Volume 9, Nomor 1, Juni 2023
EXPLORING FUTURE RESEARCH HORIZONS IN ARTIFICIAL INTELLIGENCE-BASED MARKETING MANAGEMENT FOR THE F&B
INDUSTRY
Fitria Mumtazah
Universitas Islam Kalimantan Muhammad Arsyad Al Banjari, Indonesia. E-mail: [email protected]
ARTICLE INFO ABSTRACT Keywords:
Artificial Intelligence;
Food and Beverage Industry; Marketing Management.
Received:
2 August 2023 Revised:
15 August 2023 Accepted:
31 August 2023
This study examines how Artificial Intelligence (AI) is transforming the field of marketing management in the Food and Beverage (F&B) industry. By offering an understanding of AI's disruption of the traditional 4C marketing mix strategy, this research identifies challenges and threats that emerge from integrating AI into F&B marketing management. This study discovers a noticeable disassociation between marketing and the F&B supply chain, which was uncovered by VOSviewer network visualization outcomes. The visualization presents interesting avenues for future research, particularly in the field of AI-Enhanced Marketing Strategies for the F&B Industry, Integration of AI, IoT, and Industry 4.0 in the F&B Supply Chain, and Cross-Disciplinary Collaboration and Innovation. This study contributes in suggesting additional research directions for the use of AI in marketing management of the F&B industry.
Penelitian ini mengeksplorasi bagaimana Artificial Intelligence (AI) mengubah kajian terkait manajemen pemasaran di industri Makanan dan Minuman (F&B). Penelitian ini juga mengidentifikasi tantangan dan ancaman yang muncul dari pengintegrasian AI ke dalam manajemen pemasaran F&B, melalui pemahaman tentang disrupsi AI terhadap strategi bauran pemasaran 4C tradisional. Hasil penelitian ini menemukan bahwa menurut hasil visualisasi jaringan VOSviewer, keterkaitan antara kajian tentang pemasaran dan F&B pada penelitian terdahulu terpisah dalam jarak yang cukup jauh. Visualisasi ini mengindikasikan peluang penelitian di masa depan, terutama di bidang strategi pemasaran berbasis AI untuk industri F&B, integrasi AI, IoT, dan Industri 4.0 dalam rantai pasokan F&B, serta kolaborasi dan inovasi lintas disiplin dalam kajian tersebut. Penelitian ini berkontribusi dalam menambahkan saran terkait arah penelitian berikutnya dalam hal penggunaan AI di bidang manajemen pemasaran industri F&B.
DOI: http://dx.doi.org/10.31602/iqt.v9i1.12734
Introduction
In the current era of digitalization, technology has notably transformed multiple sectors, including the food and beverage (F&B) industry. In particular, the F&B industry
has experienced significant enhancements in logistics organization, supply chain management, and competitiveness due to technological advancements. The advancements in Information and Communication Technology (ICT) have impacted diverse areas of businesses, including marketing management. Specifically, in the F&B industry, technology has resulted in significant improvements in logistics organization, supply chain management, and increasing competitiveness (Gao D. et al., 2020; Husin et al., 2021).
Artificial intelligence (AI) is transforming the marketing management strategies in the F&B industry. With the ability to conduct massive data analysis, natural language processing, and machine learning, AI can perform tasks that were once solely the domain of humans. The implementation of AI in the F&B industry can even enhance responsiveness and improve the customer experience (Gao J. et al., 2022).
The integration of AI within the F&B industry may entail the utilization of intelligent chatbots to enhance the quality and tailored nature of customer service. This, in turn, may foster customer loyalty and augment value co-creation (Nguyen et al, 2023). Such potentials have urged some F&B companies such as McDonald's, Brush and Palette Coffee, and Halal F&B companies in Malaysia to utilize AI in their marketing strategy (Fathullah & Belgiawan, 2023, Sardjono et al., 2023, Sham et al., 2021).
