Management & Organization Research Works

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    Fanbois and Fanbots: Tesla’s Entrepreneurial Narratives and Corporate Computational Propaganda on Social Media
    (MDPI, 2023-02-05) Kirsch, David A.; Chowdhury, Mohsen A.
    This paper reports the discovery of a series of computational social media accounts (Fanbots) on Twitter that may have played a critical role in sustaining the entrepreneurial narratives of Tesla, the electric-vehicle maker. From 2010 to 2020—a period of trial, error, and eventual success for Tesla—these computational agents generated pro-firm tweets (Corporate Computational Propaganda, CCP), accounting for more than 10% of the total Twitter activity that included the cashtag, $TSLA, and 23% of activity that included the hashtag, #TSLA. Though similar to programmed social media content in the political sphere, the activities of these accounts predate the existence of political computational propaganda associated with foreign support for, for instance, Brexit in the United Kingdom (2016) and Donald Trump in the United States (2016). The paper (a) characterizes the extent of Fanbot content in two large Tesla tweet corpora, (b) identifies possible motivations for the creation of these accounts in relation to the firm’s entrepreneurial narratives, and (c) explores possible mechanisms by which the Fanbots might have acted. Although we are unable to directly observe the source or stated purpose of these accounts, based upon the timing of Fanbot creation and other indirect indicators, we infer that these accounts and the social media activity they generated were intended to influence social perception of Tesla. The conclusion assesses the generalizability of a Fanbot-based strategy, highlighting contextual limitations, while also pointing to ways that firms may already be using CCP to manage social approval in emerging-industry contexts.
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    Fulfillment scheduling for buy-online-pickup-in-store orders
    (Wiley, 2022-04-09) Wu, Xueqi; Chen, Zhi-Long
    One of the most popular ways of shopping in an omnichannel retailing environment is buy-online-pickup-in-store (BOPS). Retailers often promise BOPS shoppers short in-store pickup ready times. We study fulfillment scheduling decisions of BOPS orders destined for a single store of a retailer. There are two fulfillment options for BOPS orders: they can be either processed at a fulfillment center (FC) and delivered to the store or processed at the store without needing delivery. There are two types of trucks available to deliver the BOPS orders fulfilled at the FC: prescheduled trucks that are already committed to replenishing store inventory and have some spare capacity that can be utilized, and additional trucks that can be hired from third-party logistics providers. There is a fixed cost for using each truck; the cost for using a prescheduled truck is lower than that for using an additional truck. If an order is fulfilled at the store, it incurs a processing cost and a processing time, whereas the processing cost and time are negligible if an order is fulfilled at the FC. The problem is to determine where to fulfill each order (FC vs. the store), how to assign the orders fulfilled at the FC to trucks for delivery, and how to schedule the orders fulfilled at the store for store processing, so as to minimize the total fulfillment cost, including the delivery cost from the FC to the store incurred by the orders processed at the FC, and the processing cost for fulfilling the rest of the orders at the store, subject to the constraint that each order is ready for pickup at the store by its promised pickup ready time. We consider various cases of the problem by clarifying their computational complexity, developing optimal algorithms and heuristics, and analyzing theoretical performance of the heuristics. We also conduct computational experiments to validate the effectiveness of the proposed heuristics in both static and dynamic settings and derive important insights about how the presence of prescheduled trucks and the presence of store fulfillment option impact the fulfillment cost and decisions.
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    Innovation Strategies for Digital Assets & Wealth Management
    (2021-12) Shorter, Charles; Taylor, James; Jones, Jessica; Sanford, Kevin; Kobloth, Nicholas; Dastidar, Protiti
    The University of Maryland ALP team serves as an independent research body to the Wealth ecosystem to explore Innovation Strategies for Digital Assets & Wealth Management. During the consult the team conducted a comprehensive case study to identify key data points for solution development which would support the incorporation of Cryptocurrencies into ‘traditional' financial advisory segments. The research is built in conjunction with industry leader Envestnet which specializes in the B2B FinTech, Wealth & Financial Advisory segments for independent advisors and high net worth individuals. The authors presents the essentials of the Wealth Management industry from the perspective of Registered Independent Advisor (RIA) firms that provide financial advice and planning for clients.