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The main goal of this paper is to identify the attributes of consumer experience in Michelin-starred restaurants and to estimate their effects on restaurant ratings.

A sample of 70,233 online reviews of 224 Spanish Michelin-starred restaurants were analysed with the latent Dirichlet allocation algorithm. A sentiment analysis and a logistic regression analysis were also employed to estimate the effect of attributes on restaurant ratings.

Customer attention, food quality, decor and ambience and value for money are frequently used to define restaurant experience. However, it is shown in this study that the experience in a Michelin-starred restaurant goes beyond the evaluation of those four attributes. Furthermore, the effect of the factors that were identified on customer satisfaction differed depending on the restaurant ratings.

The findings are linked to the context of Spanish Michelin-starred restaurants. It is also assumed in this study that online reviews are based on truthful opinions.

Restaurant managers should primarily focus on customer attention and food quality to achieve customer satisfaction. In addition, those restaurants with an error-free service and a highly appreciated wine list among diners are more likely to achieve the culinary excellence that deserves a 5-star rating on TripAdvisor.

The attributes of the restaurant experience are frequently identified in literature reviews. Research based on text-mining analyses of customer reviews to discover a posteriori the factors that define a restaurant experience is scarce, and particularly difficult to find in the context of Michelin-starred restaurants.

The paper attempts to address the following research questions (RQs): RQ1: What are the main research topics within PSS research? RQ2: What are future trends for PSS research?

Twenty years of research (1999–2018) on product-service systems (PSS) produced a significant amount of scientific literature on the topic. As the PSS field is relatively new and fragmented across different disciplines, a review of the prior and relevant literature is important in order to provide the necessary framework for understanding current developments and future perspectives. This paper aims to review and organize research contributions regarding PSS. A machine-learning algorithm, namely Latent Dirichlet Allocation, has been applied to the whole literature corpus on PSS in order to understand its structure.

The adopted approach resulted in the definition of eight distinct and representative topics able to deal adequately with the multidisciplinarity of the PSS. Furthermore, a systematic review of the literature is proposed to summarize the state-of-the-art and limitations in the identified PSS research topics. Based on this critical analysis, major gaps and future research challenges are presented and discussed.

On the basis of the results of the topic landscape, the paper presents some potential research opportunities on PSSs. In particular, challenges, transversal to the eight research topics and related to recent technology trends and digital transformation, have been discussed.

The purpose of this paper is to offer a comprehensive overview of current research on customer behavior in the business-to-business (B2B) context and propose a research agenda for future studies. Despite being a relatively recent area of interest for academics and practitioners, a literature review that synthesizes existing knowledge into coherent topics and outlines a research agenda for future research is still lacking.

Drawing on a systematic literature review of 219 papers and using a text-mining approach based on the Latent Dirichlet Allocation algorithm, this paper enhances the existing knowledge of B2B customer behavior and provides a descriptive analysis of the literature.

From this review, ten major research topics are found and analyzed. These topics were analyzed through the lens of the Theory, Context, Characteristics and Method framework, providing a summary of key findings from prior studies. Additionally, an integrative framework was developed, offering insights into future research directions.

This study presents a novel contribution to the field of B2B by providing a systematic review of the topic of customer behavior, filling a gap in the literature and offering a valuable resource for scholars and managers seeking to advance the field.

Digital transformation (DT) is a major challenge for incumbent organisations, as research on this phenomenon has revealed a high failure rate. Given this consideration, this paper reviews the literature on DT in incumbent organisations to identify the main themes and research directions to be undertaken.

The authors adopt a systematic literature review (SLR) and computational literature review (CLR) employing a machine learning algorithm for topic modelling (LDA) to surface the themes discussed in 103 peer-reviewed studies published between 2010 and 2022 in a multidisciplinary article sample.

The authors identify and discuss the five main themes emerging from the studies, offering the state-of-the-art of DT in established firms' literature. The authors find that the most discussed topics revolve around the DT of healthcare, the process of renewal and change, the project management, the changes in value performances and capabilities and the consequences on the products of DT. Accordingly, the authors identify the topics overlooked by literature that future studies could tackle, which concern sustainability and contextualisation of the DT phenomenon.

