very few buyers ever viewed them. The redesign worked well because the most relevant information was embedded directly in the badge buyers were already noticing, rather than hidden behind additional clicks. This design choice illustrates how platforms can increase the impact of information not by adding more signals but by repositioning existing ones into the main decision path.

This design choice illustrates how platforms can increase the impact of information not by adding more signals but by repositioning existing ones into the main decision path.

At a broader level, the redesign had consequences for market structure. Certification rates became more similar across categories, but sales also shifted toward larger sellers in some markets. When quality is assessed at the seller level, incumbents with higher volumes can gain an advantage, even if smaller sellers perform well on individual listings. This raises questions about how platforms can preserve trust while maintaining opportunities for smaller participants.

We spoke with the authors to better understand what motivated the redesign, how buyers and sellers responded, and what managers should take away when thinking about trust systems:

Q: The paper studies eBay鈥檚 major redesign of its quality certificate. What marketplace observation first made you think that this redesign could meaningfully change seller behavior and buyer trust?

A: The redesign was motivated by eBay鈥檚 perception that the prior badge was unfair to sellers because it relied heavily on consumer reviews that could reflect factors outside sellers鈥� control, rather than verifiable seller actions. It also mattered because the eTRS badge was highly salient. At the time, it was the only reputation signal shown on the search results page, so changing its criteria could meaningfully change both seller incentives and buyer trust.

Q: When platforms redesign a trust badge, they often change what the badge 鈥渕eans鈥� to consumers. How should a platform communicate the new badge criteria to buyers in a way that increases trust without overwhelming them with details?

A: While we do not directly observe how buyers got the information, seller-facing communications at the time of the rollout framed the new administrative criteria as a 鈥渟impler and more objective standard.鈥� The platform focused on ensuring sellers understood the change to these fair metrics. Our results show that buyers changed their behavior in a way that is consistent with understanding this shift, valuing the badge more in markets where the new objective criteria were more relevant to their experience.

Q: The results suggest buyers value the certificate more when administrative data are more correlated with consumer reports. How should platforms decide whether to use a single certificate rule everywhere versus tailoring the certificate across categories or markets?

A: Tailoring can improve relevance when administrative metrics better track consumer experience in some categories than others, but it can reduce the clarity and salience of a single badge and increase complexity for sellers operating across markets. Even with a single rule, effects will be heterogeneous because markets differ in how hard it is to meet the same thresholds and how buyers evaluate performance. Managers must keep this tradeoff in mind when deciding between a single rule and tailoring.

Q: Your setting shows that administrative metrics can be more controllable for sellers but not always equally meaningful for buyers. When relevance is weaker, what complementary information should platforms display alongside the badge so customers still feel confident in their decision?

A: Our study highlights that complementary information, such as detailed seller ratings, was already available on seller profile pages but was rarely viewed by buyers鈥攍ess than one percent. The redesign worked by integrating more relevant data into the salient badge itself. The implication is that simply displaying extra data may have a limited effect unless it is integrated into the main visible certification signal.

Q: You note that certification can lead to sales that are more concentrated sales on large sellers. What is one platform policy that could preserve the trust benefits of certification without unintentionally disadvantaging smaller sellers?

A: One policy is listing-level certification rather than seller-level certification. Certifying individual listings based on shipping performance and return handling allows smaller sellers to earn trust on specific products without requiring large cumulative sales histories. This preserves the trust value of certification while reducing the mechanical advantage that seller-level aggregation gives to large sellers. Platforms can also use levers other than certification, such as organic ranking or sponsored search, to direct traffic toward high-quality small sellers.

Q: In incentive systems, sellers may focus on the measured dimensions and ignore unmeasured ones. From a managerial standpoint, what guardrails can platforms put in place to prevent quality from declining in areas that are not directly included in the certification criteria?

A: Platforms should assume sellers will optimize to measured thresholds. The key managerial guardrail is balancing focus and dilution. Include the dimensions that matter most to buyers, but avoid so many metrics that incentives become diffuse. Operationally, platforms should continuously monitor for threshold targeting and deterioration in unmeasured dimensions and update criteria by adding or reweighting dimensions when needed.

Source: Xiang Hui, Ginger Zhe Jin, and Meng Liu (2025), “,” Journal of Marketing Research, 62 (1), 40鈥�60. doi:.

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Our research team analyzed how firms responded to the 鈥淐hina Shock,鈥� a surge of imports that disrupted many U.S. industries between 2000 and 2019. We discovered that firms with influential marketing departments and well-established market-based assets鈥攍ike differentiation and customer capital鈥攚ere better able to weather these competitive pressures. Specifically, we found that:

1. Marketing Leadership is Crucial

Firms where marketing had a strong voice in strategic decisions outperformed their peers. By aligning cross-functional teams and advocating for customer-driven innovation, these firms launched initiatives that enhanced brand loyalty, improved product innovation, and strengthened competitive positioning.

2. Strategic Differentiation Matters

Differentiation also proved to be a powerful tool. Firms that emphasized unique product features, higher quality, or sustainability outperformed those competing solely on price. For example, branding efforts like 鈥淢ade in America鈥� or customization helped firms justify premium pricing and retain customers, even when faced with cheaper imports.

