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The luxury experience is a growing and crucial component of luxury marketing. Experiences inspire consumers to engage with luxury brands. Although several research studies have shed light on the origin, development, and prominence of luxury experiences among consumers, there is a scarcity of research that analyzes the current knowledge holistically. As a result, this study uses a systematic literature review technique to better understand the trends in the luxury experience and consumer behavior literature and suggests future research directions to further develop the subject area.

Using the theory-context-characteristics-methodology (TCCM) framework, this study examines 130 articles on the luxury experience and consumer behavior.

Most research on luxury experiences has focused on the luxury service experience in the context of hospitality and tourism. Future researchers should explore avenues for providing luxury experience to consumers in the luxury products industry. In addition, more research is needed into the influences of the recent COVID-19 outbreak and technological advancements on consumers' luxury experiences.

The study is unique as it (1) presents a state-of-the-art understanding of the luxury experience and consumer behavior literature by analyzing the applied theories, research contexts, study characteristics, and methods used in the past studies and (2) suggests future research opportunities to advance the field. The findings will also assist luxury brand managers in designing a consumer's exceptional luxury experience.

While different attempts have been made to use artificial intelligence (AI) to codify communicative behaviors and analyze startups’ video presentations in relation to crowdfunding projects, less is known about other forms of access to entrepreneurial finance, such as video pitches for candidacies into startup accelerators and incubators. This research seeks to demonstrate how AI can enable the startup selection process for both entrepreneurs and investors in terms of video pitch evaluation.

An AI startup (Speechannel) was used to predict the outcomes of startup video presentations by analyzing text, audio, and video data from 294 video pitches sent to a leading European startup accelerator (LUISS EnLabs). 7 investors were also interviewed in Silicon Valley to establish the differences between humans and machines.

This research proves that AI has profound implications with regards to the decision-making process related to fundraising and, in particular, the video pitches of startup accelerators and incubators. Successful entrepreneurs are confident (but not overconfident), engaging in terms of speaking quickly (but also clearly), and emotional (but not overemotional).

This study not only fills the existing research gap but also provides a practical guide on AI-driven video pitch evaluation for entrepreneurs and investors, reshaping the landscape of entrepreneurial finance thanks to AI. On the one hand, entrepreneurs could use this knowledge to modify their behaviors, enabling them to increase their likelihood of being financially backed. On the other hand, investors could use these insights to better rationalize their funding decisions, enabling them to select the most promising startups.

This paper makes a significant contribution by bridging the gap between theoretical research and the practical application of AI in entrepreneurial finance, marking a notable advancement in this field. At a theoretical level, it contributes to research on managerial decision-making processes – particularly those related to the analysis of video presentations in a fundraising context. At a practical level, it offers a model that we called the “AI-enabled video pitch evaluation”, which is used to extract features from the video pitches of startup accelerators and incubators and predict an entrepreneurial project’s success.

Sustainability issues are crucial in today’s competitive environment. The integration of technology plays a vital role in the attainment of sustainability objectives. The study aims to investigate the relationship between green intellectual capital (IC), green information systems (IS), green management initiatives (GMI) and green technology adoption in light of natural resource-orchestration theory (ROT). Moreover, digital technology adoption mediates between green IC, green IS, GMI and sustainable performance. Finally, digital transformation strategy is used as a moderator between green technology adoption and sustainable performance.

A sample of 484 managers from automobile manufacturing companies was used in this study to evaluate the proposed relationships using the Structural Equation Modeling (SEM) methodology.

Findings reveal that green IC, green IS and GMI significantly influence green technology adoption. Besides, green technology adoption plays a crucial role in improving sustainable performance. Moreover, green technology adoption significantly mediates between green IC, green IS, GMI and sustainable performance. Finally, a digital transformation strategy significantly strengthens the relationship between green technology adoption and sustainable performance.

The organizations need green technology adoption to address environmental concerns, respond to consumer demand, achieve cost savings and comply with government regulations. Besides, in decision-making, organizations must focus on green IC, green IS, GMI, green technology adoption and digital transformation strategy to boost sustainable performance.

The originality of this study lies in its use of the natural ROT as a framework to examine the impact of multiple green resources on green technology adoption, leading to sustainable performance. Digital transformation strategy is used as a moderator between green technology adoption and sustainable performance. This study provides a comprehensive and integrated perspective on the subject with empirical evidence and relevant insights, contributing to the advancement of the field.