Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
skip to main content
research-article

When does social desirability become a problem? Detection and reduction of social desirability bias in information systems research

Published: 01 November 2021 Publication History

Abstract

Social desirability (SD) bias occurs in self-report surveys when subjects give socially desirable responses by over- or underreporting their behavior. Despite knowledge of SD as a potential threat to the validity of information systems (IS) research, little has been done to systematically assess its extent. Furthermore, we are uncertain of how to recover reliable estimates of the relationships between research variables contaminated by SD bias. We sought in this study to assess the extent of SD bias in causal inferences when independent and/or dependent variables are contaminated. We also evaluated whether an SD scale in conjunction with partial correlation could effectively and efficiently correct SD bias when it is found. To achieve these purposes, we designed a survey study and collected data from Amazon's Mechanical Turk in the context of mobile loafing, which refers to employees’ personal use of the mobile Internet during business hours. Using various detection methods, we found that SD bias existed in the context of mobile loafing. From the results of the variance reduction rate and a covariate technique, we found that SD bias becomes problematic when both the independent and dependent variables are susceptible to SD bias. Overall, our study contributes significantly to the IS literature by revealing the extent of SD bias and the magnitude of the possible correction for it in IS research.

References

[1]
S.A. McIntire, L.A. Miller, Foundations of Psychological Testing, McGraw-Hill, New York, NY, 2000.
[2]
H.J. Arnold, D.C. Feldman, M. Purbhoo, The role of social-desirability response bias in turnover research, Acad. Manag. J. 28 (1985) 955–966.
[3]
C.M. Hart, T.D. Ritchie, E.G. Hepper, J.E. Gebauer, The balanced inventory of desirable responding short form, Sage Open 5 (2015) 1–9.
[4]
D.L. Paulhus, Measurement and control of response bias, (Eds.) in: J. Robinson, P. Shaver, L. Wrightsman (Eds.), Measures of Personality and Social Psychological Attitudes, Academic, San Diego, CA, 1991, pp. 17–59.
[5]
M. Gergely, V.S. Rao, Social desirability bias in software piracy research: evidence from pilot studies, in: Proceedings of the International Conference on Innovations in Information Technology, IEEE, Al Ain, 2016, pp. 1–4.
[6]
S.S. Kwan, M.K. So, K.Y. Tam, Applying the randomized response technique to elicit truthful responses to sensitive questions in IS research: the case of software piracy behavior, Inf. Syst. Res. 21 (2010) 941–959.
[7]
J.-B.E.M. Steenkamp, M.G. De Jong, H. Baumgartner, Socially desirable response tendencies in survey research, J. Mark. Res. 47 (2010) 199–214.
[8]
R.Y.K. Chan, J.W.M. Lai, Does ethical ideology affect software piracy attitude and behaviour? An empirical investigation of computer users in China, Eur. J. Inf. Syst. 20 (2011) 659–673.
[9]
T.K.H. Chan, C.M.K. Cheung, R.Y.M. Wong, Cyberbullying on social networking sites: the crime opportunity and affordance perspectives, J. Manag. Inf. Syst. 36 (2019) 574–609.
[10]
M. Gergely, V.S. Rao, Social desirability bias in software piracy research, in: Proceedings of the European Conference on Information Systems, Tel Aviv, Israel, 2014, pp. 1–11.
[11]
D.-.H. Kwak, P. Holtkamp, S.S. Kim, Measuring and controlling social desirability bias: applications in information systems research, J. Assoc. Inf. Syst. 20 (2019) 317–345.
[12]
M. Sojer, O. Alexy, S. Kleinknecht, J. Henkel, Understanding the drivers of unethical programming behavior: the inappropriate reuse of internet-accessible code, J. Manag. Inf. Syst. 31 (2014) 287–325.
[13]