3. Customer Relationships Drive Resilience

Customer relationship capital rounded out the trio of success factors. Firms that invested in building long-term trust and loyalty with their customers faced less risk of losing market share. Strong customer ties created switching costs, making it harder for competitors to lure away buyers.

What Does this Mean for the C-Suite?

These insights have significant implications for executives. Many firms respond to financial pressures by cutting marketing budgets or sidelining marketing leaders from strategic discussions. However, our findings highlight the need to elevate marketing as a core function. Boards and CEOs can support marketing by increasing its decision-making authority and ensuring it is involved in board-level discussions.

Policymakers also have a role to play. While trade policies and tariffs are commonly used to protect domestic industries, our research suggests that empowering firms with marketing resources can offer a market-driven alternative to counter import competition. Public鈥損rivate partnerships focused on branding, differentiation, and customer engagement could strengthen the competitiveness of domestic firms.

The need for marketing-driven strategies will only grow. Experts warn of a potential 鈥淐hina Shock 2.0,鈥� which could flood global markets with low-cost imports in sectors like electric vehicles and solar panels. Firms must proactively strengthen their marketing leadership and differentiation efforts to withstand future competition.

For firms navigating a volatile global trade landscape, strong marketing capabilities can make the difference between thriving and folding.

Source: Nandini Ramani, 鈥�,鈥� Journal of Marketing, 89 (5), 47鈥�65.

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In recent years, marketing scholars and practitioners have expressed growing concern about the diminishing influence of marketing departments. Against this backdrop, a examines how governance networks may determine marketing department power (MDP). Drawing on data from over 6,000 publicly traded firms from 2007 to 2021, the researchers show that directors鈥� exposure through board service at other firms (i.e., board interlocks) affects MDP in the firms on whose boards they also serve (i.e., focal firms). More importantly, the strength of this effect hinges on three interlocking dimensions:

1. the reach of a firm鈥檚 board network,
2. the richness of marketing information within that network, and
3. the firm鈥檚 receptivity to information furnished by the board interlock network.

This work shifts the lens to upstream factors that shape MDP, suggesting that marketing鈥檚 influence is not built solely internally鈥攊t is also transmitted through board interlocks, making the board not only a governance body but also a conduit for influencing a firm鈥檚 MDP. For scholars of marketing鈥檚 organizational role, functional power, and network diffusion effects, this study offers a fresh vantage point and a reminder that if marketing wants to increase its power in firms, the conversation must extend beyond the CMO鈥檚 office into the boardroom.

For marketers, the core takeaway is clear: the board matters. Firms whose directors are connected to companies where marketing holds greater influence are more likely to elevate marketing鈥檚 importance within their own organization. These networks shape how leaders think about growth, which is a key priority for every board. While firms often call on marketers when facing serious challenges or major opportunities, marketing should not be reserved for exceptional circumstances. A key priority for marketers and CMOs is to educate their boards on how and why marketing drives firm growth, a shared goal across virtually all boards.

A key priority for marketers and CMOs is to educate their boards on how and why marketing drives firm growth, a shared goal across virtually all boards.

In short, governance structures are not just background context; they are powerful levers that can strengthen or diminish marketing鈥檚 strategic voice in the firm.

We recently had the opportunity to meet with all three authors of this research, who kindly offered additional insights into their motivations, managerial implications, and prospective avenues for future research.

Q: Your research shows that boards of directors, often overlooked in marketing, can shape marketing鈥檚 strategic importance. What led you to recognize the board as a missing piece in the marketing power puzzle, and how did this idea develop into the published study?

A: The idea grew from our , where we found that firms that employ CMOs tend to perform better. But we also noticed that the presence of marketers on top management teams and the overall influence of marketing within firms has declined over time. That pattern made us think about what other forces might shape marketing鈥檚 standing in firms, beyond what happens inside the organization. The board of directors emerged as a natural next place to look, because directors serve on multiple boards and can bring with them ideas about what marketing should look like. When the Wharton Customer Analytics Initiative released a call for projects offering access to large-scale data on board linkages, it gave us a perfect opportunity to test this idea. That combination of prior research, the open question around marketing鈥檚 declining power, and the new data on board interlocks ultimately came together in this study.

Q: Do firms need a formal 鈥淢arketing Department鈥� to have influence at the top, or is it enough to possess strong marketing capabilities and a deep understanding of what marketing brings to the organization?

A: That鈥檚 more of a philosophical question. While marketing today is highly cross-functional, a formal marketing function still matters. Having a defined department or leadership structure gives marketing visibility and accountability at the top. Without it, the customer perspective can easily get lost amid competing priorities. As we often say, 鈥渨hen something is everybody鈥檚 responsibility, it ends up being nobody鈥檚 responsibility.鈥� A clear advocate, like a marketing department, helps ensure that the customer鈥檚 voice is represented in key decisions.

At the same time, the role of marketing looks very different across industries. In consumer goods and retail, marketing tends to have comprehensive control and plays a central strategic role. In banking, it often has a narrower focus on promotions or communications. In professional services like accounting, marketing is more standardized and peripheral. Unlike functions such as accounting, which look similar across most firms, marketing differs widely in scope, influence, and integration. That diversity makes it distinctive: its impact depends on how the organization chooses to empower it. Companies with a more comprehensive marketing approach tend to outperform those with limited marketing responsibilities. Ultimately, marketing power depends on balancing formal structure with shared responsibility.

