Search results

The increasing importance and proliferation of text data provide a unique opportunity and novel lens to study human communication across a myriad of business and marketing applications. For example, consumers compare and review products online, individuals interact with their voice assistants to search, shop, and express their needs, investors seek to extract signals from firms' press releases to improve their investment decisions, and firms analyze sales call transcripts to increase customer satisfaction and conversions. However, extracting meaningful information from unstructured text data is a nontrivial task. In this chapter, we review established natural language processing (NLP) methods for traditional tasks (e.g., LDA for topic modeling and lexicons for sentiment analysis and writing style extraction) and provide an outlook into the future of NLP in marketing, covering recent embedding-based approaches, pretrained language models, and transfer learning for novel tasks such as automated text generation and multi-modal representation learning. These emerging approaches allow the field to improve its ability to perform certain tasks that we have been using for more than a decade (e.g., text classification). But more importantly, they unlock entirely new types of tasks that bring about novel research opportunities (e.g., text summarization, and generative question answering). We conclude with a roadmap and research agenda for promising NLP applications in marketing and provide supplementary code examples to help interested scholars to explore opportunities related to NLP in marketing.

The purpose of this study is to examine how the usage of mobile devices influences text-posting behavior in the online review-generation process. This study attempts to improve the understanding of the negative impacts of mobile channels on the quality of online reviews.

The authors develop a series of hypotheses to investigate the text-posting behaviors with mobile device usage. To examine the authors' hypotheses, the authors collect online reviews posted in London hotels on Booking.com. The authors first use a logistic regression model to examine the relationship between the usage of mobile devices and text-posting behavior. Then, the authors explored the characteristics of textual content in mobile reviews compared to reviews written via traditional devices.

The authors' finding shows that the use of mobile devices negatively influences text-posting behavior. Compared to traditional devices, consumers are less likely to post texts in their reviews with mobile devices. Although consumers decide to post text comments in consumers' reviews, the quality of textual content is relatively low – short in length, with limited analytical thinking and less authenticity.

To the best of the authors' knowledge, no study has attempted to explore text generation in review-posting behaviors in the context of mobile channels. Also, the authors' findings show the negative effects of using mobile channels on the value of generated information, which is counterintuitive to previous research.

With the advent of ChatGPT, a sophisticated generative artificial intelligence (AI) tool, maintaining academic integrity in all educational settings has recently become a challenge for educators. This paper discusses a method and necessary strategies to confront this challenge.

In this study, a language model was defined to achieve high accuracy in distinguishing ChatGPT-generated essays from human written essays with a particular focus on “not falsely” classifying genuinely human-written essays as AI-generated (Negative).

Via support vector machine (SVM) algorithm 100% accuracy was recorded for identifying human generated essays. The author discussed the key use of Recall and F2 score for measuring classification performance and the importance of eliminating False Negatives and making sure that no actual human generated essays are incorrectly classified as AI generated. The results of the proposed model's classification algorithms were compared to those of AI-generated text detection software developed by OpenAI, GPTZero and Copyleaks.

AI-generated essays submitted by students can be detected by teachers and educational designers using the proposed language model and machine learning (ML) classifier at a high accuracy. Human (student)-generated essays can and must be correctly identified with 100% accuracy even if the overall classification accuracy performance is slightly reduced.

This is the first and only study that used an n-gram bag-of-words (BOWs) discrepancy language model as input for a classifier to make such prediction and compared the classification results of other AI-generated text detection software in an empirical way.

This paper examines the role of some basic variables that may be critical in children with difficulties in expressive writing. Preliminary data demonstrating the role of a series of variables are presented. In particular, based on these data, a model was derived using structural equations showing how orthography, neuropsychological functions (idea generation and planning), and revision affect the performance of tasks requiring children to describe the content of pictures. These variables appeared to significantly discriminate between children with good and poor expressive writing skills.

Text classification is a widely accepted and adopted technique in organizations to mine and analyze unstructured and semi-structured data. With advancement of technological computing, deep learning has become more popular among academicians and professionals to perform mining and analytical operations. In this work, the authors study the research carried out in field of text classification using deep learning techniques to identify gaps and opportunities for doing research.

The authors adopted bibliometric-based approach in conjunction with visualization techniques to uncover new insights and findings. The authors collected data of two decades from Scopus global database to perform this study. The authors discuss business applications of deep learning techniques for text classification.

The study provides overview of various publication sources in field of text classification and deep learning together. The study also presents list of prominent authors and their countries working in this field. The authors also presented list of most cited articles based on citations and country of research. Various visualization techniques such as word cloud, network diagram and thematic map were used to identify collaboration network.

The study performed in this paper helped to understand research gaps that is original contribution to body of literature. To best of the authors' knowledge, in-depth study in the field of text classification and deep learning has not been performed in detail. The study provides high value to scholars and professionals by providing them opportunities of research in this area.

