Abstract
This article examines the role of artificial intelligence (AI) in enhancing marketing creativity by analyzing the synergy between computational and human creative processes. Through two studies, the authors investigate nongenerative and generative AI applications within marketing contexts using a conceptually driven and empirically derived approach. In Study 1, the authors observe how creative individuals, particularly artists, utilize AI and its effects on their creative experiences, revealing AI's role as (1) a new instrumental resource, (2) a tool for exploring possibilities, and (3) a means to deconstruct the creative process. Study 2 assesses 1,036 AI systems (2015–2021) and 241,292 AI models (2022–2024), categorizing them into four clusters and three levels of observed creativity. From these insights, the authors introduce a framework for AI-enabled creativity: (1) inspiring agile methods, (2) augmenting human creativity, and (3) inspiring unconventional thinking. Validated by three workshops, this framework equips marketing leaders with a deeper comprehension of AI's creative potential. The authors advocate for AI integration within agile, augmented, and unconventional marketing approaches, advancing our understanding of AI's contribution to marketing creativity. Additionally, they propose a research roadmap for empirical validation in real-world applications.
Keywords
The dynamic and ever-changing business landscape is shaped by various influential factors, such as lessons learned from the pandemic, rapid technological advancements, shifting consumer behaviors, stakeholder-oriented business approaches, regulatory changes, socioeconomic conditions, increasing economic and digital disparities, and an unpredictable geopolitical climate. Traditional marketing faces challenges in this dynamic landscape, necessitating a reevaluation of strategies, especially in interactive marketing. Artificial intelligence (AI), and more specifically generative AI, is instrumental in driving innovation and adaptability (Pagani and Champion 2023). These AI tools are designed to streamline tasks, tailor content, and offer insights based on data, thereby enhancing the efficiency of marketing strategies.
Technologies like generative adversarial networks (GANs) and large language models (LLMs) are transforming the marketing landscape. They personalize consumer interactions and optimize campaigns revolutionizing the way marketing is done.
One of the significant advantages of AI, particularly generative AI, is its ability to personalize messages and offerings on a large scale. This capability allows for “segments of one” personalization, a concept that has long been a dream for marketers. Recently, Open AI has introduced memory, which could greatly enhance personalization by tailoring it to the user's actual experience.
While AI enhances marketing efficiency through data analysis, content customization, and process automation (Chaisatitkul et al. 2024; Pagani and Champion 2020), it also raises concerns about job displacement and the potential overshadowing of human creativity. These concerns are amplified, particularly given the unpredictable nature and limited oversight of generative AI.
Our study addresses a gap in research on AI's impact on marketing creativity, focusing on the synergy between computational and human creativity. Aligned with recent calls for deeper exploration in this area (Ameen, Sharma, et al. 2022; Ameen, Tarhini, et al. 2022; Huang and Rust 2021; Korteling et al. 2021; Peres et al. 2023), our research seeks to shed light on how AI enhances creativity in marketing. We propose a conceptually driven and empirically derived framework to guide marketing executives in leveraging AI for creative enhancement.
In Study 1, we investigate how creative individuals, particularly artists (n = 37), integrate AI into their work, revealing the dynamic interaction between technology and human agency. We carefully distinguish between the use of nongenerative and generative AI systems in artistic endeavors. To validate our findings, we conduct interviews with three marketing managers. In Study 2, we scrutinize how AI systems claiming to enhance creativity achieve this goal. We analyze a longitudinal dataset comprising 1,036 AI systems (2015–2021) and a dataset of 241,292 AI models (2022–2024) categorized into 35 tasks. AI systems serve as overarching infrastructure integrating various AI technologies, consisting of hardware, software, algorithms, and data processing mechanisms working toward defined objectives. Examples include virtual assistants like Siri or Alexa, self-driving vehicles, and recommendation engines. In contrast, AI models represent specific algorithms tailored for particular tasks. Trained on large datasets, they discern patterns and make predictions enabling systems to interpret information and generate outputs.
Building on the insights gleaned from these studies, our integrative framework, validated in three workshops, outlines three categories of AI-facilitated creativity and suggests a strategic approach for its application in marketing. We advocate for application of AI in agile, augmented, and unconventional marketing approaches, enriching our understanding of AI's role in shaping interactive marketing creativity. Additionally, we propose a roadmap for further research to investigate and validate these concepts in real-world settings.
Conceptual Underpinnings
This article explores AI's potential to augment creativity in marketing, examining the interplay between computational and human creativity. The discussion starts with an explanation of key concepts related to creativity, the potential of AI to boost creativity, and how AI-enhanced creativity can impact marketing and interactive marketing strategies.
Creativity, as commonly understood, involves generating novel and useful ideas (Amabile 2013). When AI enhances creativity, it collaborates with human creativity, improving tasks like idea generation, pattern recognition, and problem-solving. In marketing, AI-empowered creativity focuses on using AI tools to optimize creative strategies, content creation, and campaign optimization. This connection between traditional creativity and AI highlights their evolving relationship, particularly in marketing's strategic domain.
Creativity in Marketing
Creativity plays a central role in marketing, involving the generation of novel and useful ideas to tackle various challenges. Amabile (2013) defines creativity as the production of ideas or outcomes that are both novel and appropriate to a particular goal, highlighting the significance of novelty and relevance. Amabile’s (1983) framework for creativity revolves around three fundamental components: novelty, connectedness, and meaningfulness.
Given the inherent generative nature of AI, which reproduces data from training sets, it becomes crucial to examine the concept of novelty as defined by Amabile. This exploration is essential due to the apparent gap between AI's ability to generate entirely new outputs and the essential aspect of creativity entailing a sense of novelty. Additionally, Amabile (1988) introduces a foundational model that underscores (1) domain-relevant skills, (2) creativity-relevant processes, (3) intrinsic task motivation or passion, and (4) social environment to foster creative performance, supplementing the discussion on creativity in marketing.
In the context of ideation, creativity necessitates both novelty and utility (Amabile 2013; Frare and Beuren 2021; Verganti, Vendraminelli, and Iansiti 2020). This fusion of fresh concepts is essential for both artists and marketers, emphasizing the need for ideas that not only optimize efficiency but also meet genuine consumer needs.
Our study explores how creative individuals, particularly artists, known for their boundary-pushing endeavors, demonstrate creativity in idea generation (Fernandez 2017). Understanding this creative process among artists provides marketers with valuable insights, enabling them to foster a mindset that goes beyond the ordinary and drives innovation. Artists often produce thought-provoking content that questions societal norms, contributing to broader cultural shifts (Fernandez 2017). Marketers can draw inspiration from this approach, learning to design novel solutions to enhance customer experience and craft novel campaign ideas that not only promote products or services but also engage in meaningful cultural dialogues.
