A introduces NADE (Natural Affect DEtection), that bridges this gap. NADE goes beyond sentiment analysis by leveraging the power of emojis. It first “emojifies” text and then translates those emojis into eight well-established emotions like joy, sadness, and anger. This innovative approach allows a more nuanced and accurate understanding of consumer emotions, providing deeper insights into their thoughts and feelings.

NADE’s key innovation lies in using emojis as an intermediate emotional signal. Social media users naturally self-label their posts with emojis, offering implicit emotional cues. As a “text-to-emoji-to-emotion” converter, NADE utilizes these cues in a two-stage process: The model first learns to predict which emojis best match a given text, then, using established emotion models like , NADE converts these emojis into emotional intensities. This method outperforms traditional sentiment analysis by capturing more nuanced consumer emotions.

Using NADE for Better Consumer Sentiment Analysis

NADE has wide-ranging applications across industries, helping companies gain deeper insights and make data-driven decisions:

• In social media management, it empowers companies to go beyond simple sentiment analysis. NADE enables real-time monitoring of online conversations, allowing for rapid identification and effective mitigation of potential crises. Moreover, it can serve as a valuable proxy for traditional metrics like TV ratings, providing insights into audience engagement and sentiment surrounding specific events or campaigns.
  
  
• In product development, NADE can be a powerful tool for understanding customer emotions. By analyzing customer feedback, companies can pinpoint product features that evoke specific emotions such as frustration or excitement. This granular understanding can guide product improvements and ensure that products resonate with customer desires.
  
  
• Within customer service, NADE enhances both human agent and chatbot interactions. By providing real-time insights into customer emotions, NADE equips service agents with the information they need to respond empathetically and effectively. This can lead to improved customer satisfaction, reduced resolution times, and increased customer loyalty.
  
  
• Beyond these specific applications, NADE supports innovative advertising tactics. Mood-based targeting allows advertisers to reach specific audience segments based on their current emotional state, maximizing the impact of their campaigns.
  
  
• Additionally, NADE can be leveraged for market research, enabling more accurate emotion-driven demand prediction and providing valuable insights into brand loyalty and market trends.
  
  
• Finally, NADE empowers content creators by providing valuable insights into the emotional impact of their content. By understanding how their content resonates with audiences on an emotional level, creators can design and curate more engaging and effective user experiences.

Advantages of NADE for Researchers

For researchers, NADE offers several key advantages. First, it democratizes research by making sophisticated emotion analysis accessible to researchers with limited budgets. While commercial tools like LIWC offer similar capabilities, NADE provides more nuanced emotion analysis and is entirely free, opening doors for researchers who may have been previously deterred by technical or financial constraints. This removes a significant financial barrier, enabling broader participation in high-level research.

Second, NADE’s user-friendly interface allows researchers to conduct in-depth analyses without requiring extensive programming expertise. Finally, the availability of R and Python packages provides researchers with the flexibility to adapt and extend NADE to other languages, emojis, and emotion theories, enabling further advancements in the field.

Visit the NADE App to explore how it can enhance your research or business insights:

Source: Christian Hotz-Behofsits, Nils Wlömert, and Nadia ÂÜÀòÉçčÙÍű Nabout, “,” Journal of Marketing.

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Given the growing use and implications of generative AI (GenAI), this special issue seeks to offer new, pertinent insights related to how individuals and firms can and should address it, as well as which types of policies and regulations are necessary to ensure its promise is not overcome by its perils. This special issue brings together nine articles that collectively examine the multifaceted (potential) effects of GenAI on marketing practices and its associated public policy implications. .

Articles in the Special Issue Include:

“,” by V. Kumar, Philip Kotler, Shaphali Gupta, and Bharath Rajan

By evaluating the pattern of generative AI (GAI) use by businesses in marketing, this study aims to understand the subsequent impact on society and develop policy implications that promote its beneficial use. To this end, the authors develop an organizing framework that contends that the usage of GAI models by businesses for marketing purposes creates promises and perils for society through a specific business process. This business process is represented by the action → capabilities → transformation → impact link in the proposed framework. Additionally, the authors find that the level of technology infrastructure, skilled personnel, and data access moderates the influence of GAI on businesses’ ability to develop technology-driven capabilities. Furthermore, adaptive leadership and management strategies moderate the impact of these capabilities on technology-enabled business transformations. This research is the first study to critically evaluate the use of GAI in marketing from a public policy perspective. The study concludes with an agenda for future research.

“,” by Erik Hermann and Stefano Puntoni

Generative AI (GenAI) is breaking new ground in emulating human capabilities, and content generation may only be the beginning. In this work, the authors systematize and illustrate promising areas of application of GenAI in marketing. They lay out a conceptual framework along two dimensions: (1) GenAI impact (i.e., human enhancement, human replacement) and (2) the marketing cycle stage (i.e., marketing research, marketing strategy formulation, marketing actions related to the marketing mix instruments). Based on the AI ethics literature, the authors then introduce a set of principles (i.e., ASSURANCE: Autonomy, Security, SUstainability, Representativeness, Accountability, Nonbiasedness and nondiscrimination, Crediting, Empowerment) to enable marketers to address the risks and challenges of GenAI and thereby achieve beneficial outcomes for companies, consumers, and society at large. Finally, they delineate the public policy implications for each principle and illustrate avenues for future research.

“,” by Manhui Jin, Zhiyong Yang, Traci L. Freling, and Narayanan Janakiraman

Many policy makers and governmental organizations have started using generative artificial intelligence (AI) to provide advice to individuals. However, prior research paints an unclear picture of individuals’ receptiveness to the outputs generated by AI, relative to those from human advisers. While some studies show that individuals prefer outputs generated by humans over AI, others present an opposite pattern. To reconcile these mixed findings, this research differentiates two perspectives where relative preferences have been widely examined: (1) a bystander perspective, where consumers evaluate the content generated by human versus AI agents, and (2) a decision-maker perspective, where consumers accept recommendations made by the agents. The authors find that although there is a general trend of preferring human advice over AI advice in individual decision-making—exhibiting a “human superiority effect”—there is no significant difference between human and AI content preferences during bystander evaluations. Additionally, psychological distance constitutes an important contextual moderator explaining the relative preference for human versus AI recommendations. Specifically, when decision-making circumstances are perceived to be psychologically distant (e.g., low personal relevance), the human superiority effect is attenuated. Theoretical contributions are discussed, along with practical implications for businesses and governmental organizations.

“,” by Wolfgang Messner, Tatum Greene, and Josephine Matalone

Large language models (LLMs) are able to engage in natural-sounding conversations with humans, showcasing unprecedented capabilities for information retrieval and automated decision support. They have disrupted human–technology interaction and the way businesses operate. However, technologies based on generative artificial intelligence are known to hallucinate, misinform, and display biases introduced by the massive datasets on which they are trained. Existing research indicates that humans may unconsciously internalize these biases, which can persist even after they stop using the programs. In this study, the authors explore the cultural self-perception of LLMs by prompting ChatGPT (OpenAI) and Bard (Google) with value questions derived from the GLOBE (Global Leadership and Organizational Behavior Effectiveness) project. The findings reveal that LLMs’ cultural self-perception is most closely aligned with the values of English-speaking countries and countries characterized by economic competitiveness. It is crucial for all members of society to understand how LLMs function and to recognize their potential biases. If left unchecked, the “black-box” nature of AI could reinforce human biases, leading to the inadvertent creation and training of even more biased models.

