Authors: Aghasi, A., Rai, A., and Xia, Y.
Given the abundance of images related to operations that is being captured and stored, it behooves firms to innovate systems using image processing to improve operational performance that refers to any activity that can save labor cost. In this paper, we use deep learning techniques, combined with classic image/signal processing methods, to propose a pipeline to solve certain types of object counting and layer characterization problems in firm operations. Using data obtained by us through a collaborative effort with real manufacturers, we demonstrate that the proposed pipeline method is able to achieve higher than 93% accuracy in layer and log counting. Theoretically, our study conceives, constructs, and evaluates proof-of-concept of a novel pipeline method in characterizing and quantifying the number of defined items with images, which overcomes the limitations of methods based only on deep learning or signal processing. Practically, our proposed method can help firms significantly reduce labor costs and/or improve quality and inventory control by recording the number of products in real time, more accurately and with minimal upfront technological investment.

Authors: Lin, Y. K. and Rai, A.
Software is the foundation of the digital economy. The scope of software patent protection and the social and private value of software patenting are hence central to recent policy debates. The landmark 2014 U.S. Supreme Court ruling in Alice Corp v. CLS Bank had a profound impact on software patenting, drastically narrowing the scope of patent protection on software innovation. We theorize and provide empirical evidence that a narrower scope of software patent protection is associated with a greater degree of open-source activities and improved sales in software firms. We also evaluate how individual firm’s ex ante software patent stock may moderate the proposed relationships. These findings offer insights into the roles of patent scope for software innovation in the digital economy and have important implications for the software patent and digital innovation literatures. They also have compelling implications for patent policies on software and software-related inventions, such as artificial intelligence, blockchains, cloud computing, and other emerging digital innovations.

Authors: Jiang, Z., Rai, A., Hua, S., Cheng, N., and Hu, Y.
Digital platforms facilitate the flow of information and the execution of transactions. This study investigates the impact of signals from platform-provided online information regarding search and experience attributes of products on the prices of their offline transactions. We situate our theorizing and empirical work in the context of digital real estate platforms. Our results suggest that online information pertaining to properties’ experience attributes has a significant influence on the prices of offline property transactions. The amount of online information relating to experience attributes—specifically, length of textual property description and the number of photos—positively influences the sale price of a property. In contrast, the amount of online property information related to search attributes—specifically, facts and features—has no significant influence on the property’s sale price. In addition, online property information on experience attributes has a significant impact on the sale price of uncommon properties (those valued significantly above or below their neighborhood averages), whereas its impact on the price of common properties (those valued close to their neighborhood averages) is insignificant. The findings are robust to various model specifications and across property transactions in different years, seasons, and geographical regions. They are also neither subject to confounding effect of real estate agents’ service quality nor driven by unobserved property heterogeneities. The findings shed light on how signals from online property information are used by home buyers and sellers for different types of properties. The insights have implications for how real estate professionals can better utilize digital platforms to convey signals regarding properties and facilitate property transactions and for how the platforms can be designed to support the exchange of information that provides signals on the quality of offline goods that are highly risky and experiential.

Authors: Pye, J., Rai, A., and Dong, J.
Prior research has differentiated intrafirm information technology (IT) capabilities that reduce internal coordination costs and interfirm IT capabilities that reduce external transaction costs. However, the influence of developing these capabilities on business value has not been explored in the realm of institutional governance—the regulatory context that defines the rules of the game for firms. We suggest that the value of a firm’s investments in different types of IT capabilities development (ITCD) is evaluated by the financial market contingent on the firm’s regulatory context. Our study is situated in the U.S. electric utility industry undergoing a market restructuring process to understand the impacts of intrafirm and interfirm ITCD on market value conditional on a firm’s regulatory context characterized by the extent to which its business is located in states that allow consumer choice (i.e., deregulation), as well as the extent to which its business is located in states that deliberate regulations regarding price control, value chain configuration, and information control (i.e., regulatory uncertainty). We find that intrafirm ITCD for enhancing efficiency is rewarded in a firm’s market valuation under a high level of deregulation. We further find that under a high level of regulatory uncertainty, interfirm ITCD for fostering flexibility can hedge against regulatory uncertainty and increase firm value. A key contribution of our work is demonstrating external institutional governance can influence the market value that firms accrue from different types of ITCD, thereby elaborating the complementarity in theoretical explanations of IT capabilities and institutional governance.

