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A Framework for Collaborative Artificial Intelligence in Marketing
Abstract
We develop a conceptual framework for collaborative artificial intelligence (AI) in marketing, providing systematic guidance for how human marketers and consumers can team up with AI, which has profound implications for retailing, which is the interface between marketers and consumers. Drawing from the multiple intelligences view that AI advances from mechanical, to thinking, to feeling intelligence (based on how difficult for AI to mimic human intelligences), the framework posits that collaboration between AI and HI (human marketers and consumers) can be achieved by 1) recognizing the respective strengths of AI and HI, 2) having lower-level AI augmenting higher-level HI, and 3) moving HI to a higher intelligence level when AI automates the lower level. Implications for marketers, consumers, and researchers are derived. Marketers should optimize the mix and timing of AI-HI marketing team, consumers should understand the complementarity between AI and HI strengths for informed consumption decisions, and researchers can investigate innovative approaches to and boundary conditions of collaborative intelligence.
... While the operational advantages of service robots, such as reduced wait times and improved service consistency, are well-documented, there is a notable gap in research examining their comprehensive impact on customer experience across different stages of the customer journey (Van Doorn et al., 2017). Existing studies have often focused on short-term interactions, failing to capture the evolving nature of customer-robot relationships over time or how these interactions influence customer loyalty and longterm satisfaction (Huang & Rust, 2021). Furthermore, factors that motivate or inhibit robot adoptionsuch as cultural attitudes, demographic differences, privacy concerns, and ethical considerations-have been underexplored, yet they play a significant role in shaping customer perceptions of robots in service contexts Bartneck et al., 2024). ...
... Despite their operational advantages, the chapter emphasizes the importance of complementing robotic efficiency with human empathy to ensure customer satisfaction, particularly in high-contact service industries. This study contributes to the growing discourse on human-robot interaction, offering practical insights for organizations and suggesting future research directions, such as longitudinal studies on customer loyalty and the ethical implications of widespread service robot deployment (Huang & Rust, 2021;Belanche et al., 2021). ...
... Concerns about job displacement due to automation, including service robots, contribute to resistance against their adoption. Employees may perceive robots as a threat to job security, leading to resistance within organizations, especially in laborintensive sectors like retail (Huang & Rust, 2021). There are also ethical concerns about robots' increasing role in human-centric settings, where some argue that reliance on automation reduces opportunities for meaningful human interaction and negatively impacts social skills (Selesi-Aina et al., 2024). ...
Service robots are increasingly transforming customer service by automating tasks traditionally performed by human employees, creating a hybrid of technological and human interaction across various industries. This chapter examines the integration of service robots into sectors such as hospitality, retail, healthcare, and finance, focusing on their impact on customer experience (CX)The findings highlight that motivations for adopting service robots include productivity gains, novelty, and convenience (Ivanov & Webster, 2019), while barriers such as high costs, privacy concerns, and limited social acceptance remain significant (Bartneck et al., 2020; Gursoy et al., 2019). This study contributes to the growing discourse on human-robot interaction, offering practical insights for organizations and suggesting future research directions, such as longitudinal studies on customer loyalty and the ethical implications of widespread service robot deployment (Huang & Rust, 2021; Belanche et al., 2021a).
... Artificial intelligence (AI) is revolutionizing our world and marketing practices are not an exception (Ameen et al., 2022;Huang & Rust, 2022). Traditionally, the advertising process comprises advertising research, market analysis, strategic planning, ad creation, media mix, media planning and buying, performance evaluation, and feedback (Qin & Jiang, 2019). ...
... However, there are still several areas that need further research. One of them is the use of AI for creative purposes in the advertising domain (Ameen et al., 2022;Huang & Rust, 2022;Hubert et al., 2024). It is necessary to compile and organize previous research about the uses of AI in creativity (Ameen et al., 2022;Rincón, 2023). ...
... AI may bring some benefits to advertising agencies in terms of convenience and lower costs (Arango et al., 2023;Huang & Rust, 2022). Moreover, artificial intelligence can enhance the creation and development of advertising campaigns (Enache, 2020;Rincón, 2023), conferring companies a huge competitive advantage (Sabharwal et al., 2022). ...
The advances in technologies and the irruption of artificial intelligence (AI) represent a challenge for advertising agencies and for creative management. Generative AI may be used to enhance creativity and to create content that depicts a highly convincing version of reality. The use of AI in advertising has been related to terms such as synthetic advertising, computational creativity or intelligent advertising. This chapter aims to explain the use of artificial intelligence in the following areas of advertising creativity: text, image, voice, audio, music and video generation. It also addresses the impact that the use of artificial intelligence may have on the recipient of the advertising campaign. The results obtained by recent research are identified and organized depending on whether receivers are found to react positively or negatively to the use of AI in creativity. Main conclusions and future lines of research are presented at the end of the chapter.
... GenAI may support and augment human input in a synergistic relationship established by integrating AI with human creativity instead of replacing human roles (Anantrasirichai and Bull, 2022). Huang and Rust (2022) suggested that GenAI has the potential to enhance human capacities in strategic planning and creative processes, leading to improved marketing effectiveness and innovation. GenAI can boost organisational innovation by supporting idea generation and expediting repetitive tasks, enabling marketers to focus on higher-value activities (Cillo and Rubera, 2024). ...
... According to the data structure, the framework delineates four dimensions of factors influencing the behavioural intention of marketing professionals: triggers, convenience, facilitating conditions and fear of becoming obsolete. Consistent with recent research (Huang and Rust, 2022;Lin and Ruan, 2023;Al Naqbi et al., 2024), the findings concur that GenAI is poised to reshape the communication landscape by enhancing content creation and marketing communication activities. It is anticipated to unlock opportunities for generating novel and innovative visual, textual, and multimedia content, offering unprecedented avenues for personalisation and efficiency. ...
Purpose
Generative Artificial Intelligence (GenAI) is reshaping the marketing landscape by enhancing creativity, enabling personalised customer engagement, and streamlining operations. Its transformative impact on content creation opens new opportunities for marketing communication. This study examines how marketing professionals perceive its strategic value and investigates the psychological factors shaping their expectations and attitudes towards its adoption.
Design/methodology/approach
This study adopts a qualitative research approach, using semi-structured interviews with marketing professionals in the pharmaceutical sector to explore their expectations for GenAI adoption. The pharmaceutical industry’s complex communication needs, strict regulatory requirements, and focus on innovation make it an ideal context for this investigation. Additionally, its underrepresentation in academic marketing research adds further relevance to the study.
Findings
Marketing experts do not view GenAI as an immediate threat. However, they are concerned about its long-term impact, particularly its potential to automate complex creative and strategic tasks, reducing the need for human involvement. The results emphasise the lasting importance of human creativity and sensibility, especially in pharmaceutical marketing, where effective communication relies on nuance and empathy. This study contributes to theory by incorporating psychological factors, such as the fear of obsolescence, into traditional technology adoption models, offering new insights into how GenAI can enhance rather than replace human creativity.
Originality/value
This study offers fresh insights into the adoption and implications of GenAI in marketing by focusing on the perspectives of industry professionals. Unlike prior research centred on technical advancements or consumer outcomes, this paper highlights the managerial viewpoint, exploring how GenAI enhances creativity, transforms content creation, and supports strategic goals in marketing communication. It also addresses key challenges, such as regulatory hurdles and the psychological impact of automation, providing a comprehensive understanding of how GenAI is poised to redefine marketing practices.
... Even though AI has been around for decades, there is no best definition to define what exactly AI is [15]. [16] highlight what AI includes, such as theories, statistics, sciences, the computation of neurobiology, and the impersonation of human cognition. AI refers to an object that learns to think on its own as an agent, which is developed by humans [17]. ...
... Some scholars use the term "Machine Learning" or "Robotization" instead of AI [19]. [16] believe AI is becoming more important day by day, and today, it is an integral part of operations for different companies in different sectors, particularly in activities central to value creation for customers. The global total corporate AI investments from 2015 to 2022 have reached about $91.9 billion compared to $12.75 billion and are expected to grow due to attracting more investment [20]. ...
BSTRACT: This study investigates the role of Artificial Intelligence (AI) in targeting and segmentation in the real
estate sector in Iraqi Kurdistan. Rapid technological advancements and AI usage in different sectors around the world
brought researchers' attention to exploring AI's impact on customers' experiences and marketing professionals'
willingness and readiness to leverage this technological development. To quantify their willingness and readiness, as
well as whether AI influences the effectiveness of targeting and segmentation strategy, this research uses quantitative
methods. Means, regression, and correlation analysis are used to investigate the relationship and impact between
dependent and independent variables. Moreover, the survey includes both close-ended questions with open-ended
questions. Open-ended items give researchers a chance to dive deeper into qualitative insights from respondents. This
paper provides mixed results. Even though marketing professionals see AI as a useful tool in segmentation practices,
their responses did not match their perception; thus, statistically, the findings were not significant. In addition, the
findings reveal that AI has a positive relationship with segmentation strategies despite challenges such as data quality,
lack of infrastructure, and issues related to system integration. More research on other sectors, particularly on human
resources readiness and operational challenges, is needed.
Keywords. AI, Artificial Intelligence, Marketing Professionals, Segmentation, Real Estate.
... Even though AI has been around for decades, there is no best definition to define what exactly AI is [15]. [16] highlight what AI includes, such as theories, statistics, sciences, the computation of neurobiology, and the impersonation of human cognition. AI refers to an object that learns to think on its own as an agent, which is developed by humans [17]. ...
... Some scholars use the term "Machine Learning" or "Robotization" instead of AI [19]. [16] believe AI is becoming more important day by day, and today, it is an integral part of operations for different companies in different sectors, particularly in activities central to value creation for customers. The global total corporate AI investments from 2015 to 2022 have reached about $91.9 billion compared to $12.75 billion and are expected to grow due to attracting more investment [20]. ...
This study investigates the role of Artificial Intelligence (AI) in targeting and segmentation in the real
estate sector in Iraqi Kurdistan. Rapid technological advancements and AI usage in different sectors around the world
brought researchers' attention to exploring AI's impact on customers' experiences and marketing professionals'
willingness and readiness to leverage this technological development. To quantify their willingness and readiness, as
well as whether AI influences the effectiveness of targeting and segmentation strategy, this research uses quantitative
methods. Means, regression, and correlation analysis are used to investigate the relationship and impact between
dependent and independent variables. Moreover, the survey includes both close-ended questions with open-ended
questions. Open-ended items give researchers a chance to dive deeper into qualitative insights from respondents. This
paper provides mixed results. Even though marketing professionals see AI as a useful tool in segmentation practices,
their responses did not match their perception; thus, statistically, the findings were not significant. In addition, the
findings reveal that AI has a positive relationship with segmentation strategies despite challenges such as data quality,
lack of infrastructure, and issues related to system integration. More research on other sectors, particularly on human
resources readiness and operational challenges, is needed.