“,” by Yingting Wen and Sandra Laporte

As generative AI technologies advance, understanding their capability to emulate human-like experiences in marketing communication becomes crucial. This research examines whether generative AI can create experiential narratives that resonate with humans in terms of embodied cognition, affect, and lexical diversity. An automated text analysis reveals that while reviews generated by ChatGPT 3.5 exhibit lower levels of embodied cognition and lexical diversity compared with reviews by human experts, they display more positive affect (Study 1a). However, human raters struggle to notice these differences, rating half of the selected reviews from AI higher in embodied cognition and usefulness (Study 1b). Instances of hallucination in AI-generated content were detected by human raters. For social media posts, the more sophisticated ChatGPT 4 model demonstrates superior perceived lexical diversity and leads to higher purchase intentions in unbranded content compared with human copywriters (Study 2). This research evaluates the performance of large language models in generating experiential marketing narratives. The comparative studies reveal the models’ strengths in presenting positive emotions and influencing purchase intent while identifying limitations in embodied cognition and lexical diversity compared with human-authored content. The findings have implications for marketers and policy makers in understanding generative AI’s potential and risks in marketing.

“,” by Shashank Shaurya Dubey, Vivek Astvansh, and Praveen K. Kopalle

The advent of generative AI (GenAI) has caused consternation across the industrial landscape. The financial industry is no exception. The scramble to find GenAI solutions in the financial industry has led to a proliferation in the academic and practitioner literature on the subject. However, the field of knowledge remains scattered. The authors offer four deliverables. First, using a survey of the literature and interviews of managers in financial firms, they create a funnel-shaped, two-stage framework of how GenAI can empower financial businesses. The top stage comprises seven GenAI value propositions for financial firms, condensed into the EMPOWER acronym. The bottom stage includes three functions for each proposition. Second, the authors propose ten novel GenAI-based applications spanning the five verticals of financial services, thus extending the current industrial focus of GenAI applications. Third, they outline the benefits and risks of these GenAI applications, visualizing them in a benefit–risk matrix to assist financial managers in prioritizing these applications. Fourth, they propose research questions to guide academic research and policy making at the intersection of GenAI and finance.

“,” by Lisa BrĂŒggen, Robert Gianni, Floris de Haan, Jens Hogreve, Darian Meacham, Thomas Post, and Minou van der Werf

This article presents a first step in identifying the ethical issues of AI-based financial advice. Consumers must navigate an ever more complex array of financial decisions. (Generative) AI-based financial advice may increase access to and acceptance of financial advice and strengthen consumers’ financial well-being. However, significant ethical challenges exist in designing, developing, and deploying AI-based financial advice. To analyze the perils and pitfalls of AI-based financial advice, the authors develop a definition of what constitutes good AI-based financial advice and provide a first assessment of ethical challenges related to AI-based financial advice. The iterative multistakeholder approach, including workshops and semistructured interviews with consumers and experts, results in an ethical discourse structured around the four fundamental values of the European Commission’s Ethics Guidelines for Trustworthy AI—human autonomy, explicability, fairness, and prevention of harm—and trust as the overall objectives. Based on the analyses, the authors derive a simple yet comprehensive AI Ethics Framework for Financial Advice. This reflection framework guides public policy makers, managers of financial service providers, and technology developers in incorporating ethical discourse in developing and deploying (generative) AI-based financial advice.

“,” by Meike Eilert and Stefanie Robinson

Generative artificial intelligence (GenAI) has sparked a lot of innovation in the servicescape to improve consumer experiences, primarily due to its ability to interact with consumers and personalize information based on the consumer’s input. The authors develop a framework grounded in the social model of disability to propose how GenAI can be a tool to cocreate otherwise disabling servicescape information design. Consumers with disabilities can use this technology to modify, transform, prioritize, and generate servicescape information to fit their individual accessibility needs and mitigate disabling servicescape conditions, resulting in more positive servicescape experiences, better access, and inclusion. Institutions such as industry, government, and higher education play a dual role in this framework. While these institutions are responsible for creating servicescapes with disabling information design, they are also key collaborators that support consumers with disabilities in cocreating GenAI solutions and ensuring their effective and safe use. This framework has important implications for the universal design of servicescapes and technologies supporting consumers with disabilities, as well as the various institutions that can collaborate to facilitate inclusive and safe technology-enabled, smart environments.

“,” by Unnati Narang, Vishal Sachdev, and Ruichun Liu

Generative artificial intelligence (GAI) is increasingly being integrated into marketing education and is reshaping the skill sets required in marketing careers. While research has highlighted the promise and perils of incorporating GAI into education, there remains a need for a comprehensive framework to guide its effective use. In this research, the authors conduct a multipronged analysis, including a review of marketing course syllabi, a survey of marketing educators, and follow-up qualitative interviews. Building on role theory and the community of inquiry model, they propose that GAI can assume three roles in marketing education: tutor, teammate, and tool. Each role influences teaching, social, and cognitive presence differently, shaping the learning experience and preparing workplace-ready marketing graduates. For instance, as a tutor, GAI can aid students in grasping theoretical concepts, while as a teammate, it can foster collaboration by supporting brainstorming and problem-solving activities. However, ethical considerations such as data privacy, plagiarism, dependency on AI, and fairness in assessment must be addressed to ensure its responsible adoption in marketing education. The authors provide concrete examples for GAI’s careful integration in marketing courses and discuss its implications for marketing educators, learners, and policy makers.

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A finds that ALN is effective—but only under specific conditions. Some businesses experience significant increases in sales, while others see little to no impact. The key factors determining success include the nature of the sales cycle, deal complexity, and whether the customer is new or returning.

We uncover a critical insight: ALN enhances lead interactions and improves the quality of sales conversations, but it does not guarantee higher conversion rates across all industries. ALN works best when used for new leads, short sales cycles, and lower-value deals. However, its benefits decline in high-ticket purchases or industries where buyers conduct extensive independent research.

ALN works best when used for new leads, short sales cycles, and lower-value deals. However, its benefits decline in high-ticket purchases or industries where buyers conduct extensive independent research.

This distinction has major implications for businesses investing in ALN. Many companies measure ALN success using vanity metrics—email opens, click-through rates, and engagement levels—without assessing whether those interactions lead to actual sales. Our findings suggest that firms should rethink how they evaluate automation success and shift their focus to measuring ALN’s impact on meaningful outcomes like sales meetings and conversions.