The authors further propose managerial insights which equip managers with a revolutionary mindset that is not constraining but, rather, integration-seeking. DT is not only about technology (Tabrizi B et al., 2019). Successful DT initiatives require managerial capabilities that foster a sustainable departure from the current organising logic (Markus, 2004). This study pinpoints and prioritises the role that paradox-informed thinking can have to sustain an effective digital mindset (Eden et al., 2018) that allows for the building of momentum in DT initiatives and facilitates the renewal process. Indeed, managers lagging behind DT could shift from an “either-or” solutions mindset where one pole is preferred over the other (e.g. digital or physical) to embracing a “both-and-with” thinking balancing between poles (e.g. digital and physical) to successfully fuse the digital and the legacy (Lewis and Smith, 2022b; Smith, Lewis and Edmondson, 2022), enact the renewal, and build and maintain momentum for DTs. The outcomes of adopting a paradox mindset in managerial practice are enabling learning and creativity, fostering flexibility and resilience and, finally, unleashing human potential (Lewis and Smith, 2014).

The authors propose insight that will equip managers with a mindset that will allow DT to fail less often than current reported rates, which failure may imply potential organisational collapse, financial bankrupt and social crisis.

The authors offer a multidisciplinary review of the DT complementing existing reviews due to the focus on the organisational context of established organisations. Moreover, the authors advance paradoxical thinking as a novel lens through which to study DT in incumbent organisations by proposing an array of potential research questions and new avenues for research. Finally, the authors offer insights for managers to help them thrive in DT by adopting a paradoxical mindset.

Though the circular economy (CE) is a current buzzword, this still lacks a precise definition. In the absence of a clear notion of what that term includes, actions taken by the government and companies may not be well informed. In particular, those actions need to consider what people mean when people talk about the CE, either to refocus people's decisions or to undertake a more effective communications strategy.

Since people voice people's opinions mainly through social media nowadays, special attention has to be paid to discussions on those media. In this paper, the authors focus on Twitter as a popular social platform to deliver one's thoughts quickly and fast. The authors' research aim is to get the perceptions of people about the CE. After collecting more than 100,000 tweets over 16 weeks, the authors analyse those tweets to understand the public discussion about the CE. The authors conduct a frequency analysis of the most recurring words, including the words' association with other words in the same context and categorise them into a set of topics.

The authors show that the discussion focuses on the usage of resources and materials that heavily endanger sustainability, i.e. carbon and plastic and the harmful habit of wasting. On the other hand, the two most common good practices associated with the CE and sustainability emerge as recycling and reuse (the latter being mentioned far less). Also, the business side of the CE appears to be relevant.

The outcome of this analysis can drive suitable communication strategies by which companies and governments interested in the development of the CE can understand what is actually discussed on social media and call for the attention.

This paper addresses the lack of a standard definition the authors highlighted in the Introduction. The results confirm that people understand CE by looking both at CE's constituent activities and CE's expected consequences, namely the reduction of waste, the transition to a green economy free of plastic and other pollutants and the improvement of the world climate.

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 paper aims to review the topic of “luxury fashion consumption”, a field of recent interest for academics and practitioners. However, a literature review that can map the existing knowledge and aggregate it into relevant topics and offers a research agenda for future research is still lacking.

This paper uses a systematic review and a text mining approach to analyse 73 articles on luxury fashion consumption aiming to clarify, rationalise and critically interpret the literature on luxury fashion consumption; identify the core topic, create an integrative framework of core constructs; and offer research gaps and suggest a research agenda for future studies.

From this analysis, eight major research topics are found and analysed (brand desire, authenticity, luxury markets, value perceptions, luxury retail experience, luxury brands communication, responsible consumption and sustainability and status signalling). Based on these topics and following the TCM framework, this review offers directions for future research.

This research offers a text-mining review of luxury fashion consumption to help scholars and managers further develop this field, as there is no comprehensive review on the topic exploring the themes, theories, constructs and methods used in prior studies.

Este artículo pretende revisar el “consumo de moda de lujo”, un tema de reciente interés para académicos y profesionales. Sin embargo, sigue faltando una revisión de la literatura que pueda ordenar el conocimiento existente y aglutinarlo en temas relevantes y que ofrezca una agenda de investigación futura.

Este trabajo emplea una revisión sistémica de la literatura y la minería de textos para analizar 73 artículos sobre el consumo de moda de lujo con el objetivo de (i) aclarar, racionalizar e interpretar críticamente la literatura sobre el consumo de moda de lujo, (ii) identificar el tema central, crear un marco integrador de constructos clave y (iii) presentar las lagunas de la investigación y sugerir una agenda de investigación para futuros estudios.

A partir de este análisis, se identifican y analizan ocho temas principales de investigación (el deseo de marca, la autenticidad, los mercados de lujo, las percepciones de valor, la experiencia de la venta al por menor de lujo, la comunicación de las marcas de lujo, el consumo responsable y la sostenibilidad, y la señalización de estatus). Sobre la base de estos temas y siguiendo el marco del TCM, esta revisión propone líneas para futuras investigaciones.