... This finding is consistent with research on consumer responses to AI agents that shows that consumer responses to AI agents are context-dependent (e. g., Adam, Roethke, and Benlian 2023;Garvey, Kim, and Duhachek 2023) and are increasingly negative when consumers' knowledge about AI increases (Tully, Longoni, and Appel 2025). Thus, when people become aware of the power, control, and authority that AI service agents have over them, which is likely to become more pervasive as retailers use more advanced AI technologies (Huang and Rust 2022), they are likely to develop privacy concerns in their presence. The marginally significant difference in privacy concerns between AI and human agents in the control group may have been due to our use of an article that was unrelated to the second part of the study. ...
... In addition, future research could add to our findings by testing the moderating role of a retailer's transparency about customer data use and service agents' power. Finally, retesting the effects in the future may be critical because AI technologies are developing rapidly, consumer knowledge of them is spreading, and AI service agents are likely to gain power as they develop further (Huang and Rust 2022). ...
... Additionally, it introduces an extra sub-step to problem definition: running simulations, such as modelling human behaviours and interactions -to validate the problem definition and reduce uncertainty, as previously discussed (Brand et al., 2023;Ilagan et al., 2024). At the core of this framework is a structured and cooperative humans-LLMs relationship, where humans and LLMs collaborate to leverage their respective strengths while compensating for each other's limitations (Huang & Rust, 2022). This approach aligns with recent studies on human-LLM collaboration (e.g., Ameen et al., 2024;Bouschery et al., 2024;Boussioux et al., 2024;Dell'acqua et al., 2023;Mollick, 2024), which emphasise moving beyond the traditional division of tasks based solely on individual strengths (Choudhary et al., 2023). ...
This study explores the role of Large Language Models (LLMs) in problem finding (PF) for ill-structured, wicked, and multi-stakeholder problems-an essential yet underexamined aspect of organisational innovation. While prior research has examined artificial intelligence (AI) in problem-solving (PS) and, more recently, the contributions of LLMs, their role in PF remains largely unex-plored. Given that PF lays the foundation for effective PS, overlooking it can result in missed opportunities and inefficient resource allocation, ultimately hindering the innovation process. Drawing on a cognitive-behavioural perspective rooted in Simon's foundational work, this study identifies the key activities and cognitive skills essential for PF and examines how human-LLM collaboration can enhance this process. While humans possess innate PF abilities, cognitive limitations such as bounded rationality, satisficing, and uncertainty avoidance constrain their effectiveness. LLMs, with their advanced reasoning and data-processing capabilities, can help overcome these constraints by expanding the search space, generating alternative problem framings, and stimulating creativity. However, their inherent limitations, including biases, hallucinations, and challenges in handling less structured problems, necessitate a structured approach to human-LLM collaboration. To address this, we propose a framework that defines this interaction and illustrates its application through case studies in product development and social innovation. Our findings have significant implications for organisations, emphasising the need for structured implementation , workforce training, and AI governance. We conclude with research propositions to guide future investigations into humans-LLMs collaboration in PF, positioning it as a critical driver of innovation in the era of Generative AI.
... Such promise suggests the need to understand and specify the requirements for enabling human and AI co-creation. Some existing HAICC models emphasise the dynamics of the interaction, as when Huang and Rust (2022) outline the uses of AI by both marketers and consumers. In turn, they argue that effective collaboration requires the acknowledgment and integration of distinct intelligences, such as the mechanical and analytical intelligence of AI, as well as the contextual, intuitive, and feeling intelligence held by humans. ...
The rapid advancement of artificial intelligence (AI) capabilities has extended into creative realms, presenting opportunities for creative collaboration between human brand professionals and AI in support of brand voice efforts. However, there remains little clarity regarding the implementation of this creative interaction. With a conceptual approach, the current research proposes a three-level framework of human–AI co-creation for creative brand voice that highlights key factors that can facilitate brand efficiency and effectiveness at the individual (AI task roles, co-creation teaming, knowledge and skills), organisational (infrastructure and brand voice database, socialisation), and societal (responsibility and accountability, AI transparency, brand voice copyright) levels. Each level presents different challenges and insights. At the individual level, it is critical to consider operational processes; at the organisational level, managing the interactions is key; and at the societal level, external influences must be accounted for, to manage the brand. This research contribution in turn offers theoretical guidance, aligned with a high-level brand management perspective, on how to pursue efficiency and effectiveness at three defined levels, as well as relevant avenues for further research.
... Research has shown that digital literacy is a crucial factor affecting individuals' ability to adapt to emerging technologies, and further influences their intention to use the technology (Arias López et al., 2023) [3]. Therefore, AI literacy has become a key determinant of whether users accept AIGC (Chen & Bai, 2023) [4]. This work, based on the TAM, explores how AI literacy influences the public's intention to use AIGC through how useful and easy the tool is to use. ...
In the contemporary digital and intelligent society, the accelerated evolution of Artificial Intelligence-Generated Content (AIGC) technology is profoundly transforming a multitude of industries. Nevertheless, there is a notable discrepancy between the public's awareness and acceptance of AIGC, which is largely contingent upon individual AI literacy. This study, which is based on the Technology Acceptance Model (TAM), explores the impact of AI literacy on the intention to use AIGC, with a particular focus on the mediating roles of Perceived Usefulness and Perceived Ease of Use. The results of the regression analysis and structural equation modelling (SEM), based on 630 valid questionnaires, demonstrate that AI literacy has a significant positive influence on Perceived Usefulness and Perceived Ease of Use, which in turn have a positive impact on wanting to use AIGC. Perceived Usefulness and Perceived Ease of Use act as partial mediators between AI literacy and intention to use AIGC. This work provides theoretical support for the widespread adoption of AIGC and offers practical guidance for businesses and policymakers to improve public AI literacy.
... İlgili değişkenlerin açıklamaları Tablo 2'de verilmiştir. Çalışma kapsamında incelenen söz konusu 32 orta-üst gelir gurubunda yer alan ülkeler ise sırasıyla; 1; Arnavutluk, 2; Arjantin, 3;Ermenistan,4;Botsvana,5;Brezilya,6;Bulgaristan,7;Çin,8;Kolombiya,9;Kosta Rika,10;Dominik Cumhuriyeti,11;Ekvator,12;Fiji,13;Gabon,14;Guatemala,15;Guyana,16;Irak,17;Jamaika,18;Ürdün,19;Kazakistan,20;Libya,21;Malezya,22;Mauritius,23;Meksika,24;Namibya,25 (Bozkurt vd., 2021;Pekmezci ve Bozkurt, 2021). ...
Çalışma, travmaya duyarlı uygulamaların bireyi çevresiyle birlikte ele almasının, hem bireyin yaşam kalitesini artırdığını hem de toplumsal bütünleşmeyi desteklediğini vurgulamaktadır. Bu bağlamda, profesyoneller arası iş birliğinin güçlendirilmesi, travmaya duyarlı hizmetlerin etkinliğini artırmak adına kritik öneme sahiptir.
... As marketing entities in the digital environment, enterprises can track and describe user behavior through intelligent marketing methods such as online search engines and big data platforms at lower costs and complexities, forming precise and clear user profiles to enhance the performance of digital marketing tools and improve decision-making efficiency [55,56]. Leveraging the "connection dividend" brought by digital scenarios such as mobile internet, the IoT, and open media platforms, user groups can easily communicate with similar-demand individuals and participate deeply in the entire process of product application, promotion, and improvement optimization through online experiences and community reviews [57,58]. This effectively bridges the information gap between consumers and enterprises, ensuring the seamless alignment of user needs with enterprise resources, providing a favorable market environment for digital marketing. ...
Achieving digital transformation in enterprises is vital for advancing the digital economy. Using the World Bank’s China Enterprise Survey data, this study investigates how local digital economic growth impacts enterprise transformation. Findings suggest that higher local digital growth significantly boosts enterprise transformation, thereby improving short-term operations and long-term innovation. Remarkably, threshold regression reveals a stronger impact on larger enterprises and those with higher human capital. Additional analyses demonstrate that effective access to digital dividends enhances enterprises’ production, R&D, and management. These results offer guidance for local governments, supporting digital shifts and helping enterprises tailor transformation strategies.
... Digital marketing enhances consumer engagement through targeted interactions tailored to individual needs, fostering strong customer relationships (Norah, 2024). Artificial intelligence (AI) has emerged as a transformative tool, enabling marketers to analyze consumer behavior, preferences, and interactions across digital platforms and deliver personalized experiences (Ming-Hui & Rust, 2022). Globally, AI-driven personalization has revolutionized digital marketing, but in Saudi Arabia, its adoption is still in its early stages, presenting opportunities for development (Alqasa, 2023). ...
b>Research background and purpose: This study examines AI-driven impact personalization on consumer engagement within Saudi Arabia’s digital marketing landscape, aligning with Vision 2030 objectives. It underscores the transformative potential of artificial intelligence in enhancing customer interaction, satisfaction, and loyalty by delivering tailored experiences that address consumer preferences. The research focuses on key factors—ethical considerations, technological readiness, organizational culture, and cost—that influence the effectiveness of AI-driven personalization, providing insights into fostering robust consumer relationships and supporting Saudi Arabia’s digital transformation initiatives. Design/methodology/approach: The study uses a descriptive-analytical approach to explore the relationship between AI-driven personalization and consumer engagement. Researchers collected data through a structured questionnaire distributed to a randomly selected sample of 350 participants and analyzed 300 valid responses. They applied statistical methods, including descriptive statistics and correlation analysis, to examine the relationships between variables. Additionally, Cronbach’s alpha evaluated the reliability of the research instruments. Findings: The study reveals a significant positive relationship between AI-driven personalization and consumer engagement. Ethical considerations, particularly data privacy and transparency (correlation coefficient = 0.81), play the most influential role by emphasizing the need for secure and transparent data practices to build trust. Organizational culture (0.75) also plays a crucial role, with innovation and professionalism strengthening consumer trust and loyalty. Technological readiness and cost further enhance engagement, as organizations leverage advanced AI technologies and strategic pricing to deliver personalized experiences. Participants appreciate the convenience, efficiency, and tangible benefits provided by these personalized services. Value added and limitations: This study provides critical insights into the role of AI in Saudi Arabia’s digital economy, emphasizing the integration of ethical standards and technological innovation to gain a competitive edge. However, reliance on self-reported data and a geographically confined sample may limit generalizability. Future research should include broader demographics and additional variables to expand these findings.
... Furthermore, the emerging concept of human-AI collaborative intelligence focus on the combination of humans and AIs working together to solve problems, leveraging the strengths of both parties and enhancing each other's capabilities [19,20]. Although still in its early stages, an increasing number of studies demonstrate that human-AI collaboration can lead to superior performance in accomplishing complex tasks [19,21,22]. ...