When ALN Works—and When It Doesn’t

For companies selling relatively simple products or services with shorter sales cycles, ALN can be a powerful tool. By delivering targeted content at the right time, ALN reduces uncertainty for potential buyers and ensures that sales teams engage with more informed prospects. Our research finds that in such cases, ALN can lead to a 23 percentage point increase in conversion rates.

However, for industries with long and complex sales cycles, such as B2B enterprise software or industrial equipment, ALN’s impact is less clear. In these cases, buyers rely on detailed research, peer recommendations, and in-depth consultations rather than automated content. ALN may increase engagement but does not necessarily lead to more closed deals.

Returning customers also respond differently to ALN compared to first-time buyers. Since they already have a relationship with the brand, they are less likely to need automated content to guide their purchase decision. This means companies must differentiate how they nurture new versus existing leads, rather than applying a one-size-fits-all approach.

Are Businesses Measuring the Wrong Metrics?

One of the biggest mistakes we observe is companies focusing too much on engagement metrics rather than true business outcomes. Many firms evaluate ALN success on the basis of email opens, website visits, or social media interactions. Although these indicators suggest interest, they do not necessarily translate into revenue.

Our research suggests that businesses should measure ALN effectiveness by tracking:

• Lead-to-sales meeting conversion rates (Does ALN drive actual conversations between buyers and sales teams?)
• Sales cycle speed (Does ALN shorten the time it takes to close a deal?)
• Revenue impact (Does ALN increase the number of closed deals and overall profitability?)

Shifting to these meaningful metrics will help businesses make informed decisions about ALN’s true value.

How Companies Can Use ALN Strategically

We find that ALN works best as an enhancement—not a replacement—for human sales interactions. Companies that rely too heavily on automation risk alienating high-value prospects who expect personalized, consultative selling. Instead of viewing ALN as a standalone solution, businesses should:

• Segment their leads and tailor ALN for different customer groups (e.g., new vs. returning buyers).
• Use ALN to complement human interactions, rather than replace them, particularly for complex sales.
• Refine ALN strategies over time by tracking real business outcomes rather than engagement metrics.

For marketing leaders, the takeaway is clear: ALN can be a powerful tool, but only if it is applied strategically. Businesses should test its impact before fully committing, ensuring that automation aligns with their sales process rather than relying on industry hype.

Source: Johannes Habel, Nathaniel Hartmann, Phillip Wiseman, Michael Ahearne, and Shashank Vaid, “,” Journal of Marketing.

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Our research team uncovers how firms can strategically time their technology launches by aligning internal coordination with stakeholder readiness. Success comes when managers treat timing as a dynamic, strategic process that creates trust, clarity, and excitement among stakeholders.

Key Findings 

The Four Timing Scenarios

We identify four timing scenarios that shape the outcomes of tech launches: 

1. Antagonistic Timing: Low firm coordination and low stakeholder readiness create a “delegitimate” launch moment. For example, Google Glass failed in 2013 because of privacy concerns and cultural resistance. 

2. Synergistic Timing: High firm coordination and high stakeholder readiness lead to a successful launch. Ray-Ban Meta Smart Glasses exemplify this, entering a market now open to augmented reality eyewear. 

3. Flexible Timing: High stakeholder readiness but low firm coordination. Stakeholders drive the market, requiring firms to act swiftly to meet demand. 

4. Inflexible Timing: High firm coordination but low stakeholder readiness. Firms must work to build trust and align expectations to overcome skepticism. 

Timing as a Strategic Process

Timing is a social game that requires tact, patience, and foresight. Launching too early risks overwhelming stakeholders, while launching too late can result in missed opportunities. Success lies in calibrating firm actions to meet stakeholder readiness. 

Firms must build market readiness by addressing four key factors: utility, legislative standards, shared values, and interpersonal trust. These efforts ensure stakeholders view the launch as credible, relevant, and aligned with their needs. 

Lessons from Technology Markets

The journey from antagonistic to synergistic timing often involves reintroducing products that failed previously. For instance, the augmented reality market took a decade to mature after Google Glass, paving the way for current successes. Flexible and inflexible timing scenarios are transitional stages. Managers navigating these moments must focus on bridging gaps between stakeholder expectations and firm actions. For example, firms facing inflexible timing need to create boundaries and trust to make disruptive technologies more accessible. 

Practical Recommendations for Managers 

Understand the Timing Scenarios: Managers must assess whether their launch moment aligns with stakeholder readiness and internal coordination. Identifying the current scenario—antagonistic, synergistic, flexible, or inflexible—provides a roadmap for action.

Managers should be aware that individuals’ timing norms may differ by technology type, as evidenced by Google’s various product launches occurring in different market timing situations: Google Glass was launched in antagonistic timing, Google Gemini and its extension Google Lumiere are facing flexible timing, and Google Fitbit 6 was launched in an inflexible timing situation.

Build Stakeholder Readiness: Invest in education, marketing, and regulatory alignment to create a foundation of trust and familiarity. These steps help stakeholders understand the product’s value and reduce resistance. 

Treat Timing as a Continuous Process: Rather than viewing timing as a single decision, managers should approach it as a series of adjustments. This dynamic approach ensures launches remain aligned with evolving market conditions.

Decision Tree

So how can managers make the right decision? We provide a decision tree with suggestions for marketing research:

Before launching a product, managers must ensure alignment between their firm’s and stakeholders’ timing norms (e.g., consumers, influencers, regulators). This involves market research through surveys or interviews to identify optimal timing (see potential questionnaire below). If timing norms align, the market is ready and a launch date can be set immediately. Misalignment requires further analysis of stakeholders’ willingness to adapt, using specific questions to gauge flexibility.

If stakeholders are willing to adapt, managers should use strategies like preannouncements, demos, and soft releases to cocreate an ideal launch moment. Publicizing minor imperfections can help build readiness, especially in market-driving situations. For stakeholders unwilling to adapt, managers should focus on building trust by controlling the product’s scope and allowing gradual changes to prepare the market.

If these approaches fail, managers should consider waiting for the market to mature naturally before revisiting the decision-making process. However, if the market remains resistant, any launch risks failure, necessitating a revision of the product.

Sample Questionnaire

Questions to gauge if a firm’s employee and stakeholder timing norms are aligned:

1. Do you watch out for new technology releases?
   a. Probe: If so, for which product categories?
   b. Probe: If so, how do you hear about new tech product releases?
   
   
2. How do prospective technology innovation releases make you feel? (e.g., excited, horrified,
   worried, hopeful)
   a. Probe: What kinds of technologies are you most excited about?
   b. Probe: What kinds of technologies are you most scared of?
   • i. Probe: What changes would have to happen to switch your fear to enthusiasm
     for the new technology?
     
     
3. Do you feel equipped to incorporate prospective technology innovations at your workplace?
   a. Probe: How do you feel equipped or not?
   
   
4. Do you feel equipped to incorporate prospective technology innovations in your home?
   a. Probe: How do you feel equipped or not?
   
