Search results

Customer service conversations are becoming increasingly digital and automated, leaving service encounters impersonal. The purpose of this paper is to identify how customer service agents and conversational artificial intelligence (AI) applications can provide a personal touch and improve the customer experience in customer service. The authors offer a conceptual framework delineating how text-based customer service communication should be designed to increase relational personalization.

This paper presents a systematic literature review on conversation styles of conversational AI and integrates the extant research to inform the development of the proposed conceptual framework. Using social information processing theory as a theoretical lens, the authors extend the concept of relational personalization for text-based customer service communication.

The conceptual framework identifies conversation styles, whose degree of expression needs to be personalized to provide a personal touch and improve the customer experience in service. The personalization of these conversation styles depends on available psychological and individual customer knowledge, contextual factors such as the interaction and service type, as well as the freedom of communication the conversational AI or customer service agent has.

The article is the first to conduct a systematic literature review on conversation styles of conversational AI in customer service and to conceptualize critical elements of text-based customer service communication required to provide a personal touch with conversational AI. Furthermore, the authors provide managerial implications to advance customer service conversations with three types of conversational AI applications used in collaboration with customer service agents, namely conversational analytics, conversational coaching and chatbots.

Dynamic knowledge interaction is interaction that brings about mutual understanding and knowledge evolution in a community. Proposes a communication medium called conversational medium that provides the user with a means for interacting with the content in a conversational fashion, and presents a traveling conversation model in which the community knowledge process is modeled as circulation of conversational contents that represent small talks in a community. Shows several pilot systems based on these ideas, including the public opinion channel which is an interactive broadcasting system that collects small talks and broadcasts stories reorganized from the archive of small talks; EgoChat which is a system based on a talking‐virtualized‐egos metaphor; Voice Café which is a system consisting of a physical object and a conversational agent that allows artifacts to make conversation with people or other artifacts; and embodiment communication for communicating more vivid information by introducing non‐verbal communication facilities.

This paper reviews the contributions of Gordon Pask, cybernetician, to the discipline of psychology as a methodologist and as a theoretician. Pask was prolific in many fields but especially so in psychology both as an innovative experimenter and an innovative theorist. Over four decades, Pask carried out experimental studies of learning, problem solving and decision making, often using sophisticated computer‐based environments. These latter, in turn, inspired the design of teaching machines and learning environments with practical applications in education and training. Pask’s theorizing while addressing detailed methodological and conceptual issues was also holistic and unifying in intent, not least in his proposals for rapprochements between process, individual and social psychologies.

There is now available the technology to substantially improve the quality of online information retrieval at low cost. It is possible by using microcomputers to economize on search costs, increase the convenience of searching, improve the quality of a search, and make the searching process easier for the unskilled user. Some of the facilities have already been implemented on a microcomputer costing £650 ($950), and also on a microprocessor box which plugs in between an ordinary terminal and modem. Savings of search costs of more than 50% have been achieved. Some of the possible enhancements are discussed and the most appropriate way of introduing these features is surveyed.

Artificial Intelligence (AI) has revolutionised the tourism industry, offering personalised experiences and streamlining operations. AI provides customised recommendations for travellers through data analysis and machine learning, making their journeys more meaningful. It has also improved efficiency through automated processes, chatbots and enhanced security measures. AI's ability to analyse large volumes of data enables tourism organisations to make data-driven decisions and target their marketing strategies effectively. One of the most notable contributions of AI in tourism is its ability to offer personalised recommendations. By analysing vast travel history, preferences and online behaviour, AI systems can provide tailored suggestions for destinations, accommodations, activities and dining options. This level of customisation enhances the overall travel experience, making it more relevant and satisfying for individual travellers. AI has also greatly improved operational efficiency within the tourism sector. Chatbots, powered by natural language processing, are increasingly being deployed by hotels, airlines and travel agencies to provide instant customer support and assistance. These chatbots can answer queries, offer recommendations and handle booking processes, reducing waiting times and enhancing customer satisfaction. In addition, facial recognition technology allows for quick and accurate identity verification at airports, hotels and other travel-related facilities. This improves security and provides travellers with a seamless and efficient experience. As technology advances, we expect AI to play a more prominent role in augmented reality, voice recognition and virtual assistants, further enhancing the travel experience and facilitating seamless interactions. In conclusion, AI has transformed the tourism industry by providing personalised recommendations, improving operational efficiency, enhancing security measures and enabling data-driven destination management.

