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This paper aims to introduce the concept of consumer impatience, empirically explore how it relates to time-based logistics performance (delivery speed and delivery timeliness) and discuss its impact on consumer satisfaction. This research argues that gaining insights related to delivery performance from a consumer’s perspective can help the development of more effective time-based logistics strategies for e-commerce home deliveries.

Hypotheses in this study are developed using attribution theory and tested with empirical data collected through an online behavioral consumer experiment. Middle-range theorizing is used to develop an understanding of the mechanisms that impact the relationship between time-based logistics performance and consumer satisfaction.

Findings indicate that consumer impatience with delivery speed and delivery timeliness play an essential role in the relationship between time-based delivery performance and consumer satisfaction. Issues with delivery timeliness are shown to have a more negative impact on consumer satisfaction than issues with delivery speed, while delivery communication is demonstrated to have a positive relationship with consumer satisfaction.

This empirical study adds to existing time-based competition literature by taking a consumer-centric perspective and bringing a largely overlooked but critical concept – consumer impatience – into the logistics and supply chain management setting. Middle-range theorizing allows for a conceptualized understanding of consumers’ delivery experiences that can help companies develop proactive actions in their time-based competition initiatives.

The purpose of this paper is to study the importance of image data in hotel selection-recommendation using different types of cognitive features and to explore whether there are reinforcing effects among these cognitive features.

This study represents user-generated images “cognitive” in a knowledge graph through multidimensional (shallow, middle and deep) analysis. This approach highlights the clustering of hotel destination imagery.

This study develops a novel hotel selection-recommendation model based on image sentiment and attribute representation within the construction of a knowledge graph. Furthermore, the experimental results show an enhanced effect between different types of cognitive features and hotel selection-recommendation.

This study enhances hotel recommendation accuracy and user satisfaction by incorporating cognitive and emotional image attributes into knowledge graphs using advanced machine learning and computer vision techniques.

This study advances the understanding of user-generated images’ impact on hotel selection, helping users make better decisions and enabling marketers to understand users’ preferences and trends.

This research is one of the first to propose a new method for exploring the cognitive dimensions of hotel image data. Furthermore, multi-dimensional cognitive features can effectively enhance the selection-recommendation process, and the authors have proposed a novel hotel selection-recommendation model.