   
5. Do you feel equipped to incorporate prospective technology innovations in your hobbies and
   leisure activities?
   a. Probe: How do you feel equipped or not?
   
   
6. Is [the specific function] of [firm’s new technology] useful to you? (Question relates to
   pragmatic legitimacy pillar)
   a. Probe: If no, can you describe a future situation where [specific function] of this
   technology would become useful to you?
   
   
7. Does [specific function] of [firm’s new technology] make you feel anxious? annoyed? angry?
   displeased?
   a. Probe: If yes, can you describe a future situation where [specific function] of [firm’s
   new technology] would not make you feel positive emotions?
   
   
8. In your opinion, are there current laws and official regulations in place to regulate [specific
   function] of [firm’s new technology]? (Question relates to regulative legitimacy pillar)
   a. Probe: If yes, please describe the current laws and regulations that you think apply.
   b. Probe: If not, what laws and regulations should be put in place in the future to regulate
   [specific function] of this technology?
   
   
9. Do you think the world would be a better place overall with [firm’s new technology]?
   (Question relates to normative legitimacy pillar)
   a. Probe: Please describe your answer.
   
10. Do you think [specific function] of [firm’s new technology] can improve your standing
    among your peers at work? Among your family and friends? (Question relates to relational
    legitimacy pillar)
    a. Probe: If no, can you describe a future situation where [specific function] would not
    compromise you with your peers at work? At home and in your social circles?
    
    
11. Can you currently make sense of [specific function] of [firm’s new technology]? (Question
    relates to regulative cultural-cognitive legitimacy pillar)
    a. Probe: If no, can you describe a future situation where [specific function] of this
    technology would make sense to you?
    
    
12. When do you think [firm’s new technology] should be launched?
    a. Probe: Please justify your answer.

Questions to gauge if stakeholders are willing to change their timing norms:

1. Are you willing to change your practices and habits now if a new technology was created that
   significantly improved society?
   a. Probe: If no, can you imagine a future where you would change your practices and
   habits for this prospective technology? What would this future look like?
   
   
2. Are you willing to change your practices and habits now if a new technology was created that
   made your work routines easier and/or more efficient?
   a. Probe: If no, can you imagine a future where you would change your practices and
   habits at work for this prospective technology? What would this future look like?
   
   
3. Are you willing to change your practices and habits now if a new technology was created that
   made your home life and routines easier and/or more efficient?
   a. Probe: If no, can you imagine a future where you would change your practices and
   habits at home for this prospective technology? What would this future look like?
   
   
4. Are you willing to change your practices and habits now if a new technology was created that
   made your hobbies and leisure time more entertaining?
   a. Probe: If no, can you imagine a future where you would change your practices and
   habits during your leisure time for this prospective technology? What would this future
   look like?
   
   
5. Are there certain industries where you are comfortable with a company releasing an unfinished
   technological innovation for consumers to try and test?
   a. Probe: Which industries?
   
   
6. Are there specific industries where you think companies should never release a technological
   innovation before it is fully finished and thoroughly tested?
   a. Probe: Which industries?

Timing is not just about “when” but about “how.” Firms that treat timing as a strategic tool can transform innovation into market success. Whether rescuing a failed product or launching a groundbreaking new technology, aligning firm actions with stakeholder readiness is key to achieving synergistic timing. 

Source: Thomas Robinson and Ela Veresiu, “,” Journal of Marketing.

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Reports are shorter papers (<4,000 words excluding references, abstract, tables, and figures) that focus on timely topics with clear implications for practice (marketers, consumers, or policy makers).

“Because Reports explore urgent, time-sensitive topics, it is important that they receive an expedited peer review process—without compromising on rigor, of course,” notes Verlegh. “To ensure that these papers move quickly, we’ll issue a reject or conditional accept decision after the first round.”

After conditional acceptance, Reports will be handled by the editor (if needed, with assistance from an associate editor), who will work with the authors to get the paper to an unconditionally accepted version within three months. Given the fast turnaround, Reports should be well polished at submission.

All JNM papers (full articles, special issue articles, and Reports) should be submitted via .

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The interconnected nature of modern technologies means that continued use depends on a products’ capacity to interact with other devices, objects, and infrastructures. Consider gaming consoles that interact with televisions, Bluetooth connections, internet connections, and electricity infrastructures. Their continued use is facilitated or disrupted depending on whether they can establish and maintain these connections.

In a new , we find that customers take active roles in extending a technology product’s lifecycle and that companies must consider this “entropy work” before limiting or encouraging these activities. Entropy work spans maintenance and repair activities, such as checking connections, resetting/cleaning devices, applying updates, replacing parts, and consulting others for help. When people cannot perform entropy work, they experience declines in the usefulness and ease-of-use of their devices.

The increasing prevalence of smart technologies means that these connectivity problems are increasing the amount of entropy work required from users. Moreover, technology companies often restrict users’ abilities to maintain and repair devices and connections. For instance, using third-party parts to replace failing displays or batteries tends to result in annoying notifications or reduced device functionality for iPhone users.

Continued Use Trajectories

By exploring this issue through the lived experiences of technology consumers, we identify four “continued use trajectories” that chart common events during the lifecycle of a variety of technology products from adoption to disposal.

1. First, some products enjoy a supporting trajectory in which devices work seamlessly with others, automatically connecting and functioning for long periods. For instance, Samsung partners with iFixit, a firm that empowers consumers to maintain their own devices through kits and guides. As such, Samsung sanctions its customers to maintain Galaxy smartphones with the support of a trusted third party.
   
   
2. Second, a decaying trajectory occurs when a tech product is easy to use in its early years but thereafter sees gradual declines in performance. Batteries drain faster, programs get slower, and connections to other tech products become complex.
   
   This situation can be caused by the nature of after-sales support: When consumers receive support to perform entropy work early on but this help recedes in later years, the usefulness of a device will likely decay. For instance, AppleCare is available for two to three years after purchase and, once that warranty ends, customers must consult costly certified technicians or attempt entropy work without support.
   
   
3. The third trajectory is a taxing trajectory in which tech products quickly fail to function as expected and consumers need immediate help. Famously, Samsung immediately recalled and replaced many of its smartphones in 2016 after reports of overheating and explosions. By immediately owning the problem, Samsung salvaged its brand image.
   
   
4. Finally, tech products can exist in oscillating trajectories, going back and forth between functioning properly and running into problems. These situations are frustrating because they force consumers to do unpredictable amounts and kinds of entropy work.

When users cannot derive the useful benefits of a device, they are more likely to abandon it, but they also get frustrated with brands. And if a company restricts consumers’ ability to receive help from outside sources and funnels them toward their own services, consumers can feel trapped.

To navigate these different trajectories, companies can provide resources such as guides for common recurring problems. Moreover, they can establish or endorse platforms that offer free troubleshooting advice, like Reddit communities and Adobe’s community, which offer support for products.