A.A. Soror, B.I. Hammer, Z.R. Steelman, F.D. Davis, M.M. Limayem, Good habits gone bad: explaining negative consequences associated with the use of mobile phones from a dual-systems perspective, Inf. Syst. J. 25 (2015) 403–427.
[14]
O. Turel, A. Serenko, The benefits and dangers of enjoyment with social networking websites, Eur. J. Inf. Syst. 21 (2012) 512–528.
[15]
O. Turel, A. Serenko, P. Giles, Integrating technology addiction and use: an empirical investigation of online auction users, MIS Q., 35, 2011, pp. 1043–1061.
[16]
A. Vance, P.B. Lowry, D.L. Eggett, Increasing accountability through the user interface design artifacts: a new approach to addressing the problem of access-policy violations, MIS Q. 39 (2015) 345–366.
[17]
V. Venkatesh, T. Sykes, F.K.Y. Chan, J.Y.L. Thong, P.J.H. Hu, Children's internet addiction, family-to-work conflict, and job outcomes: a study of parent-child dyads, MIS Q. 43 (2019) 903–927.
[18]
Y. Wang, N. Haggerty, Individual virtual competence and its influence on work outcomes, J. Manag. Inf. Syst. 27 (2011) 299–334.
[19]
R.J. Fisher, Social desirability bias and the validity of indirect questioning, J. Consum. Res. 20 (1993) 303–315.
[20]
M.G. De Jong, R. Pieters, J.-.P. Fox, Reducing social desirability bias through item randomized response: an application to measure underreported desires, J. Mark. Res. 47 (2010) 14–27.
[21]
R.P. Bagozzi, Measurement and meaning in information systems and organizational research: methodological and philosophical foundations, MIS Q., 35, 2011, pp. 261–292.
[22]
L. Lazuras, V. Barkoukis, A. Rodafinos, H. Tzorbatzoudis, Predictors of doping intentions in elite-level athletes: a social cognition approach, J. Sport Exerc. Psychol. 32 (2010) 694–710.
[23]
M.M. Linehan, S.L. Nielsen, Social desirability: its relevance to the measurement of hopelessness and suicidal behavior, J. Consult. Clin. Psychol. 57 (1983) 141–143.
[24]
D.L. Paulhus, Control of social desirability in personality inventories: principal-factor deletion, J. Res. Pers. 15 (1981) 383–388.
[25]
S.V. Paunonen, E.P. LeBel, Socially desirable responding and its elusive effects on the validity of personality assessments, J. Pers. Soc. Psychol. 103 (2012) 158–175.
[26]
E. Fernandez, V. Kiageri, D. Guharajan, A. Day, Anger parameters in parolees undergoing psychoeducation: temporal stability, social desirability bias, and comparison with non-offenders, Crim. Behav. Ment. Health 28 (2017) 174–186.
[27]
E. Fernandez, Y. Woldgabreal, D. Guharajan, A. Day, V. Kiageri, N. Ramtahal, Social desirability bias against admitting anger: bias in the test-taker or bias in the test?, J. Pers. Assess. 101 (2019) 644–652.
[28]
G.-.W. Bock, S.C. Park, Y. Zhang, Why employees do non-work-related computing in the workplace, J. Comput. Inf. Syst. 50 (2010) 150–163.
[29]
L. Khansa, J. Kuem, M. Siponen, S.S. Kim, To cyberloaf or not to cyberloaf: the impact of the announcement of formal organizational controls, J. Manag. Inf. Syst. 34 (2017) 141–176.
[30]
V.K.G. Lim, The IT way of loafing on the job: cyberloafing, neutralizing and organizational justice, J. Organ. Behav. 23 (2002) 675–694.
[31]
V.K.G. Lim, T.S.H. Teo, Prevalence, perceived seriousness, justification and regulation of cyberloafing in Singapore: an exploratory study, Inf. Manag. 42 (2005) 1081–1093.
[32]
B.S. Connelly, L. Chang, A meta-analytic multitrait multirater separation of substance and style in social desirability scales, J. Pers. 84 (2016) 319–334.
[33]
R.R. McCrae, P.T. Costa, Social desirability scales: more substance than style, J. Consult. Clin. Psychol. 51 (1983) 882–888.
[34]
E. Perinelli, P. Gremigni, Use of social desirability scales in clinical psychology: a systematic review, J. Clin. Psychol. 72 (2016) 534–551.
[35]