Genome annotation is essential for understanding the functional elements within genomes. While automated methods are indispensable for processing large-scale genomic data, they often face challenges in accurately predicting gene structures and functions. Consequently, manual curation by domain experts remains crucial for validating and refining these predictions. These combined outcomes from automated tools and manual curation highlight the importance of integrating human expertise with AI capabilities to improve both the accuracy and efficiency of genome annotation. However, the manual curation process is inherently labor-intensive and time-consuming, making it difficult to scale for large datasets. To address these challenges, we propose a conceptual framework, Human-AI Collaborative Genome Annotation (HAICoGA), which leverages the synergistic partnership between humans and artificial intelligence to enhance human capabilities and accelerate the genome annotation process. Additionally, we explore the potential of integrating Large Language Models (LLMs) into this framework to support and augment specific tasks. Finally, we discuss emerging challenges and outline open research questions to guide further exploration in this area.
... Artificial intelligence (AI) has been the talk of the world in recent years. It has spread its transformative changes in various sectors, particularly in the service industry, including AI-driven automation in service efficiently, AI application in consumer experience, AI role in service personalization, and AI in frontline services (Ali et al., 2023;Bohr & Memarzadeh, 2020;Davenport et al., 2020;Huang & Rust, 2022). In addition, AI is influencing its adaptive platform to tailor educational content to learning styles and individual students' needs (Kamalov et al., 2023). ...
The digital era has forced higher educational institutions (HEIs) to use artificial intelligence (AI) to improve their services. However, it is still unexplored how artificial intelligence service agents (AISA) can influence the continued intention of AI-enabled teaching. Therefore, this used the expectation confirmation (EC) model to highlight the influence of AI service agents on the continued intention of AI-enabled teaching in HEIs of the gulf cooperation council (GCC). The data were gathered from 528 students in different universities of GCC countries using AI in teaching. The questionnaire was developed to collect the data, and analysis was done by using SmartPLS 4. The results reported that all ASIA, including anthropomorphism, availability, contact, efficiency, enjoyment and security, significantly influence the perceived usefulness of AI-enabled teaching. Moreover, only availability, contact, enjoyment, and security lead to expectation confirmation of AI-enabled teaching among these agents. Perceived usefulness is associated with confirmation, and it can lead to satisfaction. Moreover, perceived usefulness and satisfaction can significantly influence the continued intention of AI-enabled teaching in HEIs. The study used AISA to examine intention in HEIs, but further studies can be conducted on specific education sectors, private or public. Moreover, the study findings can help the policymakers of HEIs to enhance their service quality by using AISA.
... These data are of great value to supply chain enterprises. They can use these data to explore consumer demand preferences (Dekimpe & Geyskens, 2019;Gupta & Ramachandran, 2021;Nilashi et al., 2021), build consumer databases (Bradlow, Gangwar, Kopalle, & Voleti, 2017), and segment consumers to improve products and provide personalized services (Hossain, Akter, & Yanamandram, 2020;Huang & Rust, 2022). However, in reality, consumer data in the supply chain is scattered across different platforms and markets, which is disorganized and difficult to obtain. ...
Building on the use of digital technology in supply chain management, this paper integrates data governance service providers into the supply chain. Given the distinct nature of data governance services, the paper illustrated the learning effect curve and simulated their output function. Building on this, four different supply chain data governance models were proposed, namely, manufacturer single governance model, retailer single governance model, manufacturer and retailer independent governance model, and manufacturer and retailer collaborative governance model. Constructed the profit model for the supply chain within the relevant framework. By vertically comparing the optimal decisions and system performance across various models, the study concluded that the collaborative governance model maximizes supply chain profit and is more responsive to factors that enhance overall profitability.
... A key finding of this study is the emergence of a hybrid cocreative process in advertising (Wingström et al., 2022). This integrated dynamic model not only highlights the collaborative nature of human-AI collaboration (Huang and Rust, 2022) but also guides smooth transitions involving active participation and interaction among various stakeholders. ...
... A team is formed through the complementary characteristics of team members to achieve a shared goal (Furnham et al., 1993). Therefore, scholars argue that in an AI augmenting relationship, humans and AI systems complement each other's as team members since they differ in characteristics and strengths (Fuegener et al., 2022;Huang & Rust, 2022a). While teamwork is commonly surrounded by multiple subgoals that contribute to the primary goal of a team (Zercher et al., 2023), scholars find that AI in a team primarily assists humans in addressing the subgoals rather than autonomously handling entire tasks, due to the limited AI capabilities (Schneider et al., 2021). ...
The relationship between humans and artificial intelligence has sparked considerable debate and polarized opinions. A significant area of focus in this discourse that has garnered research attention is the potential for humans and AI to augment one another in order to enhance outcomes. Despite the increasing interest in this subject, the existing research is currently fragmented and dispersed across various management disciplines, making it challenging for researchers and practitioners to build upon and benefit from a cohesive body of knowledge. This study offers an organized literature review to synthesize the current literature and research findings, thereby establishing a foundation for future inquiries. It identifies three emerging themes related to the nature, impacts, and challenges of Human-AI augmentation, further delineating them into several associated topics. The study presents the research findings related to each theme and topic before proposing future research agenda and questions.
... Yapay zekâ, pazarlama alanında, müşteri davranışlarının tahmin edilmesi ve bu bilgiler doğrultusunda müşteri deneyiminin optimize edilmesi amacıyla etkin bir şekilde kullanılmaktadır (Norris, 2021). Bu bağlamda, müşteri odaklı yaklaşımla stratejik pazarlama planlarının oluşturulması, yapay zekâ destekli analizlerle güç kazanmaktadır (Huang ve Rust, 2021). Özellikle e-ticaret platformlarında bireylerin dijital davranışlarının izlenmesi ve analiz edilmesi, özelleştirilmiş pazarlama stratejilerinin geliştirilmesinde kritik bir rol oynamaktadır (Forrest ve Hoanca, 2015). ...
... There are already some design recommendations in the literature for increasing the warmth and competence of chatbotsfor example, a direct gaze direction (Pizzi et al., 2023), realistic pictures, (Pizzi et al., 2023), a human name (Zheng et al., 2023), an interactive communication style (Go and Sundar, 2019), and delaying the response to simulate the typing of a human (Gnewuch et al., 2018). To make the chatbot appear warmer and more empathetic, sentiment analysis can be used to adapt the chatbot's language to a more emotional style (Huang and Rust, 2022). ...
Chatbots in customer service often fail to meet customer expectations, largely because they are considered prone to comprehension errors. Service recovery can decisively restore perceived humanness and user satisfaction through perceived warmth and competence after a service failure. In this study, we investigate the effect of the chatbot’s gender on the user in service recovery. The majority of chatbots in customer service display female characteristics. We use a pre-study (n = 30) to determine the perceived gender of several chatbot avatars and a
scenario-based experiment (n = 300) in which the service recovery after an outcome failure and the gender of the chatbot are manipulated. The results show that the service recovery significantly improved user satisfaction with the chatbot. In addition, the chatbot was perceived as significantly warmer and more competent, which resulted in higher perceived humanness and increased user satisfaction. Male chatbots were perceived as less warm in failure situations when service recovery was not achieved. However, following service recovery, there are no differences in the perception of the chatbot’s warmth and gender. Perceived warmth is correlated with perceived competence. Gender incongruence between the chatbot and the respondent resulted in a higher perceived humanness of the chatbot in service recovery. Therefore, firms should pay particular attention to the contexts in which chatbots are used and whether gender matching is appropriate.
We develop a novel generative AI (GenAI) trajectory, “democratization-average trap-model collapse,” to identify data and model challenges posed by GenAI, from which we project the GenAI future of consumer research. This trajectory consists of three key phenomena: democratization broadens consumer participation, the average trap produces generic responses, and model collapse occurs when GenAI outputs lose human sensibilities. Data and model challenges arise as democratization enhances data representation while also embedding real-world biases. The average trap, caused by next-token prediction models, leads to generic outputs that lack individuality. Additionally, model collapse occurs when GenAI increasingly learns from its own outputs, amplifying machine bias and diverging from human behavior. To address these challenges, researchers can leverage democratization to study marginalized consumers and prioritize human-centered research over purely data-driven methods. The average trap can be mitigated by fine-tuning models with task-specific and marginalized consumption data while engineering responses for uniqueness. Preventing model collapse requires integrating human–machine hybrid data and applying theories of mind to realign AI with human-centric consumption. Finally, we outline three future research directions: preserving data distribution tails to support consumption democratization, countering the average trap in next-token prediction, and reversing the trajectory from democratization to model collapse.
In this chapter, the authors explore the transformative potential of artificial intelligence (AI) in the world of marketing, focusing on its profound ability to decode consumer behavior. The chapter delves into AI's evolution—from basic automation to advanced, real-time data analysis that empowers marketers to anticipate consumer needs and deliver personalized experiences like never before. However, the chapter will also be prompted to consider the ethical challenges that come with leveraging AI, particularly in maintaining consumer trust and ensuring responsible data usage. As the chapter navigates these complexities, authors navigate through the future of AI in marketing offering limitless possibilities. By embracing AI, marketers will be well-equipped to stay ahead in a competitive landscape, creating strategies that resonate deeply with their target audiences and positioning themselves as a leader in the next generation of marketing.
Generative AI agents represent a frontier in artificial intelligence, combining generative modeling capabilities with autonomous behavior and the ability to collaborate with humans and other agents. These systems are powered by innovative architectural designs that enable them to perceive, plan, act, and adapt within dynamic environments. This article presents a comprehensive exploration of architectural innovations that underpin autonomy and collaboration in generative AI agents. It discusses foundational models, key components, evolving architectures, and multi-agent frameworks. The study includes comparisons of agent designs, integration strategies, and future prospects while identifying challenges in scalability, ethics, and interpretability. Two tables are provided to summarize architectural comparisons and agent collaboration mechanisms. This paper is designed to support scholarly citation and provides a foundation for further exploration and development in the field of generative AI systems.
In an increasingly digital world, more and more organizations attribute their organizational success to how they study and explore the behavioural dynamics of their consumers. Consumers in different countries have their own cognitive ways of perceiving a product. Most of the time, consumers link their purchase intention to how the product is marketed via an individual or, as we call them, the digital influencers. Governments, institutional investors, and enterprises are all adopting AI to streamline their processes and further to stay competitive. Researching the role of AI and influencer marketing in brand growth can uncover valuable insights for gaining a competitive edge. The present study will shed light on the following: 1. the talismanic role of AI in influencer marketing and how they are expanding prodigiously 2. To examine the role of influencers in different sectors of the economy 3. The traction force of influencers in brand success.
