A story to sell: The influence of storytelling on consumers' purchasing behavior

Storytelling can arouse consumers' emotions and affect purchasing behavior through desires and attitudes. While the marketing literature discusses storytelling, there is a lack of consensus because of the diverse conceptual and operational definitions used. To untangle the complexities and consolidate the fragmented knowledge about storytelling in marketing, this research examines how the marketing literature has addressed the influence of storytelling on consumers' purchasing behavior. The findings aid in understanding how the topic has been discussed from a marketing perspective in consumer behavior studies. Through a systematic literature review using a bibliometric analysis, we demonstrate that the marketing literature features four strands about the uses of storytelling to influence consumers' purchasing behavior. First, storytelling stimulates the consumer's identification with the brand. Second, storytelling allows consumers to experience emotional value. Third, storytelling supports engagement behaviors. Finally, storytelling has a downside in that it also propagates harmful speech. This study concludes with a roadmap for future research about how storytelling impacts consumers' purchasing behavior.     

Testing machine learning systems in real estate

Uncertainty about the inner workings of machine learning (ML) models holds back the application of ML-enabled systems in real estate markets. How do ML models arrive at their estimates? Given the lack of model transparency, how can practitioners guarantee that ML systems do not run afoul of the law? This article first advocates a dedicated software testing framework for applied ML systems, as commonly found in computer science. Second, it demonstrates how system testing can verify that applied ML models indeed perform as intended. Two system-testing procedures developed for ML image classifiers used in automated valuation models (AVMs) illustrate the approach.     

Initiating events and independent protection layers

Layer of protection analysis (LOPA) is a semiquantitative tool for analyzing and assessing process risk. The tool has grown greatly in popularity and usefulness since the publication of the first CCPS/AIChE guidebook on the subject, Layer of Protection Analysis, Simplified Process Risk Assessment (LOPA). This article is a summary of a new CCPS guideline book that includes the following:

• Initiating events (IEs).
• Independent protection layers (IPLs).
• Criteria for identifying the value of each prospective IE and prospective IPL.
• Example IE and IPL data.

This book is a necessary reference for those applying the LOPA methodology. © 2009 American Institute of Chemical Engineers Process Saf Prog, 2009