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In today's economy, experiences are a distinct offering that have become the core selling point for some of the world's most successful companies. From banking and transportation, to home exercise and healthcare, companies have differentiated themselves by designing distinct experiences alongside their core goods and services. And at the heart of this transformation are the data, systems, processes, and culture needed to understand more about customers and employees in order to design unique experiences for every individual. In this chapter we explore how success in the experience economy is not simply a case of gathering more data, but instead looking at a different type of data – Experience Data. With examples and case studies from some of the world's most successful companies, we look at how the discipline of experience management (XM) and the technology available to organizations today is fundamentally changing how companies operate – and win – in the experience economy.

To investigate ethnographically how patient experience data, as a named category in healthcare organisations, is actively “made” through the co-creative interactions of data, people and meanings in English hospitals.

The authors draw on fieldnotes, interview recordings and transcripts produced from 13 months (2016–2017) of ethnographic research on patient experience data work at five acute English National Health Service (NHS) hospitals, including observation, chats, semi-structured interviews and documentary analysis. Research sites were selected based on performance in a national Adult Inpatient Survey, location, size, willingness to participate and research burden. Using an analytical approach inspired by actor–network theory (ANT), the authors examine how data acquired meanings and were made to act by clinical and administrative staff during a type of meeting called a “learning session” at one of the hospital study sites.

The authors found that the processes of systematisation in healthcare organisations to act on patient feedback to improve to the quality of care, and involving frontline healthcare staff and their senior managers, produced shifting understandings of what counts as “data” and how to make changes in response to it. Their interactions produced multiple definitions of “experience”, “data” and “improvement” which came to co-exist in the same systematised encounter.

The article's distinctive contribution is to analyse how patient experience data gain particular attributes. It suggests that healthcare organisations and researchers should recognise that acting on data in standardised ways will constantly create new definitions and possibilities of such data, escaping organisational and scholarly attempts at mastery.

In the era of digital disruption and customer loyalty loss, it has become even more important to shape the experience journey of a firm’s stakeholders. The benefits of experience data (XD) analysis for a competitive advantage and firm performance are well proven in the business-to-customer context. Therefore, this study aims to explore the limited exploitation of XD in the business-to-business (B2B) context.

The data of 338 B2B firms is generated through computer-assisted telephone interviewing using a structured interview guideline. A Mann–Whitney U test and binary linear regression are applied to test hypotheses derived from literature.

The results suggest that XD non-collectors see XD increase efficiency, whereas XD collectors view XD strategically beyond customer data. Additionally, the successful application of XD in firms can be fostered by connecting XD with operational data through digitalised processes, strategic usage and data collection at certain defined points of time.

This study contributes to the understanding of XD perception between collectors and non-collectors and develops determinants for the successful application of XD management. Based on the results, B2B marketing executives from academics and practice can foster the implementation of XD management to improve all firm’s stakeholders’ experiences. In this way, this study contributes to the understanding of managing not only customers’ but other stakeholders’ experiences.

The rapid advances in the technologies of mobile computing and wireless communications have facilitated the use of applications and services on handheld devices. Sharing of living experience (SOLE) is one such service, through which users can share their experience of daily activities such as shopping, entertainment, and traveling. This paper seeks to study SOLE in more detail.

The paper presents an application framework for a context‐aware SOLE which considers location, preferences, and other useful information for a seamless user experience. SOLE leverages on the service‐oriented middleware, Coalition. Entities of interest are represented as services registered with Coalition, which are organized using location information. This facilitates location‐based selection of entities for associating or retrieving experiences. In addition, Coalition incorporates the contexts of users in the discovery and delivery of experience information. Besides, to cater for resource constraints of mobile devices, the concept of “proxy” is introduced.

A detailed study of SOLE, namely, its design as well as its interaction with the middleware, information providers and information consumers, is provided. As a proof of concept, a prototype of SOLE for mobile phone users is also provided. The experiments show the ease of using SOLE and its acceptable performance in practice.

SOLE is generic and scalable, by not assuming a specific application scenario; flexible, by allowing the user to choose where to store the experience data and to specify his audience; and context‐aware by considering the contexts of the user such as location and preferences in the experience sharing process.

Guest experience and satisfaction have been central constructs in the hospitality management literature for decades. In recent years, the use of big data as an increasing trending practice in hospitality research has been characterised as a modern approach that offers valuable insights into understanding and enhancing guest experience and satisfaction. Recognising such potential, both researchers and practitioners need to better understand big data’s application and contribution in the hospitality landscape. The purpose of this paper is to critically review and synthesise the literature to shed light on trends and extant patterns in the application of big data in hospitality, particularly in research focusing on hotel guest experience and satisfaction.

This research is based on a Preferred Reporting Items for Systematic Reviews and Meta-analysis literature review of academic journal articles in Google Scholar published up to the end of 2020.

