Knowledge that Transforms

Why do users engage in digital piracy? The theoretical explanations are particularistic and the empirical findings are fragmented and divergent. Managers and academics have thus had little guidance on how to explain and combat digital piracy. To help fill this gap, the present paper provides a meta-analysis that synthesizes past research and identifies the key drivers of users’ engagement in digital piracy. The findings identify new measures and revise existing strategies to confront the global threat of digital piracy. Effective anti-piracy measures focus on breaking habits, reducing users’ control, suppressing justifications, and changing attitudes. Breaking habits requires technical control strategies such as impeding access to pirate sites, and should be accompanied by legislative demands. Impairing the attractiveness of pirate websites while providing high quality content on legitimate channels weakens illegal users’ perceived control. Enhanced features and functionality of legal products and websites that are easy accessible strengthens legal users’ perceived control. While many existing anti-piracy campaigns foster positive appeals (e.g., supporting the industry), describing the risks and loss of control seem more promising. As for targeting, psychographics provide a better portrait of pirating users than demographics—they appreciate sharing, are innovative, less risk averse, less susceptible to influence, and of lower integrity. Finally, making more legal copies available to users is discouraged.

Payoffs from information technology (IT) continue to generate interest and debate both among academicians and practitioners. The extant literature cites inadequate sample size, lack of process orientation, and analysis methods among the reasons some studies have shown mixed results in establishing a relationship between IT investment and firm performance. In this paper we examine the structural variables that affect IT payoff through a meta analysis of 66 firm-level empirical studies between 1990 and 2000. Employing logistic regression and discriminant analyses, we present statistical evidence of the characteristics that discriminate between IT payoff studies that observed a positive effect and those that did not. In addition, we conduct ordinary least squares (OLS) regression on a continuous measure of IT payoff to examine the influence of structural variables on the result of IT payoff studies. The results indicate that the sample size, data source (firm-level or secondary), and industry in which the study is conducted influence the likelihood of the study finding greater improvements on firm performance. The choice of the dependent variable(s) also appears to influence the outcome (although we did not find support for process-oriented measurement), the type of statistical analysis conducted, and whether the study adopted a cross-sectional or longitudinal design. Finally, we present implications of the findings and recommendations for future research.