Has Decreasing Innovation Hurt the Stock Price of Information Security Firms? A Time Series Analysis

Prior research has shown that information security breaches are beneficial to the stock price of information security firms, around the time that these security breaches are announced. We, however, show that the overall trend in the market value of information security firms has actually been stagnating, despite an increasing number of security threats that exploit vulnerabilities in information systems. We attribute this decrease in the stock price of information security firms, after controlling for overall market conditions, to insufficient innovation on the part of information security firms. We apply time series regression methods to analyze the relationship between R&D intensity and the stock price of information security firms. This empirical work provides a plausible explanation for the decrease in the stock price of information security firms, despite high demand for their products and services

Will the Information Security Industry Die? Applying Social Network Analysis to Sturdy Industry Convergence

In this paper, we first analyze the trends in mergers and acquisitions (M&As) activities among information security firms and other information technology (IT) firms in the US over the period 1996 to 2008. We then use social network analysis to investigate the characteristics and underlying dynamics of these M&As activities. Our results reveal an increase in cohesiveness of 200% in the network linking the information security firms and the IT firms considered in our analysis. This, in turn, implies a move towards industry convergence. In particular, we show that acquisitions of identity and access management (IAM) firms have become more central to M&As by IT firms in the US since 2004, reflecting an increasing trend among IT firms to integrate IAM technologies within their products

Human Error and Privacy Breaches in Healthcare Organizations: Causes and Management Strategies

We apply Reason’s GEMS typology to study privacy breach incidents in healthcare organizations. An interpretive analysis of transcripts of interviews with privacy officers of healthcare organizations in the U.S. Midwest helps discern the underlying causes of human error and develop a framework for error management. The study finds that organizational factors causing human error constitute a greater impediment to HIPAA Privacy Rule compliance than do human factors

Valuing the flexibility of investing in security process innovations

In this paper, we develop a decision model of a firm's optimal strategy for investment in security process innovations (SPIs) when confronted with a sequence of malicious attacks. The model incorporates real options as a methodology to capture the flexibility embedded in such investment decisions. SPIs, when seamlessly integrated with the organization's overall business dynamics, induce organizational learning and provide the flexibility of switching to more suitable technologies as the environment of malicious attacks changes. The theoretical contribution of this paper is a mathematical model of the invest-to-learn and switching options generated upon early investment in flexible SPIs. The practical significance of the paper is the application of a binomial lattice model to approximate the continuous-time model, resulting in an easy to use decision aid for managers.Information security Investment analysis Cost-benefit analysis Real options theory Dynamic programming Security process innovations

Toward a Contextual Theory of Turnover Intention in Online Crowdworking

Online crowdworking marketplaces represent an emergent labor market. Despite the ample recent literature on crowdworking marketplaces, we still lack a clear understanding of the antecedents to worker turnover behavior in such labor markets. This paper integrates the extant crowdworking literature with traditional theories of worker turnover intention, boundary spanning, and online communities to develop a Contextual Model of Turnover Intention in online Crowdworking (COMTIC). Conceptually, COMTIC complements the traditional organizational turnover perspective with a contextualized boundary spanning perspective and illustrates how the two theories work together to more accurately depict the crowdworker turnover intention. The model is tested using partial least squares structural equation modeling and is largely confirmed by the results. We discuss the theoretical implications of the proposed COMTIC framework and offer practical insights for existent and future crowdworking marketplaces

ACTUAL PRIVACY SELF-DISCLOSURE ON ONLINE SOCIAL NETWORK SITES: REFLECTIVE-IMPULSIVE MODEL

People are known to disclose their private information on social network sites (SNS) despite their concerns about threats of privacy invasions—a phenomenon dubbed as privacy paradox. Extant research on this phenomenon has primarily focused on the rational factors that affect the intentions of SNS users to disclose private information, rather than their actual disclosure behavior. We draw from the reflective-impulsive model that encompasses both rational factors (i.e., reflective system) and impulses (i.e., impulsive system) to explain users’ actual disclosure of private information in SNS. We report two main findings from a survey of SNS users. First, for the reflective system, users use privacy settings to cope with their privacy concerns before engaging in their disclosure behavior. With the inclusion of this coping response, this study extends the widely applied privacy calculus model to identify only rational factors explaining disclosure of private information. Second, disclosure impulses significantly influence users’ actual disclosure behavior in SNS, with social herding and attachment to an SNS stimulating their disclosure impulses in the SNS context. Theoretical and practical implications are discussed

Active Community Participation and Crowdworking Turnover: A Longitudinal Model and Empirical Test of Three Mechanisms

Crowdworkers, such as Mturk workers, face challenging work conditions, including low pay and unfair treatment. To overcome a lack of means to share information with other workers, they often self-organize in independent online communities, for example, TurkerNation. Although prior research has explored both the crowdwork and online community contexts, it has largely ignored crowdworkers’ dual-context roles. This research provides evidence for the dual-context phenomenon. We propose three theory-driven mechanisms―embeddedness, cross-influence, and moderated heuristics―that, together with the conventional model and the sequential-update mechanism, explained up to 72% of key behavioral outcomes in both contexts. Moreover, crowdworkers’ active participation in online communities had a persistent mitigating effect on their desires to quit working in the crowdworking environment. These findings add to a richer understanding of crowdworkers’ integrated and evolving psychology within the dual-context environment. From a managerial perspective, our findings suggest that crowdwork platforms can better retain their workers by facilitating―and actively engaging with―their discussions in an embedded online community

Impact of Prior Reviews on the Subsequent Review Process in Reputation Systems

Reputation systems have been recognized as successful online review communities and word-of-mouth channels. Our study draws upon the elaboration likelihood model to analyze the extent that the characteristics of reviewers and their early reviews reduce or worsen the bias of subsequent online reviews. Investigating the sources of this bias and ways to mitigate it is of considerable importance given the previously established significant impact of online reviews on consumers' purchasing decisions and on businesses' profitability. Based on a panel data set of 744 individual consumers collected from Yelp, we used the Markov chain Monte Carlo simulation method to develop and empirically test a system of simultaneous models of consumer review behavior. Our results reveal that male reviewers or those who lack experience, geographic mobility, or social connectedness are more prone to being influenced by prior reviews. We also found that longer and more frequent reviews can reduce online reviews' biases. This paper is among the first to examine the moderating effects of reviewer and review characteristics on the relationship between prior reviews and subsequent reviews. Practically, this study offers businesses effective customer relationship management strategies to improve their reputations and expand their clientele