Coca-Cola's Real Magic Creative Academy (Coca-Cola 2023) serves as a prime example of a space where digital artists, tech firms, and fashion brands collaborate to harness AI's power, resulting in innovative advertisements that redefine creativity and reshape the advertising industry. Additionally, it is worth noting that artists who have left a lasting impact are those who introduced new paradigms or approaches. Similarly, truly creative marketers are characterized by their willingness to confront and redefine established norms. This is demonstrated through Apple's revolutionary releases of the Macintosh, iPod, iPad, and iPhone; the innovative Pepsi Challenge; and the origination of social networks.
Creativity in marketing is a multifaceted concept with significant implications for brand management, advertising strategies, and product/service innovation (Baack et al. 2016; Mustak et al. 2021; Rosengren et al. 2020; Schiessl, Dias, and Korelo 2022). Boden (2004) categorizes creativity into combinational, exploratory, and transformational types. Xu, Mehta, and Dahl (2022) build on Boden's categorization and further discuss how these types of creativity contribute to generating inventive outputs by forming connections between broad and seemingly unrelated concepts.
Within the design thinking framework (Razzouk and Shute 2012), creativity drives innovative solutions in marketing content interactions and therefore is inherently linked to innovation, which represents the application of creative ideas (Amabile 1988). Creative endeavors in marketing are vital for crafting unique approaches that resonate with target audiences, deepen customer understanding, personalize experiences, and create engaging offerings, campaigns, and experiences.
Artists’ creative processes often mirror those of marketers, offering valuable insights into capturing attention, evoking emotions, and fostering brand loyalty. With many artists now leveraging AI for enhanced creativity, examining their approaches can provide valuable lessons for marketers looking to integrate AI into their creative processes.
Creativity in marketing intersects with entrepreneurial strategies, uncovering new opportunities and shaping strategic decisions (Biraglia and Kadile 2017). Understanding creativity in marketing draws from various theoretical perspectives, including creative cognition theory (Balietti and Riedl 2021), theory of creativity (Vitrano, Altarriba, and Leblebici-Basar 2021), individual and environmental factors theories (Subotnik, Olszewski-Kubilius, and Worrell 2019), emotional intelligence (Khalili 2016), empowerment theory (Cheng et al. 2019), self-determination theory (Fischer, Malycha, and Schafmann 2019), generative theory (Peteranetz et al. 2017), and explicit–implicit interaction theory (Helie and Sun 2009) (Table 1).
Theories Informing Creativity in Marketing.
Assessing creativity is a multifaceted task with challenges in selecting appropriate instruments, setting conditions for testing, and considering domain specificity (Aschauer, Haim, and Weber 2021; Lemons 2011; Park et al. 2016; Plucker and Makel 2010; Rafner et al. 2022; Zeng, Proctor, and Salvendy 2011). New methods for measuring creativity have been suggested by recent advancements considering convergent thinking and episodic retrieval as moderating factors (Zhu et al. 2019). The subjectivity of creativity is due to its contextual variability across time, cultures, norms, and individuals, making it a dynamic phenomenon, particularly in marketing contexts.
In marketing, creativity is pivotal as it differentiates a firm's offerings and enthralls consumers by meeting their current and future needs. Our most valued products, services, or experiences, whether represented by our reliance on an iPhone, the convenience of Uber, or the culinary delights from a creative chef, underscore the originality and creativity of the businesses behind these offerings making them unforgettable.
Creativity Empowered by AI
With the rapid evolution of AI systems, understanding the relationship between AI and creative processes is essential. AI systems mimicking human intelligence across perceptual, cognitive, and conversational functions (Huang and Rust 2018) bridge diverse disciplines, giving rise to computational creativity (Colton and Wiggins 2012; Pagani and Champion 2023). These systems engage in creative tasks across various domains, including art (Avila and Bailey 2016), literature, music, problem-solving (Russell and Norvig 2009), and gaming (Chen 2016), fostering novelty (Hoffmann 2005) and transformational creativity (Boden 2009).
Recent advances in generative AI are reshaping personalized shopping experiences (Zwanka and Zondang 2023) and influencing marketing practices (Peres et al. 2023), enabling businesses to redefine traditional marketing approaches. However, despite their ability to emulate human cognition and creativity, integrating AI into marketing tasks remains an emerging field (Ameen, Sharma, et al. 2022; Ameen, Tarhini, et al. 2022; Huang and Rust 2021; Korteling et al. 2021; Peres et al. 2023).
Current advertising and marketing practices, as demonstrated in publications like Advertising Age, Bloomberg Businessweek, and the Wall Street Journal, showcase an intensified exploration of how AI can enhance creative processes. This trend is further underscored by the proliferation of startups focused on AI. Our study aims to address this gap by comparing human creative cognition with AI creative computation within the marketing context, providing insights into how AI contributes to and interacts with creative processes in marketing.
AI-Empowered Creativity for Marketing
In the rapidly evolving marketing landscape, AI has emerged as a powerful creative tool, reshaping traditional approaches and driving industry transformation. Businesses are increasingly leveraging AI to automate tasks (Pagani and Champion 2020; Verganti, Vendraminelli, and Iansiti 2020), foster unique connections, catalyze idea generation (Mikalef and Gupta 2021), and enhance creative exploration (Wilson and Daugherty 2018).
The influence of generative AI on customer engagement (Huang and Rust 2018; Wind, Pagani, and Dischler 2023; Wind et al. 2023), personalized shopping experiences (Zwanka and Zondang 2023), and marketing practices (Peres et al. 2023) is notable. Businesses are leveraging AI tools to redefine traditional marketing approaches, leading to a significant industry transformation. Their influence extends to new product development (Harz, Hohenberg, and Homburg 2022), brainstorming sessions, and problem-solving (Chalmers, MacKenzie and Carter 2021; McCaffrey 2018), as detailed in Table 2.
Illustrative Literature on the Impact of AI in Marketing and the Gap in Research.
While AI systems in marketing serve various roles, from automating tasks to serving as generative agents and collaborative partners (Kantosalo and Jordanous 2020), there is still much to explore regarding their integration into marketing tasks, particularly in terms of their impact on creativity (Ameen, Sharma, et al. 2022; Ameen, Tarhini, et al. 2022; Huang and Rust 2021; Korteling et al. 2021; Peres et al. 2023). This gap is highlighted in Table 2, which outlines the current literature on AI's impact in marketing and emphasizes the need for further research on its role in fostering creativity.
