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Seekers who visit professional virtual communities (PVCs) are usually motivated by knowledge-seeking, which is a complex cognitive process. How do seekers search for knowledge, and how is their search linked to prior knowledge or PVC situation factors? From the cognitive process and interactional psychology perspectives, this study investigated the three-way interactions between seekers’ expertise, task complexity, and perceptions of PVC features (i.e. knowledge quality and system quality) on knowledge-seeking strategies and resultant outcomes.

A field experiment was conducted with 119 seekers in a PVC using a 2 × 2 factorial design of seekers’ expertise (i.e. expert versus novice) and task complexity (i.e. low versus high).

The study reveals three significant insights: (1) For a high-complexity task, experts adopt an ask-directed searching strategy compared to novices, whereas novices adopt a browsing strategy; (2) For a high-complexity task, experts who perceive a high system quality are more likely than novices to adopt an ask-directed searching strategy; and (3) Task completion time and task quality are associated with the adoption of ask-directed searching strategies, whereas knowledge seekers’ satisfaction is more associated with the adoption of browsing strategy.

We draw on the perspectives of cognitive process and interactional psychology to explore potential two- and three-way interactions of seekers’ expertise, task complexity, and PVC features on the adoption of knowledge-seeking strategies in a PVC context. Our findings provide deep insights into seekers’ behavior in a PVC, given the popularity of the search for knowledge in PVCs.

In the extant research on online knowledge communities (OKCs), little attention has been paid to the influence of membership fluidity on the coevolution of the social and knowledge systems. This article aims to fill this gap.

Based on the attraction-selection-attrition (ASA) framework, this paper constructs a simulation model to study the coevolution of these two systems under different levels of membership fluidity.

By analyzing the evolution of these systems with the vector autoregression (VAR) method, we find that social and knowledge systems become more orderly as the coevolution progresses. Furthermore, in communities with low membership fluidity, the microlevel of the social system (i.e. users) drives the coevolution, whereas in communities with high membership fluidity, the microlevel of the knowledge system (i.e. users' views) drives the coevolution.

This paper extends the application of the ASA framework and enriches the literature on membership fluidity of online communities and the literature on driving factors for coevolution of the social and knowledge systems in OKCs. On a practical level, our work suggests that community administrators should adopt different strategies for different membership fluidity to efficiently promote the coevolution of the social and knowledge systems in OKCs.

The purpose of this study is to explore linguistic stylometric patterns encompassing lexical, syntactic, structural, sentiment and politeness features that are found in librarians’ responses to user queries.

A total of 462 online texts/transcripts comprising answers of librarians to users’ questions drawn from the Internet Public Library were examined. A Principal Component Analysis, which is a data reduction technique, was conducted on the texts and transcripts. Data analysis illustrates the three principal components that predominantly occur in librarians’ answers: stylometric richness, stylometric brevity and interpersonal support.

The results of the study have important implications in digital information services because stylometric features such as lexical richness, structural clarity and interpersonal support may interplay with the degree of complexity of user queries, the (a)synchronous communication mode, application of information service guideline and manuals and overall characteristics and quality of a given digital information service. Such interplay may bring forth a direct impact on user perceptions and satisfaction regarding interaction with librarians and the information service received through the computer-mediated communication channel.

To the best of the authors’ knowledge, the stylometric features encompassing lexical, syntactic, structural, sentiment and politeness using Principal Component Analysis have not been explored in digital information/reference services. Thus, there is an emergent need to explore more fully how linguistic stylometric features interplay with the types of user queries, the asynchronous online communication mode, application of information service guidelines and the quality of a particular digital information service.

The rapid rise of large language models (LLMs) has propelled them to the forefront of applications in natural language processing (NLP). This paper aims to present a comprehensive examination of the research landscape in LLMs, providing an overview of the prevailing themes and topics within this dynamic domain.

