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  • Singh, Param Vir  (6)
  • Economics  (6)
  • 1
    Online Resource
    Online Resource
    The Emergence of Opinion Leaders in a Networked Online Community: A Dyadic Model with Time Dynamics and a Heuristic for Fast Estimation
    Institute for Operations Research and the Management Sciences (INFORMS) ; 2013
    In:  Management Science Vol. 59, No. 8 ( 2013-08), p. 1783-1799
    Details
    In: Management Science, Institute for Operations Research and the Management Sciences (INFORMS), Vol. 59, No. 8 ( 2013-08), p. 1783-1799
    Abstract: We study the drivers of the emergence of opinion leaders in a networked community where users establish links to others, indicating their “trust” for the link receiver's opinion. This leads to the formation of a network, with high in-degree individuals being the opinion leaders. We use a dyad-level proportional hazard model with time-varying covariates to model the growth of this network. To estimate our model, we use Weighted Exogenous Sampling with Bayesian Inference, a methodology that we develop for fast estimation of dyadic models on large network data sets. We find that, in the Epinions network, both the widely studied “preferential attachment” effect based on the existing number of inlinks (i.e., a network-based property of a node) and the number and quality of reviews written (i.e., an intrinsic property of a node) are significant drivers of new incoming trust links to a reviewer (i.e., inlinks to a node). Interestingly, we find that time is an important moderator of these effects—intrinsic node characteristics are a stronger short-term driver of additional inlinks, whereas the preferential attachment effect has a smaller impact but it persists for a longer time. Our novel insights have important managerial implications for the design of online review communities. This paper was accepted by Sandra Slaughter, information systems.
    Type of Medium: Online Resource
    ISSN: 0025-1909 , 1526-5501
    URL: Article
    DOI: 10.1287/mnsc.1120.1685
    RVK:
    QP 300
    Language: English
    Publisher: Institute for Operations Research and the Management Sciences (INFORMS)
    Publication Date: 2013
    detail.hit.zdb_id: 206345-1
    detail.hit.zdb_id: 2023019-9
    SSG: 3,2
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  • 2
    Online Resource
    Online Resource
    A Structural Model of Employee Behavioral Dynamics in Enterprise Social Media
    Institute for Operations Research and the Management Sciences (INFORMS) ; 2015
    In:  Management Science Vol. 61, No. 12 ( 2015-12), p. 2825-2844
    Details
    In: Management Science, Institute for Operations Research and the Management Sciences (INFORMS), Vol. 61, No. 12 ( 2015-12), p. 2825-2844
    Abstract: We develop and estimate a dynamic structural framework to analyze the social-media content creation and consumption behavior of employees within an enterprise. We focus, in particular, on employees’ blogging behavior. The model incorporates two key features that are ubiquitous in blogging forums: users face (1) a trade-off between blog posting and blog reading; and (2) a trade-off between work-related and leisure-related content. We apply the model to a unique data set comprising the complete details of the blog posting and reading behavior of employees over a 15-month period at a Fortune 1000 IT services and consulting firm. Despite getting a higher utility from work-related blogging, employees nevertheless publish a significant number of leisure posts. This is partially because the creation of leisure posts has a significant positive spillover effect on the readership of work posts. Counterfactual experiments demonstrate that leisure-related blogging has positive spillovers for work-related blogging, and hence a policy of abolishing leisure-related content creation can inadvertently have adverse consequences on work-related content creation in an enterprise setting. When organizations restrict leisure blogging, the sharing of online work-related knowledge decreases and this in turn can also reduce employee performance rating. Overall, blogging within enterprises by employees during their work day can have positive long-term benefits for organizations. This paper was accepted by Lorin Hitt, information systems.