This paper examines the impact of artificial intelligence (AI) agents on society, focusing on the creation of synthetic content and artificial realities. As AI agents become more advanced, the content they produce may be increasingly difficult to distinguish from human-creation, raising important ethical concerns. The study highlights how generative AI can shape perceptions and blur the boundaries between traditional, authentic content and realistic, synthetic content. It explores advancements in natural language processing and image recognition, which allow for the creation of highly convincing fake content. The paper also looks at the role of AI agents in communication and the consequent social implications. Among those implications, the paper highlights the importance of augmenting creativity rather than radically replacing it with AI agents as a tool to augment human creativity rather than replacing it. Finally, it discusses the concept of superintelligence and its potential to transform society toward post-authenticity considering balanced regulation and ethical considerations.
This study investigates the key enablers of Marketing 6.0 within metaverse environments, examining how technological integration, human-centric approaches, sustainability practices, and seamless phygital experiences drive marketing performance outcomes. As marketing evolves beyond technology-driven personalization toward more meaningful consumer relationships, the metaverse offers unprecedented opportunities for immersive brand experiences. Using a mixed-methods approach combining surveys of 412 marketing professionals and 843 consumers, in-depth interviews with 28 executives and 12 technology experts, and industry-diverse case studies, this research develops a comprehensive conceptual framework. The framework identifies four implementation approaches and demonstrates how Marketing 6.0 enablers influence brand loyalty and purchase intention, with consumer engagement acting as a mediating mechanism. Structural equation modeling revealed strong support for the hypothesized relationships, with human-centric approaches emerging as particularly influential across all performance metrics.
This chapter explores the dynamic world of consumer behaviour and buying patterns, focusing on the psychological, social, cultural, and economic factors that shape decisions. It examines how consumers manage their preferences and choices in various market situations, highlighting trends like sustainable consumption, loyalty-driven purchases, and impulsive buying. The chapter also investigates the impact of the digital revolution, including social media and e-commerce, on consumer engagement and purchasing habits. By addressing elements such as peer influence, brand perception, and decision-making processes, it emphasises the importance of understanding consumer diversity in demographics, culture, and lifestyle. Combining theoretical frameworks, real-world examples, and data-driven insights, this chapter provides businesses and researchers with a foundation for predicting demands and creating effective marketing strategies.
Over recent years, the proliferation of artificial intelligence (AI) has enabled businesses worldwide to employ AI‐driven service agents to deliver frontline services to their customers. This paradigm shift has also increased scholarly attention to consumer behavior research in AI‐driven frontline service encounters. Nevertheless, the existing body of knowledge in this domain lacks coherence and consistency, with disparate findings scattered across numerous disciplines. In this context, a critical and comprehensive overview of the existing literature in this domain is essential. Therefore, to provide an updated and comprehensive understanding of consumer research in this fast‐growing domain, we systematically analyzed 157 articles. Using the popular TCCM framework, we offer a detailed overview of the theories, contexts, characteristics, and methodologies used in the prior studies in this domain. The research also presents an integrated framework considering the independent variables, mediators, and moderators influencing customer outcomes. This analysis identifies several research gaps and suggests potential opportunities for further investigation that pertain to major emerging topics and overlooked areas. This review enhances the understanding of consumer reactions to AI‐driven frontline service encounters and offers novel insights for both the literature and managerial practice regarding the implementation of AI in frontline services.
Generative Artificial Intelligence (AI) is a transformative force reshaping businesses. Advanced technology models, such as GPT-4, can produce content in various forms, from text to music, fundamentally changing many industries. The benefits are vast: personalized content for enhanced customer experiences, improved virtual assistants, fostering creativity in product design and content generation, and streamlining operations by automating routine tasks and optimizing supply chains. While Generative AI offers advanced analytics and scenario simulations for data-driven decision-making, businesses must be prepared to address various challenges. These challenges include ethical considerations, privacy concerns, regulatory compliance, and the need for skilled personnel. It is crucial for businesses to proactively address these challenges to fully harness the power of Generative AI and drive growth, efficiency, and innovation. Using Generative AI for startups will be a game-changer by contributing creative and innovative solutions in various aspects of the business.
This paper is a comprehensive systematic literature review (SLR) on artificial intelligence (AI) applications in consumer financial behavior research, highlighting the evolving interface between AI technologies and consumer financial decisions. Employing a hybrid methodology combining bibliometric analysis and a domain‐based systematic review, we analyzed 89 peer‐reviewed articles from Scopus, Web of Science, and manual search. Our findings reveal an exponential growth in publications from 2016 to 2024, emphasizing the burgeoning interest and significant advancements in applying AI in consumer financial behavior research. Drawing on the Theory‐Context‐Construct‐Method (TCCM) review framework, this review provides a comprehensive understanding of the dominant theoretical frameworks, financial scenarios, key variables and their associations, data sources, and research approaches employed in recent AI and consumer financial behavior research; it also suggests fertile areas for future research. Specifically, three key research areas have been identified: (1) consumers' acceptance and interaction with AI technologies and their subsequent financial behaviors, (2) use of AI methodologies to investigate consumer financial behavior, and (3) ethical and regulatory challenges associated with the development of AI. Insights from expert interviews further validate these findings, providing academic, industry, and regulatory perspectives that offer nuanced understanding and practical implications for AI's role in consumer finance. Finally, this review identifies gaps overlooked by existing research and discusses directions for future research. By offering insights into the strategic integration of AI in financial practices, this study serves as an essential resource for academics, industry practitioners, and policymakers aiming to use AI in consumer financial behavior research responsibly and effectively.
In the era of digital intelligence, companies need to capture more data to portray the user profile to “grab” the “heart” of the consumer, AI is undoubtedly a powerful tool to implement this process. Nevertheless, it has also gradually sparked concerns regarding user privacy and data. Recent studies have increasingly focused on user reactions to AI data capture, yet exploration in this area remains limited. This research contributes to the existing literature by investigating the underlying psychological processes and influencing factors that prompt users to confront AI data capture. Through four studies, we found the data capture strategies have a significant negative impact on users' intention to use AI systems, and compared with the overt data capture strategy, the covert strategy would make users' intention to use AI systems lower. The impact is mediated by psychological ownership, specifically, it is mediated by perceived control of psychological ownership rather than perceived possession. Additionally, prevention‐oriented users are more likely to feel deprived of their right to be informed and their control over data. However, AI explainability can increase users' psychological ownership and intention to use by alleviating their psychological defenses in the process. These results are conducive to promoting the resolution of AI data governance issues under digital intelligence empowerment, and providing a reference for reasonable strategies adopted by enterprises in using AI data capture.
Artificial intelligence (AI) powered marketing has been gaining the attention of global marketers and academicians. Contemporary marketing now views AI not merely as a cutting-edge technology, but also a valuable channel to create value. Extant research has extensively evaluated and qualitatively substantiated the significance of AI’s proliferation in marketing. However, research based on real-life practitioner data is limited in this domain. The present study addresses this research gap with a two-pronged approach: one, using grey-DEMATAL, it quantitatively evaluates the impact of AI on contemporary marketing; and two, it establishes industry validation to the driving factors of AI in marketing derived from the extant research. The scope of the study was limited to financial services. The results indicate that enhancing customer experience by personalization at scale, increasing marketing efficiency, and driving positive brand engagement were the most powerful causal factors driving the adoption of AI in marketing. As the global digital divide continues to expand between global south and north, the present study is relevant by highlighting, for researchers, practitioners and policymakers, the need to make digital and AI discourse an integral part of their research efforts.
This research investigates consumer attitudes towards advertising driven by Artificial Intelligence (AI). It explores the influence of factors such as digital literacy, privacy concerns, transparency, privacy disclosure and ethical considerations. The study adopts a mixed-methods approach to analyse consumer perceptions, examine the impact of demographic and psychographic factors, and investigate consumer expectations regarding privacy and the ethical use of AI. Key findings reveal a positive correlation between digital literacy and the acceptance of AI advertising. Tech-savvy consumers tend to be more comfortable with AI-powered marketing. The study emphasises the critical role of transparency and ethical AI governance, highlighting that clear privacy disclosures and responsible AI practices are essential for building trust and acceptance. While AI-powered personalisation can enhance engagement, over-reliance on automation without transparency may lead to consumer scepticism. The research acknowledges certain limitations, including focusing on a specific region and the absence of a longitudinal perspective. Future research should address these gaps by conducting cross-cultural studies, tracking long-term changes in AI acceptance, and examining the impact of legal and ethical compliance on consumer confidence, brand reputation, and industry adoption. This study offers valuable insights for businesses, marketers, and policymakers. By prioritising transparency, ethics, and consumer trust, businesses can create AI-driven advertising strategies that are both effective and responsible, fostering long-term relationships with their customers in the evolving digital landscape.
Purpose
This study explores how artificial intelligence (AI) has been intertwined with rhetoric and the journey of institutionalization in selected case study firms. The mechanism of institutionalizing AI into organizational processes, future technology transformation and the driving forces behind the implementation of AI is being explored.
Design/methodology/approach
It adopts the qualitative methodology and multiple case study approach, drawing evidence from ten leading retail sector organizations that have been practicing AI for over a decade. The main data collection method was face-to-face in-depth interviews, supplemented by focus group discussion and documentary reviews. From a theoretical stance, the paper draws on the notions of rhetoric institutionalism.
Findings
Empirical findings revealed that the rhetorical power of the word AI convinces the management of the firm to embrace AI. In contrast to the hype in the media, the real application of AI in the retail sector has not lived up. Therefore, the study delves into the noticeable discrepancy between the buzz surrounding AI and its actual use in retail sectors.
Originality/value
This study contributes to research by postulating that even though AI carries rhetorical power and prompt implementation, the real organizational application is far behind the rhetorical excitements. Foregrounding rhetoric institutionalism, it extends existing institutional theory-inspired management research. The paper also offers learning points to practitioners by illustrating the rise and fall of the AI implementation story. It further showcases how AI tools and techniques could be used by a business, how AI gets implicated in a firm’s business excellence journey and the ensuing management control ramifications.
This document evaluates the development and importance of conversational AI, chatbots, and virtual assistants in shaping human behavior and decision-making,particularly in the context of e-commerce. It is designed to investigate the impact of AI on the personalization of user experiences, and maps how the automation of decision-making affects cognitive processes like counterfactual thinking and regret. The findings sugest that while conversational AI enhances the shopping experience through personalized product recommendations, it can also trigger cognitive biases such as regret by exposing consumers to alternative options, leading them to reconsider their initial choices. This raises concerns about AI risk mitigation, particularly regarding transparency, psychographic profiling,and the emotional influence of AI-driven decision-making. Importantly,the study highlights that consumers do not inherently dislike AI; rather,they seek a more ethical and culturally aware approach to its implementation. A responsible AI design could not only improve user experience but also strengthen consumer trust in AI-driven products and services.To fully understand tese dynamics, longitudinal studies are needed to assess the long-term effects of conversational AI on consumer satisfaction, loyalty,and decision-making. Additionally,cross-cultural comparisons will provide deeper insights into how consumer perceptions and interactions with AI vary across different markets.
