Browsing by Author "Zheng, Zhiqiang (Eric)"
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Item Are New IT-Enabled Investment Opportunities Diminishing for Firms?Dos Santos, B. L.; Zheng, Zhiqiang (Eric); Mookerjee, Vijay S.; Chen, Hongyu; 90649574 (Mookerjee, VS)Today, few firms could survive for very long without their computer systems. IT has permeated every corner of firms. Firms have reached the current state in their use of IT because IT has provided myriad opportunities for firms to improve performance and, firms have availed themselves of these opportunities. Some have argued, however, that the opportunities for firms to improve their performance through new uses of IT have been declining. Are the opportunities to use IT to improve firm performance diminishing? We sought to answer this question. In this study, we develop a theory and explain the logic behind our empirical analysis; an analysis that employs a different type of event study. Using the volatility of firms' stock prices to news signaling a change in economic conditions, we compare the stock price behavior of firms in the IT industry to firms in the utility and transportation and freight industries. Our analysis of the IT industry as a whole indicates that the opportunities for firms to use IT to improve their performance are not diminishing. However, there are sectors within the IT industry that no longer provide value-enhancing opportunities for firms. We also find that IT products that provided opportunities for firms to create value at one point in time, later become necessities for staying in business. Our results support the key assumption in our work. © 2012 INFORMS.Item Essays on Online Review Manipulation and Sponsored Search Advertising(2018-08) Nie, Cheng; Sarkar, Sumit; Zheng, Zhiqiang (Eric)Online reviews have been found to be important drivers for consumer’s purchase decisions. Many firms resort to online review manipulations in the hope to improve their revenues. Manipulated online reviews have been acknowledged as a critical challenge in the e-commerce industry. Two essays in my dissertation are in the domain of online review manipulation in the hotel industry. In the first essay, I study how a new type of competition from the sharing economy (specifically Airbnb.com), impacts how hotels manipulate reviews. Surprisingly, I find that hotels demote their competitors less in the presence of higher levels of Airbnb competition. For self-promotion, Airbnb’s impact varies across hotel types – high-end hotels intensify self-promotion activities while low-end hotels make no change. In the second essay, I study the economic effectiveness of different review manipulation strategies. I find that high-end hotels indeed benefit from self-promotion and are hurt from getting demoted by other hotels. Moreover, the negative impact of getting demoted is stronger for low-end hotels than for high-end hotels. My third essay is in the sponsored search advertising domain. I draw on the theory of strategic groups to investigate how advertisements from firms within such groups impact consumers’ clickstream behavior when viewing sponsored search results. I find a strong positive externality for within-group competitors relative to across-group competitors. My findings contribute to the sponsored search literature by theorizing and empirically verifying consumers’ search behaviors from the strategic group perspective.Item Three Essays on Digital Business: Prescriptive Analytics for Novel Operational and Strategic Challenges(2019-04-16) Yang, Mingwen; Mookerjee, Vijay; Zheng, Zhiqiang (Eric)My dissertation provides prescriptive solutions and managerial implications for three novel operational and strategic challenges faced by firms or platforms in online business. The first problem arises from the need to manage online customer opinions. Online review platforms such as Expedia.com and Tripadvisor.com allow firms to respond to customer complaints. However firms need to carefully decide when to respond to negative reviews. To unravel the underlying mechanics of the problem, I develop a stochastic differential equation model (SDE) that describes the evolution of review ratings over time for a given response strategy employed by the firm. This model is validated using data on online customer reviews and firm responses from two of the world’s largest online travel agents. My approach is not just predictive, but more importantly one that can be used in a prescriptive sense, namely, to prescribe a response strategy that controls review ratings in a desired manner. I operationalize the theoretical response strategy in the stochastic model to an operational prescription that a firm can implement and show the applicability of the approach for different business objectives, such as Mean control, Mean-Variance control, and Service-Level control. Finally, I demonstrate the flexibility of the SDE model by extending it to encompass multiple state variables. The second problem extends the idea of online reputation management to competitive settings. I consider a market consisting of competing firms that participate in a platform such as Expedia or Yelp. Each firm exerts effort to improve its ratings, but in doing so, also influences the mean market rating. The sales of a firm are influenced by its own ratings and the mean rating of the firms in the market. An equilibrium analysis of the mean market rating reveals several insights. A more heterogeneous market (one where the parameters of the firms are very different) leads to a lower mean market rating and higher total profit of the firms in the market. The results can inform platforms to target certain firms to join: Growing the middle of the market (firms with average ratings) is the best option considering the goals of the platform (increase total profit of the firms) and the other stakeholders, namely, incumbents and consumers. For firms, I find that a firm’s profit can increase from an adverse event (such as, a reduction in sales margin, or an increase in the cost of control) depending on how other firms in the market are affected by the event. The findings are particularly significant for platform owners who could benefit from growing the platform in a strategic manner. The third problem addresses a novel Financial Technology (Fintech) phenomenon in social trading. In social trading, less experienced investors (followers) are allowed to copy the trades of experts (traders) in real-time after paying a following fee. This raises the transparencyrevenue tension: a dilemma between the need to release trading information transparently versus the risk of followers free riding on such information. I demonstrate the tension using data from a leading social trading platform operating in the Foreign Exchange market. An optimization model is developed to maximize information transparency while respecting a money-at-risk constraint. The performances of three information release policies are compared. Finally, I optimize platform revenue using an optimal release policy.