sharing economy
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2022 ◽  
Vol 30 (2) ◽  
pp. 0-0

This study uses the critical incident technique to collect and analyze incidents of service failure and success involving a logistics sharing service in which the service providers are individuals. The authors also explore the key factors that affect customer satisfaction, along with the official and ideal recovery strategies. Data is based on interviews with 35 business users in Taiwan in 2017. A card sorting exercise is employed to classify the collected incidents and strategies into categories. The results show that the determinants of success and failure in logistics sharing services include drivers, platform operation, the matching system, and communication. Compensation is the most effective recovery strategy, whereas doing nothing is the least effective. Suggestions based on our results can help managers of the sharing economy to avoid or recover from failures and attain success.


2022 ◽  
Vol 31 (2) ◽  
pp. 1-30
Author(s):  
Fahimeh Ebrahimi ◽  
Miroslav Tushev ◽  
Anas Mahmoud

Modern application stores enable developers to classify their apps by choosing from a set of generic categories, or genres, such as health, games, and music. These categories are typically static—new categories do not necessarily emerge over time to reflect innovations in the mobile software landscape. With thousands of apps classified under each category, locating apps that match a specific consumer interest can be a challenging task. To overcome this challenge, in this article, we propose an automated approach for classifying mobile apps into more focused categories of functionally related application domains. Our aim is to enhance apps visibility and discoverability. Specifically, we employ word embeddings to generate numeric semantic representations of app descriptions. These representations are then classified to generate more cohesive categories of apps. Our empirical investigation is conducted using a dataset of 600 apps, sampled from the Education, Health&Fitness, and Medical categories of the Apple App Store. The results show that our classification algorithms achieve their best performance when app descriptions are vectorized using GloVe, a count-based model of word embeddings. Our findings are further validated using a dataset of Sharing Economy apps and the results are evaluated by 12 human subjects. The results show that GloVe combined with Support Vector Machines can produce app classifications that are aligned to a large extent with human-generated classifications.


2022 ◽  
Vol 30 (2) ◽  
pp. 1-16
Author(s):  
Shiu-Li Huang ◽  
Ya-Jung Lee

This study uses the critical incident technique to collect and analyze incidents of service failure and success involving a logistics sharing service in which the service providers are individuals. The authors also explore the key factors that affect customer satisfaction, along with the official and ideal recovery strategies. Data is based on interviews with 35 business users in Taiwan in 2017. A card sorting exercise is employed to classify the collected incidents and strategies into categories. The results show that the determinants of success and failure in logistics sharing services include drivers, platform operation, the matching system, and communication. Compensation is the most effective recovery strategy, whereas doing nothing is the least effective. Suggestions based on our results can help managers of the sharing economy to avoid or recover from failures and attain success.


2022 ◽  
Vol 102 ◽  
pp. 103151
Author(s):  
Leihan Zhang ◽  
Shengyu Xiong ◽  
Le Zhang ◽  
Lin Bai ◽  
Qiang Yan

2022 ◽  
Vol 139 ◽  
pp. 1317-1334
Author(s):  
Patcharapar Rojanakit ◽  
Rui Torres de Oliveira ◽  
Uwe Dulleck

Author(s):  
Wei Liu ◽  
Fangni Zhang ◽  
Xiaolei Wang ◽  
Chaoyi Shao ◽  
Hai Yang

This study examines the pricing strategy of a parking sharing platform that rents the daytime-usage rights of private parking spaces from parking owners and sells them to parking users. In an urban area with both shared parking and curbside parking, a choice equilibrium model is proposed to predict the number of shared parking users under any given pricing strategy of the platform. We analytically analyze how the pricing strategy of the platform (price charged on users and rent paid to parking owners or sharers) would affect the parking choice equilibrium and several system efficiency metrics. It is shown that the platform is profitable when some parking owners have a relatively small inconvenience cost from sharing their spaces, but its profit is always negative at minimum social cost. Numerical studies are conducted to illustrate the analytical results and provide further understanding.


Author(s):  
Mohammed Alyakoob ◽  
Mohammad S. Rahman

This paper examines the potential economic spillover effects of a home sharing platform—Airbnb—on the growth of a complimentary local service—restaurants. By circumventing traditional land-use regulations and providing access to underutilized inventory, Airbnb attracts visitors to outlets that are not traditional tourist destinations. Although visitors generally bring significant spending power, it is unclear whether visitors use Airbnb only primarily for lodging and thus do not contribute to the adjacent economy. To evaluate this, we focus on the impact of Airbnb on restaurant employment growth across locales in New York City (NYC). Specifically, we focus on areas in NYC that did not attract a significant tourist volume prior to the emergence of a home-sharing service. Our results indicate a salient and economically significant positive spillover effect on restaurant job growth in an average NYC locality. A one-percentage-point increase in the intensity of Airbnb activity (Airbnb reviews per household) leads to approximately 1.7% restaurant employment growth. Since home-sharing visitors are lodging in areas that are not accustomed to tourists, we also investigate the demographic and market-structure-related heterogeneity of our results. Notably, restaurants in areas with a relatively high number of White residents disproportionately benefit from the economic spillover of Airbnb activity, whereas the impact in majority-Black areas is not statistically significant. Thus, policy makers must consider the heterogeneity in the potential economic benefits as they look to regulate home-sharing activities.


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