Q: You show that marketing department power can diffuse across firms through board interlocks. In other areas, firms also learn through executive mobility, strategic alliances, shared consultants, or even investor influence. How does the kind of knowledge transfer you uncover through board ties differ from these other diffusion channels, and what kinds of marketing knowledge travels across boards?

A: Other knowledge transfer channels certainly exist, such as executive mobility or strategic alliances, and with the appropriate data, they could be modeled in a similar framework. Our study focuses specifically on board interlocks, and because we do not observe boardroom conversations directly, we can capture them only through proxies. Similar mechanisms may operate through other channels, but we cannot directly test them.

A key idea here is that boards prioritize growth. When a director sees marketing contributing to growth in one firm, that perspective may diffuse through the interlock to another board. What travels may be high-level mental models about how marketing contributes to performance, or, in some cases, even specific examples shared by directors. Still, the exact mechanism remains a conjecture because we do not observe the discussions themselves; we only observe their downstream effects.

Operationally, even though we cannot measure every variable directly, our use of instrumental-variable methods helps mitigate omitted-variable concerns in this observational setting. We also know from broader research that top management buy-in is essential. That is what makes boards distinctive: because the CEO reports directly to them, any shift in board-level thinking carries disproportionate weight. These mechanisms remain hypotheses that could be examined in more depth when richer data become available.

Q: When boards are interlocked within the same industry, marketing power may spread more easily across firms. Could that connectivity also create unintended consequences? For example, could firms converge on similar, potentially less differentiated strategies?

A: As board members generally cannot serve on the boards of direct competitors, true competitor鈥恡o鈥恈ompetitor interlocks are uncommon. However, if firms are not direct competitors but are in related industries, shared information could lead them to become more similar, potentially reducing differentiation and creating herding effects. This relates to some of the network measures we used, such as degree and brokerage. Degree centrality suggests that more connections may lead firms to behave more similarly. In contrast, in brokerage, a board member links otherwise unconnected parts of the network and can introduce more diverse and innovative ideas. So, the risk depends on the structure of the interlock network.

Technically speaking, more substantial board interlock effects may mean that firms are more likely to follow their existing connections. If boards increasingly form interlocks with boards they are already connected to, then the likelihood of convergence increases. Studying this convergence would require looking at network dynamics, how these connections form and evolve over time, presenting an interesting future direction. So, the risk depends on whether board networks become more tightly clustered. If that clustering does occur, the risk of strategic convergence increases.

Q: As marketing becomes linked to broader corporate priorities like DEI and ESG initiatives, does this interconnectedness strengthen marketing鈥檚 strategic influence or risk diluting its focus?

A: Any initiative that customers value is worth pursuing, whether it鈥檚 DEI, ESG, or something else. If diversity, equity, and inclusion lead to broader thinking and help the company better serve customers, they naturally add to both customer and corporate value. The key is to have a clear understanding of how these initiatives benefit customers. For example, empowered women entrepreneurs while also expanding distribution in rural markets. This is an example where a social initiative directly supported business goals. If firms can articulate how these priorities connect to customer value, then marketing鈥檚 role becomes more pronounced. But if the link isn鈥檛 clear, there鈥檚 a risk that marketing鈥檚 focus becomes scattered. Many companies still treat DEI and ESG as compliance initiatives rather than customer-driven ones, so marketing often isn鈥檛 leading those efforts. If marketing leads them and grounds them in what matters to customers, that can actually elevate marketing鈥檚 strategic influence rather than diluting it.

Q: If you were to extend this research further, which context or mechanism would you most like to explore to deepen our understanding of how governance structures shape marketing鈥檚 strategic importance?

A: From a technical perspective, an important next step would be to examine how board connections form and evolve. Some drivers are endogenous; for example, boards that share indirect connections are more likely to become directly connected, much like 鈥渇riends of friends鈥� becoming friends. Understanding those processes would be valuable, particularly when marketing-affiliated directors drive the connection. If a marketing-driven tie disappears and later reappears, is it due to a marketing-affiliated person? Examining these processes could deepen our understanding of marketing鈥檚 strategic influence.

More broadly, another valuable direction is to examine marketing鈥檚 organizational role and influence within firms. Some work, including , builds on the idea that marketing鈥檚 influence within firms has been declining and thus asks: how can marketers regain strategic influence? As the focus increasingly shifts to marketing activities and the creation of customer value, not merely the marketing department, future research should prioritize these value-creating functions rather than focusing solely on the department. In addition, management research suggests that board interlock effects have been weakening or disappearing. We do not see that in our data; the effect remains stable over time. That leads to an interesting question about what鈥檚 actually happening: is the board interlock effect still active?

References:

Frank Germann (2025), “Beyond the 4 Ps: Marketing’s Strategic Comeback [Special issue], NIM Marketing Intelligence Review, .