D.L. Paulhus, Socially desirable responding: the evolution of a construct, in: H. Brown, D. Jackson, D. Wiley (Eds.), The Role of Constructs in Psychological and Educational Measurement, Erlbaum, Hillsdale, NJ, 2002, pp. 67–68.
[36]
A.L. Edwards, The Social Desirability Variable in Personality Assessment and Research, Dryden Press, New York, NY, 1957.
[37]
J.S. Wiggins, Interrelationships among MMPI measures of dissimulation under standard and social desirability instruction, J. Consult. Psychol. 23 (1959) 419–427.
[38]
D.P. Crowne, D. Marlowe, A new scale of social desirability independent of psychopathology, J. Consult. Psychol. 24 (1960) 349–354.
[39]
D.L. Paulhus, Two-component models of socially desirable responding, J. Pers. Soc. Psychol. 46 (1984) 598–609.
[40]
D.L. Paulhus, Assessing Self-Deception and Impression Management in self-reports: The Balanced Inventory of Desirable Responding, Vancouver, Canada, 1988.
[41]
D.L. Paulhus, O.P. John, Egoistic and moralistic biases in self-perception: the interplay of self-deceptive styles with basic traits and motives, J. Pers. 66 (1998) 1025–1060.
[42]
W.M. Reynolds, Development of reliable and valid short forms of the Marlowe-Crowne social desirability scale, J. Clin. Psychol. 38 (1982) 119–125.
[43]
R. Ballard, M.D. Crino, S. Rubenfeld, Social desirability response bias and the Marlowe-Crowne social desirability scale, Psychol. Rep. 63 (1988) 227–237.
[44]
C.E. Lambert, S.A. Arbuckle, R.R. Holden, The Marlowe–Crowne social desirability scale outperforms the BIDR impression management scale for identifying fakers, J. Res. Pers. 61 (2016) 80–86.
[45]
D.H. Robertson, R.W. Joselyn, Projective techniques in research, J. Advert. Res. 14 (1974) 27–31.
[46]
C. Posey, B. Bennett, T. Roberts, P.B. Lowry, When computer monitoring backfires: invasion of privacy and organizational injustice as precursors to computer abuse, J. Inf. Syst. Secur. 7 (2011) 24–47.
[47]
A.P. Snow, M. Keil, L. Wallace, The effects of optimistic and pessimistic biasing on software project status reporting, Inf. Manag. 44 (2007) 130–141.
[48]
B. Jann, J. Jerke, I. Krumpal, Asking sensitive questions using the crosswise model: an experimental survey measuring plagiarism, Public Opin. Q. 76 (2012) 32–49.
[49]
G.J.L.M. Lensvelt-Mulders, J.J. Hox, P.G.M. Van Der Heijden, How to improve the efficiency of randomised response designs, Qual. Quant. 39 (2005) 253–265.
[50]
A. Serenko, O. Turel, A dual-attitude model of system use: the effect of explicit and implicit attitudes, Inf. Manag. 56 (2019) 657–668.
[51]
A. Serenko, O. Turel, Measuring implicit attitude in information systems research with the implicit association test, Commun. Assoc. Inf. Syst. 47 (2020) 397–431.
[52]
D.L. Paulhus, R. Christie, Spheres of control: an interactionist approach to locus of control assessment, (Ed.) in: Lefcourt (Ed.), Research with the Locus of Control Construct, Academic, New York, NY, 1981.
[53]
Johnson, J. Global digital population as of January 2021, (2021). https://www.statista.com/statistics/617136/digital-population-worldwide/ (accessed April 18, 2021).
[54]
N. Chesley, Technology use and employee assessments of work effectiveness, workload, and pace of life, Information, Commun. Soc. 13 (2010) 485–514.
[55]
J.S. MacCormick, K. Dery, D.G. Kolb, Engaged or just connected? Smartphones and employee engagement, Organ. Dyn. 41 (2012) 194–201.
[56]
H. Jamaluddin, Z. Ahmad, M. Alias, M. Simun, Personal internet use: the use of personal mobile devices at the workplace, Procedia Soc. Behav. Sci. 172 (2015) 495–502.
[57]
J. Vitak, J. Crouse, R. LaRose, Personal internet use at work: understanding cyberslacking, Comput. Hum. Behav. 27 (2011) 1751–1759.
[58]
S. Lee, D.-.H. Kwak, Y. Tu, X. Ma, L. Khansa, Announcement of formal control as a phase-shifting perception and its moderating role in the context of mobile loafing, in: Proceedings of the Twenty-Seventh European Conference on Information Systems,, Stockholm & Uppsala, Sweden, 2019.
