Collaborative consumption sport hosting: value and consumption constraints

PurposeThe purpose of this paper is to find evidence of the benefits and constraints of collaborative consumption experiences by investigating the perceptions of hosts and visitors that attended professional regular season basketball and baseball games in the USA.Design/methodology/approachData were collected through four focus groups with 37 total participants and were analyzed through qualitative content analysis.FindingsThe results show that participants in a collaborative consumption experience perceive four types of value: social interaction and belonging, new fandom, travel bucket list experiences and local and sport knowledge. In addition, the results provide evidence of five consumption constraints related to collaborative consumption: expenses, average experiences, seat location, interpersonal disconnects and personal risk.Research limitations/implicationsThe selection of only two sites for the study limited the data triangulation that was possible. This study should be replicated across a wider range of teams and countries to confirm the main findings of the study.Practical implicationsPractitioners can use this initial study to better understand the benefits hosts and visitors perceive in the experience, and therefore the kind of experience design that would encourage increased purchases and loyalty.Originality/valueThis paper provides qualitative insights into the benefits and detriments of a collaborative consumption sport experience, based on participants' involvement in an innovative peer-to-peer platform.

Task-Level Aware Scheduling of Energy-Constrained Applications on Heterogeneous Multi-Core System

Minimizing the schedule length of parallel applications, which run on a heterogeneous multi-core system and are subject to energy consumption constraints, has recently attracted much attention. The key point of this problem is the strategy to pre-allocate the energy consumption of unscheduled tasks. Previous articles used the minimum value, average value or a power consumption weight value as the pre-allocation energy consumption of tasks. However, they all ignored the different levels of tasks. The tasks in different task levels have different impact on the overall schedule length when they are allocated the same energy consumption. Considering the task levels, we designed a novel task energy consumption pre-allocation strategy that is conducive to minimizing the scheduling time and developed a novel task schedule algorithm based on it. After getting the preliminary scheduling results, we also proposed a task execution frequency re-adjustment mechanism that can re-adjust the execution frequency of tasks, to further reduce the overall schedule length. We carried out a considerable number of experiments with practical parallel application models. The results of the experiments show that our method can reach better performance compared with the existing algorithms.

An Experimental Analysis of Model Compression Techniques for Object Detection

Recent research on Convolutional Neural Networks focuses on how to create models with a reduced number of parameters and a smaller storage size while keeping the model’s ability to perform its task, allowing the use of the best CNN for automating tasks in limited devices, with reduced processing power, memory, or energy consumption constraints. There are many different approaches in the literature: removing parameters, reduction of the floating-point precision, creating smaller models that mimic larger models, neural architecture search (NAS), etc. With all those possibilities, it is challenging to say which approach provides a better trade-off between model reduction and performance, due to the difference between the approaches, their respective models, the benchmark datasets, or variations in training details. Therefore, this article contributes to the literature by comparing three state-of-the-art model compression approaches to reduce a well-known convolutional approach for object detection, namely YOLOv3. Our experimental analysis shows that it is possible to create a reduced version of YOLOv3 with 90% fewer parameters and still outperform the original model by pruning parameters. We also create models that require only 0.43% of the original model’s inference effort.

Negotiated Agency in the Face of Consumption Constraints: A Study of Women Entrepreneurs in Subsistence Contexts

Millions of women entrepreneurs in subsistence contexts face consumption constraints while embedded in strongly patriarchal social institutions. In these contexts, the place for women is believed to be within the home as homemakers and not in the market as entrepreneurs. Yet these women are able to overcome gender-based institutional barriers and engage with the marketplace as entrepreneurs as a way to overcome consumption constraints. The authors conducted a longitudinal qualitative study of women entrepreneurs in low-income neighborhoods of Chennai, India, to understand (1) what motivates women to overcome the gender-based institutional barriers to entrepreneurial action and (2) how they can overcome the “iron cage” of institutional norms to initiate and sustain entrepreneurial action. The findings help the authors theorize the process of negotiated agency and elaborate on the microprocesses that underlie its enactment. Substantively, they demonstrate how consumption constraints in poverty trigger entrepreneurial agency among low-income women. The authors build on the findings to offer welfare-enhancing policy recommendations.