Frank Germann, Peter Ebbes, and Rajdeep Grewal (2015), “The Chief Marketing Officer Matters!” Journal of Marketing, 79 (3), 1鈥�22. .

Unilever (2024), “Harnessing the Potential of India鈥檚 Growing Workforce,” (July 23), .

Source: Peter Ebbes, Frank Germann, and Rajdeep Grewal (2024), 鈥�,鈥� Journal of Marketing Research, 62 (1), 1鈭�21. doi:

Go to the Journal of Marketing Research

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As a result, the ability to systematically analyze visual content has become essential for both managers and academic researchers. Image analytics enables firms to move beyond subjective evaluations of creative assets. By quantifying visual characteristics at scale, firms can evaluate, optimize, and personalize visual communication strategies across markets and customer segments. For researchers, image analytics provides a way to incorporate visual data into empirical analysis. By transforming images into structured, analyzable variables, researchers can investigate how visual elements shape consumer behavior and market outcomes across contexts, thereby advancing theory in domains where visual design plays a central role.

However, unlike structured data, images do not come with predefined variables. Researchers and managers must first decide which aspects of visual content matter for a given outcome and how to extract those variables from unstructured images in a reliable and scalable way.

Image variables can be broadly organized into three categories based on the level of visual meaning:

1. Low-level features capture visual properties derived directly from pixel values, such as color, brightness, and composition.
2. Mid-level features capture what is present in the image, such as objects, people, logos, or scenes.
3. High-level features capture how images are interpreted or evaluated, such as perceived emotion, aesthetics, or brand personality.

From a managerial perspective, these levels can be viewed as part of a broader decision process that links business outcomes, feature selection, measurement methods, and validation, as shown in Figure 1. In practice, the appropriate level depends on the objective of the image analytics task, as summarized in Table 1.

This article provides a structured framework to guide feature selection and measurement, synthesizing recent research from Journal of Marketing Research and related marketing and information systems journals to show how image analytics can inform theory and managerial actions.

Figure 1: Feature Selection and Measurement Framework for Image Analytics

Table 1: Choosing the Right Level of Visual Meaning

Low-Level Features: Color, Composition, and Basic Visual Properties

Objective image attributes such as file size, resolution, orientation (portrait vs. landscape), and foundational visual features drawn from photography research, including color, composition, and figure鈥揼round relationships, provide a natural starting point for image analytics. These attributes are either directly observable or easy to extract, follow standardized definitions, and yield consistent values for the same image, enabling systematic comparison at scale.

Among intrinsic attributes, color has received the most sustained attention in marketing research (see for a comprehensive review). Color is commonly characterized along three dimensions: hue (e.g., red, blue), saturation (intensity or richness), and value (lightness versus darkness), together often referred to as the hue鈥搒aturation鈥搗alue (HSV) space. Consistent with prior reviews of color research in marketing (Labrecque 2020), much of the foundational evidence on color effects comes from controlled laboratory experiments that offer strong internal validity but limited scalability. Nowadays, image analytics enable researchers to extend color research to large-scale field datasets by measuring color properties directly from images. Image processing packages such as Python Pillow allow researchers to extract pixel-level color values, preferably in HSV space to facilitate interpretation, and construct theory-consistent color variables. For example, dominant colors are often identified using k-means clustering to group pixels with similar color characteristics and represent each cluster by its centroid (). Other studies operationalize image clarity as the proportion of pixels exceeding a brightness threshold () or measure colorfulness as one minus the combined pixel share of the three most dominant colors (; ). Although specific parameter choices vary across studies, results are generally robust to reasonable alternative specifications. Major commercial vision platforms such as Google Vision AI, Amazon Web Services (AWS) Rekognition, and Microsoft Azure offer similar color detection capabilities.

Once constructed, color-related attributes can enter regression or machine learning models directly to explain or predict outcomes. For example, Li and Xie (2020) find that colorfulness affects user engagement on social media in a category-contingent manner, while Zhang et al. (2022) show that Airbnb photos with warmer hues, greater image clarity, and more balanced color properties associate with higher demand. Using gradient-boosted regression trees, demonstrate that nuanced color composition clusters exert substantial predictive power for product returns, indicating that subtle shade differences can meaningfully affect consumer behavior. However, find that photographic attributes such as composition and brightness are less useful in predicting restaurant survival compared to the content of the photos.

Beyond their direct effects, color-related attributes also serve as building blocks or explanatory factors for higher-level perceptual constructs. show that warmer hues, higher saturation, and greater brightness evoke more positive emotions in charity crowdfunding images, while Zhang et al. (2022) find that professionally verified Airbnb photos achieve higher perceived quality in part due to more appealing color properties. further demonstrate that saturation, brightness, brightness contrast, and image clarity increase picture-evoked arousal but not valence in restaurant reviews. Complementing these findings, Labrecque et al. (2025) show that marketers systematically pair highly saturated product images with language emphasizing potency and efficacy.

Taken together, this body of work highlights the dual role of color-related attributes in image analytics. Color properties directly explain variation in consumer responses and market outcomes, while also serving as foundational visual factors that must be accounted for when studying higher-level creative strategies. In practice, failing to control for color can lead managers and researchers to misattribute performance differences to creative content or messaging when those differences instead reflect underlying visual properties (Labrecque 2020; Li and Xie 2020; Zhang et al. 2022). This risk is particularly pronounced when images vary substantially in brightness or colorfulness, when creative elements such as people presence or emotional expressions systematically co-occur with specific color patterns, or when analyses span product categories with distinct color conventions.

