Applying the Randomized Response Technique in Business Ethics Research: The Misuse of Information Systems Resources in the Workplace

Amanda M. Y. Chu (), Mike K. P. So () and Ray S. W. Chung ()
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Amanda M. Y. Chu: Hang Seng Management College
Mike K. P. So: The Hong Kong University of Science and Technology
Ray S. W. Chung: The Hong Kong University of Science and Technology

Journal of Business Ethics, 2018, vol. 151, issue 1, No 14, 195-212

Abstract: Abstract Mitigating response distortion in answers to sensitive questions is an important issue for business ethics researchers. Sensitive questions may be asked in surveys related to business ethics, and respondents may intend to avoid exposing sensitive aspects of their character by answering such questions dishonestly, resulting in response distortion. Previous studies have provided evidence that a surveying procedure called the randomized response technique (RRT) is useful for mitigating such distortion. However, previous studies have mainly applied the RRT to individual dichotomous questions (e.g., yes/no questions) in face-to-face survey settings. In this study, we focus on behavioral research examining the relationships between latent variables, which are unobserved variables measured by multiple items on Likert or bipolar scales. To demonstrate how the RRT can be applied to obtain valid answers from respondents answering a self-administered online questionnaire with Likert and bipolar scales, we build a behavioral model to study the effect of punishment severity on employees’ attitudes toward misuse of information systems resources in the workplace, which in turn influence misuse behavior. The survey findings meet our expectations. The respondents are generally more willing to disclose sensitive data about their attitudes and actual behavior related to misuse when the RRT is implemented. The RRT’s implications for causal modeling and the advantages and challenges of its use in online environments are also discussed.

Keywords: Covariance-based; Sensitive questions; Data privacy; Unrelated question design; Unethical behavior (search for similar items in EconPapers)
Date: 2018
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DOI: 10.1007/s10551-016-3240-5

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