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As businesses become more sophisticated and welcome new technologies, artificial intelligence (AI)-based methods are increasingly being used for firms' pricing decisions. In this review article, we provide a survey of research in the area of AI and pricing. On the upside, research has shown that algorithms allow companies to achieve unprecedented advantages, including real-time response to demand and supply shocks, personalized pricing, and demand learning. However, recent research has uncovered unforeseen downsides to algorithmic pricing that are important for managers and policy-makers to consider.

Artificial intelligence (AI) technology has revolutionized customers' interactive marketing experience. Although there have been a substantial number of studies exploring the application of AI in interactive marketing, personalization as an important concept remains underexplored in AI marketing research and practices. This study aims to introduce the concept of AI-enabled personalization (AIP), understand the applications of AIP throughout the customer journey and draw up a future research agenda for AIP.

Drawing upon Lemon and Verhoef's customer journey, the authors explore relevant literature and industry observations on AIP applications in interactive marketing. The authors identify the dilemmas of AIP practices in different stages of customer journeys and make important managerial recommendations in response to such dilemmas.

AIP manifests itself as personalized profiling, navigation, nudges and retention in the five stages of the customer journey. In response to the dilemmas throughout the customer journey, the authors developed a series of managerial recommendations. The paper is concluded by highlighting the future research directions of AIP, from the perspectives of conceptualization, contextualization, application, implication and consumer interactions.

New conceptual ideas are presented in respect of how to harness AIP in the interactive marketing field. This study highlights the tensions in personalization research in the digital age and sets future research agenda.

This paper reveals the dilemmas in the practices of personalization marketing and proposes managerial implications to address such dilemmas from both the managerial and technological perspectives.

This is one of the first research papers dedicated to the application of AI in interactive marketing through the lenses of personalization. This paper pushes the boundaries of AI research in the marketing field. Drawing upon AIP research and managerial issues, the authors specify the AI–customer interactions along the touch points in the customer journey in order to inform and inspire future AIP research and practices.

The purpose of this paper is to study the profit optimization of manufacturers who provide personalized products to customers, thus avoiding additional operational costs and response times in the production process of personalized product design.

First, the authors present an integrated model for optimizing profit where the design and production of personalized products are both considered. The authors propose the concept of personalization level and four cases of personalization level are considered including top, high, medium and low levels. Polynomial-time optimal rules are provided for each level by analyzing 17 subcases where all possible personalized products are considered. Then, the authors compare the obtained profit of personalized products with the optimal profit of standardized products.

At low and high levels of personalization, personalized manufacturing is more profitable for specific products with specific characteristics. At the top and middle level of individuation, assuming that the product has certain characteristics, whether the best choice to produce personalized products depends on the level of individuation chosen by customers.

An important decision issue for manufacturers is whether to produce personalized or standardized products. In this study, the authors consider this decision problem and analyze the operational functions of personalized products. The authors hope to provide theoretical support in the operational management of manufacturers offering personalized products.

Pricing is always a fundamental marketing element. In the digital marketing/e-commerce context, there are two universal phenomena: desire to micro-segment and customize, and the adverse reaction upon unfair perception of price. A third related question is how should firms consider price increases and decreases? Specifically, this paper aims to address the following three research and practice questions: What are the theoretical underpinnings of perception of fairness/unfairness in pricing, and what are the findings? What are the theoretical underpinnings of response to price increases and decreases? What should be online pricing strategy, consistent with the findings on (un)fairness perception of pricing and response to price increases and decreases?

The present approach is integrative review and critical analyses, and synthesis. The review dates back to 1960s, and is inter-disciplinary, including apposite findings in behavioral science, economics, marketing and operations management/research. The authors search for insights with significant empirical support to address these questions.

Perception of unfair price impacts consumer choice, probability of purchase, intent to buy and attitude to product/service/firm adversely. Consumers react differently to perceived unfair and fair prices. Consumers react more strongly and negatively to perceived unfair prices (compared to prices perceived to be fair) in their intent to buy and other related metrics. Consumers react differently to price increases and price decreases relative to the reference price. Consumers react more strongly to price increases than to price decreases. There is substantial heterogeneity in the magnitude of loss-aversion effect, depending on the product/service category and estimation methods.

The authors review and discuss potential viable pricing strategies. Based on the generalizable findings, this study provides actionable insights to managers for pricing in digital marketing context. Also, the authors provide useful directions for future research.

This study aims to explain briefly the history and the future of revenue and yield management, offering a graphical visual model to identify the main internal and external elements to be faced by revenue managers.

Stemming from what happened in the past, the authors predict the key elements in revenue management moving forward.