Mid-Level Features: Objects and Human Faces

Mid-level visual features capture what appears in the image rather than only how it looks at the pixel level. Common examples include detected objects (e.g., product, food, car, logo, sports equipment) and the presence and characteristics of human faces (e.g., face presence, number of faces, facial expressions). These features matter because they map directly onto content elements that consumers notice and interpret, and they often proxy for managerial choices about what to show, such as products versus lifestyle contexts or people versus objects.

Researchers can extract object- and face-based features using pretrained computer vision systems that return labels and localized regions. For example, Google Vision can identify multiple objects and faces and provide bounding boxes and facial attributes, including emotion likelihoods. Similar outputs are available from platforms such as AWS Rekognition, Microsoft Azure, and Clarifai, enabling straightforward feature construction ranging from simple presence or count measures to composition-based measures that rely on object size and location.

When standard outputs are insufficient, researchers can fine-tune pretrained deep learning models for task-specific classification. For example, train a Visual Geometry Group (VGG-16) convolutional neural network (CNN) model to distinguish consumer selfies from brand selfies, and Li et al. (2022) use transfer learning with a Residual Network (ResNet-50) architecture to classify room types in Airbnb photos. Table 2 summarizes the key conceptual steps in fine-tuning pretrained CNNs for such tasks. In practice, fine-tuning is most commonly used for mid-level (e.g., object or content detection) and high-level (e.g., perception or evaluation prediction) image analytics tasks, whereas low-level visual properties such as color or brightness are typically extracted using standard image processing tools.

Table 2: Conceptual overview of fine-tuning a pretrained Convolutional Neural Networks (CNN)

In marketing contexts, including people in images is a common and highly consequential creative decision. However, evidence across settings shows that the effects of human presence are highly context dependent. Li and Xie (2020) find that images with human faces increase attention and engagement on Twitter but not on Instagram. show that in identity-relevant contexts such as vacations or weddings, including another person can reduce liking and preference by triggering psychological ownership concerns. In social media branding, Hartmann et al. (2021) document a similar trade-off: Consumer selfies generate more likes and comments, whereas product-focused brand selfies elicit stronger brand engagement and purchase intentions. In online reviews, find that reviewer face disclosure increases subsequent product ratings by reducing uncertainty about product fit. Together, these findings indicate that human presence does not uniformly enhance image effectiveness; its impact depends on platform norms, consumption goals, and the role the image plays in the decision process. Beyond direct effects, human and object presence also shape image effectiveness indirectly by influencing emotional responses. In charity crowdfunding, show that images featuring people heighten excitement while suppressing awe and selectively amplify or reduce negative emotions, whereas images with animals evoke a distinct emotional profile. Taken together, this work underscores the importance of explicitly modeling human and object presence in image analytics, both as direct predictors and as drivers of emotional and evaluative mechanisms that influence downstream outcomes.

High-Level Features: Emotion, Quality, Aesthetics, and beyond

High-level visual features capture how people respond to and evaluate an image or an object within an image. These features reflect subjective interpretations, such as the emotions an image evokes (Hou, Zhang, and Zhang 2023), the quality or aesthetic appeal it conveys (Zhang et al. 2022, Guan et al. 2023) and person-related attributes such as celebrity potential () or attractiveness ().

Prior research demonstrates that these perceptual constructs play an important role across a wide range of contexts. For example, images that evoke specific emotions influence engagement and donation behavior in charity crowdfunding (Hou, Zhang, and Zhang 2023). Perceived visual quality and aesthetic appeal shape evaluations in hospitality and online review settings (Guan et al. 2023; Zhang et al. 2022). Face-related attributes inferred from images, such as celebrity potential or attractiveness, affect influencer selection, hiring decisions, and long-term career outcomes (Feng et al. 2025; Malik, Singh, and Srinivasan 2023; ). Together, these findings show that high-level visual features capture meaningful variation in how images shape evaluations and decisions, even though their effects often depend on context and task.

High-level visual features offer three key advantages for image analytics:

1. They align measurement with how marketing theory conceptualizes decision making. Many theories emphasize perceptions and judgments as the link between marketing stimuli and outcomes.
2. They provide a compact way to summarize complex visual information, improving stability and making comparisons easier across platforms, categories, and context.
3. They improve interpretability for both researchers and managers by translating visual variation into psychologically meaningful constructs that are easier to explain and act on.

Researchers typically construct high-level visual features using the same CNN-based framework applied to other task-specific image analytics, consistent with the workflow summarized in Table 2. The main difference lies in task definition and labeling: Instead of predicting objects or content categories, models infer perceptual judgments or attributes based on human evaluations or validated proxies. The resulting predictions then serve as quantitative measures that can be incorporated directly into empirical models.

