Despite substantial investment in augmented reality and virtual reality (AR/VR) for online retailing, evidence remains fragmented on how AR/VR creates value across the online customer journey. This study develops a journey-based framework for AR/VR value creation by examining how four core AR/VR attributes (presence, immersion, vividness, and interactivity) relate to prepurchase, purchase, and post-purchase outcomes. Using meta-analytic structural equation modeling and hierarchical linear modeling with data from 126 independent samples (N = 36,386), we show that AR/VR effects vary across attributes and customer journey outcomes. Immersion is most strongly associated with prepurchase outcomes, vividness with purchase outcomes, and presence and interactivity with post-purchase outcomes. Hedonic and utilitarian gratifications help explain how AR/VR attributes are linked to customer journey outcomes. Moderation analyses further identify the customer-, product-, retailer-, interface-, and time-related conditions under which AR/VR value is shaped by amplification, resource complementarity, functional substitutability, and attenuation. Three experiments provide causal evidence for focal effects across the three customer journey outcomes. The study clarifies how, when, and under which retailing conditions AR/VR creates customer value in online retailing.

Loyalty programs (LPs) are widely used to manage customer relationships, but evidence on their effectiveness remains mixed. We argue that LP effectiveness should be understood across the recurring customer journey rather than as a single aggregate effect. We meta-analyze 434 effect sizes from 78 independent samples comparing LP members and nonmembers; classify outcomes into pre-purchase, purchase, and post-purchase stages; and test LP design characteristics and product types as moderators. Results show that LP membership is positively associated with outcomes across all three stages, with the strongest integrated effect in the purchase stage. Moderator analyses further indicate that LP effectiveness is stage-specific and depends on both program design and product type. These findings extend LP research by showing that mixed evidence on LP effectiveness partly reflects differences regarding where outcomes occur in the customer journey and under which conditions LP membership effects are assessed. For retailers, the results suggest that LPs should be designed around stage-specific objectives rather than treated as uniform incentive systems.

Meizhi Pan, Markus Blut, Arezou Ghiassaleh, and Zach W. Y. Lee explain which factors in influencer marketing drive both transactional and non-transactional marketing metrics and how managers can adapt their strategies to different platforms and products.

Manufacturers in business-to-business (B2B) industries aim to gain a competitive edge by adopting the concept of customer centricity in their strategy. Acknowledging manufacturers' challenges in implementing new technologies, we showcase how digital product passports, augmented/virtual reality, smart products, and digital twins foster customer centricity. We classify these technologies based on their use context and introduce the CCTECHframework, which delineates the impact of (1) experiential, (2) performance-enhancing, and (3) automated technologies on customer-centric processes. This research explores the opportunities for utilizing specific emerging technologies to enhance four customer-centric processes: (1) interactive customer relationship management (discovering implicit needs), (2) customer integration (systematic involvement of customers in decisionmaking), (3) internal integration (aligning business activities around customer value), and (4) external integration (supply chain-level coordination to respond to customization required by customers). Further, we provide a technology roadmap for manufacturers and suggest a research agenda to guide future research.

Purpose Building on the unified theory of acceptance and use of technology (UTAUT), this study examines how five individual-level cultural orientations influence mobile payment adoption. Direct versus moderating effects theoretical models of cultural value orientations are compared to elucidate how culture shapes consumers’ beliefs and behavior. Design/methodology/approach The study uses survey data from 679 US respondents to examine two competing theoretical models. Culture is measured at the individual rather than the national level. Findings The results provide stronger support for the direct effect than for the moderating effect model. We find that all five cultural value orientations are related to at least one UTAUT construct. Furthermore, performance expectancy, effort expectancy, hedonic motivation, social influence, facilitating conditions, habit and trust are related to the intention to use mobile payments. More importantly, all five cultural value orientations are related to at least one UTAUT construct. Originality/value Studies on culture in the adoption of mobile payments differ in their approach to measuring culture at the national and individual levels and in how they model cultural orientations as main or moderating effects. Our study is among the first to compare the two theoretical models, taking into account various adoption antecedents. It advances the understanding of the role of individual-level cultural orientations in the adoption of new technologies, particularly mobile payments.

