Taylor (2012)
Contents
Source Details
| Taylor (2012) | |
| Title: | Evaluating digital piracy intentions on behaviors |
| Author(s): | Taylor, S. A. |
| Year: | 2012 |
| Citation: | Taylor, S. A. (2012). Evaluating digital piracy intentions on behaviors. Journal of Services Marketing, 26(7), 472-483. |
| Link(s): | Definitive |
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| About the Data | |
| Data Description: | The sample represents a cross section of students from across all major campus disciplines of a Midwestern university in the USA. A total of 321 respondents accepted the invitation to participate in the study. Respondents visited an on-campus computer laboratory where they completed a survey delivered through an interactive custom computer program developed specifically for the study. At a second stage, a group of 267 university students participated in an enhanced version of the survey. |
| Data Type: | Primary data |
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| Cross Country Study?: | No |
| Comparative Study?: | No |
| Literature review?: | No |
| Government or policy study?: | No |
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Abstract
Purpose – The purpose of this paper is to assess how well digital piracy self-report intentions predict actual digital piracy behaviors in service marketing research.
Design/methodology/approach – Study 1 collects 321 surveys to investigate potential measurement issues related to digital piracy intention formation. Study 2 replicates Study 1 based on a separate sample of 267 respondents, and additionally links digital piracy intentions to directly observed digital piracy behaviors across a peer-to-peer network.
Findings – The results first validate a strong predictive relationship between self-report intentions and observed digital piracy behaviors (R2 ¼ 0:36). Second, common method bias and measurement error do not appear to threaten the veracity of reported results. Third, a social psychological model of how digital piracy behaviors emerge is validated based upon the folk theory of the mind. Finally, a two-dimensional conceptualization of frequency of past behaviors is identified based upon exploratory factor analysis using structural equation modeling.
Research limitations/implications – The research reported here relies on experimental methods of measuring peer-to-peer network activity. Future research might consider the motivational and attitudinal antecedents to digital piracy intention formation.
Practical implications – The results afford service marketers assurance that self-report measures of digital piracy behavioral intentions can serve as predictive measures of future behaviors. This helps make the collection of data in this context both achievable and practical. Also, a methodological framework is identified to strengthen measurement models associated with this type of research.
Originality/value – The research provides a first effort to empirically relate behavioral intention data to unobtrusively observed digital piracy behaviors across peer-to-peer networks.
Main Results of the Study
This article aims at a twofold analysis of primary data in order to assess how well digital piracy self-report intentions predict actual digital piracy behaviors in service marketing research. The analysis shows that:
- There is a strong predictive relationship between digital piracy intention formation and subsequent behaviors.
- The relative contributions of the desire to engage in digital piracy behaviors and ‘frequency’ reported by Taylor et al. (2009) are validated in two independent samples.
- Evidence of substantial undetected digital piracy was identified in this study suggesting that much of the activity occurs outside of internet-based peer-to-peer networks.
Policy Implications as Stated By Author
Reliance on self-report measures of gigital piracy behavioral intentions can serve as predictive measures of behaviors. For example, the recent anti-piracy advertising campaign identified by Sweeney (2010) could conduct pre- and post-advertising exposure studies with self-reports of behavioral intentions to assess their impact.
Coverage of Study
Datasets
| Sample size: | 321 |
| Level of aggregation: | University students |
| Period of material under study: | Not stated |
| Sample size: | 267 |
| Level of aggregation: | University students |
| Period of material under study: | Not stated |