Advertising and consumer behavior research has long emphasized that visual and verbal elements are processed jointly and that their congruence shapes consumer responses (). Advances in image analytics now allow researchers and managers to measure these relationships directly and at scale. Methodologically, this stream of research uses deep learning models to generate representations for images and text and then constructs measures that capture how visual and verbal content relates across modalities. These measures can be validated against human judgments and incorporated into empirical models to study how visual and verbal cues jointly shape perceptions and decisions.

A central insight from this literature is that consumer responses depend critically on how image and text content relate to one another. show that image鈥搕ext similarity substantially improves prediction of social media content popularity and consumer engagement. Li and Xie (2020) find that stronger image鈥搕ext fit increases user engagement on Twitter but not on Instagram, underscoring platform-specific processing differences. In online reviews, and Yu et al. (2026) show that alignment between photos and text in both content and emotional valence and arousal improves review helpfulness by enhancing processing fluency. Extending beyond reinforcement, uncover a U-shaped effect of image鈥搕ext congruence in product representations, showing that both high congruence driven by relevance and deliberate incongruence driven by surprise can enhance consumer preference. Together, this steam of work highlights the importance of coordinating visual and verbal cues rather than optimizing them in isolation.

Putting the Framework into Practice: A Multilevel View of a Marketing Image

Figure 2: Example of Multilevel Image Measurement in a Social Media Post

To illustrate how image analytics can support managerial decision making, consider the Nike social media post shown in Figure 2. The same image can be analyzed at multiple levels depending on the business objective. Rather than extracting every possible visual feature, the goal is to select image variables and measurements that match the decision being supported.

At the low level, managers can measure visual style and properties such as color distribution, brightness, contrast, and background uniformity using standard image-processing tools (e.g., Python image libraries, vision APIs). These measures help ensure visual consistency within and across campaigns and help isolate the effects of higher-level creative decisions. In this example, the dark background and strong contrast visually isolate the product and increase visual salience. More broadly, by quantifying background tone and contrast across posts, Nike can test when high-contrast, minimalist imagery enhances engagement or conversion relative to visuals featuring brighter or more visually complex backgrounds. These insights allow managers to tailor visual style across platforms, product categories, and campaign objectives.

At the mid-level, managers can measure what is present in the image. Object detection or classification models can identify the product, logo, product components, and the presence or absence of human. These measures support decisions about product-focused versus lifestyle-centered creative strategy, brand visibility, and content tagging. In this example, the absence of people and the sole visual focus on the shoe signal a produce-centric creative strategy that emphasizes technology and performance. Prior research shows that such content choices have meaningful consequences: Hartmann et al. (2021) find that consumer selfies generate more likes and comments, whereas product-focused brand selfies elicit stronger brand engagement and purchase intentions. By quantifying whether images feature products alone or include people, Nike can align its creative strategy with campaign objectives. Product-centric imagery can be emphasized when the goal is to strengthen purchase intent and brand evaluation. In contrast, incorporating human elements may be more effective when the objective is to increase social interaction.

At the high level, managers can gauge how consumers interpret the image. Custom models or human-assisted coding can be used to measure perceived excitement, performance intensity, or innovation cues. These constructs are directly linked to engagement, purchase intent, and brand perception (Guan et al. 2023; Hou, Zhang, and Zhang 2023; Zhang et al. 2022). In the Nike example, the dark background, strong contrast, and focused presentation of the shoe collectively convey a high-performance and technologically advanced impression. Such measures allow managers to evaluate whether an image communicates the intended brand meaning or emotional tone before deployment at scale.

When text is present, managers can also evaluate image鈥搕ext alignment. Here, the performance-focused copy aligns closely with the high-energy technical visual, reinforcing the product message. This alignment strengthens the overall consumer interpretation. Prior research demonstrates that alignment between visual and verbal cues can enhance engagement and processing fluency, while strategic incongruence may also increase attention in some contexts (Cao, Li, and Zhang 2025; Ceylan, Diehl, and Proserpio 2024; Shin et al. 2020; Li and Xie 2020; Yu et al. 2026). Extending this approach across posts allows Nike to evaluate when alignment between images and text enhances engagement and conversion outcomes and when alternative strategies may be more effective.

Together, these levels provide complementary insights, moving from visual style, to content strategy, to consumer interpretation and market outcomes.

Summary

This article offers a practical framework for making sense of visual content in digital marketing. It shows how images can be analyzed at three levels鈥攂asic visual properties, content elements such as products and people, and higher-level perceptions such as emotion and quality鈥攁nd explains when each level is most useful for understanding performance. The article also highlights the growing importance of analyzing images together with text, since consumers often interpret visual and verbal cues jointly. By synthesizing recent research and outlining scalable analytic approaches, the framework helps managers and researchers choose the right visual features, avoid misleading conclusions, and design images that communicate more effectively across platforms and contexts.

References

Cao, Jingcun, Xiaolin Li, and Lingling Zhang (2025), 鈥淚s Relevancy Everything? A Deep-Learning Approach to Understand the Effect of Image-Text Congruence,鈥� Management Science, 71 (12), 10579鈥�10602.

Ceylan, Gizem, Kristin Diehl, and Davide Proserpio (2024), 鈥淲ords Meet Photos: When and Why Photos Increase Review Helpfulness,鈥� Journal of Marketing Research, 61 (1), 5鈥�26. 