In this paper, we define AI as a computer science field focused on creating machines that can perform tasks that usually require human intelligence, encompassing visual perception, speech recognition, decision-making, and language translation. The field is rapidly evolving and intriguing. AI systems implement algorithms and statistical models to analyze data, learn from it, and draw predictions or make decisions based on their learning. Artificial Intelligence (AI) falls into two primary categories: narrow or weak AI, which is tailored for specific task execution, and general or strong AI, with the capacity to tackle any intellectual endeavour that a human can. Although AI has an extensive history spanning several decades, recent strides in machine learning and deep learning have ushered in remarkable advancements within this domain.
Consequently, AI finds practical application in various sectors, including autonomous driving, medical diagnostics, and financial trading. Nevertheless, concerns endure regarding the ethical and societal implications associated with AI, encompassing issues such as job displacement, bias, and privacy considerations. Researchers of various discipline continue to work towards addressing these challenges as the field of AI continues to advance (Laato et al., 2020; Zeller & Dwyer, 2022).
Numerous research studies have investigated the possibilities and potentials of AI in multiple areas, encompassing agriculture, journalism, and public administration (Chertovskikh O., & Chertovskikh, M., 2019; Kadyrova et al., 2021; Rozhkova et al., 2022). Some studies have concentrated on the history and development of artificial intelligence, involving the implementation of neuroevolution to create artificial brains and the advancement of machine translation technology (Adami, 2021; Jiang & Lu, 2021). Additional academic studies have explored the ethical and legal ramifications of artificial intelligence, particularly its legal standing in civil law (Amboro &
Komarhana, 2021). Some have also questioned the negative impacts of AI in
revolutionizing consumer behavior and management techniques (Hermann, 2022; Kim et al., 2023; Su et al., 2023), while others view it as a promising avenue in various business sectors (Samala & Raganathan, 2020; Thiebaut, 2019; Wang Y. & Wang H, 2022). However, the use of AI in marketing management, especially in the context of F&B industry is rarely discussed. This study aims to fill the gap by conceptualizing AI in marketing management within the F&B industry and develop a framework for implementing AI-powered marketing strategies.
Method
This study utilizes a systematic approach to identify and analyze the current literature on the application of AI in marketing management within the F&B industry. A keyword search was conducted using the terms "artificial intelligence marketing management food and beverage industry" to retrieve scholarly articles. Initially, 674 articles were retrieved and subjected to a systematic screening process, resulting in removal of duplicates to refine the dataset. Subsequently, we screened the articles for relevance to the study's focus, resulting in a final set of articles for analysis. For data analysis, we used VOSviewer, a visualization and analysis tool, to create network visualizations based on bibliometric data from the selected articles. These visualizations helped identify key research themes, connections between research areas, and potential literature gaps. Based on insights gained from the network visualizations, the research questions were developed. The questions seek to address emerging themes, challenges, and research opportunities associated with AI in F&B marketing management. This methodology facilitated the methodical investigation of the influence of AI on F&B marketing management, identified research lacunae, and devised research inquiries to direct forthcoming studies in this domain.
Results and Discussion
1. Artificial Intelligence: Definitions, Concepts, and Dimensions
The term "artificial intelligence" was coined in 1956 by McCarthy, Minsky, Rochester, and Shannon during the Dartmouth Conference. This term refers to the simulation of human intelligence in machines that are programmed to think and learn like humans (Monett & Winkler 2019). This field involves developing algorithms and computer programs capable of performing tasks requiring human- like intelligence.
Various conceptual frameworks have been created to comprehend the effects of AI in the business context. Chen L. et al. (2022) offered a conceptual framework for AI integration in business-to-business (B2B) marketing was formulated using a content analysis of 59 papers published in peer-reviewed academic journals, which identifies the drivers, barriers, practices, and consequences of adopting AI in B2B marketing. This conceptual framework builds on the principles of information processing theory and organizational learning theory (OLT).