By data types, user-generated content, especially online reviews and ratings, was at the centre of attention for hospitality-related big data research. By variables, the hospitality-related big data fell into two crucial factor categories: physical environment and guest-to-staff interactions.

This paper shows that big data research can create new insights into attributes that have been extensively researched in the hospitality field. It facilitates a thorough understanding of big data studies and provides valuable insights into future prospects for both researchers and practitioners.

The purpose of this paper is to investigate how scientists’ prior data-reuse experience affects their data-sharing intention by updating diverse attitudinal, control and normative beliefs about data sharing.

This paper used a survey method and the research model was evaluated by applying structural equation modelling to 476 survey responses from biological scientists in the USA.

The results show that prior data-reuse experience significantly increases the perceived community and career benefits and subjective norms of data sharing and significantly decreases the perceived risk and effort involved in data sharing. The perceived community benefits and subjective norms of data sharing positively influence scientists’ data-sharing intention, whereas the perceived risk and effort negatively influence scientists’ data-sharing intention.

Based on the theory of planned behaviour, the research model was developed by connecting scientists’ prior data-reuse experience and data-sharing intention mediated through diverse attitudinal, control and normative perceptions of data sharing.

This research suggests that to facilitate scientists’ data-sharing behaviours, data reuse needs to be encouraged. Data sharing and reuse are interconnected, so scientists’ data sharing can be better promoted by providing them with data-reuse experience.

This is one of the initial studies examining the relationship between data-reuse experience and data-sharing behaviour, and it considered the following mediating factors: perceived community benefit, career benefit, career risk, effort and subjective norm of data sharing. This research provides an advanced investigation of data-sharing behaviour in the relationship with data-reuse experience and suggests significant implications for fostering data-sharing behaviour.

The purpose of this paper is to examine how data reuse experience, attitudinal beliefs, social norms, and resource factors influence internet researchers to share data with other researchers outside their teams.

An online survey was conducted to examine the extent to which data reuse experience, attitudinal beliefs, social norms, and resource factors predicted internet researchers’ data sharing intentions and behaviors. The theorized model was tested using a structural equation modeling technique to analyze a total of 201 survey responses from the Association of Internet Researchers mailing list.

Results show that data reuse experience significantly influenced participants’ perception of benefit from data sharing and participants’ norm of data sharing. Belief structures regarding data sharing, including perceived career benefit and risk, and perceived effort, had significant associations with attitude toward data sharing, leading internet researchers to have greater data sharing intentions and behavior. The results also reveal that researchers’ norms for data sharing had a direct effect on data sharing intention. Furthermore, the results indicate that, while the perceived availability of data repository did not yield a positive impact on data sharing intention, it has a significant, direct, positive impact on researchers’ data sharing behaviors.

This study validated its novel theorized model based on the theory of planned behavior (TPB). The study showed a holistic picture of how different data sharing factors, including data reuse experience, attitudinal beliefs, social norms, and data repositories, influence internet researchers’ data sharing intentions and behaviors.

Data reuse experience, attitude toward and norm of data sharing, and the availability of data repository had either direct or indirect influence on internet researchers’ data sharing behaviors. Thus, professional associations, funding agencies, and academic institutions alike should promote academic cultures that value data sharing in order to create a virtuous cycle of reciprocity and encourage researchers to have positive attitudes toward/norms of data sharing; these cultures should be strengthened by the strong support of data repositories.

In line with prior scholarship concerning scientific data sharing, this study of internet researchers offers a map of scientific data sharing intentions and behaviors by examining the impacts of data reuse experience, attitudinal beliefs, social norms, and data repositories together.

This study aims to conceptualize the relationship of perceived artificial intelligence (AI) credibility with consumer-AI experiences. With the widespread deployment of AI in marketing and services, consumer-AI experiences are common and an emerging research area in marketing. Various factors affecting consumer-AI experiences have been studied, but one crucial factor – perceived AI credibility is relatively underexplored which the authors aim to envision and conceptualize.

This study employs a conceptual development approach to propose relationships among constructs, supported by 34 semi-structured consumer interviews.

This study defines AI credibility using source credibility theory (SCT). The conceptual framework of this study shows how perceived AI credibility positively affects four consumer-AI experiences: (1) data capture, (2) classification, (3) delegation, and (4) social interaction. Perceived justice is proposed to mediate this effect. Improved consumer-AI experiences can elicit favorable consumer outcomes toward AI-enabled offerings, such as the intention to share data, follow recommendations, delegate tasks, and interact more. Individual and contextual moderators limit the positive effect of perceived AI credibility on consumer-AI experiences.

This study contributes to the emerging research on AI credibility and consumer-AI experiences that may improve consumer-AI experiences. This study offers a comprehensive model with consequences, mechanism, and moderators to guide future research.

The authors guide marketers with ways to improve the four consumer-AI experiences by enhancing consumers' perceived AI credibility.

This study uses SCT to define AI credibility and takes a justice theory perspective to develop the conceptual framework.