Esta investigación ofrece una revisión de la minería de textos sobre el consumo de moda de lujo para ayudar a los académicos y gestores a seguir desarrollando este campo, ya que no existe una revisión exhaustiva sobre el tema que explore los conceptos, teorías, constructos y métodos utilizados en estudios previos.

Revisión de la literatura

本文旨在回顾 “奢侈时尚消费”, 这是学术界和从业人员最近关注的一个话题。然而, 目前仍然未能将现有知识分类并为未来研究提供议程的文献回顾。

本文使用系统的文献综述和文本挖掘, 分析了73篇关于奢侈时尚消费的文章。此文目的是：（1）批判性地解释关于奢侈时尚消费的文献; （2）确定中心主题, 建立综合框架; （3）提出研究缺憾, 为未来的研究提出议程。

从这个分析中, 我们发现并分析了八个主要的研究主题（品牌欲望、真实性、奢侈品市场、价值认知、奢侈品零售体验、奢侈品品牌传播、负责任的消费和可持续性、以及地位信号）。基于这些主题并遵循TCM框架, 本评论提出了未来研究的方向。

目前还没有关于该主题的全面文献回顾, 以探索以前研究中使用的概念、理论、构造和方法。本研究对奢侈时尚消费的文本挖掘进行了回顾, 以帮助学者和管理者进一步发展该领域。

文献评论

Following Hovland’s persuasion theory, this paper aims to develop a conceptual model and analyzes characteristics of online political deliberation behavior from three aspects (i.e. information, situation and manager). Based on the whole interactive process of online political deliberation, this paper aims to reveal the key points that affect the response effect of the government from the persuasive perspective of online political consultation.

Based on more than 40,000 netizens’ posts and government responses from 2011 to the first half of 2019 of the Chinese political platform, this paper used the text analysis and machine learning methods to extract measurement variables of online political deliberation characteristics and the econometrics analysis method to conduct empirical research.

The results showed that the textual information, political environment and identity of the political objects affect the effectiveness of government response. Furthermore, for different position categories of political officials, the length of political texts, topic categories and emotional tendencies have different effects on the response effectiveness. Additionally, the effect of political time on the effectiveness of response differs.

The findings will help ascertain the characteristics of online political deliberation behavior that affect how effective government response is and provide a theoretical basis for why the public should express their political concerns.

This study is motivated to provide a theoretically informed, data-driven assessment of the consequences associated with the participation of non-human bots in social accountability movements; specifically, the anti-inequality/anti-corporate #OccupyWallStreet conversation stream on Twitter.

A latent Dirichlet allocation (LDA) topic modeling approach as well as XGBoost machine learning algorithms are applied to a dataset of 9.2 million #OccupyWallStreet tweets in order to analyze not only how the speech patterns of bots differ from other participants but also how bot participation impacts the trajectory of the aggregate social accountability conversation stream. The authors consider two research questions: (1) do bots speak differently than non-bots and (2) does bot participation influence the conversation stream.

The results indicate that bots do speak differently than non-bots and that bots exert both weak form and strong form influence. Bots also steadily become more prevalent. At the same time, the results show that bots also learn from and adapt their speaking patterns to emphasize the topics that are important to non-bots and that non-bots continue to speak about their initial topics.

These findings help improve understanding of the consequences of bot participation within social media-based democratic dialogic processes. The analyses also raise important questions about the increasing importance of apparently nonhuman actors within different spheres of social life.

The current study is the first, to the authors’ knowledge, that uses a theoretically informed Big Data approach to simultaneously consider the micro details and aggregate consequences of bot participation within social media-based dialogic social accountability processes.

This study furthers one’s understanding of the motivations of the crowdfunding crowd by empirically examining critical factors that influence the crowd's decision to support a crowdfunding project.

Backer's comments from a sample of the top 100 most funded technology product projects on KickStarter were collected. A latent Dirichlet allocation (LDA) analysis strategy was adopted to investigate critical motivational factors. Three experts mapped those factors to the known theoretical constructs of social exchange theory (SET).

Although backers are motivated by value, they are also motivated by far less tangible social factors including trust and a feeling of psychological ownership. Findings suggest that the crowd is far more than a passive group of investors or customers and should be viewed as participatory stakeholders. This study serves as guidance for project owners hoping to motivate the crowd and for future investigators examining backer motivations in other types of crowdsourcing projects.

Online chatter in the form of user-generated comments is an excellent data source for researchers to mine for value and meaning.

Given strong feelings of psychological ownership, project owners should actively engage the crowd and solicit the crowd for advice and help in order to motivate them.

The study presents the first empirical exploration of backer motivations using LDA guided by theory and the knowledge of experts. A framework of latent motivational factors is proposed.