Drawing from an extensive corpus of 198 records published between 1996 to 2023 from the relevant academic database encompassing journal articles, books, book chapters, conference papers and selected working papers, this study delves deep into the multifaceted world of LLM research. In this study, the authors employed the BERTopic algorithm, a recent advancement in topic modeling, to conduct a comprehensive analysis of the data after it had been meticulously cleaned and preprocessed. BERTopic leverages the power of transformer-based language models like bidirectional encoder representations from transformers (BERT) to generate more meaningful and coherent topics. This approach facilitates the identification of hidden patterns within the data, enabling authors to uncover valuable insights that might otherwise have remained obscure. The analysis revealed four distinct clusters of topics in LLM research: “language and NLP”, “education and teaching”, “clinical and medical applications” and “speech and recognition techniques”. Each cluster embodies a unique aspect of LLM application and showcases the breadth of possibilities that LLM technology has to offer. In addition to presenting the research findings, this paper identifies key challenges and opportunities in the realm of LLMs. It underscores the necessity for further investigation in specific areas, including the paramount importance of addressing potential biases, transparency and explainability, data privacy and security, and responsible deployment of LLM technology.

The analysis revealed four distinct clusters of topics in LLM research: “language and NLP”, “education and teaching”, “clinical and medical applications” and “speech and recognition techniques”. Each cluster embodies a unique aspect of LLM application and showcases the breadth of possibilities that LLM technology has to offer. In addition to presenting the research findings, this paper identifies key challenges and opportunities in the realm of LLMs. It underscores the necessity for further investigation in specific areas, including the paramount importance of addressing potential biases, transparency and explainability, data privacy and security, and responsible deployment of LLM technology.

This classification offers practical guidance for researchers, developers, educators, and policymakers to focus efforts and resources. The study underscores the importance of addressing challenges in LLMs, including potential biases, transparency, data privacy, and responsible deployment. Policymakers can utilize this information to shape regulations, while developers can tailor technology development based on the diverse applications identified. The findings also emphasize the need for interdisciplinary collaboration and highlight ethical considerations, providing a roadmap for navigating the complex landscape of LLM research and applications.

This study stands out as the first to examine the evolution of LLMs across such a long time frame and across such diversified disciplines. It provides a unique perspective on the key areas of LLM research, highlighting the breadth and depth of LLM’s evolution.

This study aims to examine the role of hope and hate in political leaders’ messages in influencing liberals versus conservatives’ social-distancing behavior during the COVID-19 pandemic. Given the increasing political partisanship across the world today, using the appropriate message framing has important implications for social and public policy.

The authors use two Natural Language Processing (NLP) methods – a pretrained package (HateSonar) and a classifier built to implement our supervised neural network-based model architecture using RoBERTa – to analyze 61,466 tweets by each US state’s governor and two senators with the goal of examining the association between message factors invoking hate and hope and increased or decreased social distancing from March to May 2020. The authors examine individuals’ social-distancing behaviors (the amount of nonessential driving undertaken) using data from 3,047 US counties between March 13 and May 31, 2020, as reported by Google COVID-19 Community Mobility Reports and the New York Times repository of COVID-19 data.

The results show that for conservative state leaders, the use of hate increases nonessential driving of state residents. However, when these leaders use hope in their speech, nonessential driving of state residents decreases. For liberal state leaders, the use of hate displays a directionally different result as compared to their conservative counterparts.

Amid the emergence of new analytic techniques and novel data sources, the findings demonstrate that the use of global positioning systems data and social media analysis can provide valuable and precise insights into individual behavior. They also contribute to the literature on political ideology and emotion by demonstrating the use of specific emotion appeals in targeting specific consumer segments based on their political ideology.

The findings have significant implications for policymakers and public health officials regarding the importance of considering partisanship when developing and implementing public health policies. As partisanship continues to increase, applying the appropriate emotion appeal in messages will become increasingly crucial. The findings can help marketers and policymakers develop more effective social marketing campaigns by tailoring specific appeals given the political identity of the consumer.

Using Neural NLP methods, this study identifies the specific factors linking social media messaging from political leaders and increased compliance with health directives in a partisan population.

