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Nurturing Online Communities: An Empirical Investigation
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2019 | Management Information Systems Quarterly | Citations: 43
Authors: Bapna, Sofia; Benner, Mary J.; Liangfei Qiu

Abstract: Online brand communities can be valuable to firms, but how do firms cultivate su ... Expand

Abstract: Online brand communities can be valuable to firms, but how do firms cultivate such communities? We find that engagement, that is, “likes” in response to firm posts, in the online brand community is associated with subsequent growth in the community. We theorize that when individuals engage with firms’ posts, the social media platform broadcasts such interactions to others, who are not necessarily part of the firm’s online brand community. Such social diffusion of information about the interaction and the related firm content provides individuals with new information about the firm, based on which they may decide to join the brand community of the firm. We find that firm posts that convey firm credibility through product and industry knowledge, convey organizational achievements through information about firm milestones, partnerships, or awards, seek opinions, and convey promotions or offers, are associated with engagement. Such posts have a significantly greater effect on engagement for early stage brand communities, that is, for those in their first year and a half, than for later stage brand communities. Collapse

Semantic filters: long short memory network

Topics: Facebook online community social media Google+ business model

Methods: regression analysis method experimental group longitudinal research survey robustness check
Predictive Power of Online and Offline Behavior Sequences: Evidence from a Micro-finance Context

2017 | International Conference on Information Systems | Citations: 0
Authors: Mehrotra, Rishabh; Bhattacharya, Prasanta; Tan, Tianhui; Phan, Tuan Q.

Abstract: Microfinance based institutions have emerged as a potential solution to the fina ... Expand

Abstract: Microfinance based institutions have emerged as a potential solution to the financial exclusion problem in developing economies around the world. A key challenge facing such micro-lending firms is assessing the credit risk of borrowers, owing to the lack of formal financial histories and collaterals. A number of micro-lending companies have, therefore, started leveraging social media and digital communication data from applicants to assess their ability and willingness to repay loans. In our study, we demonstrate a novel approach of leveraging online and offline behavior sequences, as captured from the borrowers’ browsing logs and mobility traces to accurately predict the borrowers’ creditworthiness. Our preliminary results show that using such sequence data, we can provide micro-lending firms with a cheap and reliable strategy for assessing credit risk of borrowers at the time of loan creation. We contend that such bigdata based strategies are critical to the sustainability of micro-lending institutions. Collapse

Semantic filters: long short memory network

Topics: mobile phone website mobile application Google+ application programming interface

Methods: deep learning machine learning experiment artificial neural network Keras

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Nurturing Online Communities: An Empirical Investigation

Predictive Power of Online and Offline Behavior Sequences: Evidence from a Micro-finance Context