Our study seeks to bridge this gap by comparing human creative cognition with AI creative computation in the marketing context. By emphasizing the need for empirical evidence and practical insights, we aim to shed light on the relationship between human and computational creativity. Ultimately, we believe that both generative and nongenerative AI have the potential to significantly enhance creativity in marketing strategies, paving the way for innovative approaches in the field.
Research Design
The research framework guiding this article's structure is conceptually driven and empirically derived, informed by McCracken’s (1988) methodology. This framework, illustrated in Figure 1, comprises four main stages to structure the research process.
Research Framework.
Stage 1: Exploring the Demand Side (Study 1)
This initial phase focused on exploring the demand side (Study 1). The primary objective was to understand how creative individuals, particularly artists (n = 37), utilize AI systems, including generative AI systems. A content analysis approach was employed to compare AI and creativity approaches among artists using generative AI and nongenerative AI. To ensure the reliability of findings, independent coders were engaged in the analysis. Through collaborative discussions, the category definitions were refined to enhance the accuracy and clarity of the coding system (Thomas and Harden 2008). Additionally, three marketing managers were interviewed to validate insights.
This study aimed to understand how individuals utilize AI technologies to enhance and complement their creative processes, with a specific emphasis on the potential impact of AI utilization on choice and autonomy within the creative process. Methods employed by these artists were examined, distinguishing between generative and nongenerative AI, to shed light on the intricate interaction between AI systems and the preservation of creative agency.
Stage 2: Exploring the Supply Side (Study 2)
In the second stage, we explored the supply side (Study 2) to evaluate the creative capabilities of AI systems, particularly their ability to generate novel and useful outcomes. Through content analysis, we assessed a dataset consisting of 1,036 AI systems from 2015 to 2021. Then, using a second dataset spanning 2022 to 2024 and consisting of 241,292 AI models (61% generative AI), we conducted content analysis specifically on 35 precategorized tasks into which the AI models are grouped. This analysis evaluated the creative abilities of each AI system and group of AI models (grouped by task), differentiating between nongenerative AI systems, which focus on reasoning, decision-making, and problem-solving, and generative AI systems, capable of producing new content. We also conducted a follow-up analysis on a sample of 350 AI models, each associated with specific tasks. This enabled us to directly evaluate each model's novelty and usefulness with regard to the task it performed.
Additionally, insights from personal interviews with managers of AI system providers shed light on design intent and considerations, particularly in their use by marketers. This second stage complemented the insights from the first stage, contributing to a deeper understanding of how creative individuals, AI systems, and creativity enhancement mechanisms interact.
Stage 3: Developing a Conceptual Framework
In this stage, we developed a conceptual framework based on insights from the two studies. These insights guided the creation of guidelines for marketing executives to effectively use AI in enhancing creativity. The synthesized findings from both the demand and supply sides informed this framework.
Stage 4: Validating the Conceptual Framework
The proposed conceptual framework underwent validation through three workshops and nine in-depth interviews with senior leaders. The first workshop, where the framework categories were presented and discussed, was held at a business school in the United Kingdom and involved scholars and PhD students. The second workshop, at Meta’s headquarters in New York City, included over 70 senior leaders from various sectors, including chief marketing officers and marketing scholars. Fifteen participants from this group had focused discussions on the framework, confirming its structure. Additionally, a third workshop with 46 marketing professionals from a global lifestyle and fashion magazine, along with nine in-depth interviews with key leaders, further supported and validated the study's findings.
Study 1: Unraveling AI's Role in Creativity
In Study 1, we examined a diverse group of artists, including painters, writers, choreographers, musicians, and artist entrepreneurs, with 43% specifically using generative AI. Our principal objective was to explore the complex relationship between AI and creative processes, delving into the interplay between technological intervention and human agency.
The decision to focus on artists was grounded in the creative characteristics they share with entrepreneurs, designers (Chaston and Sadler-Smith 2012), and marketers in executing creative tasks (Paige and Littrell 2002). Marketing strategies often demand swift, nonlinear changes beyond conventional practices (Amabile and Pratt 2016; Lies 2021), relying on intense imagination and the generation of impactful ideas.
Our study sought insights from creative individuals utilizing AI systems and generative AI, aiming to go beyond previous conceptualizations focused solely on technology use. Through a mixed-method approach, we adopted an exploratory design to uncover the intricacies of creative practice with AI systems and identify new avenues for enhancing the creativity of marketing strategies.
The Instrument, Sample, and Data Gathering
For Study 1, we employed exploratory personal interviews and open-ended questionnaires as our main data-gathering methods. Initially, we conducted in-depth interviews with three artists (two music composers and one sculptor) and the chief digital officer of a lifestyle magazine in Taiwan, all actively using generative AI in their creative processes. These interviews, lasting around 45 minutes each, were recorded to grasp how AI influenced their creative activities and served as our initial analysis to familiarize ourselves with the subject.
In the second stage, we reached out to AIArtists.org, the largest community of artists exploring AI in their creative endeavors. Our goal was to gather insights from the experiences of these artists. We collected data from 37 international community members who agreed to participate in the study (see Appendix A). The respondents (68% male, 32% female) varied in age and geographical representation. All the respondents were multidisciplinary artists with experience using different types of AI systems, with 43% specifically using generative AI and GANs, as indicated in Appendix A.
We administered an open-ended questionnaire, allowing respondents to provide detailed answers to three analytical questions about their artistic practice with AI:
Describe a specific artwork created using AI, including the AI system used, and the artwork's features. What questions are you exploring using AI? How has technology impacted your creative practice?
To delve deeper into emerging themes and capture nuanced perspectives, we conducted three in-depth interviews with chief marketing officers from three large B2C companies. These interviews were tailored to select managers who could provide rich experiential insights, ensuring meticulous rigor and profound validation of our study's insights within the marketing domain.
Data Analysis
For this research, we adopted a structured data analysis approach comprising three interconnected phases, each serving a specific purpose to fulfill the study's objectives.
Phase 1: Qualitative analysis using the phenomenological approach
In this initial phase, we focused on qualitative data analysis, employing a phenomenological approach to gain insights into the experiences and perspectives of creative individuals using AI. Systematic analytical procedures, including categorization, abstraction, comparison, dimensionalization, integration, iteration, and refutation (McCracken 1988), were applied. The rigor and reliability of our analysis were ensured through the engagement of three independent coders with specialized training. Category definitions were refined through collaborative discussions to enhance the accuracy and clarity of the coding system (Thomas and Harden 2008). An audit trail was diligently maintained following Bowen's (2009) guidelines, ensuring credibility and transparency. The analysis revealed consistent themes and patterns across diverse cultural contexts, reinforcing the validity of the findings.