This study aims to investigate the effects of interaction in agricultural product live rooms, including personalization, responsiveness, and entertainment, on consumers’ purchase intention.

The SOR model has been applied to formulate hypotheses. This study is based on an online survey conducted with a sample of 433 valid questionnaires from Chinese consumers on the TikTok agricultural products live platform. A partial least squares structural equation modeling (PLS-SEM) is used to test the hypotheses.

The findings indicate that (1) interactions (personalization, responsiveness, and entertainment) in the agricultural products live room have a positive effect on perceived values (utilitarian value, symbolic value, and entertainment value); (2) utilitarian value positively affects purchase intention and mediates the effects of personalization and entertainment interactions on purchase intention, respectively; (3) entertainment value positively influences purchase intention and mediates the influence of entertainment interaction on purchase intention.

This study offers theoretical insights into live marketing of agricultural products and practical implications for practitioners of agricultural products in live streaming commerce.

The rapid growth of artificial intelligence is disrupting various industries, including the tourism sector. This paper aims to outline the applications, benefits and risks of ChatGPT and large language models in general on tourism. It also aims to establish a research agenda for investigating the implications of these models in tourism.

Drawing on the available literature on ChatGPT, large language models and artificial intelligence, the paper identifies areas of application of ChatGPT for several tourism stakeholders. Potential benefits and risks are then considered.

ChatGPT and other similar models are likely to have a profound impact on several tourism processes. They will contribute to further streamline customer service in front-of-house operations and increase productivity and efficiency in back-of-house operations. Although negative consequences for human resources are expected, this technology mostly enhances tourism employees.

To the best of the authors’ knowledge, this is one of the first studies that explore the potential implications of ChatGPT in tourism and hospitality.

人工智能的快速发展正在颠覆包括旅游业在内的各个行业。 本文旨在概述ChatGPT和大型语言模型在旅游业中的应用、好处和风险。同时, 旨在建立一个研究议程, 以调查这些模型在旅游业中的影响。

本文借鉴了关于ChatGPT、大型语言模型和人工智能的现有文献, 确定了ChatGPT在几个旅游利益相关者中的应用范围, 然后考虑了潜在的好处和风险。

ChatGPT和其他类似的模型可能会对一些旅游过程产生深远的影响。它们将有助于进一步简化前台业务的客户服务, 并提高后台业务的生产力和效率。虽然对人力资源的负面影响是可以预见的, 但这项技术主要是增强旅游业的员工能力。

这是首批探索ChatGPT在旅游业和酒店业潜在影响的研究之一。

A partir de la bibliografía disponible sobre ChatGPT, grandes modelos lingüísticos e inteligencia artificial, este artículo identifica las posibles áreas de aplicación de ChatGPT y actores que se pueden beneficiar. De igual forma, se examinan los posibles beneficios y riesgos.

El rápido crecimiento de la inteligencia artificial está afectando diversas industrias, incluyendo la del turismo. Este artículo pretende esbozar las aplicaciones, ventajas y riesgos de ChatGPT, así como los grandes modelos lingüísticos, en turismo. También pretende establecer una agenda de investigación para estudiar las implicaciones de estos modelos en el turismo.

Es probable que ChatGPT y otros modelos similares tengan un profundo impacto en varios procesos turísticos, contribuyendo a racionalizar, aún más, el servicio al cliente en las operaciones de front-of-the-house y aumentando la productividad y eficiencia en el back-of-the-house. Aunque se prevén consecuencias negativas para los recursos humanos, esta tecnología servirá sobre todo para potenciarlos.

Éste es uno de los primeros estudios que exploran las implicaciones potenciales de ChatGPT en el turismo y la hostelería.

Online reviewers' identity information is an essential cue by which consumers judge reviews on ecommerce platforms. However, few studies have explored how prior anonymous reviews and focal reviews affect reviewers' preference for anonymity. The purpose of this paper is to investigate why reviewers seek anonymity in terms of prior anonymous reviews and focal reviews.

Based on restaurant reviews collected from meituan.com, one of the largest group-buying ecommerce platforms in China, this study employed logistic regression to examine how prior anonymous reviews and focal reviews are associated with reviewers' preference for anonymity.

