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In this article, we reflect on how smart technology is transforming service research discourses about service innovation and value co-creation. We adopt the concept of technology smartness’ to refer to the ability of technology to sense, adapt and learn from interactions. Accordingly, we seek to address how smart technologies (i.e. cognitive and distributed technology) can be powerful resources, capable of innovating in relation to actors’ agency, the structure of the service ecosystem and value co-creation practices.

This conceptual article integrates evidence from the existing theories with illustrative examples to advance research on service innovation and value co-creation.

Through the performative utterances of new tech words, such as onlife and materiality, this article identifies the emergence of innovative forms of agency and structure. Onlife agency entails automated, relational and performative forms, which provide for new decision-making capabilities and expanded opportunities to co-create value. Phygital materiality pertains to new structural features, comprised of new resources and contexts that have distinctive intelligence, autonomy and performativity. The dialectic between onlife agency and phygital materiality (structure) lies in the agencement of smart tech–enabled value co-creation practices based on the notion of becoming that involves not only resources but also actors and contexts.

This paper proposes a novel conceptual framework that advances a tech-based ecology for service ecosystems, in which value co-creation is enacted by the smartness of technology, which emerges through systemic and performative intra-actions between actors (onlife agency), resources and contexts (phygital materiality and structure).

Turn-taking is beneficial to conversational search success, but the increase in turns and time can also increase the cognitive load of the user. Therefore, in this research paper, the authors view conversational search sessions through the lens of economic theory and use the economic models of search to analyze the various costs and benefits of information-seeking interactions.

First, the authors built a cost-benefit model for conversational search sessions by defining action types and performing an intellectual mapping of actual sessions into sequences of these actions (using thematic analyses). The authors used the hypothesized cost and benefit actions (obtained from the user-system dialogs), along with the number of turns, utterances and time-related parameters, to propose the mathematical model. Next, the authors tested the model empirically by comparing the model scores to the user satisfaction and task success scores (collected through questionnaires). By representing each session as a bag of actions, the authors developed linear regression models to predict task success and user satisfaction.

Through feature analysis and significance testing, the authors identify the different parameters that contribute significantly to user satisfaction and task success scores. Error analysis shows that the model predicts task success and user satisfaction reasonably well, with the average prediction error being 0.5 for both (on a 5-point scale).

The authors' research is an initial step toward building a mathematical model for predicting user satisfaction and task success in conversational search sessions.

This research work aims to arise from developing new communication channels for customer service in micro, small and medium enterprises (MSMEs), such as chatbots. In particular, the results of the usability testing of three chatbots implemented in MSMEs are presented.

The methodology employed includes participants, chatbot development platform, research methodology, software development methodology and usability test to contextualize the study's results.

Based on the results obtained from the System Usability Scale (SUS) and considering the accuracy of the chatbot's responses, it is concluded that the level of satisfaction in using chatbots is high; therefore, if the chatbot is well integrated with the communication systems/channels of the MSMEs, the client receives an excellent, fast and efficient service.

The paper analyzes chatbots for customer service and presents the usability testing results of three chatbots implemented in MSMEs.

The study aims to examine the impact of an artificial intelligent service agent (AISA) on customer services to the rural population provided by KAYA, Kotak Life's AI-enabled insurance chatbot avatar that offers quality insurance services.

Multi-stage cluster sampling method was adopted to collect the responses from the 707 customers across the rural population of southern states of India. SPSS V.2 and Smart PLS 4 were used to apply simple percentage analysis, multiple linear regression analysis, and structural equation modeling (SEM) to validate the hypothesis. The dependent variables are economic performance and market performance based on the independent variables: efficiency, security, availability, enjoyment and contact.

The study revealed that efficiency and security are the highest predictors and the most influencing variables in predicting the economic and market performance of the insurance companies in determining the quality of service when rendered through AISA among the customers. Efficiency, security, availability, contact and enjoyment are the critical dimensions of AISA. It has a more significant impact on quality service (claim processing) to the rural population. It improves the economic and market performance among the insurance companies and the rural population.