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Purpose - This study delves into the relationship between value co-creation and business performance in travel agencies. Furthermore, the study examines the mediating role of artificial intelligence (AI) marketing strategies in travel agencies. Methodology - The study used questionnaires to collect primary data from the respondents, which were subsequently analyzed using the Smart-PLS software. Data collection focused on individuals employed in travel agencies within the Republic of Serbia, aiming to empirically test the study's hypotheses. Findings - The findings highlight the importance of value co-creation in achieving superior business performance. They also suggest that implementing artificial intelligence marketing strategies positively correlates with the business performance of travel agencies in the Republic of Serbia. Finally, the findings illustrate a significantly positive relationship between AI-based marketing strategies, value co-creation, and business performance of travel agencies in the Republic of Serbia. Implications - Artificial intelligence has become a key topic for tourism organizations. A marketing strategy based on artificial intelligence, combined with feedback from service users, is likely to enhance the performance of service organizations.
This study explores the role of artificial intelligence (AI) in modern marketing practices, focusing on four AI-powered platforms: Content Studio, Predis.AI, Visme, and Mailchimp. By comparing their AI functions, pricing structures, offerings, solutions, capabilities, propositions, and industry integrations, the research highlights the potential of AI to enhance consumer engagement, operational efficiency, and decision-making processes. The findings demonstrate the platforms' ability to optimise marketing strategies, streamline workflows, and deliver personalised customer experiences across diverse contexts. Ethical considerations and future research directions are also discussed, providing a comprehensive understanding of AI's impact and challenges in the marketing sector.
A vállalkozói lehetőség kialakulása, feltárása, megteremtése és kihasználása az innováció és a versenyképesség alapja, a gazdaság fenntartható fejlődésének egyik kulcstényezője. A mesterséges intelligencia hozzájárul a vállalkozói lehetőségek azonosításához, elemzéséhez, illetve akár azok megvalósításához is, mely további lehetőségeket nyújt a vállalkozások számára. A tanulmány célja interdiszciplináris megközelítésben a mesterséges intelligencia szerepének elemzése a vállalkozói lehetőségekkel összefüggésben. A kutatás szisztematikus szakirodalmi áttekintés alapján vizsgálja a témakörben a Scopus adatbázisban megjelent publikációkat. A kutatás rámutat arra, hogy a mesterséges intelligencia komplex módon járul hozzá a vállalkozói lehetőségek gyorsabb és pontosabb feltárásához és kihasználásához akár bizonytalan piaci körülmények között is, mely elősegíti a vállalkozásfejlesztést. A mesterséges intelligencia fokozza a vállalkozások hatékonyságát támogatva a vállalati folyamatokat és a döntéshozatalt, elősegítve a vállalkozói lehetőségek azonosítását. Ugyanakkor a vállalkozási tevékenységbe integrált üzleti modellként hozzájárul a versenyképesség fokozásához, a termék- és szolgáltatásinnovációhoz.
The authors develop a three-stage framework for strategic marketing planning, incorporating multiple artificial intelligence (AI) benefits: mechanical AI for automating repetitive marketing functions and activities, thinking AI for processing data to arrive at decisions, and feeling AI for analyzing interactions and human emotions. This framework lays out the ways that AI can be used for marketing research, strategy (segmentation, targeting, and positioning, STP), and actions. At the marketing research stage, mechanical AI can be used for data collection, thinking AI for market analysis, and feeling AI for customer understanding. At the marketing strategy (STP) stage, mechanical AI can be used for segmentation (segment recognition), thinking AI for targeting (segment recommendation), and feeling AI for positioning (segment resonance). At the marketing action stage, mechanical AI can be used for standardization, thinking AI for personalization, and feeling AI for relationalization. We apply this framework to various areas of marketing, organized by marketing 4Ps/4Cs, to illustrate the strategic use of AI.
Artificial intelligence (AI) helps companies offer important benefits to consumers, such as health monitoring with wearable devices, advice with recommender systems, peace of mind with smart household products, and convenience with voice-activated virtual assistants. However, although AI can be seen as a neutral tool to be evaluated on efficiency and accuracy, this approach does not consider the social and individual challenges that can occur when AI is deployed. This research aims to bridge these two perspectives: on one side, the authors acknowledge the value that embedding AI technology into products and services can provide to consumers. On the other side, the authors build on and integrate sociological and psychological scholarship to examine some of the costs consumers experience in their interactions with AI. In doing so, the authors identify four types of consumer experiences with AI: (1) data capture, (2) classification, (3) delegation, and (4) social. This approach allows the authors to discuss policy and managerial avenues to address the ways in which consumers may fail to experience value in organizations’ investments into AI and to lay out an agenda for future research.
Marketers are becoming increasingly reliant on videos to market their products and services. However, there is no standard set of measures of visual information that can be applied to large datasets. This paper proposes two standard measures that can be automatically obtained from videos: visual variation and video content. The paper tests the measures on crowdfunding videos from a leading online crowdfunding website, and shows that the proposed measures have explanatory power on the funding outcomes of the projects. These measures can be effectively implemented and used for large datasets. Further, researchers can apply these measures to other sets of visual information, and marketers could use the research to guide their video design and improve their video marketing effectiveness.
Retail sales forecasting often requires forecasts for thousands of products for many stores. We present a meta-learning framework based on newly developed deep convolutional neural networks, which can first learn a feature representation from raw sales time series data automatically, and then link the learnt features with a set of weights which are used to combine a pool of base-forecasting methods. The experiments which are based on IRI weekly data show that the proposed meta learner provides superior forecasting performance compared with a number of state-of-art benchmarks, though the accuracy gains over some more sophisticated meta ensemble benchmarks are modest and the learnt features lack interpretability. When designing a meta-learner in forecasting retail sales, we recommend building a pool of base-forecasters including both individual and pooled forecasting methods, and target finding the best combination forecasts instead of the best individual method.
Autonomous vehicles (AVs) are expected to soon replace human drivers and promise substantial benefits to society. Yet, consumers remain skeptical about handing over control to an AV. Partly due to the uncertainty about the appropriate moral norms for such vehicles (e.g., should AVs protect the passenger or the pedestrian if harm is unavoidable?). Building on recent work on AV morality, the current research examined how people resolve the dilemma between protecting self versus a pedestrian, and what they expect an AV to do in a similar situation. Five studies revealed that participants considered harm to a pedestrian more permissible with an AV as compared to self as the decision agent in a regular car. This shift in moral judgments was driven by the attribution of responsibility to the AV and was observed for both severe and moderate harm, and when harm was real or imagined. However, the effect was attenuated when five pedestrians or a child could be harmed. These findings suggest that AVs can change prevailing moral norms and promote an increased self-interest among consumers. This has relevance for the design and policy issues related to AVs. It also highlights the moral implications of autonomous agents replacing human decision-makers.
Taking three recent business books on artificial intelligence (AI) as a starting point, we explore the automation and augmentation concepts in the management domain. Whereas automation implies that machines take over a human task, augmentation means that humans collaborate closely with machines to perform a task. Taking a normative stance, the three books advise organizations to prioritize augmentation, which they relate to superior performance. Using a more comprehensive paradox theory perspective, we argue that, in the management domain, augmentation cannot be neatly separated from automation. These dual AI applications are interdependent across time and space, creating a paradoxical tension. Over-emphasizing either augmentation or automation fuels reinforcing cycles with negative organizational and societal outcomes. However, if organizations adopt a broader perspective comprising both automation and augmentation, they could deal with the tension and achieve complementarities that benefit business and society. Drawing on our insights, we conclude that management scholars need to be involved in research on the use of AI in organizations. We also argue that a substantial change is required in how AI research is currently conducted in order to develop meaningful theory and to provide practice with sound advice.
Technologies are becoming increasingly autonomous, able to make decisions and complete tasks on behalf of consumers. Virtual assistants already take care of grocery shopping by replenishing used up ingredients while cooking machines prepare these ingredients and implement recipes. In the future, consumers will be able to delegate substantial parts of the shopping process to autonomous shopping systems. Whereas the functional benefits of these systems are evident, they challenge psychological consumption motives and ingrained human–machine relationships due to the delegation of decisions and tasks to technology. The authors take a cross-disciplinary approach drawing from research in marketing, psychology, and human–computer interaction to examine barriers to adoption of autonomous shopping systems. They identify different types of psychological and cultural barriers, and suggest ways to craft the online and bricks-and-mortar retail environment to overcome these barriers along the consumer journey. The article finishes with implications for policy makers and a future research agenda for researchers examining autonomous technologies.
In the future, artificial intelligence (AI) is likely to substantially change both marketing strategies and customer behaviors. Building from not only extant research but also extensive interactions with practice, the authors propose a multidimensional framework for understanding the impact of AI involving intelligence levels, task types, and whether AI is embedded in a robot. Prior research typically addresses a subset of these dimensions; this paper integrates all three into a single framework. Next, the authors propose a research agenda that addresses not only how marketing strategies and customer behaviors will change in the future, but also highlights important policy questions relating to privacy, bias and ethics. Finally, the authors suggest AI will be more effective if it augments (rather than replaces) human managers.
Anthropomorphism is the attribution of human characteristics to a non-human object. Past research shows that anthropomorphism changes how we perceive objects (e.g., believing them to be more attractive). Does this mean we would be willing to pay more for them? We examined whether displaying a product in an anthropomorphized form influenced how much a consumer was willing to pay. We examined two design aspects, visual (i.e., a face) and auditory (e.g., a voice), in the context of an online auction, and proposed three theoretical routes by which an anthropomorphic product display might affect willingness to pay (emotional, product attachment, and product quality). Results show that adding visual anthropomorphizing features to the way a product was displayed increased the amount bid by 7 percent, but adding auditory anthropomorphizing features had no effect. The visual anthropomorphizing features increased product attachment but had no effect on emotions or perceptions of product quality. Therefore, we conclude that anthropomorphizing the way a product is displayed increases willingness to pay primarily through the theoretical route of creating attachment to the product. There is an additional, as yet undiscovered, theoretical route through which anthropomorphism influences willingness to pay. The results also suggest that the conventional wisdom that the combination of visual and auditory design features is best for triggering anthropomorphism is not always true.
Artificial intelligence (AI) is revolutionizing healthcare, but little is known about consumer receptivity toward AI in medicine. Consumers are reluctant to utilize healthcare provided by AI in real and hypothetical choices, separate and joint evaluations. Consumers are less likely to utilize healthcare (study 1), exhibit lower reservation prices for healthcare (study 2), are less sensitive to differences in provider performance (studies 3A-3C), and derive negative utility if a provider is automated rather than human (study 4). Uniqueness neglect, a concern that AI providers are less able than human providers to account for their unique characteristics and circumstances, drives consumer resistance to medical AI. Indeed, resistance to medical AI is stronger for consumers who perceive themselves to be more unique (study 5). Uniqueness neglect mediates resistance to medical AI (study 6), and is eliminated when AI provides care (a) that is framed as personalized (study 7), (b) to consumers other than the self (study 8), or (c) only supports, rather than replaces, a decision made by a human healthcare provider (study 9). These findings make contributions to the psychology of automation and medical decision making, and suggest interventions to increase consumer acceptance of AI in medicine.