[59]
A.L. Blanchard, C.A. Henle, Correlates of different forms of cyberloafing: the role of norms and external locus of control, Comput. Human Behav. 24 (2008) 1067–1084.
[60]
M.S. Ascher, P. Levounis, The Behavioral Addictions, American Psychiatric Publishing, Washington, DC, 2015.
[61]
A. Serenko, O. Turel, Directing technology addiction research in information systems: part I. Understanding behavioral addictions, Data Base Adv. Inf. Syst. 51 (2020) 81–96.
[62]
M.D. Griffiths, Classifying behavioural addictions: the DSM, and over-pathologising everyday life, Psychol. Rev. 23 (2018) 18–21.
[63]
M. Siponen, A. Vance, Neutralization: New insights into the problem of employee information systems security policy violations, MIS Q., 34, 2010, pp. 487–502.
[64]
M. Silic, J.B. Barlow, A. Back, A new perspective on neutralization and deterrence: predicting shadow IT usage, Inf. Manag. 54 (2017) 1023–1037.
[65]
G.M. Sykes, D. Matza, Techniques of neutralization: a theory of delinquency, Am. Sociol. Rev. 22 (1957) 664–670.
[66]
F.D. Davis, Perceived usefulness, perceived ease of use, and user acceptance of information technology, MIS Q. 13 (1989) 319–339.
[67]
A. Bhattacherjee, G. Premkumar, Understanding changes in belief and attitude toward information technology usage: a theoretical model and longitudinal test, MIS Q. 28 (2004) 229–254.
[68]
S.S. Kim, The integrative framework of technology use: An extension and test, MIS Q. 33 (2009) 513–537.
[69]
S.S. Kim, N.K. Malhotra, A longitudinal model of continued IS use: an integrative view of four mechanisms underlying postadoption phenomena, Manage. Sci. 51 (2005) 741–755.
[70]
S. Huh, N.D. Bowman, Perception of and addiction to online games as a function of personality traits, J. Media Psychol. 13 (2008) 1–31.
[71]
C.R. Mynatt, M.E. Doherty, R.D. Tweney, Confirmation bias in a simulated research environment: an experimental study of scientific inference, Q. J. Exp. Psychol. 29 (1977) 85–95.
[72]
P.C. Wason, On the failure to eliminate hypotheses in a conceptual task, Q. J. Exp. Psychol. 12 (1960) 129–140.
[73]
R.L. Akers, Deviant Behavior: A Social Learning Approach, Wadsworth, Belmont, CA, 1977.
[74]
J.B. Barlow, M. Warkentin, D. Ormond, A. Dennis, Do not even think about it! The effects of antineutralization, informational, and normative communication on information security compliance, J. Assoc. Inf. Syst. 19 (2018) 689–715.
[75]
M. Siponen, A. Vance, R. Willison, New insights into the problem of software piracy: the effects of neutralization, shame, and moral beliefs, Inf. Manag. 49 (2012) 334–341.
[76]
S. Byun, C. Ruffini, J.E. Mills, A.C. Douglas, M. Niang, S. Stepchenkova, S.K. Lee, J. Loutfi, J.-.K. Lee, M. Atallah, Internet addiction: metasynthesis of 1996–2006 quantitative research, CyberPsychol. Behav. 12 (2009) 203–207.
[77]
J.P. Charlton, A factor-analytic investigation of computer “addiction” and engagement, Br. Psychol. Soc. 93 (2002) 329–344.
[78]
M.D. Griffiths, Internet addiction: fact or fiction?, Psychologist 12 (1999) 246–251.
[79]
R.A. Davis, A cognitive-behavioral model of pathological internet use, Comput. Hum. Behav. 17 (2001) 187–195.
[80]
R.A. Davis, G.L. Flett, A. Besser, Validation of a new scale for measuring problematic internet use: implications for pre-employment screening, Cyberpsychol. Behav. 5 (2002) 331–345.
[81]
J.P. Charlton, I.D.W. Danforth, Distinguishing addiction and high engagement in the context of online game playing, Comput. Hum. Behav. 23 (2007) 1531–1548.
[82]
J. Kuem, S. Ray, M. Siponen, S.S. Kim, What leads to prosocial behaviors on social networking services: a tripartite model, J. Manag. Inf. Syst. 34 (2017) 40–70.
[83]
Paulhus, D.L, Manual for the Paulhus deception scales: BIDR version 7, Toronto, Canada: Multi-Health Systems, (1998).
[84]