Another study formulated a conceptual framework has been for implementing AI in project management. This framework outlines the fundamental concepts for the integration of AI into project management while considering the requirements of both AI and project management. The framework organizes the potential use cases of AI in project management and facilitates targeted solution design for businesses (Auth et al., 2021).
In case of Covid-19, AI has the potential to automate business processes and enhance user engagement in marketing. It integrates traditional marketing practices into an overarching structure that can be implemented by structured AI (Zhang, 2022).
Additionally, AI has also been developed for the use of Robots, AI, and Service Automation (RAISA) in companies related to Travel, Tourism, and Hospitality (TTH).
The framework offers an inclusive analysis of issues pertaining to the utilization of RAISA technology in the TTH sector. It covers the factors driving RAISA implementation in tourism, competitive advantages and drawbacks of RAISA technologies versus human resources, managerial determinations, and the effects of RAISA deployment on business operations (Ivanov & Webster, 2019).
AI differs greatly from past technologies in numerous dimensions. One of the main dissimilarities relates to intelligence. AI entails the effort to equip machines with intelligence, allowing them to operate competently and with foresight in their surroundings (Nasir et al., 2023). This contrasts with previous technologies, which were created to execute specific tasks devoid of intelligence.
The complexity of AI is another point of divergence. AI is a multifaceted field encompassing the development of theory, methodology, technology, and application systems to simulate and expand human intelligence (Liu et al., 2022).
The complexity of AI is attributed to designing systems that can learn and adapt to new situations, necessitating advanced algorithms and computational power.
AI also has the potential to replicate human intelligence and abilities, including natural language processing, image recognition, and decision-making (Samuel et al., 2022). This represents a significant departure from prior technologies, which were usually developed to execute particular tasks without any human-like capabilities.
However, the swift evolution of AI technologies has prompted numerous ethical and trustworthiness concerns, especially in safety-critical environments like healthcare. Given growing concerns over the potential harm that AI could cause people, it is essential that AI technologies can be trusted, enabling people to live in harmony with them.
2. Artificial Intelligence Disruptions to Marketing Management in Food and Beverages Industry
AI has the potential to revolutionize marketing strategy in the F&B industry in numerous ways. Referring to the recent 4C (Co-creation, Currency, Communal
Activation, and Conversation) marketing mix strategy developed by Kotler et al., (2016), the following represents the current involvement of AI in marketing management:
a. AI can improve restaurant co-creation service quality and enhance the customer experience. By leveraging robots, chatbots, facial recognition, voice-activated tech, and sentiment analysis, eateries can now utilize tech for recommender systems and personalized services (Cheong et al., 2022). Chatbots can answer customer queries, provide recommendations, while facial recognition can personalize their experience. Voice-activated technology can facilitate order- taking and reservation-making, whereas sentiment analysis may enhance customer feedback analysis and service quality improvement (Sadiku et al., 2020). AI algorithms, like machine and deep learning, can intelligently predict and optimize food production, processing, and quality. Machine learning algorithms can predict customer demand and optimize inventory management, while deep learning algorithms can improve food quality and consistency. Co- creation with AI can enhance restaurant service quality, increase customer satisfaction, and optimize operations.
b. In terms of currency, AI has the potential to allow for "dark nudges" by transnational food and beverage companies to influence consumer behavior. An example is the use of AI-enabled dark nudges to change the availability, position, functionality, and presentation of products. This prompts further purchases based on current selections (Brooks et al., 2022). As an illustration, AI has the capability to scrutinize a consumer's buying patterns and preferences, enabling it to forecast the products they are inclined to purchase. Subsequently, the company can strategically manage the accessibility and display of these products to incentivize the consumer to engage in additional purchases. AI can also generate personalized recommendations and promotions that are tailored to each specific consumer, further increasing the likelihood of a purchase. Such utilization of dark nudges does trigger ethical concerns regarding the manipulation of consumer behavior and the possibility of harm. For this reason, organizations need to implement AI in a responsible and transparent approach to guarantee that consumers are not being exploited or deceived.