Phase 2: Structural topic modeling
In this phase, we utilized structural topic modeling (STM), a machine learning technique designed for natural language processing. The primary aim was to identify key themes and topics within the corpus of written responses, with three main objectives:
The integration of STM enriched the insights by refining our topic analysis, demonstrating the robustness of our findings.
Phase 3: Linguistic characteristics analysis
In this phase, we employed the Linguistic Inquiry and Word Count (LIWC2015) software to analyze the emotional, cognitive, and structural components of written responses. The goal was to interpret word usage and its psychological implications for each of the three identified themes (Pennebaker et al. 2015). This analysis served two main objectives:
Additionally, we used the Speak AI software to assess user sentiment.
Results
In the artistic domain, the results showed that AI's integration is prompted by two primary triggers: problem-solving and curiosity. Artists perceive AI as a versatile tool, utilizing it to address artistic challenges and fuel their exploration.
Theme 1: AI as an instrumental new resource
Artists view AI, machine learning, and generative AI as novel resources in their creative toolkits. They see AI as a medium for both technological exploration and artistic expression. It's acted as a new medium to explore both on the technological side (What else can this do?) and on the artistic side (How do I control or guide this thing, to express myself). (Male artist and Google engineer) For me, machine learning is a tool, just like ink or charcoal. It does different things and offers a different history when you choose to work with it. (Female painter exploring machine learning's creative potential)
Theme 2: AI as a tool to explore possibilities
Artists perceive AI as a catalyst for imaginative thinking, simulating new artistic ideas, and expanding creative horizons. I think it is expanding human creativity to new levels. The machine is good at exploring possibilities, providing lots of new ideas in the art space. (Male artist and CEO of an AI technology startup for the art market) My relationship with technology is fundamental. I’m fascinated by how AI might explore the potential of choreography. The canvas is way bigger. (Male choreographer)
Theme 3: AI as a tool to deconstruct the creative process:
AI is seen as a collaborative tool that aids artists in understanding and deconstructing their creative processes. It fosters self-awareness and challenges traditional views of creativity. The “artist” must tune the imagery put into the “machine” to craft its interpretation of nature. (Male designer and technologist) Engaging with mark-making in collaboration with a robot means not always knowing what I’m doing. It's a new stage for examining authorship and agency. (Female painter)
However, some artists expressed discomfort with relinquishing control to AI algorithms, questioning AI's agency and its impact on traditional notions of creativity (Dietvorst, Simmons, and Massey 2015). When people think about AI there is a tendency to ascribe considerable agency. It starts to question, who is in control and who we want to be in control. (Female painter)
Figure 2 illustrates the distribution of 37 respondents based on their motivations for considering AI systems. The comparison between artists using generative AI and those not using it reveals distinct motivations. As shown in the figure:
These results highlight that while both groups recognize the value of AI as a new resource, users of generative AI are more inclined to see it as a means to explore possibilities and deconstruct the creative process.
Distribution of Respondents by the Reasons Provided.
Topic modeling and dimensional analysis
To understand how AI impacts creative processes, we conducted a quantitative analysis using STM on comments from 37 respondents identifying nine topics (see Table 3). Unlike other methods like principal component analysis and factor analysis, STM distinguishes between different data classes, with each topic characterized by keywords with the highest frequency of occurrence in the corpus, indicating a strong association with that specific topic compared with others. Furthermore, we explored the proximity between various topics and arranged them along three principal dimensions derived through collaborative discussions among three expert coders proficient in AI, creativity, and marketing:
Dominant Topics in Artists’ Experiences Using AI.
Topic numbers are autogenerated by the algorithms used (STM).
Labels are assigned by the researchers according to the underlying commonalities.
The percentage for each topic in STM is calculated by determining the proportion of words in a document that are assigned to that particular topic. This is done by considering the prevalence of terms within each topic relative to the entire corpus. The proportions are then normalized to ensure that they sum to 100% across all topics within the document. This process provides insights into the specific themes captured by each topic within the structural context of the document.
We validated these findings through collaborative discussions among three coders, ensuring reliability and consistency. Table 3 provides detailed results from STM, while the coders’ discussion offers nuanced insights into each topic's contributions to the three dimensions. Additionally, coherence measures were used to validate logical and semantic coherence within each topic, further enhancing validation.
Descriptive analysis of text linguistic characteristics
The LIWC2015 tool facilitated a systematic examination of linguistic features in the text, quantifying dimensions like analytical thinking, social awareness, authenticity, and emotional tone. Analytical thinking, as defined by the ability to approach a topic thoughtfully and systematically, scored notably high at 72.4. This highlights the structured approach of respondents in their reflections. Awareness of relative status (clout), representing individuals’ awareness of their social positioning in discussions related to AI in the creative process, registered at 53.7. This numerical score suggests a moderate understanding of social positioning in discussions about AI and creativity. The authenticity metric scored 50.4, indicating a modest level of sincerity and transparency in expressing thoughts on AI's integration into creative processes. Emotional tone received a high score of 80.4, reflecting strong positive feelings associated with AI's role in creativity. 1
Additionally, the Speak AI software assessed user sentiment, revealing varying positive-to-negative ratios across different themes (Figure 3). This indicates the strength of positive resonance toward specific aspects of AI integration into creativity. For instance, Theme 3 (tool to deconstruct the creative process) showed a higher positive-to-negative ratio, indicating stronger positive sentiment, likely due to its perceived value in artistic development. Theme 1 (instrumental new resource) also exhibited positive sentiment, though with some reservations, as reflected in its positive-to-negative ratio of 7.
Overall Sentiments: Positive-to-Negative Ratio.
Table 4 illustrates findings from our analyses, shedding light on the relationship between AI, creativity, and user experience. Our analyses reveal the multifaceted role of AI in creativity and marketing, highlighting its function as both a tool and a catalyst. Through coding and categorizations, we identified AI as an instrumental resource, a means to explore new creative possibilities, and a helper in deconstructing the creative process. Topic modeling added depth by detailing specific dimensions within these themes, while a linguistic analysis provided sentiment insights, showing generally positive perceptions but also some reservations and discomfort. This integrated approach offers a nuanced understanding of AI's potential and challenges, guiding marketers and creatives in effectively leveraging AI for innovation.
Summary of Findings of the Three Analyses in Study 1.
Study 2: How Do AI Systems Enhance the Creativity of Their Users?