Customers need convenience when making claims. Even little challenges might lead to stress and unhappiness, depending on the situation. Restrictions on where customers can file claims may not be the most outstanding service insurance firms can offer, given rising travel and commuting costs and widening geographical borders. Customers value proactive communication from service providers about the status of their insurance claims.

This study explores how chatbot gender and symbolic service recovery may improve the satisfaction of angry customers in the context of service failures. It provides a strategy for companies to deploy chatbots effectively in customer anger.

This research relies upon a systematic literature review to propose three hypotheses, and we recruit 826 participants to examine the effect of chatbot gender on angry customers through one lab study and one field study.

This research shows that female chatbots are more likely to increase the satisfaction of angry customers than male chatbots in service failure scenarios. In addition, symbolic recovery (apology vs. appreciation) moderates the effect of chatbot gender on angry customers. Specifically, male (vs. female) chatbots are more effective in increasing the satisfaction of angry customers when using the apology method, whereas female (vs. male) chatbots are more effective when using the appreciation method.

The rapid advancements in artificial intelligence technology have significantly enhanced the effectiveness of chatbots as virtual agents in the field of interactive marketing. Previous research has concluded that chatbots can reduce negative customer feedback following a service failure. However, these studies have primarily focused on the level of chatbot anthropomorphism and the design of conversational texts, rather than the gender of chatbots. Therefore, this study aims to bridge that gap by examining the effect of chatbot gender on customer feedback, specifically focusing on angry customers following service failures.

ChatGPT is a versatile technology with practical use cases spanning many professional disciplines including marketing. Being a recent innovation, however, there is a lack of academic insight into its tangible applications in the marketing realm. To address this gap, the current study explores ChatGPT’s application in marketing by mining social media data. Additionally, the study employs the stages-of- growth model to assess the current state of ChatGPT’s adoption in marketing organizations.

The study collected tweets related to ChatGPT and marketing using a web-scraping technique (N = 23,757). A topic model was trained on the tweet corpus using latent Dirichlet allocation to delineate ChatGPT’s major areas of applications in marketing.

The topic model produced seven latent topics that encapsulated ChatGPT’s major areas of applications in marketing including content marketing, digital marketing, search engine optimization, customer strategy, B2B marketing and prompt engineering. Further analyses reveal the popularity of and interest in these topics among marketing practitioners.

The findings contribute to the literature by offering empirical evidence of ChatGPT’s applications in marketing. They demonstrate the core use cases of ChatGPT in marketing. Further, the study applies the stages-of-growth model to situate ChatGPT’s current state of adoption in marketing organizations and anticipate its future trajectory.

The integration of artificial intelligence (AI) technologies like conversational AI and HR chatbots in international human resource development (HRD) presents both productivity benefits and ethical challenges. This study aims to examine the ethical dimensions of AI-driven HR chatbots, emphasizing the need for fairness, autonomy and nondiscrimination. It discusses inherent biases in AI systems and addresses linguistic, cultural and accessibility issues. The paper advocates for a comprehensive risk assessment approach to guide ethical integration, proposing a “risk management by design” framework. By embracing ethical principles and robust risk management strategies, organizations can navigate AI-driven HR technologies while upholding fairness and equity in global workforce management.

Systematic literature review.

The paper advocates for a comprehensive risk assessment approach to guide ethical integration, proposing a “risk management by design” framework.

By embracing ethical principles and robust risk management strategies, organizations can navigate AI-driven HR technologies while upholding fairness and equity in global workforce management.

This study explores the intricate ethical landscape surrounding AI-driven HR chatbots, spotlighting the imperatives of fairness, autonomy, and nondiscrimination. Uncovering biases inherent in AI systems, it addresses linguistic, cultural, and accessibility concerns. Proposing a pioneering “risk management by design” framework, the study advocates for a holistic approach to ethical integration, ensuring organizations navigate the complexities of AI-driven HR technologies while prioritizing fairness and equity in global workforce management.