c. AI can also be used to automate and optimize communal activation initiatives, enabling F&B industry to create personalized and precise marketing campaigns tailored to meet the specific needs and preferences of consumers. Crafting customized marketing messages is feasible through analyzing vast datasets of consumer behavior, preferences, and purchasing habits. This meticulous study offers deeper insights into individual customers and promotes better customer engagement. The emergence of Digital Marketing and Advertising 5.0, marked by the integration of cutting-edge technologies like AI, machine learning, and robust big data analytics, entails a transformational potential to revolutionize how F&B companies establish connections and foster engagement with their target audience. Data-driven decision-making allows companies to gain a deeper
comprehension of their clientele, leading to more efficient marketing campaigns customized to individual specifications and preferences (Sardjono et al., 2023).
This can result in enhanced customer engagement, loyalty, and ultimately, greater sales and revenue for F&B establishments. Additionally, AI can assist F&B firms in maximizing their marketing expenditures by identifying the most impactful platforms and messaging for reaching their desired audience.
Analyzing customer behavior and preferences through data can enable AI algorithms to discern the optimal marketing channels and messages for each individual client.
d. Lastly, with regard to conversations, AI has become a widely adopted tool for enhancing customer service and engagement in businesses. One method to achieve this goal involves implementing chatbots, computer programs that simulate human-like conversations with users. These chatbots possess the ability to handle frequently asked questions, process orders, and offer recommendations, among numerous other tasks. Integrating across digital platforms enables customers to interact through various channels like websites, social media, and messaging applications. This diverse set of interaction opportunities enhances the customer experience. They can learn from customer interactions to enhance their accuracy in providing helpful responses, making them an effective customer service tool over time. Additionally, voice-activated technology is another AI application that can improve customer service. This technology enables customers to interact with businesses via voice, allowing for hands-free ordering and payment (Cheong et al., 2022). Customers have the capability to utilize voice-activated assistants like Amazon's Alexa or Google Assistant to place orders, make reservations, and acquire information regarding products and services. This technological advancement is particularly beneficial for individuals with disabilities or those who prefer not to engage with a keyboard or touch screen interface.
3. Challenges and Threats Emerging Through Artificial Intelligence for Marketing Management in Food and Beverage Industry
The application of AI in marketing management for the F&B sector can result in various challenges, primarily concerning data privacy. AI depends on significant amounts of data to determine, and this data might contain confidential information related to customers, including their personal tastes, buying behaviors, and even whereabouts. Access by unauthorized individuals could lead to exploitation or mishandling of data, causing serious consequences for both customers and businesses.
In June 2021, CNN (2021) reported McDonald’s announcement that it had been affected by a data breach that exposed private information of customers and employees in South Korea and Taiwan. The breach resulted in the exposure of personal data like emails, phone numbers, and addresses. Gore (2023) of al.com news has also reported that Chick-fil-A confirmed a data breach of their mobile app that exposed customers’ personal information, in which the hacker used email
addresses and passwords from a third-party to access the system and acquire data including membership numbers, names, emails, addresses, and more.
To obviate consequent occurrences, corporations need to implement extra safety precautions to secure the customers' data. Researchers have proposed various privacy-preserving techniques for AI, such as federated learning, differential privacy, and encryption (Aratesh et al., 2023; Cheng et al., 2020; Iqbal et al., 2023).
Additionally, implementing security measures such as encryption and multi-factor authentication, maintaining transparent data collection and utilization policies, and obtaining explicit consent from customers prior to utilizing their data, along with establishing a comprehensive strategy for addressing data breaches - including promptly notifying affected customers and taking measures to mitigate any potential harm - can further encourage ethical and responsible applications of artificial intelligence in the food and beverage industry.
Furthermore, over-reliance on technology can cause companies to miss essential insights, nuances, and contextual factors that AI may not be able to capture (Li et al., 2023). Depending too heavily on AI can result in faulty decision-making since AI systems are only as effective as the data they are trained on and the algorithms they employ, and as it is still crucial to also consider human intuition and experience.
