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Co-creation of services (CCOS) is a collaborative strategy that emphasises customer involvement and their expertise to increase the value of the service experience. In the service ecosystem, artificial intelligence (AI) plays a key role in value co-creation. Therefore, this study is undertaken to empirically uncover how AI can empower CCOS.

The source data were collected from 305 service provider respondents and quantitative methodology was applied for data analysis.

New service development augmented with AI provides tangible value to service providers while also providing intangible value to supportive customers. With AI, service providers adapt to new innovations and enrich additional information, which eventually outperforms human-created services.

AI adoption for CCOS empowerment in service businesses brings “service-market fit”, which represents the significant benefits wherein customers contribute to creativity, intuition, and contextual awareness of services, and AI contributes to large-scale service-related analysis by handling volumes of data, service personalisation, and more time to focus on challenging problems of the market.

This study presents theoretical concepts on AI-empowered CCOS, AI technological innovativeness, customer participation in human-AI interaction, AI-powered customer expertise, and perceived benefits in CCOS, and subsequently discusses the CCOS empowerment framework. Then, it proposes a novel conceptual model based on the theoretical concepts and empirically measures and validates the intention to adopt AI for CCOS empowerment. Overall, the study contributes to novel insight on empowering service co-creation with AI.

Customers can participate in new product development (NPD) in many ways. Drawing on the knowledge-based view (KBV) and innovation literature, this study aims to contrast two main product development activity types, i.e. problem-solving and decision-making. It proposes customers play distinct roles if they get involved in these activities, which influence NPD outcomes differently. It also explores customer need specificity as a boundary condition for the above-mentioned relationships.

The authors collected survey data from 308 managers in the innovation domain.

Customer involvement in problem-solving and decision-making distinctively influences new product innovativeness and development speed. Customer need specificity interacts with the two co-development types differently to impact these NPD outcomes further.

This research extends the KBV and addresses the inconsistent findings in the literature regarding customer involvement as co-developers in innovation. It also provides novel insights into how knowledge characteristics like customer need specificity can direct co-developing activities to generate distinct NPD results.

This paper offers practical implications for firms on how to involve customers in developing innovative new products while managing development speed.

Prior research has yet to distinguish customer responsibilities related to co-development activities. This research fills this gap and offers novel insights that problem-solving and decision-making have opposite impacts on different NPD outcomes. This research demonstrates that finer knowledge about customer involvement responsibilities is needed for critical NPD outcomes.

In recent times, the field of corporate intelligence has gained substantial prominence, employing advanced data analysis techniques to yield pivotal insights for instantaneous strategic and tactical decision-making. Expanding beyond rudimentary post observation and analysis, social media analytics unfolds a comprehensive exploration of diverse data streams encompassing social media platforms and blogs, thereby facilitating an all-encompassing understanding of the dynamic social customer landscape. During an extensive evaluation of social media presence, various indicators such as popularity, impressions, user engagement, content flow, and brand references undergo meticulous scrutiny. Invaluable intelligence lies within user-generated data stemming from social media platforms, encompassing valuable customer perspectives, feedback, and recommendations that have the potential to revolutionize numerous operational facets, including supply chain management. Despite its intrinsic worth, the actual business value of social media data is frequently overshadowed due to the pervasive abundance of content saturating the digital realm. In response to this concern, the present study introduces a cutting-edge system known as the Enterprise Just-in-time Decision Support System (EJDSS).

Leveraging deep learning techniques and advanced analytics of social media data, the EJDSS aims to propel business operations forward. Specifically tailored to the domain of marketing, the framework delineates a practical methodology for extracting invaluable insights from the vast expanse of social data. This scholarly work offers a comprehensive overview of fundamental principles, pertinent challenges, functional aspects, and significant advancements in the realm of extensive social data analysis. Moreover, it presents compelling real-world scenarios that vividly illustrate the tangible advantages companies stand to gain by incorporating social data analytics into their decision-making processes and capitalizing on emerging investment prospects.

To substantiate the efficacy of the EJDSS, a detailed case study centered around reverse logistics resource recycling is presented, accompanied by experimental findings that underscore the system’s exceptional performance. The study showcases remarkable precision, robustness, F1 score, and variance statistics, attaining impressive figures of 83.62%, 78.44%, 83.67%, and 3.79%, respectively.