Consumer robots are predicted to be employed in a variety of customer-facing situations. As these robots are designed to look and behave like humans, consumers attribute human traits to them—a phenomenon known as the “Eliza Effect.” In four experiments, we show that the anthropomorphism of a consumer robot increases psychological warmth but decreases attitudes, due to uncanniness. Competence judgments are much less affected and not subject to a decrease in attitudes. The current research contributes to research on artificial intelligence, anthropomorphism, and the uncanny valley phenomenon. We suggest to managers that they need to make sure that the appearances and behaviors of robots are not too human-like to avoid negative attitudes toward robots. Moreover, managers and researchers should collaborate to determine the optimal level of anthropomorphism.
People are increasingly purchasing (e.g., food, clothes) and consuming (e.g., movies, courses) online where, traditionally, the sensory interaction has mostly been limited to visual, and to a lesser extent auditory, inputs. However, other sensory interfaces (e.g., including touch screens, together with a range of virtual, and augmented solutions) are increasingly being made available to people to interact online. Moreover, recent progress in the field of human-computer interaction means that online environments will likely engage more of the senses and become more connected with offline environments in the coming years. This expansion will likely coincide with an increasing engagement with the consumer's more emotional senses, namely touch/haptics, and possibly even olfaction. Forward-thinking marketers and researchers will therefore need to appropriate the latest tools/technologies in order to deliver richer online experiences for tomorrow's consumers. This review is designed to help the interested reader better understand what sensory marketing in a digital context can offer, thus hopefully opening the way for further research and development in the area.
Online social media drive the growth of unstructured text data. Many marketing applications require structuring this data at scales non-accessible to human coding, e.g., to detect communication shifts in sentiment or other researcher-defined content categories. Several methods have been proposed to automatically classify unstructured text. This paper compares the performance of ten such approaches (five lexicon-based, five machine learning algorithms) across 41 social media datasets covering major social media platforms, various sample sizes, and languages. So far, marketing research relies predominantly on support vector machines (SVM) and Linguistic Inquiry and Word Count (LIWC). Across all tasks we study, either random forest (RF) or naive Bayes (NB) performs best in terms of correctly uncovering human intuition. In particular, RF exhibits consistently high performance for three-class sentiment, NB for small samples sizes. SVM never outperform the remaining methods. All lexicon-based approaches, LIWC in particular, perform poorly compared with machine learning. In some applications, accuracies only slightly exceed chance. Since additional considerations of text classification choice are also in favor of NB and RF, our results suggest that marketing research can benefit from considering these alternatives.
>>> Purpose – The service sector is at an inflection point with regard to productivity gains and service industrialization similar to the industrial revolution in manufacturing that started in the 18th century. Robotics in combination with rapidly improving technologies like artificial intelligence (AI), mobile, cloud, big data and biometrics will bring opportunities for a wide range of innovations that have the potential to dramatically change service industries. This conceptual paper explores the potential role service robots will play in the future and advances a research agenda for service researchers.
>>> Design/methodology/approach – This paper uses a conceptual approach that is rooted in the service, robotics, and AI literature.
>>> Findings – The contribution of this article is threefold. First, it provides a definition of service robots, describes their key attributes, contrasts their features and capabilities with those of frontline employees, and provides an understanding for which types of service tasks robots will dominate and where humans will dominate. Second, this article examines consumer perceptions, beliefs and behaviors as related to service robots, and advances the service robot acceptance model (sRAM). Third, it provides an overview of the ethical questions surrounding robot-delivered services at the individual, market and societal level.
>>> Practical implications – This article helps service organizations and their management, service robot innovators, programmers and developers, and policymakers better understand the implications of a ubiquitous deployment of service robots.
>>> Originality/value – This is the first conceptual article that systematically examines key dimensions of robot-delivered frontline service and explores how these will differ in the future.
The amount of digital text available for analysis by consumer researchers has risen dramatically. Consumer discussions on the internet, product reviews, and digital archives of news articles and press releases are just a few potential sources for insights about consumer attitudes, interaction, and culture. Drawing from linguistic theory and methods, this article presents an overview of automated text analysis, providing integration of linguistic theory with constructs commonly used in consumer research, guidance for choosing amongst methods, and advice for resolving sampling and statistical issues unique to text analysis. We argue that although automated text analysis cannot be used to study all phenomena, it is a useful tool for examining patterns in text that neither researchers nor consumers can detect unaided. Text analysis can be used to examine psychological and sociological constructs in consumerproduced digital text by enabling discovery or by providing ecological validity. © The Author 2017. Published by Oxford University Press on behalf of Journal of Consumer Research, Inc. All rights reserved.
The authors extend evaluative conditioning research by examining how differences in emotional ability impact implicit and explicit attitude formation from conditioning. Across five studies, the ability to experience emotional information enhanced the valence of implicit attitudes toward a conditioned stimulus (CS). Conversely, the ability to reason about emotional information reduced the impact of implicit CS attitudes on subsequent explicit evaluations. Furthermore, we examine how brand familiarity and the timing of CS-US pairings impacts attitude formation. Implications for associative learning and persuasion are provided.
Artificial intelligence (AI) is increasingly reshaping service by performing various tasks, constituting a major source of innovation, yet threatening human jobs. We develop a theory of AI job replacement to address this double-edged impact. The theory specifies four intelligences required for service tasks—mechanical, analytical, intuitive, and empathetic—and lays out the way firms should decide between humans and machines for accomplishing those tasks. AI is developing in a predictable order, with mechanical mostly preceding analytical, analytical mostly preceding intuitive, and intuitive mostly preceding empathetic intelligence. The theory asserts that AI job replacement occurs fundamentally at the task level, rather than the job level, and for “lower” (easier for AI) intelligence tasks first. AI first replaces some of a service job’s tasks, a transition stage seen as augmentation, and then progresses to replace human labor entirely when it has the ability to take over all of a job’s tasks. The progression of AI task replacement from lower to higher intelligences results in predictable shifts over time in the relative importance of the intelligences for service employees. An important implication from our theory is that analytical skills will become less important, as AI takes over more analytical tasks, giving the “softer” intuitive and empathetic skills even more importance for service employees. Eventually, AI will be capable of performing even the intuitive and empathetic tasks, which enables innovative ways of human–machine integration for providing service but also results in a fundamental threat for human employment.
Service innovation is intertwined with service design, and knowledge from both fields should be integrated to advance theoretical and normative insights. However, studies bridging service innovation and service design are in their infancy. This is because the body of service innovation and service design research is large and heterogeneous, which makes it difficult, if not impossible, for any human to read and understand its entire content and to delineate appropriate guidelines on how to broaden the scope of either field. Our work addresses this challenge by presenting the first application of topic modeling, a type of machine learning, to review and analyze currently available service innovation and service design research (n = 641 articles with 10,543 pages of written text or 4,119,747 words). We provide an empirical contribution to service research by identifying and analyzing 69 distinct research topics in the published text corpus, a theoretical contribution by delineating an extensive research agenda consisting of four research directions and 12 operationalizable guidelines to facilitate cross-fertilization between the two fields, and a methodological contribution by introducing and demonstrating the applicability of topic modeling and machine learning as a novel type of big data analytics to our discipline.
Artificial intelligence (AI) has penetrated many organizational processes, resulting in a growing fear that smart machines will soon replace many humans in decision-making. To provide a more proactive and pragmatic perspective, this article highlights the complementarity of humans and AI, and examines how each can bring their own strength in organizational decision making processes typically characterized by uncertainty, complexity, and equivocality. With a greater computational information processing capacity and an analytical approach, AI can extend humans' cognition when addressing complexity, whereas humans can still offer a more holistic, intuitive approach in dealing with uncertainty and equivocality in organizational decision-making. This premise mirrors the idea of 'intelligence augmentation': AI systems should be designed with the intention of augmenting, not replacing, human contributions.
Retailers have embraced a variety of technologies to engage their customers. This article focuses on " The Future of Retailing " by highlighting five key areas that are moving the field forward: (1) technology and tools to facilitate decision making, (2) visual display and merchandise offer decisions, (3) consumption and engagement, (4) big data collection and usage, and (5) analytics and profitability. We also suggest numerous issues that are deserving of additional inquiry, as well as introduce important areas of emerging applicability: the internet of things, virtual reality, augmented reality, artificial intelligence, robots, drones, and driverless vehicles.
Deciphering consumers' sentiment expressions from big data (e.g., online reviews) has become a managerial priority to monitor product and service evaluations. However, sentiment analysis, the process of automatically distilling sentiment from text, provides little insight regarding the language granularities beyond the use of positive and negative words. Drawing on speech act theory, this study provides a fine-grained analysis of the implicit and explicit language used by consumers to express sentiment in text. An empirical text-mining study using more than 45,000 consumer reviews demonstrates the differential impacts of activation levels (e.g., tentative language), implicit sentiment expressions (e.g., commissive language), and discourse patterns (e.g., incoherence) on overall consumer sentiment (i.e., star ratings). In two follow-up studies, we demonstrate that these speech act features also influence the readers' behavior and are generalizable to other social media contexts, such as Twitter and Facebook. We contribute to research on consumer sentiment analysis by offering a more nuanced understanding of consumer sentiments and their implications.
Technology is rapidly changing the nature of service, customers’ service frontline experiences, and customers’ relationships with service providers. Based on the prediction that in the marketplace of 2025, technology (e.g., service-providing humanoid robots) will be melded into numerous service experiences, this article spotlights technology’s ability to engage customers on a social level as a critical advancement of technology infusions. Specifically, it introduces the novel concept of automated social presence (ASP; i.e., the extent to which technology makes customers feel the presence of another social entity) to the services literature. The authors develop a typology that highlights different combinations of automated and human social presence in organizational frontlines and indicates literature gaps, thereby emphasizing avenues for future research. Moreover, the article presents a conceptual framework that focuses on (a) how the relationship between ASP and several key service and customer outcomes is mediated by social cognition and perceptions of psychological ownership as well as (b) three customer-related factors that moderate the relationship between ASP and social cognition and psychological ownership (i.e., a customer’s relationship orientation, tendency to anthropomorphize, and technology readiness). Finally, propositions are presented that can be a catalyst for future work to enhance the understanding of how technology infusion, particularly service robots, influences customers’ frontline experiences in the future.