In terms of cost, implementing AI systems can be expensive and time-consuming, and may demand substantial investments in technology and personnel (Asif et al., 2023). Companies that invest excessively in AI technology without a clear strategy or comprehension of how it will benefit their business may squander resources and fail to attain their objectives. This is proven by the 2019 MIT Sloan Management Review and Boston Consulting Group AI survey which found that 7 out of 10 companies reported minimal or no value from their AI investments (Davenport &
Bean, 2022). Hence, a thorough planning and deliberation when implementing AI systems are needed to prevent potential hazards of overinvesting in technology without comprehending its limitations and potential drawbacks.
Additionally, the use of AI will also necessitate the external thread of intense competition among companies to attract their target audience. AI may be used by multiple firms to analyze identical data and create similar marketing strategies, resulting in amplified marketplace competition. Alcoholic beverages companies such as AB InBev, Diageo, and Heineken are known to compete in using AI to improve their consumer experience. Nestle has also used AI technology to develop customer engagement, personalized diet-recommending methods, and supply chain management
AI can also potentially lead to job displacement as companies optimize and automate their marketing strategies. In 2019, McDonald's implemented AI in the food and beverage industry by acquiring an AI company called Dynamic Yield, which specializes in decision logic technology and personalization (Zhang et al., 2022).
McDonald's intends to use this technology to create personalized menus that can adapt to various factors, such as the time of day, weather, and trending menu
items. PepsiCo (2023) has also collaborated with Stanford Institute for Human- Centered Artificial Intelligence for optimizing its supply chain and manufacturing.
Although these examples of AI use show enhances in efficiency and profitability, it may also cause job loss for workers replaced by machines (Santhosh et al., 2023), as workers are no longer needed to take orders, make recommendations, or oversee inventory and production.
4. Research Road Map: The Way Forward for Artificial Intelligence-Based Marketing in Food and Beverages Industry
AI has brought opportunities for the marketing aspect of F&B sector (Brooks et al., 2022). By amalgamating AI technology with marketing approaches, companies have the potential to innovate the way they interact with consumers, streamline supply chains, and elevate decision-making processes (Dellaert et al., 2020; Cheng
& Jiang, 2021; Gerlich et al., 2023). We conducted A systematic literature review to analyze 674 articles retrieved from ScienceDirect utilizing "artificial intelligence marketing management food and beverages industry" as keywords. The resulting network visualization created by VOSviewer illustrates the current state of research, the interconnectivity of various research domains in AI-based marketing in the F&B industry, and potential areas for future exploration.
Figure 1. Network Visualization of Artificial Intelligence Marketing Management F&B. Source: VOSviewer (2023)
Figure 1 shows that the "marketing" dot has limited direct links to the "food supply chain" and its derivatives. It must pass through various intermediate dots such as
"internet of things," "industry 4.0," and "artificial intelligence" to connect with the food dots. This suggests a possible gap in the current literature about merging AI- enabled marketing practices with the F&B supply chain and related technologies.
The limited connection between marketing and the food supply chain in the network visualization highlights a promising avenue for research. Future studies
should investigate the seamless integration of AI-driven marketing strategies into the F&B supply chain. This integration can involve AI-assisted demand forecasting, supply chain optimization, inventory management, and real-time food product tracking.
Furthermore, the network visualization indicates that the "internet of things" and
"industry 4.0" data points are nearer to the "marketing" data point than the "food supply chain." This signifies an opportunity for interdisciplinary research across these fields. In future research, it may be valuable to explore how AI-driven marketing approaches can take advantage of IoT and Industry 4.0 technologies to enhance consumer involvement, product traceability, and sustainability in the F&B industry.
Finally, the network visualization highlights the significance of promoting cross- disciplinary collaboration among marketing and supply chain management researchers, as well as those in the fields of AI, IoT, and Industry 4.0 in the F&B sector. By bridging gaps between these domains, scholars can reveal innovative insights and create comprehensive strategies for AI-based marketing. To further clarify these three research categories, Table 1 presents potential research queries to investigate in forthcoming studies.