Avatars, which are used as a technology and marketing tactic, can embody consumer-facing employees and mimic their real-life roles on companies' websites, thereby playing a key role in enhancing the relationships between consumers and brands in the online environment. Academics and practitioners have increasingly acknowledged the significance of the consumer-brand relationship in both traditional and online contexts. However, the impersonal nature of the online environment is considered to be a hindrance to the development of these relationships. Despite the importance of this technology, little attention has been paid to the investigation of the avatar concept from a marketing perspective. This paper explores the nature of the avatar concept, including its main characteristics, dimensions, and conditions as well as the attitudinal and behavioural consequences of avatar users.

Adopting the qualitative design, a taxonomy was developed from interviews. In total, 42 interviews were conducted with current university students. 30 participants participated in the exploratory interviews. A total of 12 interviews were conducted during the in-depth stage based on findings in the preceding research.

Based on the qualitative data analysis, a taxonomy was developed. The idea of the taxonomy is summarized in that different dimensions of the avatar are considered the main base (first phase) of the taxonomy. There are consequential three parts: the attitudinal consequences related to the website; the attitudinal consequences related to the brand; the behaviours towards the brand. These behaviours represent the final phase of the taxonomy.

By developing a taxonomy of using avatars on brands' websites, the authors advance the understanding consumer-brands relationships. Using avatars' verbal interactions helps in shaping consumers' cognitive, affective, attitudinal and behavioural responses and add vital empirical evidence to the increasing body of research and practices involving avatar usage in the interactive marketing area.

The purpose of this paper is to examine the motivation behind Google’s development of Gemini and its potential impact on the information industry.

This viewpoint paper relies on a comprehensive analysis of the advancements in artificial intelligence (AI) technology, specifically in the field of chatbots.

The findings reveal that Gemini is designed to enhance user experiences by providing personalized and contextually relevant information. It aims to streamline information retrieval, improve customer service and offer tailored content recommendations. The competition among companies in building AI-powered chatbots is driving rapid advancements and innovation in the field.

The originality of this paper lies in its analysis of Google DeepMind’s Gemini and its potential impact on the information industry. It highlights the significance of AI-powered chatbots in transforming how users access and interact with information. This paper contributes to the existing literature by examining the competition in building AI tools and its implications for the future of the information industry. It offers insights into the motivation behind Google’s development of Gemini and its potential value in enhancing user experiences and driving monetization opportunities.

This study investigates how artificial intelligence (AI) chatbots persuade customers to accept their recommendations in the online shopping context.

Drawing on the elaboration likelihood model, this study establishes a research model to reveal the antecedents and internal mechanisms of customers' adoption of AI chatbot recommendations. The authors tested the model with survey data from 530 AI chatbot users.

The results show that in the AI chatbot recommendation adoption process, central and peripheral cues significantly affected a customer's intention to adopt an AI chatbot's recommendation, and a customer's cognitive and emotional trust in the AI chatbot mediated the relationships. Moreover, a customer's mind perception of the AI chatbot, including perceived agency and perceived experience, moderated the central and peripheral paths, respectively.

This study has theoretical and practical implications for AI chatbot designers and provides management insights for practitioners to enhance a customer's intention to adopt an AI chatbot's recommendation.

1. The study investigates customers' adoption of AI chatbots' recommendation.

2. The authors develop research model based on ELM theory to reveal central and peripheral cues and paths.

3. The central and peripheral cues are generalized according to cooperative principle theory.

4. Central cues include recommendation reliability and accuracy, and peripheral cues include human-like empathy and recommendation choice.

5. Central and peripheral cues affect customers' adoption to recommendation through trust in AI.

6. Customers' mind perception positively moderates the central and peripheral paths.

The study investigates customers' adoption of AI chatbots' recommendation.

The authors develop research model based on ELM theory to reveal central and peripheral cues and paths.

The central and peripheral cues are generalized according to cooperative principle theory.

Central cues include recommendation reliability and accuracy, and peripheral cues include human-like empathy and recommendation choice.

Central and peripheral cues affect customers' adoption to recommendation through trust in AI.

Customers' mind perception positively moderates the central and peripheral paths.