Authors: Chen, L., Hsieh, Po-An JJ, and Rai, A.
Intelligent systems are transforming the nature of work as humans and machines collectively perform tasks in novel ways. While intelligent systems empower employees with algorithm-generated knowledge, they require employees to adapt how they work to enhance their job performance. We draw upon the coping-adaptation framework as the overarching theoretical lens to explain how employees’ perceptions of IntelSys knowledge as an empowering external coping resource affect the mechanisms through which they adapt to IntelSys-induced changes to their work, as well as how their internal coping resources regulate their adaptation. Our coping-adaptation explanation of intelligence augmentation integrates (i) the empowering role of external coping resources, specifically IntelSys knowledge, captured as intelligent system knowledge empowerment (ISK-Emp), (ii) the benefit-maximizing adaptation mechanism (through infusion use enhancement) and the disturbance-minimizing adaptation mechanism (through role conflict reduction) that channel the impact of ISK-Emp on job performance, and (iii) the regulating role of internal resources, specifically, employees’ work experience, in influencing the importance of the adaptation mechanisms for the employee. We conduct studies in three distinct settings in which different intelligent systems were implemented to support employees’ knowledge work. Our findings show that ISK-Emp increases job performance through each of the two adaptation mechanisms. The benefit-maximization mechanism (via enhanced infusion use) plays a more important role for novice employees than for experienced employees, whereas the disturbance-minimization mechanism (via reduced role conflict) has higher importance for experienced employees than for novice employees. Our work provides insights into the critical role of adaptation mechanisms in linking ISKEmp with performance outcomes and into the relative importance of the adaptation mechanisms through which job performance payoffs are realized by novice and experienced employees.

Authors: Lin, Y., Rai, A., and Yang, Y.
Subscription-based crowdfunding (SBC) is an emerging platform for creators of digital content (e.g., music, comics, stories, and videos) to build their person brands and garner support from fans. A unique feature that sets SBC apart from other crowdfunding models is the creator-centered freemium model. That is, creators can offer free content to attract fans, who can then subscribe to financially support a creator; in return, the backers gain access to the creator’s premium content and exclusive perks. In doing so, creators receive regular (e.g., monthly) subscription payments from their backers to sustain their ongoing creative activities. The SBC context engenders unique creator-centric dynamics and mechanisms that are ill-understood in crowdfunding literature. In this study, we investigate the effects of SBC creators’ two information control strategies: (1) earnings concealment and (2) private postings. From a brand management perspective, we theorize that earnings concealment improves brand authenticity, whereas private postings foster brand differentiation, co-creation, and attachment. These will lead to a positive impact on the financial (backer base) and nonfinancial (fan participation) outcomes of an SBC campaign. Furthermore, we propose a reinforcing dynamic between information control and SBC performance whereby better SBC outcomes will increase the creators’ tendencies to engage in information control. To test our hypotheses, we obtained panel data from a large SBC platform that contains monthly observations of 92,850 creators from August 2016 to December 2017. The results of our empirical analyses provide evidence for the benefits of the two information control strategies in SBC and demonstrate the reinforcing relationships between information control and SBC outcomes for creators. We discuss the theoretical and practical implications of our findings.

Authors: Sambhara, C., Rai, A., and Xu, X.
We investigate how public firms configure their enterprise systems (ES) portfolio when faced with information risk, which refers to the likelihood that corporate financial information is of poor quality. We focus on firms’ configuration of their ES portfolio by introducing a novel construct: ES portfolio balance, or the relative proportion of two categories of ES modules, operational and functional. We draw on the theory of information processing to hypothesize the impact of information risk on ES portfolio balance and how this impact is affected by internal controls. We construct a multi-source panel dataset of 697 firms and 1,993 firm-year observations from 2005–2008 and use econometric and multivariate procedures to test our hypotheses. We find that when faced with an increase in information risk, firms change their ES portfolio balance more toward operational modules. However, when such firms are also faced with materially weak internal controls, they change their ES portfolio balance more toward functional modules instead. These findings expand our understanding of how firms’ information processing needs drive the configuration of their ES portfolio and, more broadly, IT resources portfolio.