Social media sites have created a reverberating "echoverse" for brand communication, forming complex feedback loops ("echoes") between the "universe" of corporate communications, news media, and user-generated social media. To understand these feedback loops, the authors process longitudinal, unstructured data using computational linguistics techniques and analyze them using econometric methods. By assembling one of the most comprehensive data sets in the brand communications literature with corporate communications, news stories, social media, and business outcomes, the authors document the echoverse (i.e., feedback loops between all of these sources). Furthermore, the echoverse has changed as online word of mouth has become prevalent. Over time, online word of mouth has fallen into a negativity spiral, with negative messages leading to greater volume, and firms are adjusting their communications strategies in response. The nature of brand communications has been transformed by online technology as corporate communications move increasingly from one to many (e.g., advertising) to one to one (e.g., Twitter) while consumer word of mouth moves from one to one (e.g., conversations) to one to many (e.g., social media). The results indicate that companies benefit from using social media (e.g., Twitter) for personalized customer responses, although there is still a role for traditional brand communications (e.g., press releases, advertising). The evolving echoverse requires managers to rethink brand communication strategies, with online communications becoming increasingly central.
This article develops a strategic framework for using artificial intelligence (AI) to engage customers for different service benefits. This framework lays out guidelines of how to use different AIs to engage customers based on considerations of nature of service task, service offering, service strategy, and service process. AI develops from mechanical, to thinking, and to feeling. As AI advances to a higher intelligence level, more human service employees and human intelligence (HI) at the intelligence levels lower than that level should be used less. Thus, at the current level of AI development, mechanical service should be performed mostly by mechanical AI, thinking service by both thinking AI and HI, and feeling service mostly by HI. Mechanical AI should be used for standardization when service is routine and transactional, for cost leadership, and mostly at the service delivery stage. Thinking AI should be used for personalization when service is data-rich and utilitarian, for quality leadership, and mostly at the service creation stage. Feeling AI should be used for relationalization when service is relational and high touch, for relationship leadership, and mostly at the service interaction stage. We illustrate various AI applications for the three major AI benefits, providing managerial guidelines for service providers to leverage the advantages of AI as well as future research implications for service researchers to investigate AI in service from modeling, consumer, and policy perspectives.
The authors introduce emotional ability similarity to explain consumer satisfaction in interactions with frontline sales and service employees and other consumers beyond the effects of traditional relational variables in the similarity–attraction paradigm. Four studies examine how and why similar abilities for using emotional information between two people promote relational success in marketplace exchanges. We find that when interacting with others, consumers who exchange nonverbal information with their partners experience (dis)similarity in their emotional ability (EA). Similar dyads who rely on expressive (high-high EA pairs) or inexpressive (low-low EA pairs) emotion norms experience significantly greater satisfaction in their interactions than consumers with dissimilar norms (high-low EA pairs). Together, these findings advance the understanding of consumer relationships and satisfaction by establishing EA similarity as a new avenue for consumer research.
Many online retailers, such as Amazon, use automated product recommender systems to encourage customer loyalty and cross-sell products. Despite significant improvements to the predictive accuracy of contemporary recommender system algorithms, they remain prone to errors. Erroneous recommendations pose potential threats to online retailers in particular, because they diminish customers’ trust in, acceptance of, satisfaction with, and loyalty to a recommender system. Explanations of the reasoning that lead to recommendations might mitigate these negative effects. That is, a recommendation algorithm ideally would provide both accurate recommendations and explanations of the reasoning for those recommendations. This article proposes a novel method to balance these concurrent objectives. The application of this method, using a combination of content-based and collaborative filtering, to two real-world data sets with more than 100 million product ratings reveals that the proposed method outperforms established recommender approaches in terms of predictive accuracy (more than five percent better than the Netflix Prize winner algorithm according to normalized root mean squared error) and its ability to provide actionable explanations, which is also an ethical requirement of artificial intelligence systems.
Recent years have seen a growing focus on automated personalized services, with music recommendations a particularly prominent domain for such contributions. However, while most prior work on music recommender systems has focused on preferences for songs and artists, a fundamental aspect of human music perception is that music is experienced in a temporal context and in sequence. Hence, listeners’ preferences also may be affected by the sequence in which songs are being played and the corresponding song transitions. Moreover, a listener’s sequential preferences may vary across circumstances, such as in response to different emotional or functional needs, so that different song sequences may be more satisfying at different times. It is therefore useful to develop methods that can learn and adapt to individuals’ sequential preferences in real time, so as to adapt to a listener’s contextual preferences during a listening session. Prior work on personalized playlists either considered batch learning from large historical data sets, attempted to learn preferences for songs or artists irrespective of the sequence in which they are played, or assumed that adaptation occurs over extended periods of time. Hence, this prior work did not aim to adapt to a listener’s current song and sequential preferences in real time, during a listening session. This paper develops and evaluates a novel framework for online learning of and adaptation to a listener’s current song and sequence preferences exclusively by interacting with the listener, during a listening session. We evaluate the framework using both real playlist datasets and an experiment with human listeners. The results establish that the framework effectively learns and adapts to a listeners’ transition preferences during a listening session, and that it yields a significantly better listener experience. Our research also establishes that future advances of online adaptation to listener’s temporal preferences is a valuable avenue for research, and suggests that similar benefits may be possible from exploring online learning of temporal preferences for other personalized services.
Words are part of almost every marketplace interaction. Online reviews, customer service calls, press releases, marketing communications, and other interactions create a wealth of textual data. But how can marketers best use such data? This article provides an overview of automated textual analysis and details how it can be used to generate marketing insights. The authors discuss how text reflects qualities of the text producer (and the context in which the text was produced) and impacts the audience or text recipient. Next, they discuss how text can be a powerful tool both for prediction and for understanding (i.e., insights). Then, the authors overview methodologies and metrics used in text analysis, providing a set of guidelines and procedures. Finally, they further highlight some common metrics and challenges and discuss how researchers can address issues of internal and external validity. They conclude with a discussion of potential areas for future work. Along the way, the authors note how textual analysis can unite the tribes of marketing. While most marketing problems are interdisciplinary, the field is often fragmented. By involving skills and ideas from each of the subareas of marketing, text analysis has the potential to help unite the field with a common set of tools and approaches.
The capability of AI is currently expanding beyond mechanical and repetitive to analytical and thinking. A “Feeling Economy” is emerging, in which AI performs many of the analytical and thinking tasks, and human workers gravitate more toward interpersonal and empathetic tasks. Although these people-focused tasks have always been important to jobs, they are now becoming more important to an unprecedented degree. To manage more effectively in the Feeling Economy, managers must adapt the nature of jobs to compensate for the fact that many of the analytical and thinking tasks are increasingly being performed by AI, and, thus, human workers must place increased emphasis on the empathetic and emotional dimensions of their work.
Understanding the effects of contextual factors is crucial in designing contextbased marketing. This paper focuses on product recommendations and studies how time and crowd pressures-two prominent contextual effects in the consumer behavior literature- can impact the effectiveness of recommendations. Measuring these effects is not straightforward because the joint distribution of consumer choice, time, and crowdpressures is rarely observed outside the laboratory and recommendations are often endogenously determined. We overcome these issues using data from an experiment conducted with vending machines in railway stations across Tokyo. Themachines are equipped with a facial recognition system to make recommendations, and recommendations are changed exogenously in the experiment. This setup provides us with well-measured variables of the time and crowd pressures that affect the effectiveness of recommendations. After showing that recommendations increase the sales of both the recommended and nonrecommended products,we showthat time pressures moderate the effectiveness of product recommendations for both recommended products directly and nonrecommended products indirectly. Crowd pressures weaken the direct effect on the recommended products, although its impact on the nonrecommended products is small and not robust in some cases. These results indicate that, when marketers make context-based recommendations, they should be mindful of the consumers under time pressure.
Consumers often learn the weights they ascribe to product attributes (“preference weights”) as they search. For example, after test driving cars, a consumer might find that he or she undervalued trunk space and overvalued sunroofs. Preference-weight learning makes optimal search complex because each time a product is searched, updated preference weights affect the expected utility of all products and the value of subsequent optimal search. Product recommendations, which take preference-weight learning into account, help consumers search. We motivate a model in which consumers learn (update) their preference weights. When consumers learn preference weights, it may not be optimal to recommend the product with the highest option value, as in most search models, or the product most likely to be chosen, as in traditional recommendation systems. Recommendations are improved if consumers are encouraged to search products with diverse attribute levels, products that are undervalued, or products for which recommendation-system priors differ from consumers’ priors. Synthetic data experiments demonstrate that proposed recommendation systems outperform benchmark recommendation systems, especially when consumers are novices and when recommendation systems have good priors. We demonstrate empirically that consumers learn preference weights during search, that recommendation systems can predict changes, and that a proposed recommendation system encourages learning.
The data files and online appendix are available at https://doi.org/10.1287/mksc.2018.1144 .
Interactions between consumers and humanoid service robots (HSRs; i.e., robots with a human-like morphology such as a face, arms, and legs) will soon be part of routine marketplace experiences. It is unclear, however, whether these humanoid robots (compared with human employees) will trigger positive or negative consequences for consumers and companies. Seven experimental studies reveal that consumers display compensatory responses when they interact with an HSR rather than a human employee (e.g., they favor purchasing status goods, seek social affiliation, and order and eat more food). The authors investigate the underlying process driving these effects, and they find that HSRs elicit greater consumer discomfort (i.e., eeriness and a threat to human identity), which in turn results in the enhancement of compensatory consumption. Moreover, this research identifies boundary conditions of the effects such that the compensatory responses that HSRs elicit are (1) mitigated when consumer-perceived social belongingness is high, (2) attenuated when food is perceived as more healthful, and (3) buffered when the robot is machinized (rather than anthropomorphized).
Pricing managers at online retailers face a unique challenge. They must decide on real-time prices for a large number of products with incomplete demand information. The manager runs price experiments to learn about each product’s demand curve and the profitmaximizing price. In practice, balanced field price experiments can create high opportunity costs, because a large number of customers are presented with suboptimal prices. In this paper, we propose an alternative dynamic price experimentation policy. The proposed approach extends multiarmed bandit (MAB) algorithms from statistical machine learning to include microeconomic choice theory. Our automated pricing policy solves this MAB problem using a scalable distribution-free algorithm. We prove analytically that our method is asymptotically optimal for any weakly downward sloping demand curve. In a series of MonteCarlo simulations,we showthat the proposed approach performs favorably compared with balanced field experiments and standard methods in dynamic pricing from computer science. In a calibrated simulation based on an existing pricing field experiment, we find that our algorithm can increase profits by 43% during the month of testing and 4% annually.
Personalized point-of-interest (POI) recommendation is important to location-based social networks (LBSNs) for helping users to explore new places and for helping third-party services to launch targeted advertisements. Discovering effective features or representations from check-in data is the key to POI recommendation. Deep learning is a representation-learning method with multiple levels for discovering intrinsic features to better represent user preferences. We analyzed users’ check-in behavior in detail and developed a deep learning model to integrate geographical and social influences for POI recommendation tasks. We used a semi-restricted Boltzmann machine to model the geographical similarity and a conditional layer to model the social influence. Experiments with real-world LBSNs showed that our method performed better than other state-of-the-art methods. Theoretically, our study contributes to the effective usage of data science and analytics for social recommender system design. In practice, our results can be used to improve the quality of personalized POI recommendation services for websites and applications.