    Type of Medium: Online Resource
    ISSN: 0025-1909 , 1526-5501
    URL: Article
    DOI: 10.1287/mnsc.2014.2125
    RVK:
    QP 300
    Language: English
    Publisher: Institute for Operations Research and the Management Sciences (INFORMS)
    Publication Date: 2015
    detail.hit.zdb_id: 206345-1
    detail.hit.zdb_id: 2023019-9
    SSG: 3,2
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  • 3
    Online Resource
    Online Resource
    Algorithmic Transparency with Strategic Users
    Institute for Operations Research and the Management Sciences (INFORMS) ; 2023
    In:  Management Science Vol. 69, No. 4 ( 2023-04), p. 2297-2317
    Details
    In: Management Science, Institute for Operations Research and the Management Sciences (INFORMS), Vol. 69, No. 4 ( 2023-04), p. 2297-2317
    Abstract: Should firms that apply machine learning algorithms in their decision making make their algorithms transparent to the users they affect? Despite the growing calls for algorithmic transparency, most firms keep their algorithms opaque, citing potential gaming by users that may negatively affect the algorithm’s predictive power. In this paper, we develop an analytical model to compare firm and user surplus with and without algorithmic transparency in the presence of strategic users and present novel insights. We identify a broad set of conditions under which making the algorithm transparent actually benefits the firm. We show that, in some cases, even the predictive power of the algorithm can increase if the firm makes the algorithm transparent. By contrast, users may not always be better off under algorithmic transparency. These results hold even when the predictive power of the opaque algorithm comes largely from correlational features and the cost for users to improve them is minimal. We show that these insights are robust under several extensions of the main model. Overall, our results show that firms should not always view manipulation by users as bad. Rather, they should use algorithmic transparency as a lever to motivate users to invest in more desirable features. This paper was accepted by D. J. Wu, information systems. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2022.4475 .
    Type of Medium: Online Resource
    ISSN: 0025-1909 , 1526-5501
    URL: Article
    DOI: 10.1287/mnsc.2022.4475
    RVK:
    QP 300
    Language: English
    Publisher: Institute for Operations Research and the Management Sciences (INFORMS)
    Publication Date: 2023
    detail.hit.zdb_id: 206345-1
    detail.hit.zdb_id: 2023019-9
    SSG: 3,2
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  • 4
    Online Resource
    Online Resource
    Why Bitcoin Will Fail to Scale?
    Institute for Operations Research and the Management Sciences (INFORMS) ; 2022
    In:  Management Science Vol. 68, No. 10 ( 2022-10), p. 7323-7349
    Details
    In: Management Science, Institute for Operations Research and the Management Sciences (INFORMS), Vol. 68, No. 10 ( 2022-10), p. 7323-7349
    Abstract: Bitcoin falls dramatically short of the scale provided by banks for payments. Currently, its ledger grows by the addition of blocks of ∼2,000 transactions every 10 minutes. Intuitively, one would expect that increasing the block capacity would solve this scaling problem. However, we show that increasing the block capacity would be futile. We analyze strategic interactions of miners, who are heterogeneous in their power over block addition, and users, who are heterogeneous in the value of their transactions, using a game-theoretic model. We show that a capacity increase can facilitate large miners to tacitly collude—artificially reversing back the capacity via strategically adding partially filled blocks in order to extract economic rents. This strategic partial filling crowds out low-value payments. Collusion is sustained if the smallest colluding miner has a share of block addition power above a lower bound. We provide empirical evidence of such strategic partial filling of blocks by large miners of Bitcoin. We show that a protocol design intervention can breach the lower bound and eliminate collusion. However, this also makes the system less secure. On the one hand, collusion crowds out low-value payments; on the other hand, if collusion is suppressed, security threatens high-value payments. As a result, it is untenable to include a range of payments with vastly different outside options, willingness to bear security risk, and delay onto a single chain. Thus, we show economic limits to the scalability of Bitcoin. Under these economic limits, collusive rent extraction acts as an effective mechanism to invest in platform security and build responsiveness to demand shocks. These traits are otherwise hard to attain in a disintermediated setting owing to the high cost of consensus. This paper was accepted by Kartik Hosanagar, information systems.
