Every year I talk to many entrepreneurs about business transfers and acquisitions. Only rarely do they tell me that it was a cinch. Buying or selling a business is complex. For a start, a business should be shipshape from an organizational and administrative perspective, while several legal and fiscal matters also affect the transaction. Moreover, many parties are involved in a business transfer: the buyer and the seller, of course, but also the employees, the spouse and/or family of the entrepreneur, the customers and suppliers. Emotions and trust also play a central role in selling a firm. Many owner/managers find it hard to abandon their business. The fact that a transaction of fixed assets may also be involved is another complicating factor. Is it a good thing to include fixed assets in the sale, or in fact the reverse? Considering that most people find it quite hard to sell their own house, engaging an estate agent to do it for them, it is understandable that buying and selling a business is a transaction fraught with difficulties.
Recent advancements in mobile sensing and wearable technologies create new opportunities to improve our understanding of how people experience their environment. This understanding can inform urban design decisions. Currently, an important urban design issue is the adaptation of infrastructure to increasing cycle and e-bike use. Using data collected from 12 cyclists on a cycle highway between two municipalities in The Netherlands, we coupled location and wearable emotion data at a high spatiotemporal resolution to model and examine relationships between cyclists' emotional arousal (operationalized as skin conductance responses) and visual stimuli from the environment (operationalized as extent of visible land cover type). We specifically took a within-participants multilevel modeling approach to determine relationships between different types of viewable land cover area and emotional arousal, while controlling for speed, direction, distance to roads, and directional change. Surprisingly, our model suggests ride segments with views of larger natural, recreational, agricultural, and forested areas were more emotionally arousing for participants. Conversely, segments with views of larger developed areas were less arousing. The presented methodological framework, spatial-emotional analyses, and findings from multilevel modeling provide new opportunities for spatial, data-driven approaches to portable sensing and urban planning research. Furthermore, our findings have implications for design of infrastructure to optimize cycling experiences.
MULTIFILE