The Right to Repair

Given the worsening cost-of-living crisis, we can understand why consumers increasingly demand the “right to repair” their own devices via access to third-party services and parts. Oregon, Colorado, and the European Union have all enacted right-to-repair laws, illustrating a growing movement’s momentum to guarantee consumers’ ability to perform entropy work and maintain their devices.

Mindful of these movements, companies must consider how they support or limit consumers’ entropy work. We offer several suggestions for chief marketing officers:

• Keep in mind that as the connectivity of a tech product increases, the chances for these connections to enable problems to emerge increases.
  
  
• Incentivize customers to upgrade to a new device to improve ease-of-use when entropy work overwhelms them.
  
  
• Implement holistic investigations into which technologies, people, and other objects have the capacities to increase the entropy work customers must do to maintain their device’s continued use.
  
  
• Establish enduring service relationships when tech product issues are likely to recur to help customers maximize periods of stable continued use.

Source: Paolo Franco, Robin Canniford, Marcus Phipps, and Amber M. Epp, “,” Journal of Marketing.

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The business world has witnessed the emergence of new digital technologies and business models in the past two decades. For example, the rise of digital platform business models in retail and IT services has disrupted traditional models, making firms reconsider their business strategies and creating new opportunities for marketers to create value for buyers and other stakeholders (Perren and Kozinets 2018). Digital platforms intermediate between sellers, buyers, and other stakeholders via digital architecture often manifested as mobile and web applications (e.g., sharing economy platforms; Kozlenkova et al. 2021). The platform firm decides on the extent of governance accountability it will take in the entire value-creation process. Ecosystems, of which platforms can be a part, are networks of independent but interdependent actors participating in an industry’s or economic sector’s value chain (Nambisan, Zahra, and Luo 2019). Both platforms and ecosystems create value via direct and indirect network externalities (Kumar, Nim, and Agarwal 2021; Sridhar, Mantrala, Naik, Thorson 2011; ), with value cocreation at the core of actors’ business models and strategies.

In today’s Internet Age, digital platforms have no geographic borders. Platform business models are becoming a go-to strategy for international firms across the globe. For example, retailers are increasingly considering the platformization of their brands to add more value to their core offerings (Wichmann, Wiegand, and Reinartz 2022). With the help of digital technologies, it has become easier to expand into multiple markets simultaneously without diluting the supply chain advantages and brand positioning. Consider the low-cost e-commerce firm Temu from China, operating in more than 50 global markets and developing a strong ecosystem after launching in 2022. Temu consumers get access to various global sellers, making the domestic and international markets more competitive (Deighton 2023).

At the same time, marketers with new ways to create and capture value get access to an expanded target market. For example, as an entertainment platform, Netflix has launched different product and subscription pricing strategies in markets like India to compete with Disney, Amazon, and Reliance (Sull and Turconi 2021). Crowdfunding platforms like Kickstarter connect project creators and backers across the globe. Social media platforms have further amplified the reach of such retail and commerce platforms across both business and consumer markets (Gao et al. 2018). Thus, digital platforms and ecosystems can be considered a contemporary approach to internationalization and thereby are of great interest to marketing managers, policy makers, and regulators in both developed and emerging markets (Hewett et al. 2022).

However, research and knowledge of the dynamics of digital platforms and ecosystems in international marketing is still rather limited. A deeper understanding of how digital platforms can be utilized in the global marketing efforts of businesses is needed. Understanding digital platforms utilized across markets for sustainability, water conservation, health care, and other pressing issues and from the perspectives of NGOs and governments is urgently needed (Falcke, Zobel, and Comello 2024).

The purpose of this special issue, therefore, is to significantly advance research investigating the role of platforms and ecosystem business models across various facets of international marketing. Of special interest are papers focusing on the evolution and formation of digital platform-based global marketing strategies and business models, providing concepts, frameworks, theories, and empirical insights helpful for customers, firms, regulators, policy makers, and governments.

Research bearing on (but not limited to) the following questions is welcome: 

1. Different modes of platforms and ecosystems as internationalization approaches:
   
   a. How orchestrator firms build digital and nondigital architecture across different markets while entering or growing in a market.
   b. How these modes differ due to within and across market heterogeneity leading to different marketing strategies.
   c. How the consumer culture of a market complements the different modes.
   
   
2. Impact of platform and ecosystem approach on the international marketing mix:
   
   a. How stakeholders drive or impact product development and innovation processes.
   b. How integrating private label brands by platforms and using seller data impacts platform outcomes.
   c. How pricing strategies evolve and change over time with platforms and ecosystem approaches.
   d. How subscription and non-subscription pricing strategies evolve across various markets.
   e. How the supply chain and distribution network strengthens or weakens as the industry moves toward platform and ecosystem approaches.
   f. How the orchestrator firm develops or chooses partners for various marketing activities across and within developed and developing markets.
   g. How the promotion mix of the platform and ecosystem-based offering differ from traditional business models within and across industries (B2B vs. B2C) and markets.
   h. How culture interacts with platform and ecosystem strategies, and how this impacts firms and other stakeholders.
   
   
3. Impact on market structure, competition, and consumer and stakeholder welfare:
   
   a. Do platforms and ecosystems command higher market power, impacting the welfare of consumers and other stakeholders?
   b. Do platform and ecosystems approaches vary across industries (e.g., retail, energy, transportation)? How does these approaches impact the marketing mix in a market?
   c. Does higher platform power lead to consumer and stakeholder welfare erosion?
   d. How can marketers navigate the competition and coopetition to make a platform successful across various markets?
   e. What lessons can be learned from technology platforms like Google and Apple to navigate the tricky technological and regulatory landscape?
   f. What is the role of government and regulatory bodies in supporting or deterring the platform’s growth to ensure the welfare of stakeholders?
   g. Do dark patterns affect customer welfare? (For example, subscription pricing charges and policies that are not visible to consumers and the role of regulators.)
   h. Is there a loss of local livelihood that affects sellers as platforms integrate private label brands?
   i. What is the impact of the rise of circular platforms on sustainable value chains and stakeholders across developed and developing markets?
   
   
4. Customer attitudes and actions within and across platforms and ecosystems:
   
   a. Conceptual similarity for customer-based outcomes for platform firms and other stakeholders.
   b. Measurement of customer experience, satisfaction, and engagement with digital platforms and ecosystems.
   c. Management of failures and customer recovery in multisided platforms and ecosystems.
   d. Customer journey management across various digital and nondigital touchpoints in platforms and ecosystems.
   e. Interdependence of consumers’ relationships with and perceptions of brands or partners operating across platforms and ecosystems.
   f. How marketers can explore circular platforms and ecosystems to help firms be sustainable value chains and positive customer attitudes.
   g. Platform exploitation by customers and disintermediation.
   

This list of topics and questions is reflective but not exhaustive of the current state of industry and academic literature. We call for more interdisciplinary and foundational research to expand the horizons of platforms and ecosystems literature in International Marketing. We invite all types of research—qualitative, behavioral, and empirical—and encourage researchers to identify multiple sources of data and motivation for this special issue.

