Concentration and Platform Growth in the Sharing Economy: A Resource Partitioning Perspective

2022 | International Conference on Information Systems | Citations: 0

Authors: Lu, Angela; Mishra, Abhay; Du, Jintao

Abstract: The emergence and growth of sharing economy platforms have engendered significa ... Expand

Abstract: The emergence and growth of sharing economy platforms have engendered significant research interests recently. These platforms have witnessed increased entry of professional service providers, who have large amounts of excess assets and standardized business practices. Meanwhile, sharing economy platforms have witnessed an astounding growth, much of which is not attributed to professional service providers. This paper examines two seemingly contradictory phenomena -increased concentration among professional service providers and rapid growth of non-professionals on sharing economy platforms. Using the resource partitioning theory from the organizational literature, we explain how these two phenomena are inherently related. We further emphasize the role of income inequality that affects the resource partitioning process. The empirical analysis uses 1.4 million zip-code level Airbnb data, with Airbnb Plus policy as a natural experiment. Findings reveal that professional service provider concentration facilitates non-professional growth but reduces their performance, and the effects are significantly moderated by income inequality. Collapse

Topics: Airbnb sharing platform digital platform digital service accounting

Methods: natural experiment experimental group longitudinal research survey difference in differences

Theories: organizational theory

Good Robot, Bad Robot: Customer Responses to Norm-Compliant and Norm-Violating Service Robots

2022 | International Conference on Information Systems | Citations: 0

Authors: Stock-Homburg, Ruth; Heitlinger, Lea

Abstract: Service robots that interact with customers have penetrated various industries. ... Expand

Abstract: Service robots that interact with customers have penetrated various industries. With a basis in social identity theory, this study examines how customers respond to frontline service robots (FSRs) by investigating norm-compliant versus norm-violating behaviors compared with similar behaviors by human frontline employees (FLEs). In experimental studies, a black sheep effect occurs, such that customers downgrade norm-violating FLE behaviors more than similar behaviors by FSRs. They also upgrade norm-compliant behaviors by human FLEs more than those of FSRs. In service failures, this effect manifests as greater anger and frustration toward the FLE. We establish the underlying mechanism driving the black sheep effect: customers assign FSRs to an outgroup but categorize FLEs to their social ingroup, across different service encounters and independent of interaction frequency. Collapse

Topics: robotic customer satisfaction customer loyalty social presence technology adoption

Methods: experiment longitudinal research survey design statistical power analysis parametric test

Theories: social identity theory

A novel social media competitive analytics framework with sentiment benchmarks

2015 | Information & Management | Citations: 30

Authors: He, Wu; Wu, Harris; Yan, Gongjun; Akula, Vasudeva; Shen, Jiancheng

Abstract: In today's competitive business environment, there is a strong need for business ... Expand

Abstract: In today's competitive business environment, there is a strong need for businesses to collect, monitor, and analyze user-generated data on their own and on their competitors’ social media sites, such as Facebook, Twitter, and blogs. To achieve a competitive advantage, it is often necessary to listen to and understand what customers are saying about competitors’ products and services. Current social media analytics frameworks do not provide benchmarks that allow businesses to compare customer sentiment on social media to easily understand where businesses are doing well and where they need to improve. In this paper, we present a social media competitive analytics framework with sentiment benchmarks that can be used to glean industry-specific marketing intelligence. Based on the idea of the proposed framework, new social media competitive analytics with sentiment benchmarks can be developed to enhance marketing intelligence and to identify specific actionable areas in which businesses are leading and lagging to further improve their customers’ experience using customer opinions gleaned from social media. Guided by the proposed framework, an innovative business-driven social media competitive analytics tool named VOZIQ is developed. We use VOZIQ to analyze tweets associated with five large retail sector companies and to generate meaningful business insight reports. Collapse

Topics: social media marketing intelligence Twitter social media site competitive advantage

Methods: natural language processing sentiment analysis language model survey experiment

Organizational Learning from Extreme Performance Experience: The Impact of Success and Recovery Experience

2009 | Organization Science | Citations: 22

Authors: Kim, June-Young; Kim, Ji-Yub (Jay); Miner, Anne S.

Abstract: This paper argues that two different types of a firm's own extreme performance e ... Expand

Abstract: This paper argues that two different types of a firm's own extreme performance experiences--success and recovery--and their interactions can generate survival-enhancing learning. Although these types of experience often represent valuable sources of useful learning, several important learning challenges arise when a firm has extremely limited prior experience of the same type. Thus, we theorize that a certain threshold of a given type of experience is required before each type of experience becomes valuable, with low levels of experience harming the organization. Furthermore, we propose that success and recovery experience will interact to enhance each other's value. These conditions can help overcome learning challenges such as superstitious learning or learning from small samples. We investigate our ideas using a sample of the U.S. commercial banks founded between 1984 and 1998. Our results indicate that both success and recovery experience of a firm generate survival-enhancing learning, but only after a certain level of experience is reached. Furthermore, success and recovery experience enhance each other's learning value, consistent with the theories that emphasize the importance of richer and contrasting experience in providing useful knowledge. Our framework advances organizational learning theory by presenting a contingent model of the impact of success and recovery experience and their interaction. Collapse

Topics: organization learning anonymity financial performance absorptive capacity organizational memory

Methods: theory development hazard function likelihood-ratio test qualitative interview descriptive statistic

Theories: contingency theory organizational learning theory