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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.

Hotels offer high-quality guest experiences to positively impact their emotions, satisfaction, perceived value, word-of-mouth (WOM) and electronic word-of-mouth (eWOM). This study aims to investigate the impacts of the quality perceived by hotel guests on their positive emotions, negative emotions, perceived value and satisfaction; verify the impacts of the price on perceived value and satisfaction; examine the impacts of satisfaction on WOM and eWOM; and test the moderating effect of hotel guests’ behavioural engagement on social networking sites (HGBE-SNS) on the relationship between satisfaction and eWOM.

This survey included 371 guests who assessed their experiences at three Brazilian hotels. Structural equation modelling tested the hypothetical model supported by the stimulus-organism-response (S-O-R) theory (Mehrabian and Russell, 1974).

The quality perceived by hotel guests (stimulus) positively impacts perceived value, positive emotions and satisfaction and negatively affects negative emotions (organism). Price (stimulus) negatively impacts perceived value but does not affect satisfaction. Perceived value positively impacts satisfaction. Satisfaction positively impacts WOM and eWOM (responses). The HGBE-SNS moderates the relationship between satisfaction and eWOM.

To the best of the authors’ knowledge, this study is the first that simultaneously demonstrates the relationships between perceived quality, price, perceived value, positive and negative emotions, satisfaction, WOM, eWOM and HGBE-SNS. Hotels must offer their guests high-quality services to positively impact’ perceived value, positive emotions, satisfaction and WOM. Low prices boost the perceived value but do not directly increase guest satisfaction. Satisfied hotel guests share their experiences via WOM, but high HGBE-SNS is crucial to boost eWOM.

Los hoteles ofrecen experiencias de alta calidad a sus huéspedes para influir positivamente en sus emociones, satisfacción, valor percibido, boca a boca (WOM) y boca a boca electrónico (eWOM). Este estudio tiene como objetivo a) investigar el impacto de la calidad percibida por los huéspedes del hotel en sus emociones positivas, emociones negativas, valor percibido y satisfacción; b) verificar el impacto del precio en el valor percibido y la satisfacción; c) examinar el impacto de la satisfacción en el WOM y eWOM; d) probar el efecto moderador del compromiso conductual de los huéspedes del hotel en las redes sociales (HGBE-SNS) en la relación entre satisfacción y eWOM.

En esta encuesta participaron 371 huéspedes que evaluaron sus experiencias en tres hoteles brasileños. La modelización de ecuaciones estructurales puso a prueba el modelo hipotético apoyado en la teoría estímulo-organismo-respuesta (S-O-R) (Mehrabian y Russell, 1974).

La calidad percibida por los clientes del hotel (estímulo) influye positivamente en el valor percibido, las emociones positivas y la satisfacción, y negativamente en las emociones negativas (organismo). El precio (estímulo) afecta negativamente al valor percibido, pero no a la satisfacción. El valor percibido afecta positivamente a la satisfacción. La satisfacción afecta positivamente al WOM y al eWOM (respuestas). El HGBE-SNS modera la relación entre satisfacción y eWOM.

Este estudio es el primero que demuestra simultáneamente las relaciones entre calidad percibida, precio, valor percibido, emociones positivas y negativas, satisfacción, WOM, eWOM y HGBE-SNS. Los hoteles deben ofrecer a sus clientes servicios de alta calidad para influir positivamente en el valor percibido, las emociones positivas, la satisfacción y el WOM. Los precios bajos aumentan el valor percibido pero no incrementan directamente la satisfacción de los huéspedes. Los huéspedes satisfechos comparten sus experiencias a través del WOM, pero un alto nivel de HGBE-SNS es crucial para impulsar el eWOM.

酒店提供高质量的宾客体验, 对宾客的情绪、满意度、感知价值、口碑（WOM）和电子口碑（eWOM）产生积极影响。本研究旨在 a) 调查酒店客人感知到的质量对其积极情绪、消极情绪、感知价值和满意度的影响; b) 验证价格对感知价值和满意度的影响; c) 检验满意度对 WOM 和电子口碑的影响; d) 检验酒店客人在社交网站上的行为参与（HGBE-SNS）对满意度和电子口碑之间关系的调节作用。

本次调查包括 371 位客人, 他们对自己在巴西三家酒店的入住体验进行了评估。结构方程模型检验了由刺激-组织-反应（S-O-R）理论（Mehrabian 和 Russell, 1974 年）支持的假设模型。

酒店客人感知到的质量（刺激因素）对感知价值、积极情绪和满意度有积极影响, 而对消极情绪（有机体）有消极影响。价格（刺激因素）对感知价值有负面影响, 但不影响满意度。感知价值对满意度有积极影响。满意度对 WOM 和 eWOM（反应）产生积极影响。HGBE-SNS 可调节满意度与网络口碑之间的关系。

本研究首次同时展示了感知质量、价格、感知价值、积极和消极情绪、满意度、WOM、eWOM 和 HGBE-SNS 之间的关系。酒店必须为客人提供高质量的服务, 才能对 “感知价值"、"积极情绪"、"满意度 “和 “WOM “产生积极影响。低价会提升感知价值, 但不会直接提高客人满意度。满意的酒店客人会通过 WOM 分享他们的体验, 但高 HGBE-SNS 对促进 eWOM 至关重要。

The study aims to identify the status quo of artificial intelligence in entrepreneurship education with a view to identifying potential research gaps, especially in the adoption of certain intelligent technologies and pedagogical designs applied in this domain.

A scoping review was conducted using six inclusive and exclusive criteria agreed upon by the author team. The collected studies, which focused on the adoption of AI in entrepreneurship education, were analysed by the team with regards to various aspects including the definition of intelligent technology, research question, educational purpose, research method, sample size, research quality and publication. The results of this analysis were presented in tables and figures.

Educators introduced big data and algorithms of machine learning in entrepreneurship education. Big data analytics use multimodal data to improve the effectiveness of entrepreneurship education and spot entrepreneurial opportunities. Entrepreneurial analytics analysis entrepreneurial projects with low costs and high effectiveness. Machine learning releases educators’ burdens and improves the accuracy of the assessment. However, AI in entrepreneurship education needs more sophisticated pedagogical designs in diagnosis, prediction, intervention, prevention and recommendation, combined with specific entrepreneurial learning content and entrepreneurial procedure, obeying entrepreneurial pedagogy.

This study holds significant implications as it can shift the focus of entrepreneurs and educators towards the educational potential of artificial intelligence, prompting them to consider the ways in which it can be used effectively. By providing valuable insights, the study can stimulate further research and exploration, potentially opening up new avenues for the application of artificial intelligence in entrepreneurship education.