In Study 2 we analyzed two datasets to understand how AI systems and models enhance creativity and generate novel and useful outcomes.
The first dataset, DeepIndex (https://yiu.co.uk/deepindex/), includes 1,036 AI systems from 2015 through 2021 across 19 areas. These AI systems, as defined by the European Union's definition (European Parliament and Council of the European Union 2024), operate with varying autonomy levels and can influence physical or virtual environments. Examples include virtual assistants like Siri or Alexa and advanced language models like GPT-4. The second dataset, from Hugging Face (https://huggingface.co/datasets?sort=trending), includes 241,292 AI models (61% generative AI) from 2022 through 2024, classified into 35 tasks. These models are designed to perform tasks by learning from data, recognizing patterns, making decisions, and solving problems. The tasks within the Hugging Face dataset represent specific objectives that the models strive to achieve, such as language translation, question answering, or image recognition.
We categorized these AI systems and model based on their creative prowess. This analysis provides insights into how AI intersects with and enhances creative processes, offering valuable information for marketing practitioners and researchers about AI-driven creativity enhancement.
Methodology
We conducted a supply-side analysis to identify and classify the unique characteristics of AI systems and models that contribute to creativity. Our focus was on understanding how these systems and models can generate results that are both novel and useful. To gain deeper insights, we conducted personal interviews with managers from companies that provide AI services, such as installation, maintenance, and customization of AI systems.
For the 1,036 AI systems in the DeepIndex dataset, we reviewed press releases and coded secondary data to create a structured dataset. This dataset included system descriptions, launch dates, and specific interactive marketing business applications. We also analyzed a second dataset of 241,292 AI models classified into 35 tasks (Hugging Face dataset).
Our assessment process involved three expert judges who independently evaluated each AI system (DeepIndex dataset) and each task for every group of AI models (Hugging Face dataset). Regarding this second dataset, we also conducted a follow-up analysis on a sample of 350 Hugging Face models, each associated with specific tasks. This allowed us to directly evaluate each model's novelty and usefulness within the task it performed.
We adopted the Consensual Assessment Technique proposed by Amabile (1983), as detailed in Appendix B. The judges assessed the dimensions of “novelty” and “usefulness,” derived from the creative products semantic scale (Besemer and O’Quin 1986) using a seven-point Likert scale. The “novelty” dimension, including three items, “new,” “unique,” and “unusual,” considered the ability of the systems and group of AI models to generate new processes, concepts, and other elements of newness. The “usefulness” dimension, measured by three items, “feasible,” “useful,” and “operable,” evaluated the system's ability to address real market problems and meet real needs for marketing strategies and tactics. The judges assessed novelty and usefulness based on the application context in a specific use case.
To initiate the evaluation process, each judge was prompted to engage with the object of analysis.
For the DeepIndex dataset, judges were provided with links to press releases corresponding to each AI system, which they reviewed to familiarize themselves with each AI system's scope, objectives, and requirements. For the Hugging Face dataset, judges were directed to visualize the AI models associated with each task, enabling them to understand the scope and objectives.
We conducted a factor analysis on the six attributes, three each for novelty and usefulness, which led to the formation of these two dimensions. These items for each dimension demonstrated high internal consistency (Cronbach’s α > .7). Interrater reliability was assessed, and judges agreed on novelty measurements in 71.3% of cases and on usefulness measurements in 68.9% of cases.
A median split was performed to create a 2 × 2 matrix to understand the distribution further. Subsequently, a K-means clustering algorithm was applied as a robustness check. Using the elbow method utilizing the within-cluster sum of squares across varying cluster numbers, we identified four main clusters as the optimal number for analysis. The analysis confirmed that the results of the median split were consistent with the clustering, supporting the validity. The same analysis was conducted for each task of the AI models in the second dataset. This approach helped us understand the potential impact of each AI system and task of AI models to enhance creativity and generate novel and useful outcomes, and its implications for marketing strategies and tactics.
Results
The findings emerging from the analysis of the first dataset (DeepIndex) comprising 1,036 AI systems show that usefulness and novelty are distinct constructs, with a relatively low negative correlation of −.192. Descriptive statistics for each variable are as follows: usefulness: median = 5.00, mean = 4.89; novelty: median = 3.00, mean = 3.06.
Figure 4 provides an overview of the 2 × 2 matrix and the emerging four clusters. The judges, including one of the authors, conducted a detailed analysis of the AI systems within each cluster to establish a distinct profile. The same analysis was conducted on the second dataset of 241,292 AI models classified into 35 tasks (Table 5) to measure the level of novelty and usefulness of each task for every group of AI models.
Cluster 1 (19.51%): low usefulness and low novelty. AI systems in this cluster focus on replicating specific cognitive tasks, demonstrating lower creativity (low novelty and usefulness). They excel in recognition, analysis, and decision-making tasks within defined parameters. They are also utilized for automating business processes, leveraging machine learning for consumer insights, and implementing intelligent marketing automation. Cluster 2 (19.02%): high novelty and low usefulness. AI systems in this cluster display medium-level creativity, particularly in exploring the design space. These systems can combine various concepts using machine learning to remix and recombine data. Cluster 2 comprises AI-augmented management software optimizing warehouse flows and robots enhancing production line efficiency in intelligent factory automation. The creativity process involves additive or subtractive remixing, generating innovative approaches. Cluster 3 (36.72%): low novelty and high usefulness. AI systems in this cluster excel in practical tasks with a medium level of creativity. These systems focus on solving well-defined problems using established methodologies. Although they may bring some innovation in terms of application and optimization, they do not aim for groundbreaking creativity. Instead, their main priorities are functionality, reliability, and efficiency. Examples of AI systems in this cluster include customer service chatbots, data entry and validation systems, and expense report automation. Cluster 4 (24.75%): high novelty and high usefulness. AI systems in this cluster demonstrate high creativity with a focus on discovering new patterns. These systems can compose music, create artwork, design objects, and engage in tasks such as writing songs or poems. DALL-E 2, DaVinci, and ChatGPT are part of this cluster. These systems showcase high creativity by generating unique visual outputs, creating coherent text compositions, and engaging in conversational interactions. Advances in deep learning AI enable them to perform a variety of creative tasks.
Clusters and Centroids of Data Points with Proportional Sizes in Python (n = 1,036; DeepIndex Dataset).
Creativity Types of AI Systems (2024) from the Hugging Face Dataset (n = 241,292).
Notes: G = generative, NG = nongenerative.
