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This study aims to investigate how chatbots’ warmth and competence affect customer behavioural expectation (i.e. purchase, recommendation) through perceived humanness and perceived persuasiveness. Moreover, prior knowledge of chatbot is considered the boundary condition of the effects of chatbots’ warmth and competence.

A lab-in-field experiment with 213 participants and a scenario-based experiment of 186 participants were used to test the model using partial least squares structural equation modelling via SmartPLS 4.

Chatbot warmth positively affects customer behavioural expectation through perceived humanness while chatbot competence positively affects customer behavioural expectation through perceived persuasiveness. Prior knowledge of chatbot positively moderates the effect of chatbot warmth on perceived humanness.

This study provides nuanced insights into the effects of chatbots’ warmth and competence on customer behavioural expectation. Future studies could extend the model by exploring additional boundary conditions of the effects of chatbots’ warmth and competence in different generations.

This study offers insightful suggestions for marketing managers on how to impress and convert online customers through designing verbal scripts in customer−chatbot conversations that encourage the customers to anthropomorphise the chatbots.

This study probes into the effects of chatbots’ warmth and competence on customer behavioural expectation by proposing and examining a novel research model that incorporates perceived humanness and perceived persuasiveness as the explanatory mechanisms and prior knowledge of chatbot as the boundary condition.

The study examines the impact of behavioral biases, such as herd behavior, overconfidence and reactions to ESG News, on Socially Responsible Investing (SRI) decisions in the Indian context. Additionally, it explores gender differences in SRI decisions, thereby deepening the understanding of the factors shaping SRI choices and their implications for sustainable finance and gender-inclusive investment strategies.

The study employs Bayesian linear regression to analyze the impact of behavioral biases on SRI decisions among Indian investors since it accommodates uncertainties and integrates prior knowledge into the analysis. Posterior distributions are determined using the Markov chain Monte Carlo technique, ensuring robust and reliable results.

The presence of behavioral biases presents challenges and opportunities in the financial sector, hindering investors’ SRI engagement but offering valuable opportunities for targeted interventions. Peer advice and hot stocks strongly predict SRI engagement, indicating external influences. Investors reacting to extreme ESG events increasingly integrate sustainability into investment decisions. Gender differences reveal a greater inclination of women towards SRI in India.

The sample size was relatively small and restricted to a specific geographic region, which may limit the generalizability of the findings to other areas. While efforts were made to select a diverse sample, the results may represent something different than the broader population. The research focused solely on individual investors and did not consider the perspectives of institutional investors or other stakeholders in the SRI industry.

The study's practical implications are twofold. First, knowing how behavioral biases, such as herd behavior, overconfidence, and reactions to ESG news, affect SRI decisions can help investors and managers make better and more sustainable investment decisions. To reduce biases and encourage responsible investing, strategies might be created. In addition, the discovery of gender differences in SRI decisions, with women showing a stronger propensity, emphasizes the need for targeted marketing and communication strategies to promote more engagement in sustainable finance. These implications provide valuable insights for investors, managers, and policymakers seeking to advance sustainable investment practices.

The study has important social implications. It offers insights into the factors influencing individuals' SRI decisions, contributing to greater awareness and responsible investment practices. The gender disparities found in the study serve as a reminder of the importance of inclusivity in sustainable finance to promote balanced and equitable participation. Addressing these disparities can empower individuals of both genders to contribute to positive social and environmental change. Overall, the study encourages responsible investing and has a beneficial social impact by working towards a more sustainable and socially conscious financial system.

This study addresses a significant research gap by employing Bayesian linear regression method to examine the impact of behavioral biases on SRI decisions thereby offering more meaningful results compared to conventional frequentist estimation. Furthermore, the integration of behavioral finance with sustainable finance offers novel perspectives, contributing to the understanding of investors, investment managers, and policymakers, therefore, catalyzing responsible capital allocation. The study's exploration of gender dynamics adds a new dimension to the existing research on SRI and behavioral finance.

While the proliferation of chatbots allows companies to connect with their customers in a cost- and time-efficient manner, it is not deniable that they quite often fail expectations and may even pose negative impacts on user experience. The purpose of the study is to empirically explore the negative user experience with chatbots and understand how users respond to service failure caused by chatbots.

This study adopts a qualitative research method and conducts thematic analysis of 23 interview transcripts.

It identifies common areas where chatbots fail user expectations and cause service failure. These include their inability to comprehend and provide information, over-enquiry of personal or sensitive information, fake humanity, poor integration with human agents, and their inability to solve complicated user queries. Negative emotions such as anger, frustration, betrayal and passive defeat were experienced by participants when they interacted with chatbots. We also reveal four coping strategies users employ following a chatbots-induced failure: expressive support seeking, active coping, acceptance and withdrawal.

Our study extends our current understanding of human-chatbot interactions and provides significant managerial implications. It highlights the importance for organizations to re-consider the role of their chatbots in user interactions and balance the use of human and chatbots in the service context, particularly in customer service interactions that involve resolving complex issues or handling non-routinized tasks.

An AI concierge is a technologically advanced, intelligent and personalized assistant that is designated to an individual customer, proactively taking care of that customer’s needs throughout the service journey. This article envisions the idea of AI concierges and discusses how to leverage AI concierges in the customer journey.

This article takes a conceptual approach and draws insights from literature in service management, marketing, psychology, human-computer interaction and ethics.

This article delineates the fundamental forms of AI concierges: dialog interface (no embodiment), virtual avatar (embodiment in the virtual world), holographic projection (projection in the physical world) and tangible service robot (embodiment in the physical world). Key attributes of AI concierges are the ability to exhibit semantic understanding of auditory and visual inputs, maintain an emotional connection with the customer, demonstrate proactivity in refining the customer’s experience and ensure omnipresence through continuous availability in various forms to attend to service throughout the customer journey. Furthermore, the article explores the multifaceted roles that AI concierges can play across the pre-encounter, encounter and post-encounter stages of the customer journey and explores the opportunities and challenges associated with AI concierges.

This paper provides insights for professionals in hospitality, retail, travel, and healthcare on leveraging AI concierges to enhance the customer experience. By broadening AI concierge services, organizations can deliver personalized assistance and refined services across the entire customer journey.

This article is the first to introduce the concept of the AI concierge. It offers a novel perspective by defining AI concierges’ fundamental forms, key attributes and exploring their diverse roles in the customer journey. Additionally, it lays out a research agenda aimed at further advancing this domain.