Results show that the volume and sequence of prior anonymous review are positively associated with the likelihood of reviewers' preference for anonymity, whereas focal review valence is negatively correlated with this preference. Focal review length is positively correlated with reviewers' preference for anonymity but negatively moderates the roles of review valence and prior anonymous reviews on this preference.

This study expands the information disclosure literature by exploring determinants of user identity disclosure from a reviewer perspective. This research also offers a methodological contribution by employing a more accurate measure to calculate reviewers' preference for anonymity, enhancing the empirical results. Lastly, this work supplements the online review literature on how prior anonymous reviews and focal reviews are associated with reviewers' identity disclosure.

Global adoption of the internet and mobile usage results in a huge variation in the cultural backgrounds of consumers who generate and consume electronic word-of-mouth (eWOM). Unsurprisingly, a research trend on cross-cultural eWOM has emerged. However, there has not been an attempt to synthesize this research topic. This paper aims to bridge this gap.

This research paper conducts a systematic literature review of the current research findings on cross-cultural eWOM. Journal articles published from 2006 to 2021 are included. This study then presents the key issues in the extant literature and suggests potential future research.

The findings show that there has been an upward trend in the number of publications on cross-cultural eWOM since the early 2010s, with a relatively steeper increase toward 2020. The findings also synthesize cross-cultural eWOM research into four elements and suggest potential future research avenues.

To the best of the authors’ knowledge, there is currently no exhaustive/integrated review of cross-cultural eWOM research. This research fills the need to summarize the current state of cross-cultural eWOM literature and identifies research questions to be addressed in the future.

El boca a boca electrónico cross-cultural: una revisión sistemática de la literatura

La adopción global de Internet y los móviles da lugar a una enorme diferencia en el origen cultural de los consumidores que generan y consumen el boca a boca electrónico (eWOM). No es de extrañar que haya surgido una tendencia de investigación sobre el eWOM transcultural. Sin embargo, no se ha intentado sintetizar este tema de investigación. El objetivo de este artículo es subsanar esta carencia.

Este trabajo de investigación realiza una revisión bibliográfica sistemática de las investigaciones realizadas sobre eWOM transcultural. Se incluyen artículos de revistas publicados desde 2006 hasta 2021. A continuación, el estudio presenta las cuestiones clave de la literatura existente y sugiere posibles investigaciones futuras.

Los resultados muestran que ha habido una tendencia al alza en el número de publicaciones sobre eWOM intercultural desde principios de la década de 2010, con un aumento relativamente creciente hacia 2020. Los resultados también sintetizan la investigación sobre eWOM intercultural en cuatro elementos y sugieren posibles vías de investigación futuras.

Actualmente no existe una revisión exhaustiva/integrada de la investigación sobre el eWOM cross-cultural. Esta investigación satisface la necesidad de resumir el estado actual de la literatura sobre eWOM cross-cultural e identifica las cuestiones de investigación que deben abordarse en el futuro.

跨文化电子口碑研究：系统性文献回顾

摘要

在互联网全球化以及移动手机的广泛使用的背景下, 不同文化背景的消费者都在贡献电子口碑（eWOM）。这使得电子口碑存在文化差异。然而, 还没有人试图对这个研究课题进行综合分析。本文的目的就是要弥补这一空白。

本研究论文对目前关于跨文化eWOM的研究成果进行了系统的文献回顾。包括2006年至2021年发表的期刊文章。然后, 本研究提出了现有文献中的关键问题, 并提出了潜在的未来研究。

研究结果显示, 自2010年初以来, 关于跨文化eWOM的出版物数量呈上升趋势, 到2020年时增幅相对较大。研究结果还总结了跨文化eWOM研究的四个要素, 并提出了潜在的未来研究途径。

目前还没有关于跨文化eWOM研究的详尽/综合的回顾。这项研究填补了总结跨文化电子WOM文献现状的需要, 并确定了未来要解决的研究问题。