Authors: Dong, J.Q., Karhade, P., Rai, A., and Xu, X.
While prior research has established that information technology (IT) investment has a significant impact on firm performance, relatively few studies have provided insights into the antecedents of IT investment decisions. By integrating the behavioral theory of the firm and agency theory, we propose a behavioral agency theory to explain performance shortfalls and corporate governance, which monitors and controls managers’ tendency of overinvestment or underinvestment in IT, as key drivers that jointly determine IT investment. As such, IT investment facilitates a firm’s problemistic search that generates innovation in response to performance gaps. We further examine the role of innovation outputs as a mediating mechanism linking IT investment to firm performance. Our econometric analysis of a large-scale panel dataset provides empirical evidence corroborating our theory. Overall, this study contributes a behavioral agency theory to deepen our understanding about performance drivers and outcomes of IT investment decisions.

Authors: Cheng, Z., Rai, A., Tian, F., and Xu, X.
We use a social learning perspective to extend our understanding of information technology (IT) investment and return. Specifically, we investigate social learning in the context of interlocks between corporate boards, which allow firms to share knowledge and experiences with respect to their IT investments. Using a large dataset of firm-years from 2001 to 2008, we find (a) a positive relationship exists between a focal firm’s IT investment and that of its interlocked firms; (b) this positive relationship is amplified by the interlocked firms’ IT capability, but only if the focal firm has an active board, which devotes time to allow sufficient communication among directors; and (c) the component of the focal firm’s IT investment that is attributable to board interlock influence is positively related to the firm’s performance, but only if the firm has an active board. Collectively, these findings support our central thesis: social learning through board interlocks can play a significant role in influencing a firm’s IT investments and enhancing their payoff. That said, attaining such benefits requires boards to incorporate those firms with high IT management capability and to strengthen board activity so interlocked members can substantively share their knowledge and experiences with IT investments.

Authors: Rai, A.
Deep learning methods to develop artificial intelligence (AI) systems produce black-box models that achieve high levels of prediction accuracy but are inscrutable to human users. Explainable AI (XAI) techniques offer the potential to achieve both prediction accuracy and explainability objectives with AI applications by converting black-box models to glass-box models that can be interpreted. Understanding how to effectively use XAI in marketing opens up exciting research avenues relating to the role of different classes of XAI in redefining the tradeoff between prediction accuracy and explainability, creating trustworthy AI applications, achieving AI fairness, and modifying the privacy calculus of consumers. It also raises important questions related to the levels of explainability and transparency for different users and how explainability can create value for different stakeholders involved in the development and deployment of marketing AI applications.

Authors: Jia, N., Rai, A., and Xu, X.
We investigate how firms use IT implementation to mitigate an anomaly in capital markets: investors underreacting to new public information. The theory of information uncertainty (IU) suggests that the anomaly is amplified with IU; that is, with ambiguity in information about firm value. We theorize that a firm’s IT in general—and enterprise systems (ES) in particular—can mitigate IU, thus reducing the IU-induced underreaction anomaly. Based on a difference-in-differences analysis of a sample of 572 ES implementations, our main finding is that ES implementation does reduce IU-induced underreaction anomaly. This is achieved through a reduction in the firm’s fundamentals volatility and an improvement in information quality. We also find that firms with greater IT capability are better positioned to realize the anomaly-reducing benefits of ES implementation and that ES’s anomaly-reducing effect is most pronounced when high levels of both functional and operational ES modules are implemented. We obtain remarkably consistent results when using alternate empirical design, samples, and measures of news. Such IT impacts are economically highly consequential as they improve capital market efficiency.