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Advancements in technology are radically transforming service, and increasingly providing the underlying basis for service strategy. In this paper, we develop a typology and positioning map for service strategy, in the context of rapid technological change, and outline the process for firms to position or reposition their service strategies. Which strategy to choose is based on the degree to which customer demand is heterogeneous, and the degree to which potential customer lifetime value varies across customers. This results in four strategies: the McService strategy that is standardized and transactional, the Relational Service strategy that is standardized and relational, the Customized Transaction strategy that is personalized and transactional, and the Adaptive Personalization strategy that is personalized and relational. We provide firms a roadmap for identifying a “sweet spot” strategy in relation to a segment’s realized or potential customer lifetime value, combined with the firm’s technological capabilities. Because technological capabilities inevitably advance, firms will tend to move from standardized to personalized and from transactional to relational over time, implying that firms should be alert to technological opportunities to personalize their relationships with customers. Our strategic framework also produces a useful bridge from marketing practice to the conceptual evolution of the service literature, showing how the historical trends toward continuing customer relationships and co-productive personalization should drive strategic thinking in service.

Content on Twitter’s home timeline is selected and ordered by personalization algorithms. By consistently ranking certain content higher, these algorithms may amplify some messages while reducing the visibility of others. There’s been intense public and scholarly debate about the possibility that some political groups benefit more from algorithmic amplification than others. We provide quantitative evidence from a long-running, massive-scale randomized experiment on the Twitter platform that committed a randomized control group including nearly 2 million daily active accounts to a reverse-chronological content feed free of algorithmic personalization. We present two sets of findings. First, we studied tweets by elected legislators from major political parties in seven countries. Our results reveal a remarkably consistent trend: In six out of seven countries studied, the mainstream political right enjoys higher algorithmic amplification than the mainstream political left. Consistent with this overall trend, our second set of findings studying the US media landscape revealed that algorithmic amplification favors right-leaning news sources. We further looked at whether algorithms amplify far-left and far-right political groups more than moderate ones; contrary to prevailing public belief, we did not find evidence to support this hypothesis. We hope our findings will contribute to an evidence-based debate on the role personalization algorithms play in shaping political content consumption.

Purpose This paper aims to explore the realm of literature about personalization of digital library services. This paper focuses on users’ unique needs and will identify different types of personalized services. Therefore, this study has identified different types of services personalization in the context of digital academic libraries. Design/methodology/approach In this research, the systematic review method has been used to obtain the relevant indicators of different types of personalization in the context of libraries. To explain basic indicators, a Delphi method has been used. The Delphi panel’s members consisted of 15 experts (faculty members, researchers, professional users and software designers). A purposeful sampling and the Delphi fulfillment process were performed in three rounds. After collecting data, descriptive statistics (mean and standard deviation), inferential statistics (binomial distribution test) and the Kendall coordination coefficient were used to determine the consensus rate among experts. Findings A total of 103 indicators were extracted for different types of personalization through a systematic literature review. Of these, 90 indicators were considered significant in the experts’ view. Generally, content personalization, interactive personalization, collaborative personalization and information retrieval personalization are the main components of personalization types, each of which has its own indicators. Originality/value This study has dealt with the issue of what is personalized in the context of digital academic library. The findings should be helpful and effective in the development of a holistic view on personalization of services in digital libraries.

PurposeArtificial 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.Design/methodology/approachDrawing 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.FindingsAIP 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.Research limitations/implicationsNew 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.Practical implicationsThis 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.Originality/valueThis 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.

With emerging digital technologies, personalization has become a key activity for marketing strategy to gain competitive success in customer relationships. The aim of this study is to develop and empirically assess a general measurement model of perceived personalization. Multiple data gathering processes and rigorous empirical testing procedures are employed to assess and validate the proposed measurement model. The perceived personalization scale developed in the study rests on the focus of what is personalized and includes three main categories: (1) individuallevel, (2) social-level, and (3) situation-based personalization. A multidimensional measure of personalization is developed based on these categories and is validated via several tests, including a test of nomological validity exploring the effects of perceived personalization on critical customer responses such as positive emotions, negative emotions, perceived sincerity, satisfaction, and behavioral intentions. These findings shed light on and open new avenues of development for this growing practice for both researchers and practitioners in marketing.

Given the increasingly specific ways marketers can target ads, consumers and regulators are demanding ad transparency: disclosure of how consumers’ personal information was used to generate ads. We investigate how and why ad transparency impacts ad effectiveness. Drawing on literature about offline norms of information sharing, we posit that ad transparency backfires when it exposes marketing practices that violate norms about “information flows”—consumers’ beliefs about how their information should move between parties. Study 1 inductively shows that consumers deem information flows acceptable (or not) based on whether their personal information was: 1) obtained within versus outside of the website on which the ad appears, and 2) stated by the consumer versus inferred by the firm (the latter of each pair being less acceptable). Studies 2 and 3 show that revealing unacceptable information flows reduces ad effectiveness, which is driven by increasing consumers’ relative concern for their privacy over desire for the personalization that such targeting affords. Study 4 shows the moderating role of platform trust: when consumers trust a platform, revealing acceptable information flows increases ad effectiveness. Studies 5a and 5b, conducted in the field with a loyalty program website (i.e., a trusted platform), demonstrate this benefit of transparency.

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.

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 optimizing marketing-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, tuming to the future, the authors identify trends that will shape marketing analytics as a discipline as well as marketing analytics education.

Purpose Drawing on the personalization–privacy paradox and guided by means–end analysis, this study explores how consumers balance their concerns for privacy and the benefits of smart home device personalization and the role that trust plays in the process. More specifically, this study aims to investigate how perceptions of smart device personalization and privacy concerns are shaped by consumers’ experiences and the role of trust in the deliberation process. Design/methodology/approach In-depth interviews were conducted across diverse demographic groups of smart device users to shed light on the balancing act between personalization and privacy. Findings The study found that product experience, ownership type, perceived value of convenience and control and quality of life via “smart things” are key motivators for product usage. The benefits of tailored recommendations and high relevance are balanced against the risks of echo chamber effects and loss of control. The results also show the role of active involvement in the privacy calculus and trust level. The study points to the significance of an ecosystem-based service/business model in gaining consumer confidence when they balance between personalization and privacy. Originality/value Although many studies have explored trust, privacy concerns and personalization in an artificial intelligence (AI)-related context, few have addressed trust in the context of both smart devices and the personalization–privacy paradox. As such, this study adds to the existing literature by incorporating the concept of trust and addressing both privacy concerns and personalization in the AI context.