Service research and business ethics literature intersect concerning the question of artificial intelligence (AI) service robot accountability. In financial services, there is a broad spectrum of potential ethical issues, from data usage to customer vulnerabilities. This article scrutinizes the impact of morality and where accountability resides in the use of AI service robots in financial services. To address this challenge, we discuss the role of Corporate Digital Responsibility (CDR) for firms and illustrate how to implement a conceptual framework on the ethical implications of AI service robot applications, drawing on normative ethical theory. The framework elaborates on how the locus of morality (from human to AI agency) and moral intensity combine within context-specific AI service robot applications, and how this might influence associated accountability. We provide examples of AI robots’ use for different purposes, differentiating between four 'accountability clusters': (1) professional norms, (2) business responsibility, (3) inter-institutional normativity, and (4) supra-territorial regulations cluster. We also discuss the CDR implications in different clusters. Ethical implications of using AI service robots and associated accountability challenges are relevant for a network of actors—from customers and designers to firms and the government. Implementation of the framework incorporates a range of internal and external stakeholders that firms need to consider. We also provide a CDR roadmap to incorporate a time perspective and to inform implementation efforts.

Purpose - This study aims to clarify the direct impact of digitalization on export performance (EP) by synthesizing previous research and testing this relationship empirically. Furthermore, the study investigates digitalization types, contextual moderators and method moderators affecting the impact of digitalization on EP. Design/methodology/approach - The study uses meta-analysis to test the digitalization-EP relationship (k = 81) using data from 106 independent samples involving 62,082 respondents across nearly 30 countries. Findings - The study finds digitalization's positive and significant effect on EP (r = 0.36). The impact of digitalization on EP is also subject to different moderators, including digitalization type (i.e. digital capabilities), contextual factors (i.e. institutions, export experience, development of the region and industry) and method factors (i.e. back translation and strategy measurement). Originality/value - Scholars have initiated studies on the impacts of diverse digitalization types on EP, while empirical findings on these effects remain inconclusive. Based on resource-based theory, the study develops and validates a comprehensive meta-analytic framework, revealing the important influence of digitalization on EP. The moderator findings further highlight the impact of internal and external contingencies on the outcomes of exporting firms' digitalization.

Customer perceived value (CPV) is a cornerstone of marketing literature. However, myriad studies have generated contradictory empirical findings. In addition, though some existing literature reviews help clarify the conceptual foundations of CPV, the literature lacks a meta-analysis of empirical evidence about the CPV model and its effects. To consolidate existing research, the current meta-analysis assesses the findings of 687 articles, involving 780 independent samples and 357,247 customers. The most integrative CPV model, which includes benefits, sacrifices, and overall value, performs best. Empirical generalizations also reveal the relative weights of various benefits and sacrifices integrated into this CPV model and causal chains between CPV and different outcomes (satisfaction, word-of-mouth, and repurchase intentions). Finally, this analysis uncovers moderating effects of multiple relational contexts: nonprofit/for-profit, public/private, contractual/non-contractual, online/offline, business-to-business/business-to-consumer, and goods/services. For scholars, this article synthesizes existing findings on CPV; for managers, the results provide suggestions for ways to increase CPV.

Given the substantive influence of the digital revolution on the sharing economy, it is timely and relevant to ask why some sharing platforms (e.g. Airbnb and Uber) achieve significant success while others fail. To determine which factors encourage customers to participate in sharing goods and services on sharing platforms, and when they do so, this study conducts a meta-analysis of empirical findings from 192 independent samples, extracted from 167 studies involving 171,344 customers. As the results clarify, customer-related factors (customer motives, customer competence, customer satisfaction and subjective norms) are key antecedents. However, platform-related factors (service quality of the platform, trust in the platform, performance expectancy and effort expectancy) and service-provider-related factors (service quality of the provider, trust in the provider and provider gender) also exert meaningful effects. To assess the generalizability of these antecedents, the meta-analysis includes contextual moderators, namely customer type (previous provider experience), provider type (private/professional supply), platform characteristics (rivalry on the platform, prestige of ownership and services/goods) and exchange type (for-profit/non-profit and ownership transfer). The findings advance the literature on the sharing economy and provide specific guidance for platform managers about when to focus on certain antecedents.