We extend latent Dirichlet allocation by introducing a topic model, hierarchically dual latent Dirichlet allocation, the output of which provides a basis for estimating consumers’ content preferences on the fly from their search queries.
Consumers’ interactions with smart objects have a relational nature, and extensive research has supported the “relationship metaphor” as a fruitful way to understand consumer responses to consumption objects. But, smart objects pose unique challenges for considering the emergence of consumer–object relationships, because their degrees of agency, autonomy, and authority lend them their own unique capacities for interaction. We present a new framework for consumer–object relationships based on the circumplex model of interpersonal complementarity and situated in assemblage theory and object-oriented ontology. Consumer–object relationship styles are defined in terms of two foundational dimensions of behavior, agency, and communion, based on the expressive roles played by consumer and object. The overlay of assemblage theory provides a conceptually rich understanding of the space of master–servant, partner, and unstable relationship styles, along with their concomitant positive (enabling) versus negative (constraining) consumer experiences. The model’s underlying geometry supports extensive empirical work and provides a powerful managerial framework for measuring and tracking consumer–object relationships and the journeys they take over time.
The rise of unstructured data (UD), propelled by novel technologies, is reshaping markets and the management of marketing activities. Yet these increased data remain mostly untapped by many firms, suggesting the potential for further research developments. The integrative framework proposed in this study addresses the nature of UD and pursues theoretical richness and computational advancements by integrating insights from other disciplines. This article makes three main contributions to the literature by (1) offering a unifying definition and conceptualization of UD in marketing; (2) bridging disjoint literature with an organizing framework that synthesizes various subsets of UD relevant for marketing management through an integrative review; and (3) identifying substantive, computational, and theoretical gaps in extant literature and ways to leverage interdisciplinary knowledge to advance marketing research by applying UD analyses to underdeveloped areas.
The consumer Internet of Things (IoT) has the potential to revolutionize consumer experience. Because consumers can actively interact with smart objects, the traditional, human-centric conceptualization of consumer experience as consumers' internal subjective responses to branded objects may not be sufficient to conceptualize consumer experience in the IoT. Smart objects possess their own unique capacities and their own kinds of experiences in interaction with the consumer and each other. A conceptual framework based on assemblage theory and objectoriented ontology details how consumer experience and object experience emerge in the IoT. This conceptualization is anchored in the context of consumerobject assemblages, and defines consumer experience by its emergent properties, capacities, and agentic and communal roles expressed in interaction. Four specific consumer experience assemblages emerge: enabling experiences, comprising agentic self-extension and communal self-expansion, and constraining experiences, comprising agentic self-restriction and communal self-reduction. A parallel conceptualization of the construct of object experience argues that it can be accessed by consumers through object-oriented anthropomorphism, a nonhuman-centric approach to evaluating the expressive roles objects play in interaction. Directions for future research are derived, and consumer researchers are invited to join a dialogue about the important themes underlying our framework. © The Author 2017. Published by Oxford University Press on behalf of Journal of Consumer Research, Inc. All rights reserved.
Can machine learning improve human decision making? Bail decisions provide a good test case. Millions of times each year, judges make jail-or-release decisions that hinge on a prediction of what a defendant would do if released. The concreteness of the prediction task combined with the volume of data available makes this a promising machine-learning application. Yet comparing the algorithm to judges proves complicated. First, the available data are generated by prior judge decisions. We only observe crime outcomes for released defendants, not for those judges detained. This makes it hard to evaluate counterfactual decision rules based on algorithmic predictions. Second, judges may have a broader set of preferences than the variable the algorithm predicts; for instance, judges may care specifically about violent crimes or about racial inequities. We deal with these problems using different econometric strategies, such as quasi-random assignment of cases to judges. Even accounting for these concerns, our results suggest potentially large welfare gains: one policy simulation shows crime reductions up to 24.7% with no change in jailing rates, or jailing rate reductions up to 41.9% with no increase in crime rates. Moreover, all categories of crime, including violent crimes, show reductions; these gains can be achieved while simultaneously reducing racial disparities. These results suggest that while machine learning can be valuable, realizing this value requires integrating these tools into an economic framework: being clear about the link between predictions and decisions; specifying the scope of payoff functions; and constructing unbiased decision counterfactuals.
This article was downloaded by: [128.97.27.20] On: 25 May 2016, At: 09:44 Publisher: Institute for Operations Research and the Management Sciences (INFORMS) INFORMS is located in Maryland, USA Marketing Science Publication details, including instructions for authors and subscription information: http://pubsonline.informs.org Editorial—Marketing Science and Big Data Pradeep Chintagunta, Dominique M. Hanssens, John R. Hauser To cite this article: Pradeep Chintagunta, Dominique M. Hanssens, John R. Hauser (2016) Editorial—Marketing Science and Big Data. Marketing Science 35(3):341-342. http://dx.doi.org/10.1287/mksc.2016.0996 Full terms and conditions of use: http://pubsonline.informs.org/page/terms-and-conditions This article may be used only for the purposes of research, teaching, and/or private study. Commercial use or systematic downloading (by robots or other automatic processes) is prohibited without explicit Publisher approval, unless otherwise noted. For more information, contact permissions@informs.org. The Publisher does not warrant or guarantee the article’s accuracy, completeness, merchantability, fitness for a particular purpose, or non-infringement. Descriptions of, or references to, products or publications, or inclusion of an advertisement in this article, neither constitutes nor implies a guarantee, endorsement, or support of claims made of that product, publication, or service. Copyright © 2016, INFORMS Please scroll down for article—it is on subsequent pages INFORMS is the largest professional society in the world for professionals in the fields of operations research, management science, and analytics. For more information on INFORMS, its publications, membership, or meetings visit http://www.informs.org
The authors provide a critical examination of marketing analytics methods by tracing their historical development, examining their applications to structured and unstructured data generated within or external to a firm, and reviewing their potential to support marketing decisions. The authors identify directions for new analytical research methods, addressing (1) analytics for optimizingmarketing-mix spending in a data-rich environment, (2) analytics for personalization, and (3) analytics in the context of customers' privacy and data security. They review the implications for organizations that intend to implement big data analytics. Finally, turning to the future, the authors identify trends that will shape marketing analytics as a discipline as well as marketing analytics education.
Accurate forecasting of sales/consumption is particularly important for marketing because this information can be used to adjust marketing budget allocations and overall marketing strategies. Recently, online social platforms have produced an unparalleled amount of data on consumer behavior. However, two challenges have limited the use of these data in obtaining meaningful business marketing insights. First, the data are typically in an unstructured format, such as texts, images, audio, and video. Second, the sheer volume of the data makes standard analysis procedures computationally unworkable. In this study, we combine methods from cloud computing, machine learning, and text mining to illustrate how online platform content, such as Twitter, can be effectively used for forecasting. We conduct our analysis on a significant volume of nearly two billion Tweets and 400 billion Wikipedia pages. Our main findings emphasize that, by contrast to basic surface-level measures such as the volume of or sentiments in Tweets, the information content of Tweets and their timeliness significantly improve forecasting accuracy. Our method endogenously summarizes the information in Tweets. The advantage of our method is that the classification of the Tweets is based on what is in the Tweets rather than preconceived topics that may not be relevant. We also find that, by contrast to Twitter, other online data (e.g., Google Trends, Wikipedia views, IMDB reviews, and Huffington Post news) are very weak predictors of TV show demand because users tweet about TV shows before, during, and after a TV show, whereas Google searches, Wikipedia views, IMDB reviews, and news posts typically lag behind the show.
Data, as supplemental material, are available at http://dx.doi.org/10.1287/mksc.2015.0972 .
In this paper, we propose an automated and scalable garment recommender system using real time in-store videos that can improve the experiences of garment shoppers and increase product sales. The video-based automated recommender (VAR) system is based on observations that garment shoppers tend to try on garments and evaluate themselves in front of store mirrors. Combining state-of-the-art computer vision techniques with marketing models of consumer preferences, the system automatically identifies shoppers’ preferences based on their reactions and uses that information to make meaningful personalized recommendations. First, the system uses a camera to capture a shopper’s behavior in front of the mirror to make inferences about her preferences based on her facial expressions and the part of the garment she is examining at each time point. Second, the system identifies shoppers with preferences similar to the focal customer from a database of shoppers whose preferences, purchasing and/or consideration decisions are known. Finally, recommendations are made to the focal customer based on the preferences, purchasing and/or consideration decisions of these like-minded shoppers. Each of the three steps can be implemented with several variations, and a retailing chain can choose the specific configuration that best serves its purpose. In this paper, we present an empirical test that compares one specific type of VAR system implementation against two alternative, non-automated personal recommender systems: self-explicated conjoint (SEC) and self-evaluation after try-on (SET). The results show that VAR consistently outperforms SEC and SET. A second empirical study demonstrates the feasibility of VAR in real time applications. Participants in the second study enjoyed the VAR experience, and almost all of them tried on the recommended garments. VAR should prove to be a valuable tool for both garment retailers and shoppers.
SINCE THE MID-2000S, few business topics have received as much attention as big data and business analytics,5,8,11,13 including unstructured data derived from social media, blogs, chat, and email messages. In addition to unstructured data, YouTube, Vimeo, and other video sources represent another aspect of organizations' customer services. A 2011 IBM survey of more than 4,000 IT professionals from 93 countries and 25 industries7 identified big data and business analytics as a major business trend for most organizations, along with mobile, cloud, and social business technologies.
Ernest Davis and Gary Marcus share their views on the significance of commonsense reasoning and commonsense knowledge in artificial intelligence (AI). Many intelligent tasks, such as understanding texts, computer vision, planning, and scientific reasoning require the real-world knowledge and reasoning abilities. Techniques for implementing commonsense include logical analysis, handcrafting large knowledge bases, Web mining, and crowdsourcing. Intelligent machines need not replicate human cognition directly, but a better understanding of human commonsense is needed to perform such activities.
Salespeople make two types of judgments about customers in face-to-face interactions: those that are more intuitive and those that are more deliberative. The authors evaluate the influence of accurate intuitive and deliberative judgments on the performance of salespeople. To evaluate this influence, the authors employ matched survey, observational, and objective field data obtained before, during, and after salesperson-customer interactions. The results reveal that accurate intuitive judgments improve selling performance by enabling more appropriate initial sales strategies. These judgments not only help increase the effectiveness of salespeople's selling efforts but also reduce the amount of selling time, resulting in improved selling efficiency. However, performance is compromised when inaccurate deliberative judgments follow accurate intuitive judgments. The findings also identify different antecedents to judgment accuracy. Intuitive accuracy is influenced by domain-specific experience, similarity to the customer, and empathy for the customer, whereas listening skills and customer orientation influence deliberative accuracy.