Submission Process

All manuscripts will be reviewed as a cohort for this special issue of the Journal of International Marketing. Manuscripts must be submitted between March 1, 2025 and May 30, 2025. All submissions will go through Journal of International Marketing’s double-anonymized review and follow standard norms and processes. Submissions must be made via the journal’s , with author guidelines available here. For any queries, feel free to reach out to the special issue editors.

Manuscripts must be submitted by May 30, 2025.

Special Issue Editors

Nandini Nim, Assistant Professor of Marketing, Colorado State University (email: n.nim@colostate.edu)

Murali Krishna Mantrala, Ned Fleming Professor of Marketing, University of Kansas (email: mmantrala@ku.edu)

AyƟegĂŒl Özsomer, Professor, Koç University, and Editor in Chief, Journal of International Marketing (email: aozsomer@ku.edu.tr)

References

Adner, Ron (2022), “Sharing Value for Ecosystem Success,” MIT Sloan Management Review, 63 (2), 85–90.

Deighton, John (2023), “How SHEIN and Temu Conquered Fast Fashion—and Forged a New Business Model,” Harvard Business School (April 25), .

Falcke, Lukas, Ann-Kristin Zobel, and Stephen D. Comello (2024), “How Firms Realign to Tackle the Grand Challenge of Climate Change: An Innovation Ecosystems Perspective,” Journal of Product Innovation Management, 41 (2), 403–27.

Gao, Hongzhi, Mary Tate, Hongxia Zhang, Shijiao Chen, and Bing Liang (2018). “Social Media Ties Strategy in International Branding: An Application of Resource-Based Theory. Journal of International Marketing, 26 (3), 45–69.

Hewett, Kelly, G. Tomas M. Hult, Murali K. Mantrala, Nandini Nim, and Kiran Pedada (2022), “Cross-Border Marketing Ecosystem Orchestration: A Conceptualization of Its Determinants and Boundary Conditions,” International Journal of Research in Marketing, 39 (2), 619–38.

Kozlenkova, Irina V., Ju-Yeon Lee, Diandian Xiang, and Robert W. Palmatier (2021), “Sharing Economy: International Marketing Strategies,” Journal of International Business Studies, 52, 1445–73.

Kumar, V., Nandini Nim, and Amit Agarwal (2021), “Platform-Based Mobile Payments Adoption in Emerging and Developed Countries: Role of Country-Level Heterogeneity and Network Effects,” Journal of International Business Studies, 52, 1529–58.

Nambisan, Satish, Shaker A. Zahra, and Yadong Luo (2019), “Global Platforms and Ecosystems: Implications for International Business Theories,” Journal of International Business Studies, 50, 1464–86.

Perren, Rebeca and Robert V. Kozinets (2018), “Lateral Exchange Markets: How Social Platforms Operate in a Networked Economy,” Journal of Marketing, 82 (1), 20–36.

Sridhar, Shrihari, Murali K. Mantrala, Prasad A. Naik, and Esther Thorson. “Dynamic marketing budgeting for platform firms: Theory, evidence, and application.” Journal of Marketing Research 48, no. 6 (2011): 929-943.

Sull, Donald and Stefano Turconi (2021), “Netflix Goes to Bollywood,” Teacher Resources Library, MIT Sloan School of Management (February 22), https://mitsloan.mit.edu/teaching-resources-library/netflix-goes-to-bollywood.

Wichmann, Julian R.K., Nico Wiegand, and Werner J. Reinartz (2022), “The Platformization of Brands,” Journal of Marketing, 86 (1), 109–31.

Other Resources

Adner, Ron (2017). Ecosystem as Structure: An Actionable Construct for Strategy,” Journal of Management, 43 (1), 39–58.

Akaka, Melissa A., Stephen L. Vargo, and Robert F. Lusch (2013), “The Complexity Of Context: A Service Ecosystems Approach for International Marketing,” Journal of International Marketing, 21 (4), 1–20.

Gawer, Annabelle and Michael A. Cusumano (2014). “Industry Platforms and Ecosystem Innovation,” Journal of Product Innovation Management, 31 (3), 417–33.

Glavas, Charmaine, Shane Mathews, and Rebekah Russell-Bennett (2019), “Knowledge Acquisition via Internet-Enabled Platforms: Examining Incrementally and Non-Incrementally Internationalizing SMEs,” International Marketing Review, 36 (1), 74–107.

Kanuri, Vamsi K., Murali K. Mantrala, and Esther Thorson (2017), “Optimizing a Menu of Multiformat Subscription Plans for Ad-Supported Media Platforms,” Journal of Marketing, 81 (2), 45–63.

Zhou, Qiang (Kris), B.J. Allen, Richard T. Gretz, and Mark B. Houston (2022), “Platform Exploitation: When Service Agents Defect with Customers from Online Service Platforms,” Journal of Marketing, 86 (2), 105–25.

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When a giant multinational like Unilever partners with one of its major suppliers, such as the industrial enzyme-producer Novozyme, the collaboration can fast-track innovation and improve business performance. Such a partnership between a firm and a supplier brings together knowledge, technologies, and other resources to create a product, service, or solution—and industry reports indicate that up to 85% of firms believe these collaborations are an effective means of innovation.

This broader impact of product co-development collaboration is aptly captured in the following public statement by the multinational pharmaceutical company Bristol-Myers Squibb:

As critical drivers of our strategy, external innovation and partnering have brought significant commercial success and pipeline growth. Twelve of our company’s twenty blockbuster medicines are derived from collaborations. In addition, more than sixty percent of our current development pipeline is externally sourced bringing significant external innovation to complement our internal capabilities and innovation.

However, such collaborations also expose the firm to various transactional hazards such as knowledge spillovers and opportunism. In a , we demonstrate how misaligned contracts can erode innovation outcomes of high-tech firms. The danger looms large when a firm fails to consider its positioning strategy and functional capabilities when crafting innovation collaboration contracts with its suppliers. This creates a barrier to a firm’s ability to generate sustained dividends from its broader marketing strategy.

Strategic decisions taken by firms are based on the presumed value generated from the implementation of the decisions and the presumed costs incurred in the process. As important as innovation co-developments are to a firm’s broader marketing strategy, managers should ask themselves an important question: Will such contracts help sustain any strategy dividend? The strategy dividend can be whittled away if there is no “fit” between a firm’s strategic positioning, functional capabilities, and the governance modes of co-development.

The Downsides of Joint Venture Partnerships

Consider, for example, industry observations that . Economic downturns impose a need for cost efficiencies, and JVs can be useful because of presumed close coordination between the two entities. Yet, as our hypotheses and results show, this economic dividend can only be realized when firms have high technological capabilities. For firms with similar efficiency orientations, we estimate that their technological capabilities are associated with an increase of 5.2% in innovation performance for JVs, but not for technology licensing contracts and joint development agreements.