The analysis of AI systems led to the identification of four clusters, each representing a unique combination of novelty and usefulness. However, Clusters 2 and 3 were found to share a characteristic, mixed-effect creativity, whereby one variable (novelty or usefulness) scored high while the other scored low. Instead of having four separate creativity types for each cluster, we decided to group Clusters 2 and 3 under “mixed-effect creativity.” This decision was driven by the aim to provide a more nuanced understanding of creativity, highlighting the interplay between novelty and usefulness. The following creativity types were observed:
Low creativity, represented by Cluster 1 (low novelty and low usefulness). Mixed-effect creativity, represented by two clusters: Cluster 2, characterized by high novelty and low usefulness, and Cluster 3, characterized by low novelty and high usefulness. Both of these clusters are situated in the off-diagonal segments of a matrix. This positioning indicates that they score lower on one of the variables, either novelty or usefulness. High creativity, demonstrated by Cluster 4 (high novelty and high usefulness).
The evolution of four clusters and their associated creativity types (Figure 5) from the initial dataset spanning from 2015 to 2021 was observed. Cluster 4 is notably still in its early stages but shows progressive growth. Over time, the gap between Cluster 1 and the other clusters is decreasing, suggesting changes in the distribution of creativity types over the observed period.
Evolution of Clusters over Time (2015–2021).
To focus on generative AI systems introduced in the last two years (2022–2024), we utilized data from Hugging Face. The dataset comprised 241,292 AI models categorized into 35 tasks. Among these, 146,768 were generative AI systems, and 94,524 were nongenerative AI systems (Table 5). The assessment of usefulness and novelty for each task was conducted by the same three judges, following coding guidelines outlined in Appendix B. These three coders evaluated the novelty and usefulness of each task, leading to the identification of four clusters. The results presented in Table 5 are derived from performing a median split, which was used to construct a 2 × 2 matrix. For each cluster we analyzed various aspects of the AI models, including their behavior, performance, and the domains of the tasks they perform.
Cluster 1 (26.79%): low usefulness and low novelty. Cluster 1 comprises AI models that exhibit attributes aligned with human cognitive abilities. These models engage in tasks such as learning from experience through reinforcement learning, identifying objects within images through object detection, and comprehending language through text classification. The tasks predominantly involve nongenerative domains like tabular regression, classification, and robotics, mirroring cognitive processes related to decision-making, pattern recognition, and problem-solving. While other clusters explore novel patterns or focus on generative tasks, Cluster 1's emphasis is on practical, nongenerative tasks, effectively replicating human cognitive abilities. Cluster 2 (20.15%): high novelty and low usefulness. Models in Cluster 2 display novelty with a focus on mixed-effect creativity. These models explore the design space and aim to discover new patterns through tasks such as unconditional image generation, zero-shot image classification, mask generation, zero-shot object detection, token classification, question answering, translation, and image-to-video, text-to-video, text-to-3D, image-to-3D, image-to-image, or text-to-image conversion. While these models push the boundaries of creativity and innovation across various media formats, their practical applications are limited, resulting in low usefulness. Cluster 3 (1.85%): low novelty and high usefulness. Cluster 3 consists of models that prioritize practical utility. These models rely on established techniques and methods to perform tasks such as tabular classification, video classification, depth estimation, image feature extraction, sentence similarity detection, and voice activity detection. Their focus on tasks with clear practical benefits demonstrates high usefulness despite exhibiting low novelty. Cluster 4 (51.21%): high novelty and high usefulness. Cluster 4 features models that excel in discovering new patterns and are primarily generative. These models engage in a variety of tasks, including text and image generation and audio processing, such as text-to-speech conversion and automatic speech recognition. By balancing high novelty with high usefulness, the models in Cluster 4 generate content across various domains, showcasing their ability to innovate and maintaining practical applicability.
In our study, we applied novelty and usefulness ratings to tasks completed by a cluster of AI models. A follow-up analysis was conducted on a sample of 350 Hugging Face models, each associated with specific tasks. This allowed for a direct evaluation of each model's novelty and usefulness for the task it performed.
Through the two datasets, this study classifies AI systems’ creativity levels, distinguishing between nongenerative and generative AI. For low creativity (Cluster 1), nongenerative AI ensures predictability. Mixed-effect creativity (Clusters 2 and 3) is observed in deep learning and generative AI, introducing valuable unpredictability. Well-managed generative AI (Cluster 4) brings accidental creativity, offering novel solutions but requiring careful evaluation for alignment with goals and ethical considerations due to its inherent randomness.
AI systems are larger constructs that use one or more AI models to perform tasks. These models are specific implementations of AI methods, which are the techniques used in AI. It is crucial to understand the strengths and limitations of different AI methods, such as deep learning and classical machine learning, as they lead to various adoption patterns in creative and marketing processes. Creative processes typically involve the generation of new ideas, concepts, or solutions, which could include designing a new product, creating an advertising campaign, or writing content. In the context of AI, these processes might involve using AI systems to generate new ideas or content, such as creating artwork, composing music, or writing text. In contrast, marketing processes refer to activities related to promoting and selling products or services. This could include market research, advertising, sales, distribution, customer service, and more. In the context of AI, these processes might involve using AI systems to analyze market trends, personalize advertising, or automate customer service.
Marketers need to understand these differences to ensure effective decision-making and maintain necessary checks and balances. While deep learning is beneficial for processing complex data, classical machine learning excels at capturing variations. Recognizing nuances and understanding the implications of machine learning are crucial for responsible AI use. Creative marketers should remain vigilant about potential biases, ethical considerations, and interpretability challenges. This awareness ensures effective and responsible use, preserving the integrity of artistic and marketing outputs.
In marketing processes, AI systems showcase varied creativity levels. In low creativity, nongenerative AI aids targeted advertising with predictable outcomes. Medium creativity involves generative AI, enhancing social media content creation and introducing dynamic elements. High creativity is demonstrated when well-managed generative AI provides personalized product recommendations, necessitating careful evaluation. These real-world examples illustrate the spectrum of creativity levels within AI-driven marketing processes, from predictability in advertising to innovation in content creation and recommendations.
Integrative Framework
The findings from our two studies led to the development of an integrated framework, outlined in Figure 6, illustrating three ways AI can impact marketing creativity.
Conceptual Framework: The Influence of AI on Marketers’ Creativity.
Inspire Agile Methods
In this category of creativity, AI systems excel at enhancing businesses by understanding customer behavior, streamlining decision-making, and automating routine tasks—a concept resonating with creative cognition theory (Balietti and Riedl 2021). These AI applications not only replicate human thinking (as highlighted in Study 2) but, as discussed in Study 1, also serve as invaluable resources for marketing managers, inspiring agile methodologies within the framework.