The authors discuss four notable emerging themes for a fertile revenue management application: organizational culture; dynamic pricing, both in terms of intertemporal price discrimination and inventory controls; personalized pricing; and distribution channels.

The paper offers informative knowledge to revenue managers and academics working in the area of strategic yield and revenue management.

This viewpoint paper calls for research on the social impact that comes with implementing algorithmic pricing in hospitality and tourism, in particular online price discrimination. It seeks to broaden the literature on consumer backlash and corporate social responsibility (CSR) to include algorithmic pricing.

As algorithmic pricing will become increasingly important in hospitality and tourism, the authors argue that scholarly attention should be directed to two topics.

First, there is a need for research on how algorithmic pricing triggers consumer backlash and online firestorms, and how these can be detected, prevented, and mitigated. Second, the authors need to increase our understanding of how deception, misconduct, dishonesty, and injustice in algorithmic pricing impact CSR performance, especially when differential pricing is enticed by deceptive yet legal algorithmic applications of indirect behavioral “self-selection” mechanisms.

Algorithmic price discrimination has been criticized for its potential to harm consumers, and doubt is cast upon the current ability of legal frameworks to set minimum standards of behavior.

Algorithmic pricing includes a variety of computerized pricing applications aimed at increasing revenue and minimizing opportunity costs. With early use by airlines decades ago its diffusion has gradually extended to other sectors including hospitality and tourism. While algorithms are expected to increasingly impact pricing decisions, little research can be found on the topic, with the exception of a vigorous debate in the policy literature on its ethical implications and regulatory needs.

Theories of marketing management and strategy need to evolve and change to keep pace with changes in the marketplace and in marketing practice. As the next century draws closer, it is apparent that some marketing managers are basing their relationships with customers on policies and procedures called either “individualisation”, “mass‐customisation”, or as we prefer, “personalisation”. The core of this practice involves tailoring goods and services to the individual needs and wants of specific consumers, just the opposite of one‐size‐fits‐all. We propose that personalisation is so important to marketing strategy that it should become one of the featured elements of the marketing mix, alongside product, price, promotion, place, personnel, physical assets, and procedures, to form a new marketing mix, the 8Ps.

Reference price is a key input in deciding product/service prices by organizations and has a significant influence on consumer purchase decisions. This study aims to provide a deeper understanding of reference pricing literature using bibliometric analysis and offers specific research questions for future research in this domain.

Using a sample of 309 articles published between 1977 and 2021, the study conducts bibliographic coupling, citation analysis, cluster analysis, content analysis, keyword analysis and a three-field plot to map the intellectual structure of reference price.

The content analysis gave seven research clusters: (1) modeling reference price, (2) consumer perceptions of price (un)fairness, (3) price framing, (4) comparative price-based promotion, (5) reference price formulation, (6) pay-what-you-want (PWYW) pricing and (7) range theory and price perceptions. The study also delineates reference price literature across several parameters like authorship, highest cited paper, most popular journal, institutions, region-wise publication trend and author-networks. The emerging research themes for future scholars working in this domain have also been highlighted.

This is the first comprehensive study to explore reference price from a bibliometric lens. The study highlights and discusses the recent themes on reference price, from both academic and managerial perspectives.

Practicing flexible revenue management (RM) at hotels during Covid-19 is essential. The well-performed hotels ponder how to transform the target from revenue to net profits. This paper aims, first, to develop a value stream mapping (VSM) model for a productive RM based on six key drivers: organizational culture, demand forecasting, dynamic distribution channels, competition breakdown, dynamic and customized pricing and daily reviewing, and, second, to examine the nexus between RM and hotel’s efficiency during Covid-19 using the wavelet analysis (WA) to visualize this relationship’s time and frequency-based lead–lag dynamics.

Using time-series data, a multiple case study of 31 luxury hotels in Egypt was applied based on semi-structured interviews and self-administered questionnaires.

The first phase results showed that consensus toward the RM framework was achieved, regardless of current challenges, indicating that RM managers and scholars could use it. In Phase 2, the WA confirmed a positive correlation and significant influence between Covid-19 and RM practices at most business cycle frequencies. Furthermore, overall high causal relationships between RM practices and hotel efficiency were discovered in the short and medium terms and through different occurrence cycles. Though, the dynamic pricing in the long term was apart from this relationship. The causal effects between Covid-19 and hotel efficiency are not observable in the long-run spectra, indicating that resilience efforts with Covid-19 perhaps mitigated the impact.

Hotel managers could use the RM model developed from this study during the downturn to improve efficiency. The outcome may lead to the recovery of the hotel market and the whole economy. WA maps display possible directions for hotel managers to be more efficient based on the time and frequency domains.

This study shows opportunities for RM implementation during Covid-19 based on the VSM and the WA approaches in hotels.