On the other hand, estimates show that strong marketing capabilities in the same situation are associated with a decrease of 17.9% in innovation performance for JVs. Additionally, marketing capabilities seem to be more benign for joint development agreements in high differentiation-oriented firms. For such firms, marketing capabilities are associated with an increase of 7.8% in innovation performance for joint development agreements.

One of our central themes is that the idea of fit in co-development collaborations comes with underlying notions of misalignment costs that need to be recognized. While mapping the bases of misalignment, we highlight the keystone role of the firm’s positioning strategy in innovation collaborations. Strategy frames how a firm deploys its resources and focuses its energies. So, a misalignment will naturally manifest in inefficiency, perhaps one that will emerge over time. As our empirical results bear out, misalignment between collaboration contracts, capabilities, and strategy significantly erodes innovation outcomes.

Lessons for Chief Marketing Officers

Our study offers three key takeaways:

1. For better innovation outcomes, firms need to select the collaboration form that motivates their partners to share know-how and expertise and facilitate efficient knowledge transfers. At the same time, firms must also pay attention to protecting their valuable knowledge and skills from opportunistic appropriation and ensure effective use of their deployed resources.
2. Firms need to build the “right” functional capability to yield the most benefit from innovation collaborations. For instance, a firm needs to invest in building marketing capabilities if it is driven by high differentiation and consider more arms-length arrangements such as joint development agreements with suppliers. In contrast, firms driven by efficiency considerations are better off developing their technological capabilities when considering a joint venture.
3. Firms must resist blindly copying the practices of other firms, regardless of the appearance of “industry best practices.” Considering the firm’s positioning strategy along with its capabilities is crucial to designing effective contracts. Thus, blanket prescriptions for one or the other types of contracts (e.g., joint ventures during downturns) may be misdirected.

From: Nehal Elhelaly and Sourav Ray, “,” Journal of Marketing.

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The Journal of Interactive Marketing and the (IMRC) are pleased to announce a special issue focused on exploring the cross-disciplinary approaches to solving various challenges that marketers, consumers, and societies face today. The special track represents IMRC’s tradition for a timely and highly interactive discussion of new research ideas, preliminary findings, and ongoing research. We invite all interested researchers to submit their proposals to IMRC’s special track for constructive feedback. Researchers can then implement the initial feedback before submission to the special issue.

For this special issue, we encourage novel approaches to contemporary interactive marketing problems that rely on innovative data-driven methodological approaches or theoretical conceptualizations grounded in disciplines outside of business schools. We expect at least one of the coauthors to have a primary faculty appointment outside of a business school (in a non-business-related discipline). The preference will be given to papers grounded in disciplines that have yet to be widely represented in the marketing literature, such as computer science, data science, engineering, health care, history, life science, mathematics, network science, neuroscience, philosophy, physics, and political science, among others. We also welcome submissions that introduce novel methodological approaches or theoretical conceptualizations grounded in the disciplines that marketing scholarship traditionally has been drawing from, such as communication and media studies, economics, psychology, sociology, and statistics.

When submitting the work, we ask authors to highlight the unique contributions of the disciplines/researchers in their cover letters.

Updated Submission Deadline: January 31, 2025

Submitting Your Manuscript to the Special Issue

All submissions will be considered for publication in the Journal of Interactive Marketing, via a double-anonymous peer review, drawing on prominent scholars with interest and expertise in the area. Each manuscript may also be considered by prominent marketing practitioners and thought leaders.

Submissions must be made via the journal’s , with author guidelines available here. 

Special Issue Editors: and (Northeastern University)

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Increasingly, AI is improving how automation can bring about performance improvements for marketing operations and innovations in how marketers engage with their various stakeholders. IA refers to a practice that harnesses methods and technologies, business process automation, robotic process automation (RPA), natural language processing (NLP) and generation (NLG), entity detection, and computer vision, enabling computing applications to undertake tasks and processes that were previously carried out by humans (Bornet et al. 2020). This promises to improve business performance (e.g., see Libai et al. 2020), empowering marketing workers to improve both their effectiveness and efficiency.

AI as a core enabler of IA has the potential to dramatically increase the scope and capability of automated marketing operations. The world’s largest technology companies, including Alphabet, Meta, Amazon, and Microsoft are rapidly innovating in this space. A testimony to the tremendous level of interest in IA is Microsoft’s multibillion-dollar investment in Open AI (the developer of ChatGPT). Microsoft’s “Dynamics 365 Copilot” promises to bring the power of AI and automation across the marketing organization, including sales teams, customer service agents (via conversation boosters in Power Virtual Agents; Le et al. 2023; Sajtos et al. 2020), product listing in online commerce, customer insights, supply chain coordination, and marketing planning tasks such as segmentation and targeting.

This special issue therefore aims to focus on understanding the IA of higher-order marketing tasks and processes, including evaluative thinking, agile marketing (e.g., Kalaignaman et al. 2022), design thinking (e.g., Beverland et al. 2015; Allen et al. 2018), causal reasoning, brainstorming, and idea generation (e.g., Girotra et al. 2023)

When companies invest in and deploy IA for marketing operations, they need to consider multiple factors, since marketing automations can vary widely in terms of what they do and how they work. Marketing automations differ based on the technologies they employ, the type and modality of data (structured or unstructured; text, images, speech) that they use (see Du et al. 2021), and the outcome (rule- or inference-based) they produce. For instance, validating customers’ physical or email addresses requires an automation process different from one to classifying customer queries into the correct category. These different marketing automations often focus on a single task, but companies increasingly connect these automations to form end-to-end automation chains or automation journeys, which can have an adverse effect on IA transparency and employee autonomy. However, as with employees, automation applications are not necessarily specialized, and can be employed to complete different tasks in the business depending on where the needs arise. Automation capabilities promise benefits in efficiency and effectiveness but present challenges for marketing practitioners who must redesign their internal operations.

Besides organizational workflows, marketing automations are likely to affect company brands and their communication. Marketing automations can deliver seamless experiences with brands when, for instance, a customer books an appointment, and first receives a confirmation, which is followed by timely reminders of the event or the need to make a change. These streamlined and personalized processes have the power to result in faster and personally relevant experiences and contribute to creating a stronger brand experience. At the other extreme, of course, these very same automations can be too rigid, can demand or capture too much or inappropriate information, and become frustrating for customers, which can increase the likelihood of the customer taking their business elsewhere. More importantly, as these automations become more widely available, companies need to consider whether and how their marketing automations are aligned with and support their brand values and their ethical standards prior to deploying them (Wirtz et al. 2023). Finally, companies develop automations for customers to help them in their daily routines, and for automation developers it is critical to understand the tasks and processes people are keen to automate, and how best to automate them.

Relevant Topics and Questions for Research

Considering numerous differences in how automations are built and work and the areas in which they can be employed, companies have a number of decisions to make when it comes to developing and investing in IA. Based on the issues outlined, this special issue aims to focus on particular aspects of IA by addressing some of the following questions.