This scholarly work offers a comprehensive overview of fundamental principles, pertinent challenges, functional aspects, and significant advancements in the realm of extensive social data analysis. Moreover, it presents compelling real-world scenarios that vividly illustrate the tangible advantages companies stand to gain by incorporating social data analytics into their decision-making processes and capitalizing on emerging investment prospects.

The current study aims to investigate the factors that impact the feedback received on answers to questions in social Q&A communities and whether the expertise-required question influences the role of these factors on the feedback.

To understand the antecedents and consequences that influence the feedback received on answers to online community questions, the elaboration likelihood model (ELM) is applied in this study. The authors use web data crawling methods and a combination of quantitative analyses. The data for this study came from Zhihu; in total, 353,775 responses were obtained to 1,531 questions, ranging from 49 to 23,681 responses per question. Each answer received 0 to 113,892 likes and 0 to 6,250 comments.

The answers' cognitive and emotional components and the answerer's influence positively affect user feedback behavior. In addition, the expertise-required question moderates the effects of the answer's cognitive component and emotional component on the user feedback, moderating the effects of the answerer's influence on the user approval feedback.

This study builds upon a limited yet growing body of literature on a theme of great relevance to scholars, practitioners and social media users concerning the effects of the connotation of answers (i.e. their cognitive and emotional components) and the answerer's influence on user feedback (i.e. approval and collaborative feedback) in social Q&A communities. The authors further consider the moderating role of the domain expertise required by the question (expertise-required question). The ELM model is applied to explore the relationships between questions, answers and feedback. The findings of this study add a new perspective to the research on user feedback and have implications for the management of social Q&A communities.

The integration of social media and e-commerce has resulted in a rising phenomenon among individual content providers (ICPs), who used to offer free content, to provide consumers with paid content, such as online courses, Q&As or consultations. Despite the prevalence of ICPs’ content monetization, empirical research has rarely studied its underlying mechanism. This paper examines how the characteristics of free content contributed by ICPs on social media platforms influence their paid content sales, focusing on the perspective of human brand.

The empirical setting is an online knowledge exchange platform, where users are allowed to provide free content (e.g. answers) on the social media platform and launch paid content (e.g. lectures) on the e-commerce platform. A machine learning technique is employed to construct measures for the characteristics of free content, and fixed-effects estimation is presented to confirm which factors have a significant influence on the sales of paid content.

The empirical results show that the quality, diversity and expertness of free content have a significant positive impact on the sales of the ICP-paid content, with the brand popularity of ICP playing a mediating role.

This study is the first attempt to demystify the relationship between content contribution and ICPs’ content monetization from the perspective of human brand. The findings validate the effectiveness of the “Selling by Contribution” strategy and provide valuable insights for ICPs and social media platforms.

This study aims to explore why Chinese consumers pay for digital content products by investigating the experiences of Chinese consumers living in first-tier and second-tier cities regarding paid digital knowledge products.

A total of 19 in-depth interviews were conducted to collect data, and the phenomenological reduction was adopted to analyze data.

This study reveals that Chinese consumers use paid digital knowledge products to alleviate stress and anxiety stemming from real-life competition and the fear of falling behind. While consumers acknowledge the limited assistance that paid knowledge products can offer, their acceptance and expectations of paid digital knowledge products remain positive.

Paid digital knowledge represents an innovative phenomenon, with few scholars outside China having studied it. This study contributes a conceptual framework to understand the motivations of Chinese consumers with high purchasing power residing in first-tier and second-tier cities to invest in digital content.

The model of pay-for-knowledge incentivizes individuals with financial rewards for sharing their expertise, facilitating a transactional exchange between knowledge providers (sellers) and seekers (buyers). While this model is effective in promoting paid contributions, its influence on free knowledge exchanges remains ambiguous, creating uncertainty about its overall impact on platform knowledge ecosystems. This study aims to explore the mechanim of how knowledge payment influences free knowledge contribution. Based on relational signaling theory, this study posits that a buyer’s payment for knowledge acts as a positive relational signal in the buyer–seller relationship and examines how the signaling effect varies across different social contexts through attribution theory.