Conversational agents are natural language interaction interfaces designed to simulate conversation with a real person. This paper seeks to investigate current development and applications of these systems worldwide, while focusing on their availability in Canadian libraries. It aims to argue that it is both timely and conceivable for Canadian libraries to consider adopting conversational agents to enhance – not replace – face‐to‐face human interaction. Potential users include library web site tour guides, automated virtual reference and readers' advisory librarians, and virtual story‐tellers. To provide background and justification for this argument, the paper seeks to review agents from classic implementations to state‐of‐the‐art prototypes: how they interact with users, produce language, and control conversational behaviors.

The web sites of the 20 largest Canadian libraries were surveyed to assess the extent to which specific language‐related technologies are offered in Canada, including conversational agents. An exemplified taxonomy of four pragmatic purposes that conversational agents currently serve outside libraries – educational, informational, assistive, and socially interactive – is proposed and translated into library settings.

As of early 2010, artificially intelligent conversational systems have been found to be virtually non‐existent in Canadian libraries, while other innovative technologies proliferate (e.g. social media tools). These findings motivate the need for a broader awareness and discussion within the LIS community of these systems' applicability and potential for library purposes.

This paper is intended for reflective information professionals who seek a greater understanding of the issues related to adopting conversational agents in libraries, as this topic is scarcely covered in the LIS literature. The pros and cons are discussed, and insights offered into perceptions of intelligence (artificial or not) as well as the fundamentally social nature of human‐computer interaction.

This study aims to examine the main aspects of innovation development regarding digital tools used in the tourism industry in a local area.

A qualitative approach and the descriptive analysis technique was employed to analyse completed semi-structured expert survey questionnaires created in Google Forms. The aim is to examine the opportunities and potential for digital innovation in tourism in the Baltic region. Specialists representing tourism and hotel businesses, the guide profession as well as the disciplines of business and economics were selected as experts.

The experts indicated that digital marketing was very important for tourism development, and it is still considered a new field of science with huge potential. The research results showed that when introducing/developing digital innovations in a local area, tourism companies were faced with low-quality or no data analytics at all. In addition, long-term maintenance of digital tools was not considered, and the investment outweighed the benefits of implementing the technologies.

The study contributes to the development of digital innovation and the identification of problems in a local area.

This paper questions the validity of traditional scientific method for the study of human learning and proposes five postulates for the advancement of a conversational science. It considers how an evolving capacity for lifelong learning has been constrained by inappropriate research methods and educational practice leading to a learning deficit in the population. Over 25 years of action research offers solid evidence for the humanisation of science as a conversational research process which respects the individual as a unique meaning constructing, self‐organising learning (SOL) entity. A learning conversation pedagogy which enables learners to act as personal scientists and action researchers and a SOL Systems Seven for a community of action researchers is outlined. Finally, the paper considers how SOL entities can function as catalysts for new forms of ORDER with a potential for the emergence of a new species of learning and of being human.

The purpose of this paper is to review recent developments in educational informatics relating to the provision by information systems of pedagogical support to web‐based learners, and to propose further investigation of the feasibility and potential value of web‐based “conversational” information systems to complement adaptive hypermedia and information retrieval systems.

The potential of Pask's conversation theory is considered as a potentially useful framework for the development of information systems capable of providing pedagogical support for web‐based learners, complementary to that provided by existing computer‐assisted learning and adaptive hypermedia systems. The potential role and application of entailment meshes are reviewed in relation to other forms of knowledge representation including classifications, semantic networks, ontologies and representations based on knowledge space theory.

Concludes that conversation theory could be a useful framework to support the development of web‐based “conversational” information that would complement aspects of computer‐assisted learning, adaptive hypermedia and information retrieval systems. The entailment mesh knowledge representation associated with conversation theory provides the potential for providing particularly rich pedagogical support by virtue of its properties of cyclicity, consistency and connectivity, designed to support deep and enduring levels of understanding.

Although based on a considerable body of theoretical and empirical work relating to conversation theory, the paper remains speculative in that the gap is still great between, on the one hand, what has so far been achieved and, on the other, the practical realisation of its potential to enhance web‐based learning. Much work remains to be done in terms of exploring the extent to which procedures developed and benefits found in relatively small‐scale experimental contexts can effectively be scaled to yield enhanced support for “real world” learning‐related information behaviour.

The ideas of Pask, discussed in this paper, are capable of guiding the structuring of information according to parameters designed to facilitate deep and enduring understanding via interactive “conversational” engagement between the conceptual structures of information source authors and learners. If one can scale Pask's work to “real world” learning‐related information behaviour, one can increase the range of web‐based information systems and services capable of providing pedagogical support to web‐based learners.

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.