1. What tasks or processes are most suitable for marketing automation? How should companies decide on or prioritize which tasks and processes to automate?
2. How do IA and marketing automation tools influence employee and business performance?
3. What are the competitive implications of AI-based automation? Topics addressing this question may consider how the potential for greater personalization through automation could potentially alter competitive dynamics and outcomes.  
4. How will IA and AI-enabled marketing activities change consumer behavior?
5. What are the future domains of marketing automation? How will IA transform areas that are already heavily automated, such as data analysis, content generation, real-time personalization, media buying, market research, and so on?
6. How will IA contribute to creating more effective and efficient business processes? Are consumers willing to pay more for more effective and efficient processes?
7. What tasks and processes do customers automate? What IA and marketing automation tools are available for customers to use? How does automation of marketers interact with that of customers?
8. What changes in employee skill levels or loyalty to the firm are required or can be observed as a result of introducing marketing automations in their jobs? Under what conditions does IA lead to either skill erosion (or, at the extreme, human replacement) or to skill development (human enhancement)?
9. What new roles will marketers of the future be required to undertake, and what skills would they need to be effective in these roles?
10. How should we train the next generation of marketers? Will IA and marketing automation tools change (shrink or enhance) marketers’ roles in the future—and if so, how? Will IA and marketing automation tools be likely to create new roles and responsibilities for marketing professionals (e.g., Chief Ethics Officers)?
11. How helpful is IA in building brands? Or will IA erode companies’ efforts to build humanized brands?
12. What criteria should companies use to assess the effectiveness of IA and marketing automation tools? How can ROI on automations be calculated? How is employee time saved in jobs (partial task automation) incorporated into these calculations?
13. How should companies develop their automation strategies? Do companies always first automate the processes that account for the largest share of their transactions?
14. Will the need to automate accelerate companies’ efforts to integrate customer data stored across multiple isolated databases?
15. What ethical, regulatory or moral issues are important for brands and marketers to consider in the future of hyper automated marketing organisations? What is the role of AI alignment strategies and AI safety for AI-powered and IA marketing operations? 

The special issue invites original manuscripts on these, or related topics. We especially welcome empirical work and articles focused on technological aspects of intelligent automation relevant to marketing practitioners. 

Submission Deadline: December 31, 2024

Submitting Your Manuscript to the Special Issue

All submissions will be considered for publication in the Journal of Interactive Marketing, via a double-anonymous peer review, drawing on prominent scholars with interest and expertise in the area. Each manuscript may also be considered by prominent marketing practitioners and thought leaders.

Submissions must be made via the journal’s , with author guidelines available here. 

Please contact André Bonfrer with questions (andre.bonfrer@deakin.edu.au).

Special Issue Editors

André Bonfrer is Professor of Marketing at Deakin Business School, Deakin University. He has published extensively in various leading journals in marketing on topics related to marketing technology, advertising, and branding, and has served on editorial boards for several leading journals in marketing.

Laszlo Sajtos is Associate Professor, University of Auckland Business School. He researches the impact of emergent technologies on service management. His work is published in top journals including the International Journal of Research in Marketing, Journal of Service Research, Journal of Interactive Marketing, among others.

Jochen Wirtz is Professor of Marketing and Vice Dean of MBA programs, National University of Singapore. His research focuses on services marketing and management and has been published in over 200 academic articles and books, including Services Marketing: People, Technology, Strategy (9^th edition) and Essentials of Services Marketing (4^th edition).

Erik Hermann is Permanent Affiliate Professor of Marketing, ESCP Business School Berlin. His research focuses on consumer behaviour and psychology as well as the ethical and sociotechnical issues and implications of the development and deployment of artificial intelligence in marketing. His research has led to publications in leading marketing journals such as Journal of Marketing, International Journal of Research in Marketing, Journal of the Academy of Marketing Science, Journal of Business Ethics, and Journal of Advertising.

References

Allen, B.J., Deepa Chandrasekaran, and Suman Basuroy (2018), “Design Crowdsourcing: The Impact on New Product Performance of Sourcing Design Solutions from the ‘Crowd,’” Journal of Marketing, 82 (2), 106–23.

Beverland, Michael B., Sarah  J.S. Wilner, and Pietro Micheli (2015), “Reconciling the Tension Between Consistency and Relevance: Design Thinking as a Mechanism for Brand Ambidexterity,” Journal of the Academy of Marketing Science, 43 (5), 589–609.

Bornet, Pascal, Ian Barkin, and Jochen Wirtz (2020), Intelligent Automation: Welcome to The World of Hyperautomation: Learn How to Harness Artificial Intelligence to Boost Business & Make Our World More Human. World Scientific.

Du, Rex Y., Oded Netzer,  David A. Schweidel, and Debanjan Mitra (2021), “Capturing Marketing Information to Fuel Growth,” Journal of Marketing, 85 (1), 163–83.

Girotra, Karan, Lennart Meincke, Christian Terwiesch, and Karl T. Ulrich (2023), “Ideas Are Dimes a Dozen: Large Language Models for Idea Generation in Innovation,” SSRN (August 2), .

Kalaignanam, Kartik, Kapil R. Tuli, Tarun Kushwaha, Leonard Lee, and David Gal (2021), “Marketing Agility: The Concept, Antecedents, and a Research Agenda,” Journal of Marketing, 85 (1), 35–58.

Krafft, Manfred, Laszlo Sajtos, and Michael Haenlein (2020), “Challenges and Opportunities for Marketing Scholars in Times of the Fourth Industrial Revolution,” Journal of Interactive Marketing, 51 (1), 1–8.

Le, Khan Bao Quang, Laszlo Sajtos, and Karen Veronica Fernandez (2023), “Employee-(Ro)Bot Collaboration in Service: An Interdependence Perspective,” Journal of Service Management, 34 (2), 176–207.

Libai, Barak, Yakov Bart, Sonja Gensler, Charles F. Hofacker, Andreas Kaplan, Kim  Kötterheinrich, and Eike Benjamin Kroll (2020), “Brave New World? On AI and the Management of Customer Relationships,” Journal of Interactive Marketing, 51, 44–56.

Reisenbichler Martin, Thomas Reutterer, David A. Schweidel, and Daniel Dan (2022), “Frontiers: Supporting Content Marketing with Natural Language Generation,” Marketing Science, 41 (3), 441–52.

Sajtos, L., B.G. Voyer, M. Sangle-Ferriere, and B. Sung (2020), “Algorithmic Decision-Making, Agency and Autonomy in a Financial Decision Making Context: An Experiment,” ACR North American Advances.

Skiera, Bernd (2022), “MarTech and SalesTech,” NIM Marketing Intelligence Review, 14 (2), https://www.nim.org/en/publications/nim-marketing-intelligence-review/detail-issue/martech-and-salestech.

Wirtz, Jochen, Werner Kunz, Nicole Hartley, and James Tarbit (2023), “Corporate Digital Responsibility in Service Firms and their Ecosystems,” Journal of Service Research, 26 (2), 173–90.


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