    Type of Medium: Online Resource
    ISSN: 0025-1909 , 1526-5501
    URL: Article
    DOI: 10.1287/mnsc.2021.4271
    RVK:
    QP 300
    Language: English
    Publisher: Institute for Operations Research and the Management Sciences (INFORMS)
    Publication Date: 2022
    detail.hit.zdb_id: 206345-1
    detail.hit.zdb_id: 2023019-9
    SSG: 3,2
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  • 5
    Online Resource
    Online Resource
    “Un”Fair Machine Learning Algorithms
    Institute for Operations Research and the Management Sciences (INFORMS) ; 2022
    In:  Management Science Vol. 68, No. 6 ( 2022-06), p. 4173-4195
    Details
    In: Management Science, Institute for Operations Research and the Management Sciences (INFORMS), Vol. 68, No. 6 ( 2022-06), p. 4173-4195
    Abstract: Ensuring fairness in algorithmic decision making is a crucial policy issue. Current legislation ensures fairness by barring algorithm designers from using demographic information in their decision making. As a result, to be legally compliant, the algorithms need to ensure equal treatment. However, in many cases, ensuring equal treatment leads to disparate impact particularly when there are differences among groups based on demographic classes. In response, several “fair” machine learning (ML) algorithms that require impact parity (e.g., equal opportunity) at the cost of equal treatment have recently been proposed to adjust for the societal inequalities. Advocates of fair ML propose changing the law to allow the use of protected class-specific decision rules. We show that the proposed fair ML algorithms that require impact parity, while conceptually appealing, can make everyone worse off, including the very class they aim to protect. Compared with the current law, which requires treatment parity, the fair ML algorithms, which require impact parity, limit the benefits of a more accurate algorithm for a firm. As a result, profit maximizing firms could underinvest in learning, that is, improving the accuracy of their machine learning algorithms. We show that the investment in learning decreases when misclassification is costly, which is exactly the case when greater accuracy is otherwise desired. Our paper highlights the importance of considering strategic behavior of stake holders when developing and evaluating fair ML algorithms. Overall, our results indicate that fair ML algorithms that require impact parity, if turned into law, may not be able to deliver some of the anticipated benefits. This paper was accepted by Kartik Hosanagar, information systems.
    Type of Medium: Online Resource
    ISSN: 0025-1909 , 1526-5501
    URL: Article
    DOI: 10.1287/mnsc.2021.4065
    RVK:
    QP 300
    Language: English
    Publisher: Institute for Operations Research and the Management Sciences (INFORMS)
    Publication Date: 2022
    detail.hit.zdb_id: 206345-1
    detail.hit.zdb_id: 2023019-9
    SSG: 3,2
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  • 6
    Online Resource
    Online Resource
    What Makes a Good Image? Airbnb Demand Analytics Leveraging Interpretable Image Features
    Institute for Operations Research and the Management Sciences (INFORMS) ; 2022
    In:  Management Science Vol. 68, No. 8 ( 2022-08), p. 5644-5666
    Details
    In: Management Science, Institute for Operations Research and the Management Sciences (INFORMS), Vol. 68, No. 8 ( 2022-08), p. 5644-5666
    Abstract: We study how Airbnb property demand changed after the acquisition of verified images (taken by Airbnb’s photographers) and explore what makes a good image for an Airbnb property. Using deep learning and difference-in-difference analyses on an Airbnb panel data set spanning 7,423 properties over 16 months, we find that properties with verified images had 8.98% higher occupancy than properties without verified images (images taken by the host). To explore what constitutes a good image for an Airbnb property, we quantify 12 human-interpretable image attributes that pertain to three artistic aspects—composition, color, and the figure-ground relationship—and we find systematic differences between the verified and unverified images. We also predict the relationship between each of the 12 attributes and property demand, and we find that most of the correlations are significant and in the theorized direction. Our results provide actionable insights for both Airbnb photographers and amateur host photographers who wish to optimize their images. Our findings contribute to and bridge the literature on photography and marketing (e.g., staging), which often either ignores the demand side (photography) or does not systematically characterize the images (marketing). This paper was accepted by Juanjuan Zhang, marketing.
    Type of Medium: Online Resource
    ISSN: 0025-1909 , 1526-5501
    URL: Article
    DOI: 10.1287/mnsc.2021.4175
    RVK:
    QP 300
    Language: English
    Publisher: Institute for Operations Research and the Management Sciences (INFORMS)
    Publication Date: 2022
    detail.hit.zdb_id: 206345-1
    detail.hit.zdb_id: 2023019-9
    SSG: 3,2
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