Dang, Ivy Chu, Canice M.C. Kwan, Jayson S. Jia, and Yang Shi (2026), 鈥淲hen Words Meet Visuals: How Content Composition Drives Social Media Engagement for Marketer-Generated Content,鈥� Journal of Marketing Research, 63 (1), 167鈥�90.

Dzyabura, Daria, Siham El Kihal, John R. Hauser, and Marat Ibragimov (2023), 鈥淟everaging the Power of Images in Managing Product Return Rates,鈥� Marketing Science, 42 (6), 1125鈥�42.

Feng, Xiaohang, Shunyuan Zhang, Xiao Liu, Kannan Srinivasan, and Cait Lamberton (2025), 鈥淎n AI Method to Score Celebrity Visual Potential,鈥� Journal of Marketing Research, 62 (5), 757鈥�75. 

Guan, Yue, Yong Tan, Qiang Wei, and Guoqing Chen (2023), 鈥淲hen Images Backfire: The Effect of Customer-Generated Images on Product Rating Dynamics,鈥� Information Systems Research, 34 (4), 1641鈥�63.

Hartmann, Jochen, Mark Heitmann, Christina Schamp, and Oded Netzer (2021), 鈥淭he Power of Brand Selfies,鈥� Journal of Marketing Research, 58 (6), 1159鈥�77. 

Heckler, Susan E. and Terry L. Childers (1992), 鈥淭he Role of Expectancy and Relevancy in Memory for Verbal and Visual Information: What Is Incongruency?鈥� Journal of Consumer Research, 18 (4), 475鈥�92.

Hou, Jian-Ren, Jie Zhang, and Kunpeng Zhang (2023), 鈥淧ictures That Are Worth a Thousand Donations: How Emotions in Project Images Drive the Success of Online Charity Fundraising Campaigns? An Image Design Perspective,鈥� MIS Quarterly, 47 (2), 535鈥�84.

Labrecque, Lauren (2020), 鈥淐olor Research in Marketing: Theoretical and Technical Considerations for Conducting Rigorous and Impactful Color Research,鈥� Psychology & Marketing, 37 (7), 855鈥�63.

Li, Hanwei, David Simchi-Levi, Michelle Xiao Wu, and Weiming Zhu (2022), 鈥淓stimating and Exploiting the Impact of Photo Layout: A Structural Approach,鈥� Management Science, 69 (9), 5209鈥�33.

Li, Yiyi and Ying Xie (2020), 鈥淚s a Picture Worth a Thousand Words? An Empirical Study of Image Content and Social Media Engagement,鈥� Journal of Marketing Research, 57 (1), 1鈥�19. 

Lu, Zoe Y., Suyeon Jung, and Joann Peck (2024), 鈥淚t Looks Like 鈥楾heirs鈥�: When and Why Human Presence in the Photo Lowers Viewers鈥� Liking and Preference for an Experience Venue,鈥� Journal of Consumer Research, 51 (2), 321鈥�41.

Malik, Nikhil, Param Vir Singh, and Kannan Srinivasan (2023), 鈥淲hen Does Beauty Pay? A Large-Scale Image-Based Appearance Analysis on Career Transitions,鈥� Information Systems Research, 35 (4), 1524鈥�45.

Shin, Donghyuk, Shu He, Gene Moo Lee, Andrew B. Whinston, Suleyman Centintas, and Kuang-Chih Lee (2020), 鈥淓nhancing Social Media Analysis with Visual Data Analytics: A Deep Learning Approach,鈥� MIS Quarterly, 44 (4), 1459鈥�92.

Troncoso, Isamar and Lan Luo (2022), 鈥淟ook the Part? The Role of Profile Pictures in Online Labor Markets,鈥� Marketing Science, 42 (6), 1080鈥�1100.

Yu, Yifan, Xinyao Wang, Jinghua Huang, and Yong Tang (2026), 鈥淭he Pleasant Visual Path to Review Helpfulness: Picture-Evoked Emotional Valence and Picture-Text Alignment,鈥� MIS Quarterly, 50 (1), 243鈥�68.

Zhang, Mengxia and Lan Luo (2022), 鈥淐an Consumer-Posted Photos Serve as a Leading Indicator of Restaurant Survival? Evidence from Yelp,鈥� Management Science, 69 (1), 25鈥�50.

Zhang, Shunyuan, Dokyun Lee, Param Vir Singh, and Kannan Srinivasan (2022), 鈥淲hat Makes a Good Image? Airbnb Demand Analytics Leveraging Interpretable Image Features,鈥� Management Science, 68 (8), 5644鈥�66.

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Marketers have long recognized that customers acquired through referrals tend to be more loyal and valuable. What has remained underappreciated, however, is the additional value these customers may generate through their future referral behavior. In their , Rachel Gershon (University of California, Berkeley) and Zhenling Jiang (University of Pennsylvania) uncover a 鈥渞eferral contagion鈥� and show that referred customers are not just more profitable but also more likely to refer others, setting off a multiplier effect that many firms have overlooked so far.

