Adoption of Identity Theft Countermeasures and its Short- and Long-Term Impact on Firm Value

Abstract

Previous studies on IT investment using event studies investigated different aspects of IT. For example, Dos Santos et al. (1993) studied IT innovativeness andChatterjee et al. (2002) focused on different functionality aspects of IT (e.g., infrastructure and application). Apart from general IT investment, there were also a few studies specific to IT applications (e.g., ERP, e-business, and security). Nevertheless, the steps of the event study adopted in previous studies were similar. All previous studies first analyzed abnormal return based on subsamples followed by subsampling analysis. Most studies adopted a three-day event window around IT investment announcements. Both parametric and nonparametric tests (e.g., sign test and Corrado's rank test) were used to determine the level of significance of abnormal return in the estimation period. Furthermore, most of the sample data were from the United States. Meng and Lee (2007) was the only study that compared the reaction of IT investment in the United States with that in China. A summary of previous research on IT investment using event studies is shown in Table A1.

When Blockchain Creates Shareholder Value: Empirical Evidence from International Firm Announcements

“…We also complement the understanding of the general relationship between IT initiatives and financial firm performance (Bose and Leung 2019, Hendricks et al. 2007, Melville et al.…”

“…Hence, our sample is determined by a non-random self-selection process of firms that decided to engage in blockchain, which might bias our results. To account for potential endogeneity issues, we use both a PSM approach (e.g., Boyd et al 2019, Modi et al 2015) and a Heckman two-step model (e.g., Bose andLeung 2019, Hendricks et al 2015). While PSM allows us to control for self-selection based on observable firm characteristics, we implement the Heckman model to account for self-selection based on unobservables that might influence both the decision to use blockchain and the stock market reaction (Boyd et al 2019).…”

“…While short-term event studies are dominant in the operations management and information systems literature, few long-term event studies exist. Exemplary studies examine the shareholder value impact of identity theft countermeasure announcements (Bose and Leung 2019), product recalls (Liu et al 2017), or supply chain disruptions (e.g., Hendricks and Singhal 2005). To conduct a long-term event study, we follow prior studies and compute buy-and-hold abnormal returns (BHARs) based on one-to-one firm matches (Hendricks and Singhal 2005).…”

“…where R it is the observed return for stock i on day t, R mit is the market return of the country-specific benchmark stock market index m for stock i on day t, SMB it is the small-minus-big-size portfolio return for stock i on day t, HML it is the high-minus-lowbook-to-market portfolio return for stock i on day t, UMD it is the momentum factor, introduced by Carhart (1997), for stock i on day t, α i , β i , γ i , δ i , and θ i are linear regression coefficients, and ɛ it is the residual error. Alike the majority of previous event studies (e.g., Bose andLeung 2019, Modi et al 2015), we run an ordinary least squares (OLS) regression over a period of 200 trading days, with an offset of 10 trading days prior to the announcement date to ensure isolation of the event effect. Using the resulting estimators αi , βi , γi , δi , and θi , we calculate the abnormal return AR it as the difference between the observed return and the expected return:…”

“…2018). IT initiatives are generally associated with cost reductions and operational efficiency gains, generating revenue, and firm value (see Bose and Leung 2019, Melville et al. 2004).…”