Chatbot for SMEs: Integrating customer and business owner perspectives

PurposeChatbots have been widely adopted to create more positive customer experiences as customers now spend more time in digital environments. Despite the technological advancement and benefits of chatbots for customer service, research on chatbot applications for Small and medium-sized enterprises (SMEs) is limited. The absence of research explaining the struggles faced by SMEs contributes to the gap of SMEs' chatbot adoption. This research determines the features and elements that fit with SMEs’ characteristics and their customers with chatbots.Design/methodology/approachA mixed-methods approach is used to understand SMEs' needs. Study 1 uses interviews with SME business owners and its customers; it aims to explore the features that should be provided by chatbots for SME by identifying combinations between chatbots' generic features and SMEs' customer characteristics. Study 2 tests features identified in Study 1 and surveys 315 SMEs customers to empirically test featured chatbots' influence to anthropomorphism, perceived enjoyment, perceived ease of use, perceived usefulness, and how they affect SMEs’ customer intentions to use chatbots and their shopping intentions.Findings –The findings suggest four combinations of chatbot features that match SME customer characteristics: responsive; simple steps to trigger customer actions; humanized conversations; and personalized recommendations. An experimental survey was designed by creating a chatbot prototype based on these features. The results show that the featured chatbot prototype affects higher anthropomorphism, perceived enjoyment, and perceived usefulness, compared to the standard chatbot. We also find that perceived enjoyment and usefulness positively affect customer's intention to shop and intention to use the chatbot. While anthropomorphism only affect customer's shopping intention to SMEs.OriginalityThis paper contributes to the emerging service literature on the use of chatbots service interactions, particularly for SMEs. This research provides robust explorations from the perspective of both SME owners and customers. For practice, the research provides guidelines on how to design a chatbot for SMEs that meet customers’ needs.     

Stimulating and sustaining interest in a language course: An experimental comparison of Chatbot and Human task partners

Novel technology can be a powerful tool for enhancing students' interest in many learning domains. However, the sustainability and overall impact of such interest is unclear. This study tests the longer-term effects of technology on students' task and course interest. The experimental study was conducted with students in foreign language classes (n = 122): a 12-week experimental trial that included pre- and post-course interest, and a sequence of task interest measures. Employing a counterbalanced design, at three week intervals students engaged in separate speaking tasks with each of a Human and “Chatbot” partner. Students' interest in successive tasks and in the course (pre-post), were used to assess differential partner effects and course interest development trajectories. Comparisons of task interest under different partner conditions over time indicated a significant drop in students' task interest with the Chatbot but not Human partner. After accounting for initial course interest, Structural Equation Modelling indicated that only task interest with the Human partner contributed to developing course interest. While Human partner task interest predicted future course interest, task interest under Chatbot partner conditions did not. Under Chatbot partner conditions there was a drop in task interest after the first task: a novelty effect. Implications for theory and practice are discussed.     

基于Java技术的校园智能聊天机器人--晓同

随着计算机技术,互联网以及人工智能和大数据的快速发展和应用。高校对于智能、信息化校园的建设也得以更快发展,提出了很多新思路,开发了很多实用性价值性很好的校园应用。在高校中师生获取信息还多以网页搜索浏览为主,但是想从中快速找到真正所需的信息,时间消耗多效果却常常不尽如人意。针对这些问题,从校园情景出发、着眼于信息查询等,设计并并制作了一款聊天机器人。能进行智能信息匹配,人机之间智能聊天,可通过关键字等触发相关问题的解答,及时获得一些信息。该设计通过后台服务人员以及不同用户的权限不同可以将散落在学校官网、微信公众号等的信息进行收集、归纳和整理,建立数据库并存储到后台服务器,利用微软提供的LUIS、Q&AMaker、BotFrame-work等云服务[1]完成自然语言处理、对话管理,智能匹配问题解答。最后能够比较准确地回答用户提出的各类问题以及日常人机智能聊天对话。     

Conversational artificial intelligence in the AEC industry: A review of present status,challenges and opportunities

The idea of developing a system that can converse and understand human languages has been around since the 1200 s. With the advancement in artificial intelligence (AI), Conversational AI came of age in 2010 with the launch of Apple’s Siri. Conversational AI systems leveraged Natural Language Processing (NLP) to understand and converse with humans via speech and text. These systems have been deployed in sectors such as aviation, tourism, and healthcare. However, the application of Conversational AI in the architecture engineering and construction (AEC) industry is lagging, and little is known about the state of research on Conversational AI. Thus, this study presents a systematic review of Conversational AI in the AEC industry to provide insights into the current development and conducted a Focus Group Discussion to highlight challenges and validate areas of opportunities. The findings reveal that Conversational AI applications hold immense benefits for the AEC industry, but it is currently underexplored. The major challenges for the under exploration were highlighted and discusses for intervention. Lastly, opportunities and future research directions of Conversational AI are projected and validated which would improve the productivity and efficiency of the industry. This study presents the status quo of a fast-emerging research area and serves as the first attempt in the AEC field. Its findings would provide insights into the new field which be of benefit to researchers and stakeholders in the AEC industry.     

Emerging-market consumers’ interactions with banking chatbots

Chatbot development and adoption is a growing challenge in emerging markets, which are characterised by a young population structure, low-level internet penetration and a high degree of institutional adversity. The purpose of this study is to explore how consumers in emerging markets interact and engage with banking chatbots when conducting bank transactions. Based on qualitative research using semi-structured interviews with 36 Nigerian residents, this study demonstrates that the four factors of the unified theory of acceptance and use of technology (UTAUT) can explain how emerging-market consumers interact and engage with banking chatbots. While age and technological experience are significant components in facilitating the use of chatbots, perceived expertise, responsiveness and security were found to be particularly important to users. This study delves into user experiences with conversational interfaces, specifically focusing on emerging markets.     

Artificial Intelligence-Enabled Chatbots in Mental Health: A Systematic Review

Clinical applications of Artificial Intelligence (AI) for mental health care have experienced a meteoric rise in the past few years. AI-enabled chatbot software and applications have been administering significant medical treatments that were previously only available from experienced and competent healthcare professionals. Such initiatives, which range from “virtual psychiatrists” to “social robots” in mental health, strive to improve nursing performance and cost management, as well as meeting the mental health needs of vulnerable and underserved populations. Nevertheless, there is still a substantial gap between recent progress in AI mental health and the widespread use of these solutions by healthcare practitioners in clinical settings. Furthermore, treatments are frequently developed without clear ethical concerns. While AI-enabled solutions show promise in the realm of mental health, further research is needed to address the ethical and social aspects of these technologies, as well as to establish efficient research and medical practices in this innovative sector. Moreover, the current relevant literature still lacks a formal and objective review that specifically focuses on research questions from both developers and psychiatrists in AI-enabled chatbot-psychologists development. Taking into account all the problems outlined in this study, we conducted a systematic review of AI-enabled chatbots in mental healthcare that could cover some issues concerning psychotherapy and artificial intelligence. In this systematic review, we put five research questions related to technologies in chatbot development, psychological disorders that can be treated by using chatbots, types of therapies that are enabled in chatbots, machine learning models and techniques in chatbot psychologists, as well as ethical challenges.