Beyond Acquisition: The Hidden Downstream Value of Referrals

Referral programs are ubiquitous, from ride-sharing and food delivery apps to fintech platforms and online retailers. Typically, marketers have evaluated these referral programs by counting how many new customers they bring in and how much revenue those customers generate. Gershon and Jiang argue that this approach severely underestimates the true value of referral programs.

Across multiple field data sets, they show that referred customers make between 31% and 57% more referrals than those acquired through other channels. When these secondary referrals are ignored, firms end up undervaluing the total worth of a referral by 20% to 36%. The authors demonstrate this referral contagion across a wide range of industries, including finance, software, and retail.

Why Referrals Spread: The Role of Social Appropriateness

Gershon and Jiang show that referred customers are more likely to refer others because the act of referring feels more socially appropriate to them. Drawing on insights from social psychology, they find that when people see someone else refer, they interpret the behavior as socially acceptable, reducing the fear of seeming too pushy or self-interested.

In several controlled experiments, participants who imagined joining an app through a friend鈥檚 referral rated the act of referring as more appropriate, felt lower psychological discomfort, and were significantly more likely to make referrals themselves compared to those who imagined joining through an ad. This effect was stronger when the referrer was a friend rather than an influencer, emphasizing that personal recommendations drive the norm of appropriateness more than celebrity endorsements.

The Power of a Simple Nudge: 鈥淵ou Were Referred In 鈥� Now Refer Your Friends!鈥�

To translate their insights into practice, the authors conducted a large-scale field experiment with over 10 million referred customers. A simple tweak made all the difference: instead of a generic 鈥淩efer your friends!鈥� message, half the customers received a reminder tied to their own experience: 鈥淵ou were referred in 鈥� now refer your friends!鈥� The message activated the existing social norm, made referring feel more appropriate, and ultimately boosted referrals by more than 20%.

A simple tweak made all the difference: instead of a generic 鈥淩efer your friends!鈥� message, half the customers received a reminder tied to their own experience: 鈥淵ou were referred in 鈥� now refer your friends!鈥� The message activated the existing social norm, made referring feel more appropriate, and ultimately boosted referrals by more than 20%.

The study illustrates the value of industry鈥搑esearch collaborations. Companies gain evidence-based insights that go beyond intuition, while researchers gain access to real-world data and the opportunity to test ideas at scale. We reached out to the authors to learn more about the inspiration behind their work and what their results mean for managers. In the conversation below, Gershon and Jiang share their perspective on how referral contagion works, how firms can capture its full value, and where future opportunities lie for practitioners.

Q: What first sparked your interest in exploring the 鈥渞eferral contagion鈥�?

A: The idea emerged from our observation of a robust pattern in our dataset: referred customers were substantially more likely to refer others. We found this pattern both intriguing and theoretically meaningful. When reviewing the literature, we saw that prior research had largely overlooked this downstream consequence of referral behavior, which inspired us to systematically investigate what we later termed 鈥渞eferral contagion.鈥�

Q: Your research shows that referred customers don鈥檛 just buy more, they also refer more. Based on this, how should managers rethink how they measure the total value of their referral programs? 

A: While prior studies have examined the direct benefits of referred customers (such as higher loyalty and spending), they have largely overlooked their indirect impact through subsequent referrals. Managers should incorporate these downstream effects into how they assess the value of referral programs, including it in metrics like customer lifetime value (CLV) and the effective ROI of referral incentives.

Q: A simple reminder to referred customers can boost referrals by about 21%. Where might this nudge stop working, and how could marketers adapt it in practice?

A: Reminding customers that they were once referred signals that referring is appropriate. Making this social norm salient increases referral behavior. We expect this nudge to be effective in scenarios where psychological barriers prevent customers from making referrals, likely extending across different product categories, tie strengths, and incentive types. Exploring how these factors shape the effectiveness of the reminder presents an interesting direction for future research.

Q: Referral contagion seems relevant beyond business, such as in public health. How might your findings inform policymakers?

A: The idea of referral contagion naturally extends beyond business contexts. For policymakers in public health systems, this means that investments in referral-based outreach could have a multiplier effect, as those who are referred become more likely to refer others. Programs could be strengthened by highlighting that referring others is common and appropriate.

Q: Your research relies on large-scale field data and company collaboration. What challenges did you face in building these partnerships and collecting real-world data at this scale?

A: We began by reaching out to a wide range of potential field partners. It鈥檚 a numbers game: we cast a wide net and had many conversations until we found organizations whose interests and data aligned with our research goals. We were fortunate to identify three enthusiastic and collaborative partners.

Q: What do marketing managers gain from working with academic researchers, and vice versa?

A: Collaborations are most valuable when both sides view them as a valuable exchange. For managers, they offer a chance to go beyond intuition and understand what drives customer behavior, grounded in careful experimentation and analysis. For researchers, they provide access to rich data and the opportunity to test ideas in real-world settings. Collaborations reveal the challenges of translating theoretical insights into practice and how organizational constraints, competing priorities, and practical considerations shape what鈥檚 possible. Working with firms often challenges our assumptions and helps us refine our theories to be more relevant and impactful.

Source: Rachel Gershon and Zhenling Jiang (2024), “.” Journal of Marketing Research, 62 (1), 97鈥�116. doi:.

Go to the Journal of Marketing Research

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