The increasing introduction of intelligent, interactive robots in the service industry raises concerns about the potential dehumanization of service provision and its influences on corporate brand perceptions. To avoid adverse effects, new service development (NSD) managers seemingly favor service robots that feature anthropomorphic design metaphors, so they appear more human-like. The current research investigates explicitly how customers' perception of a robot's anthropomorphic design metaphors might spill over to affect corporate brand perceptions. Study 1, a picture-based scenario study with 109 participants, reveals the impact of anthropomorphic design metaphors on untested corporate brand outcomes, such as brand trust and brand experience. Then Study 2, a video-based scenario study with 530 participants, addresses whether these effects depend on the service context. In Study 3, a field study of 393 participants, the authors examine how anthropomorphic design metaphors influence other firm-related outcomes (e.g., shopping enjoyment, sales). The combined results confirm that anthropomorphic design metaphors strongly affect brand trust and brand experience, as well as other critical firm-related outcomes; they also reveal notable context effects, such that customers of people-processing (e.g., care services) and mental-stimulus-processing (e.g., shopping assistance) services appear more likely to use anthropomorphic design metaphors as corporate brand cues. Our research encourages NSD managers and scholars to consider the effects of introducing anthropomorphic service robots on corporate brands.

Anecdotal evidence indicates a notable shift in online consumer expectations, emphasizing a desire for an enjoyable online shopping experience, beyond convenience and efficiency. This insight thus prompts key questions: Should retailers emphasize efficiency-related or experience-related website attributes, and in which contexts might one priority be superior to the other for encouraging consumer loyalty? The present study provides initial insights into the evolution of the effectiveness of different website attributes and heterogeneity in their effects. Using a rich data set, spanning vastly different contexts and time periods, the authors detect new, evolving patterns by which different website attributes relate to customer loyalty. Experience-related attributes have become more important than efficiency-related attributes in recent years, with some noteworthy contingencies, such that they are especially impactful for services (vs. products) and in cultures with long-term (vs. short-term), high (vs. low) self-indulgence, and high (vs. low) masculinity orientations. The increasing importance of experience-related attributes is driven by cultures with a low (vs. high) uncertainty avoidance. These insights in turn offer practical implications for retailers navigating the challenges associated with designing their websites to drive customer loyalty.

Influencer marketing significantly impacts consumer behavior and decision-making. However, identifying the drivers of influencer marketing effectiveness and conditions that enhance their impact remains challenging. This meta-analysis, which synthesizes 1,531 effect sizes from 251 papers, assesses influencer marketing effectiveness by examining its antecedents, mediators, and moderators. Building on the persuasion knowledge model to develop and test a framework, we identify post, follower, and influencer characteristics as key antecedents impacting both non-transactional (i.e., attitude, behavioral engagement, and purchase intention) and transactional (i.e., purchase behavior and sales) marketing outcomes. For non-transactional outcomes, follower characteristics (social identity) have the strongest effects on consumer attitudes and behavioral engagement, while post characteristics (informational value and hedonic value) exert stronger effects on purchase intention. For transactional outcomes, influencer characteristics (influencer communication) have the strongest effects on purchase behavior. These antecedents also affect marketing outcomes indirectly through persuasion knowledge and source credibility. Moderation results indicate that direct and indirect effects of antecedents depend on social media types (i.e., nature of connection and usage) and product types (i.e., information availability and status-signaling capability). These results consolidate and advance the literature and offer insights into enhancing the effectiveness of influencer marketing.