The purpose of this paper is to propose a new approach for organizing texts for researchers based on projection from researcher space (consisting of reasoning ability and research ability) onto text space (consisting of text features).

The projection from researcher space onto text space is performed on the grounds of the differences between the sets of essential text features which are consistent, respectively, for the reasoning ability (as researcher's existing ability) and the research ability (as researcher's desired ability) using the concept of a dependency graph.

Through the projection from researcher space onto text space, one can expect to find an effective text which can help a person with certain reasoning ability acquire a certain research ability in the related domain.

Acquisition of reasoning ability (ies) may call for a comprehensive questionnaire or test protocol whose validation by the expert may not necessarily be convenient. Appropriate questionnaires/test protocols, as well as knowledge‐based models for the text features at different layers (to assure derivation of more reliable features), is suggested as future work.

A salient benefit of the proposed approach is its flexibility in responding to a wide range of users with different models. It thus can be used as an efficient tool for online e‐learning and e‐research purposes in cyber‐learning environment.

The originality of the paper mostly lies in the concept of projection from researcher space onto text space as an approach for provision of appropriate text features; and the application of dependency graph as a potential means for identifying those text features whose prerequisites exist in the research abilities of the researcher.

The paper shows how access to knowledge can be enhanced by using a set of innovative approaches and technologies based on the semantic web.

Emerging trends in knowledge access are considered followed by a description of how ontologies and semantics can contribute. A set of tools is then presented which is based on semantic web technology. For each of these tools a detailed description of the approach is given together with an analysis of related and future work as appropriate.

The tools presented are at the prototype stage but can already show how knowledge access can be improved by allowing users to more precisely express what they are looking for and by presenting to them in a form that is appropriate to their current context.

The tools show promising results in improving access to knowledge which will be further evaluated within a practical setting. The tools will be integrated and trialled as part of case studies within the SEKT project. This will allow their usability and practical applicability to be measured.

Ontologies as a form of knowledge representation are increasing in importance. Knowledge management, and in particular knowledge access, will benefit from their widespread acceptance. The use of open standards and compatible tools in this area will be important to support interoperability and widespread access to disparate knowledge repositories.

The paper presents research in an emerging but increasingly important field, i.e. semantic web‐based knowledge technology. It describes how this technology can satisfy the demand for improved knowledge access, including providing knowledge delivery to users at the right time and in the correct form.

Following the recent rise in generative artificial intelligence (GenAI) tools, fundamental questions about their wider impacts have started to reverberate around various disciplines. This study aims to track the unfolding landscape of general issues surrounding GenAI tools and to elucidate the specific opportunities and limitations of these tools as part of the technology-assisted enhancement of mechanical engineering education and professional practices.

As part of the investigation, the authors conduct and present a brief scientometric analysis of recently published studies to unravel the emerging trend on the subject matter. Furthermore, experimentation was done with selected GenAI tools (Bard, ChatGPT, DALL.E and 3DGPT) for mechanical engineering-related tasks.

The study identified several pedagogical and professional opportunities and guidelines for deploying GenAI tools in mechanical engineering. Besides, the study highlights some pitfalls of GenAI tools for analytical reasoning tasks (e.g., subtle errors in computation involving unit conversions) and sketching/image generation tasks (e.g., poor demonstration of symmetry).

To the best of the authors’ knowledge, this study presents the first thorough assessment of the potential of GenAI from the lens of the mechanical engineering field. Combining scientometric analysis, experimentation and pedagogical insights, the study provides a unique focus on the implications of GenAI tools for material selection/discovery in product design, manufacturing troubleshooting, technical documentation and product positioning, among others.

The purpose of this paper is to explain the technological phenomenon artificial intelligence (AI) and how it can contribute to knowledge-based marketing in B2B. Specifically, this paper describes the foundational building blocks of any artificial intelligence system and their interrelationships. This paper also discusses the implications of the different building blocks with respect to market knowledge in B2B marketing and outlines avenues for future research.

The paper is conceptual and proposes a framework to explicate the phenomenon AI and its building blocks. It further provides a structured discussion of how AI can contribute to different types of market knowledge critical for B2B marketing: customer knowledge, user knowledge and external market knowledge.

The paper explains AI from an input–processes–output lens and explicates the six foundational building blocks of any AI system. It also discussed how the combination of the building blocks transforms data into information and knowledge.

Aimed at general marketing executives, rather than AI specialists, this paper explains the phenomenon artificial intelligence, how it works and its relevance for the knowledge-based marketing in B2B firms. The paper highlights illustrative use cases to show how AI can impact B2B marketing functions.

The study conceptualizes the technological phenomenon artificial intelligence from a knowledge management perspective and contributes to the literature on knowledge management in the era of big data. It addresses calls for more scholarly research on AI and B2B marketing.