This paper empirically tests the hypotheses by analyzing a data set comprising 630 instances from 359 unique knowledge sellers on Zhihu, a prominent knowledge-sharing platform in China. This paper use zero-inflated negative binomial models to conduct this analysis.

The findings reveal that when buyers pay for knowledge, this action positively influences sellers to contribute knowledge for free. However, the strength of this influence is moderated by the platform’s social functions: appreciation feedback tends to weaken this effect, while social network ties enhance it.

Prior research has predominantly focused on the financial incentives of pay-for-knowledge and its spillover effects on unpaid users’ activities. This study shifts the focus to the social dimensions of pay-for-knowledge, arguing that buyer-initiated knowledge payments signal buyers’ commitment to foster reciprocal relationships with sellers. It expands the literature on the relationship between knowledge payment and contribution, moving beyond financial incentives to include social factors, thus enriching our understanding of the interplay between paid and free knowledge activities. Additionally, the empirical evidence supports the efficacy of pay-for-knowledge in promoting both free and paid contributions within knowledge-sharing platforms.

Effectively engaging patients is critical for the sustainable development of online health communities (OHCs). Although physicians’ general knowledge-sharing, which is free to the public, represents essential resources of OHCs that have been shown to promote patient engagement, little is known about whether such knowledge-sharing can backfire when superfluous knowledge-sharing is perceived as overwhelming and anxiety-provoking. Thus, this study aims to gain a comprehensive understanding of the role of general knowledge-sharing in OHCs by exploring the spillover effects of the depth and breadth of general knowledge-sharing on patient engagement.

The research model is established based on a knowledge-based view and the literature on knowledge-sharing in OHCs. Then the authors test the research model and associated hypotheses with objective data from a leading OHC.

Although counterintuitive, the findings revealed an inverted U-shape relationship between general knowledge-sharing (depth and breadth of knowledge-sharing) and patient engagement that is positively associated with physicians’ number of patients. Specifically, the positive effects of depth and breadth of general knowledge-sharing increase and then decrease as the quantity of general knowledge-sharing grows. In addition, physicians’ offline and online professional status negatively moderated these curvilinear relationships.

This study further enriches the literature on knowledge-sharing and the operations of OHCs from a novel perspective while also offering significant specific implications for OHCs practitioners.

Wine producers and marketing professionals increasingly recognize the significance of online wine reviews. Emotions have long been acknowledged as influential in online review behaviors. However, considering the multisensory nature of the wine experience, consumers’ wine expertise also plays a substantial role. Hence, this study aims to examine the online review behaviors exhibited by wine consumers through the dual lens of wine expertise and emotionality.

Two studies were conducted to address the research question. Study 1 explored the relationship among expertise, emotionality and review behaviors using a panel data model, with a data set consisting of 4,600,922 reviews from Vivino.com. Study 2 used a multigroup structural equation modeling (SEM) analysis using data obtained from an online survey. Study 2 aimed to investigate the interactive impact of emotionality and expertise on online review intention mediated by customer engagement.

The findings from Study 1 demonstrated a positive correlation between emotionality and online wine reviews. In addition, expertise displayed a bell-shaped relationship with both emotionality and online wine reviews. Study 2, in turn, uncovered that novices and experts experienced a direct influence of emotionality on their review intentions. In contrast, for those classified as ordinary, the influence of emotionality on review intention occurred indirectly through the mediation of customer engagement.

This paper extends the current literature on online wine review by integrating the effect of emotion and expertise on online wine review behaviors, expanding the examination of Dunning–Kruger effect in the wine literature. It also adds value by introducing emotionality and the Evaluative Lexicon into the hospitality literature, extending the measurement of emotion from valence and extremity to a third dimension, emotionality, in hospitality and wine domains.

This chapter presents a study of ‘floral ethics’, defined as a set of standardized practices for handling flowers shared among members of the florist occupation. Drawing on 36 months of ethnographic fieldwork in flower shops in Switzerland and Chicago, it contributes to the sociology of work and professions by providing a nuanced understanding of the tensions that can arise between the professional expertise of florists and the aesthetic preferences of customers. The findings on the micro-level reveal that adherence to floral ethics serves as a strategy for protecting and maintaining occupational legitimacy. By uncovering divergent conceptions of what constitutes ‘work well done’ among florists, this chapter offers a broader argument on the power asymmetry inherent in service relationships.