Retailers rely on virtual assistants (VAs), such as Amazon's Alexa and chatbots, to deliver 24/7 customer service at low costs, as well as novel shopping opportunities. Despite improved VA capabilities due to artificial intelligence (AI), many retailers still struggle to convince customers to become repeat users of VAs. Therefore, to establish recommendations for how to facilitate VA use, this meta-analysis extracts 2,766 correlations from 244 independent samples of customers interacting with VAs. The results suggest that customer-, VA-, and shopping occasion–related factors all influence technology use. Price value is the strongest driver, followed by support, social influence, and anthropomorphism. Performance risk, competence, and trust matter to lesser extents. These factors exert strong indirect effects by triggering two customer responses: cognitive and emotional. Negative emotions emerge as a particularly important mediator. Finally, several VA types enhance or weaken the noted effects, including whether they are intelligent/less intelligent, commercial/noncommercial, voice-/text-based, and avatar-/non-avatar-based. The results suggest no one-size-fits-all approach applies for VAs, because their performance varies across customer responses. The current meta-analysis provides in-depth guidance for retailers seeking to select appealing VAs.

Scholarly understanding of customer journeys has evolved from a linear, single service provider perspective to encompass complex service delivery networks that involve multiple touchpoints governed by various service providers. This intricate setting often gives rise to experiential pain points for customers. To investigate this phenomenon within the context of airport services, our research employs critical incident and problem-centered interviews as well as an analysis of 7192 online airport reviews. In Studies 1a and 2a, we explore the crucial pain points that travelers encounter throughout their airport journey. Complementing these insights, Studies 1b and 2b assess the impact of the identified pain points on travelers' emotions. Building upon a classification of pain points into information, performance, and hospitality themes, Study 3 further examines how smart service solutions, as new technologies, can address and resolve these pain points, ultimately enhancing the customer experience (CX). By accomplishing these objectives, our work contributes a comprehensive classification scheme for experiential pain points in complex customer journeys to the academic discourse on customer journeys. Furthermore, it establishes a connection to the emerging field of research on the impact of smart service solutions on the CX.

Many retailers (e.g., Amazon, Walmart) use various types of online recommendation agents (RAs) on their websites to suggest goods and services to consumers. These RAs screen millions of options to ease consumers' information search and evaluation. To determine which RA types best support consumers' efforts, the present research reports a meta-analysis of perceived recommendation quality research, a key performance metric that gauges RAs from consumers' perspectives. To test the framework derived from this meta-analysis, the authors rely on data gathered from 32,172 consumers, reported in 122 samples. The results affirm that some RAs perform better than others in leveraging the effects of perceived recommendation quality on consumers' decision-making satisfaction, RA satisfaction, and intention to use the RA in the future. The best performing RAs feature specific algorithms (i.e., collaborative filtering, interactive RAs, and self-serving recommendations), recommendation presentations (i.e., solicited recommendation), and data sources (i.e., location-based and social network- based RAs). Moreover, the results suggest that some RAs perform better than others in leveraging the effects of decision-making and RA satisfaction on future use intentions. These insights advance RA theory and provide guidance for managers, with regard to choosing the optimal RA.(c) 2023 The Author(s). Published by Elsevier Inc. on behalf of New York University. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ )

The authors synthesize research on the relationship of customer satisfaction with customer- and firm-level outcomes using a meta-analysis based on 535 correlations from 245 articles representing a combined sample size of 1,160,982. The results show a positive association of customer satisfaction with customer-level outcomes (retention, WOM, spending, and price) and firm-level outcomes (product-market, accounting, and financial-market performance). A moderator analysis shows the association varies due to many contextual factors and measurement characteristics. The results have important theoretical and managerial implications.