Table 1. Potential Research Questions AI-Enhanced Marketing Strategies for the F&B Industry
1 How can AI-driven marketing strategies be effectively employed to personalize F&B product recommendations for consumers, enhancing their overall experience?
2 What are the key factors influencing consumer acceptance and trust in AI- powered marketing initiatives within the F&B sector?
3 How can AI-driven marketing strategies assist F&B businesses in optimizing pricing strategies while maintaining competitiveness and profitability?
4 What are the ethical considerations and potential challenges associated with utilizing AI in shaping consumer perceptions and preferences within the F&B industry?
5 How can AI-powered chatbots and virtual assistants enhance customer engagement and support in the F&B sector, and what are the implications for customer loyalty and satisfaction?
Integration of AI, IoT, and Industry 4.0 in the F&B Supply Chain
1 How can AI, IoT, and Industry 4.0 technologies be integrated into the F&B supply chain to enable real-time tracking, traceability, and quality assurance of food products?
2 How does the integration of AI contribute to the enhancement of inventory management, demand forecasting, and distribution logistics within the F&B
supply chain, and what implications does it have for improving cost-efficiency and sustainability?
3 What security and privacy considerations arise when implementing AI, IoT, and Industry 4.0 technologies in the F&B supply chain, and what strategies can be employed to proactively mitigate these challenges effectively?
4 How can AI-driven predictive maintenance models be utilized to enhance equipment reliability and minimize downtime in F&B production facilities, contributing to overall supply chain efficiency?
5 What are the potential environmental and sustainability benefits of integrating AI, IoT, and Industry 4.0 in the F&B supply chain, and how can these technologies support responsible sourcing and production practices?
Cross-Disciplinary Collaboration and Innovation
1 How can collaborative research initiatives across marketing, supply chain management, AI, and Industry 4.0 foster innovation and create synergies in the F&B industry?
2 What strategies and frameworks can facilitate effective knowledge transfer and interdisciplinary collaboration between researchers and industry stakeholders in the context of AI-based marketing for the F&B sector?
3 How can policymakers and regulatory bodies support responsible AI adoption in the F&B industry while addressing ethical, legal, and privacy considerations?
4 How can collaborative partnerships between academia and industry contribute to the advancement and adoption of AI-driven marketing and supply chain solutions within the F&B sector?
5 To what extent do cultural and regional disparities impact the adoption and effectiveness of AI-based marketing and supply chain practices in the global F&B industry, and what implications does this hold for fostering international cooperation and innovative approaches?
Source: processed by the author (2023)
These research inquiries encompass a wide array of themes and challenges situated at the intersection of AI, marketing, and the F&B sector, and provide guidance for prospective research endeavors. Given the observed limited interconnectedness between marketing and the food supply chain, there exists a promising avenue for exploring the seamless integration of AI-powered marketing strategies into the F&B supply chain. Such exploration presents researchers with a new opportunity to shape the future landscape of marketing within the F&B industry, with a focus on driving innovation, enhancing operational efficiency, and promoting sustainability.
Conclusion
This study explores of AI's implementation in the business context, particularly within the F&B industry's marketing management. The research defines and conceptualizes AI's role, highlights its disruptive impact on the conventional 4C marketing mix strategy, and identifies different challenges and threats that result from integrating AI into F&B marketing management. One noteworthy discovery from this study is the apparent disconnect between marketing and the food supply chain in the VOSviewer network visualization results. This intriguing observation implies that there is significant potential for exploring the seamless integration of AI-driven marketing strategies within the F&B supply chain. Given these insights, further studies exploring identified research gaps and unanswered questions are needed. The development of AI and its applications in marketing within the F&B sector is a constantly evolving field that presents new opportunities for scholars and practitioners to contribute to its growth and progress, and shape the future of marketing and supply chain practices in the F&B industry.
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