For instance, dynamic pricing optimization, as seen with airlines like Delta and retail giants like Walmart, involves continuous analysis of market conditions, demand fluctuations, and competitor pricing to set prices to maximize revenue. This automation allows pricing analysts to focus on more strategic endeavors, such as crafting creative promotions and marketing campaigns. Adobe Sensei's campaign automation and performance insights across multiple channels optimize campaign performance, enabling marketers to apply creative solutions for better ROI (Adobe 2024), echoing the cognitive processes of creative problem-solving emphasized in creative cognition theory (Balietti and Riedl 2021).
Spotify's personalized recommendations, driven by machine learning algorithms, analyze a user's listening habits, mimicking human thought and preferences to make music recommendations based on their history and musical preferences. In these cases, AI systems inspire agile methods, empowering marketers to concentrate on the more creative and strategic aspects of their work. This category exemplifies how AI, as an instrumental new resource, streamlines decision-making and empowers marketers to concentrate on innovative strategies.
Augment the Human Creative Process
This second category aligns perfectly with the outcomes of Study 2, considering AI's ability to augment the human creative process by uncovering new patterns or offering practical solutions and facilitating, as discussed in Study 1, the deconstruction of the creative process.
AI systems contribute significantly to retailers and brands aligning with customer values and creating innovative breakthrough products, services, and experiences. This resonates with the theory of creativity and its recent developments (Vitrano, Altarriba, and Leblebici-Basar 2021), emphasizing the role of AI in helping the generation of novel and valuable ideas. Additionally, AI assists in the evolution of business and revenue models to address changing consumer needs in the dynamic business environment.
Generative AI employs advanced technologies to uncover new patterns and inspire marketers to think creatively. It serves as a valuable tool that stimulates the deconstruction of the creative process. Notable examples include Zalando's use of generative AI in fashion design, allowing the company to analyze and offer unique designs aligned with customer preferences (Karl’s Notes 2022). OpenAI's DALL-E, generating synthetic images, and Netflix’s AI-driven creative decision-making in content production further exemplify the augmentation of human creativity through AI applications. These instances showcase the role of AI in fostering innovative thinking and generating creative outputs.
Inspire Out-of-the-Box Thinking
This category refers to AI's ability to explore the design space, as outlined in Study 2, including all the AI systems driving real-time hyperpersonalization at scale, attracting and engaging consumers while differentiating businesses from competitors. This ability not only makes AI systems a new resource or a tool to deconstruct the creative process but also enables individuals to explore possibilities (as emerged in Study 1). This reflects the interplay between explicit knowledge and implicit, unconscious processes in creative problem-solving described in the explicit–implicit interaction theory (Altarriba and Avery 2021; Helie and Sun 2009).
AI systems, particularly those utilizing technologies like GANs, delve into the design space to spark imaginative marketing strategies. Examples illustrating this influence include Salesforce's Einstein platform, which provides tailored recommendations and performance insights for marketing campaigns, enhancing efficiency and effectiveness (Salesforce 2024). IBM’s Watson, employing deep learning and natural language processing, analyzes social media to derive creative marketing strategies, showcasing how AI can inspire out-of-the-box thinking through the integration of explicit and implicit knowledge in creative processes. Both examples illustrate how AI systems delve into the design space, inspiring imaginative marketing strategies and contributing to out-of-the-box thinking.
While these three categories outlining how AI may influence creativity have distinct characteristics, they exhibit an overlapping and dynamic interplay that enhances the creative potential of marketing professionals. For instance, the first category, “inspire agile methods,” supports the third category, “inspire out-of-the-box thinking,” by automating tasks and enabling marketers to engage in innovative strategies. Simultaneously, the second category, “augment the human creative process,” benefits from AI-generated insights originating from automation-centric processes of the first category or other mechanisms designed to elevate creative decision-making. Figure 6 summarizes these three influences, categorizing AI's impact into dimensions beyond generative capabilities, revealing that AI not only enhances creativity but also helps marketers make informed decisions, inspire novel thinking, and create unique customer experiences. This structured discussion provides a nuanced understanding of AI's relationship with creativity in marketing.
In essence, our integrated framework, shaped by the findings emerging from the two studies, provides a structured and direct link between the empirical results of our research and the practical implications for AI's role in marketing creativity.
Validation
To validate the proposed framework, we conducted three workshops. The initial workshop involved 20 scholars and PhD students at a U.K. business school. The second, held at Meta’s headquarters in New York City, included over 70 participants, comprising chief marketing officers, members of the Association of National Advertisers Global CMO Growth Council, and marketing scholars. A subgroup of 15 participants engaged in a group discussion on the three types of creativity. The third workshop involved 46 marketing professionals, including CEOs, editors-in-chief, digital directors, and marketing managers from 45 country editions of a global lifestyle and fashion magazine. Additionally, nine in-depth interviews were conducted with key leaders to further reinforce the study's findings.
Insights gathered from these validation activities, including workshops and interviews, added depth and credibility to the identified ways in which AI can enhance creativity in marketing. Results confirmed that AI has the potential to enhance creativity in three main ways: (1) inspiring agile methods through the reduction of repetitive tasks; (2) augmenting the creative process with AI-powered tools generating creative outputs, such as images or paintings, via simple text commands; and (3) inspiring out-of-the-box thinking using generative models.
Despite the positive aspects, concerns were raised about privacy, clear copyright regulations, and the need to ensure proper AI tool usage to avoid hindering creativity. Participants also raised concerns about biases of the AI systems and the hallucination of generative AI. Discussions with professionals unveiled key insights:
Enhancing efficiency. Marketing professionals acknowledge AI's potential to liberate time and boost efficiency, supporting and complementing their creative efforts. Transformative role. In connection with deep learning and generative AI, AI's transformative role is underscored, fostering innovative thought processes and pushing the boundaries of conventional thinking. Skill development. Managers using generative AI and deep learning emphasized the importance of acquiring requisite skills training programs for effective AI utilization in marketing roles. Concerns and opportunities. While concerns about potential job transformation and displacement due to AI were raised, AI's role in sparking novel perspectives and complementing traditional approaches was noted, as demonstrated in our studies.
The validation stage, guided by seasoned marketing professionals, consolidated the authenticity and real-world relevance of the study's outcomes, offering a multifaceted view of AI's influence on creativity in the marketing landscape. Results reaffirmed the validity and practical applicability of the dimensions identified in Study 1, providing valuable insights into the dynamic interplay between AI and marketing creativity.