As part of their customer engagement (CE) marketing, firms use different platforms to interact with customers, in ways that go beyond purchases. Task-based CE strategies call for customers’ participation in structured, often incentivized tasks; experiential CE initiatives instead aim to stimulate pleasurable experiences for customers. But the optimal uses of these two strategies, in terms of improving customer engagement to produce more positive marketing outcomes, are unclear. With a meta-analysis and data from 395 samples, pertaining to 434,233 customers, the present study develops and tests a unifying framework of how to optimize investments in both two engagement strategies across different engagement platforms. On average, task-based initiatives are more effective in driving customer engagement, but the effects depend on the platform. If platforms support continuous or lean interactions, task-based initiatives are more effective; on platforms that encourage spot interactions, experiential initiatives are preferable. Three customer engagement dimensions (cognitive, emotional, and behavioral) in turn lead to positive marketing outcomes, though in ways that depend on the platforms’ interaction characteristics (intensity, richness, initiation) and differ across digital versus physical platforms. These results provide clear guidance for managers regarding how to plan their CE marketing activities to benefit both their firms and their customers.

Firms operating internationally need to ascertain effective relationship marketing (RM) strategies for their foreign operations. One set of RM strategies is based on understanding and using switching costs perceptions. Based on data from 1,630 customers across 16 countries, we examine the interplay between culture and switching costs perceptions using Triandis and Gelfand's four cultural personal value dimensions (CPVs), horizontal and vertical individualism and collectivism. These CPVs are assessed on external switching costs (ESC) and internal switching costs (ISC) perceptions along with additional important outcomes, including commitment and share of wallet. We find vertical individualism (VI), horizontal collectivism (HC), and vertical collectivism (VC) positively relate to ESC, and VI and VC positively relate to ISC. VI produced the strongest relationship with both switching costs. Our findings indicate the importance of including the horizontal/vertical dimension in studying cultural values. Implications for RM strategies internationally are offered.

There are both formal and informal cries that UTAUT and, by association, the stream of research on technology adoption has reached its limit, with little or no opportunities for new knowledge creation. Such a conclusion is ironic because the theory has not been sufficiently and suitably replicated. It is possible that misspecifications in the various replications, applications, and extensions have led to the incorrect conclusion that UTAUT is more robust than it really is, leaving limited opportunities for future work. Although work on UTAUT has included important variables, predictors, and moderators, absent a faithful use of the original specification, it is impossible to assess the true nature of the effects of the original and additional variables. The present meta-analysis uses 25,619 effect sizes reported by 737,112 users in 1,935 independent samples to address this issue. Consequently, we develop a clear current state-of-the-art and revised UTAUT that extends the original theory with new endogenous mechanisms from different, other theories (i.e., technology compatibility, user education, personal innovativeness, and costs of technology) and new moderating mechanisms to examine the generalizability of UTAUT in different contexts (e.g., technology type and national culture). Based on this revised UTAUT, we present a research agenda that can guide future research on the topic of technology adoption in general and UTAUT in particular.

Healthcare information technologies (HIT) can address several challenges faced by healthcare systems. To benefit from the advantages HIT offer, users must first accept them. This meta-analysis synthesizes previous research on HIT acceptance. It uses data from 214 independent samples reported in 193 articles and 83,619 technology users from 33 countries. The study contributes to the HIT literature by (1) synthesizing the empirical findings on technology acceptance factors and combining them in a comprehensive model, (2) testing the mediating mechanisms of health technology acceptance, and (3) examining contextual differences. The study finds that HIT acceptance depends on various predictors proposed by the technology acceptance model and the unified theory of acceptance and use of technology. These factors displayed strong indirect effects through effort expectancy, perceptions of the technology, performance expectancy, and attitudes toward using HIT. Studies overlooking these effects may underestimate the importance of various acceptance factors. Finally, the results suggest that technology acceptance varies across healthcare technologies (remote information systems [IS], wearables), users (staff/patients, age, voluntariness, experience), and locations (hospitals, healthcare systems, life expectancy in country). We also provide IS managers with guidance for improving technology acceptance in the healthcare industry to ensure efficient, high-quality services.