Pitfalls of AI in the creativity generation process
Understanding the collaboration between AI and humans in generating creative outputs is crucial. It is not a matter of “AI versus human,” but rather “AI and human.” A significant challenge, as highlighted in McKinsey’s (2022) findings, is the lack of explainability in AI algorithms, especially those using deep learning techniques. This lack of transparency in the reasoning behind AI-generated outputs can lead to a trust deficit and discomfort among artists, as revealed in Study 1. Future research should focus on addressing these trust issues and exploring ways to alleviate discomfort regarding AI-generated outputs.
In addition to explainability, biases within AI algorithms pose another critical challenge for creative artists and managers. For those without a deep understanding of the underlying mechanics, identifying and understanding these biases can be daunting. It is also essential to recognize situations where human involvement may be a better choice than AI, considering the potential for higher accuracy and more innovative solutions in human creativity. The creative process is interconnected, requiring careful consideration of issues like the rise of deepfakes and copyright infringement resulting from AI-enhanced solutions.
The significance of challenges tied to AI-enhanced creativity is undeniable. Our study provides a novel perspective by fostering discussion on these issues. Study 1 delved into individual experiences, uncovering challenges related to trust issues and discomfort in the face of AI-generated outputs. This exploration adds a nuanced layer to our comprehension of how individuals navigate the complex landscape of AI in the creative process. Study 2 involved empirical analysis of a large dataset of AI systems, offering a careful evaluation of AI's impact on creativity.
By combining insights from individual experiences with a quantitative assessment of AI systems, our research extends the current understanding of computational creativity and human creative processes, contributing to the ongoing discourse on AI-driven solutions in marketing and beyond.
Conclusions and Future Research
This study sheds light on the transformative impact of AI on creativity within marketing, particularly in the domain of interactive marketing. By exploring the intersection of computational creativity and creative cognition, our research provides valuable insights into how AI empowers individual creativity and enhances marketing efficiency and effectiveness. Through our initial study, we uncovered three key ways in which AI enhances the creative process: as a novel instrumental resource, a tool for exploring innovative possibilities, and a method for deconstructing the creative process itself.
Subsequent empirical analysis of AI datasets uncovered four clusters of observed creative features. Findings from the two studies suggest three actionable strategies for managers to leverage AI in boosting creativity and refining marketing strategies: (1) inspiring agile methods, (2) augmenting human creative processes, and (3) inspiring out-of-the-box thinking.
By integrating AI tools, marketers can streamline routine tasks, enabling greater focus on strategic thinking, innovation, and decision-making. AI algorithms facilitate the analysis of large datasets, uncovering patterns and providing valuable insights that inform creative decision-making and enable personalized interactive content. Notably, generative AI systems emerge as catalysts for unprecedented creativity in content generation, driving improved customer engagement and long-term profitability (Pagani and Champion 2023; Wind et al. 2023). However, alongside the potential benefits of AI adoption come associated risks, including algorithmic bias, privacy concerns, and potential hallucination by generative AI. To navigate these challenges, we advocate for a cautious approach to AI adoption, emphasizing the importance of consumer trust and ethical considerations in interactive marketing.
Looking ahead, we propose a research roadmap that directs future exploration at the intersection of AI and creativity within marketing. We propose four main research areas:
Assessing the influence of AI on marketing effectiveness. Exploring the utilization of AI tools in strategic decision-making processes. Evaluating the impact of AI on creative decision-making within interactive marketing contexts. Assessing strategies to build trust in AI recommendations.
Our study's findings serve as hypotheses for future experiments aimed at enhancing the creativity of marketing and interactive marketing strategies. To assess the impact of generative AI, we propose employing experimental methodologies that blend quantitative studies with qualitative insights from open-ended responses. We also recommend incorporating nonverbal measures such as neurological measures and eye tracking to gain a holistic understanding that spans all five senses. Through continuous meta-analysis of existing studies and fostering interdisciplinary collaborations, we aim to forge empirical generalizations about optimizing AI to enhance creative processes in marketing.
Ensuring convergence validity across diverse methods will provide holistic insights into these pressing questions. It is also important to address potential challenges, including ethical considerations, algorithmic bias, and risk mitigation strategies associated with AI adoption in marketing as well as challenges related to data privacy and general data quality.
By embracing continuous meta-analysis and innovative research designs, we can unlock groundbreaking applications of AI that bolster creativity across all facets of marketing. This will usher in an era in which strategic decisions are not only data driven but also ingeniously inspired.
Footnotes
Appendix A
Sample Description of Study 1 Respondents
Appendix B
Coding Guidelines for Judges Using Consensual Assessment Technique (Amabile 1983)
Thank you for your dedication to employing the Consensual Assessment Technique (CAT) in the evaluation of AI applications. This method is designed to bolster the reliability and validity of our assessment by fostering consensus among judges. The following guidelines outline the steps to implement CAT in the evaluation of “Novelty” and “Usefulness.”
To evaluate each AI system, please click on the provided link to access the press release for more information regarding the context of the application. This additional context may provide insights that enhance your evaluation.
- Familiarize yourself with each task (by clicking the link to the press release for the AI system in the dataset 1) or visualizing the AI models referring to the task analysed (dataset 2). - Understand the scope objective and requirements for each task.
- Assess the originality, innovation, and uniqueness on a Likert scale (1–7), where 1 signifies “Strongly Disagree,” and 7 indicates “Strongly Agree." - Independently rate each item (New, Unique, Unusual) based on your judgment.
- Evaluate practical value, feasibility, and operability on the Likert scale (1–7) following the same guidelines. - Independently rate each item (Feasible, Useful, Operable) based on your judgment.
- Engage in a structured group discussion after individual ratings. - Share your perspectives on each item, providing insights into your rationale and considerations. - Collaborate to identify areas of agreement and differences, working toward a shared understanding.
- If significant discrepancies persist, participate in a constructive discussion to reconcile differences. - Consider rerating based on insights gained during the discussion.
- Document the group discussion, highlighting areas of consensus and any conflicts that were resolved. - Clearly articulate the rationale for final ratings to ensure transparency.
- Generate final consensus ratings for each item under Novelty and Usefulness. - These ratings represent a collective judgment based on the group discussion.
Please ensure the submission of final consensus ratings within the specified timeframe.
Your commitment to following the Consensual Assessment Technique is paramount for a robust and objective evaluation process. Should you have any questions or require further clarification, please do not hesitate to reach out. Thank you for your dedication and valuable contributions to this evaluation.
Editor
Arvind Rangaswamy
Declaration of Conflicting Interests
The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding
The authors received no financial support for the research, authorship, and/or publication of this article.