W. Mason, S. Suri, Conducting behavior research on Amazon's Mechanical Turk, Behav. Res. Method 44 (2012) 1–23.
[85]
W. Hair, F. J., R.L. Tatham, R.E. Anderson, Black, Multivariate Data Analysis, Prentice-Hall, Englewood Cliffs, NJ, 2009.
[86]
J.C. Nunnally, I.H. Bernstein, Psychometric Theory, McGraw-Hill, New York, NY, 1994.
[87]
D. Gefen, D. Straub, A practical guide to factorial validity using PLS-Graph: tutorial and annotated example, Commun. Assoc. Inf. Syst. 16 (2005) 91–109.
[88]
K.A. Bollen, Structural Equations with Latent Variables, Wiley, New York, NY, 1989.
[89]
W.L. Leite, L.A. Cooper, Detecting social desirability bias using factor mixture models, Multivar. Behav. Res. 45 (2010) 271–293.
[90]
Y. Lo, N.R. Mendell, D.B. Rubin, Testing the number of components in a normal mixture, Biometrika 88 (2001) 767–778.
[91]
P.Y. Chen, P.E. Spector, Negative affectivity as the underlying cause of correlations between stressors and strains, J. Appl. Psychol. 76 (1991) 398.
[92]
S. Müller, M. Moshagen, True virtue, self-presentation, or both?: a behavioral test of impression management and overclaiming, Psychol. Assess. 31 (2019) 181–191.
[93]
L. Uziel, Look at me, I'm happy and creative: the effect of impression management on behavior in social presence, Pers. Soc. Psychol. Bull. 36 (2010) 1591–1602.
[94]
S. Sharma, I want it my way: using consumerism and neutralization theory to understand students’ cyberslacking behavior, Int. J. Inf. Manage. 53 (2020).
[95]
R. Tourangeau, T.W. Smith, Asking sensitive questions: the impact of data collection mode, question format, and question context, Public Opin. Q. 60 (1996) 275–304.
[96]
S. Gittelman, V. Lange, W.A. Cook, S.M. Frede, P.J. Lavrakas, C. Pierce, R.K. Thomas, Accounting for social-desirability bias in survey sampling: a model for predicting and calibrating the direction and magnitude of social-desirability bias, J. Advert. Res. 55 (2015) 242–254.
[97]
D. Gefen, D. Straub, M.-.C. Boudreau, Structural equation modeling and regression: guidelines for research practice, Commun. Assoc. Inf. Syst. 4 (2000) 1–78.
[98]
L. Hu, P.M. Bentler, Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives, Struct. Equ. Model. 6 (1999) 1–55.
[99]
L.R. Tucker, C. Lewis, A reliability coefficient for maximum likelihood factor analysis, Psychometrika 38 (1973) 1–10.

Cited By

View all
  • (2024)Recall for Good: Flexible Retrospective Mobile In-App Topic Tracking in a Privacy-Friendly Local-First ApproachProceedings of the International Conference on Mobile and Ubiquitous Multimedia10.1145/3701571.3703382(451-453)Online publication date: 1-Dec-2024
  • (2024)Using smart and connected health services to cope with pandemicsInformation and Management10.1016/j.im.2024.10396461:7Online publication date: 1-Nov-2024

Index Terms

  1. When does social desirability become a problem? Detection and reduction of social desirability bias in information systems research
            Index terms have been assigned to the content through auto-classification.

            Recommendations

            Comments

            Information & Contributors

            Information

            Published In

            cover image Information and Management
            Information and Management  Volume 58, Issue 7
            Nov 2021
            307 pages

            Publisher

            Elsevier Science Publishers B. V.

            Netherlands

            Publication History

            Published: 01 November 2021

            Author Tags

            1. Social desirability bias
            2. Self-report survey
            3. Social desirability scale
            4. Balanced inventory of desirable responding
            5. Indirect questioning
            6. Correlation
            7. Variance reduction rate
            8. Factor mixture model
            9. Covariate technique
            10. Mobile loafing

            Qualifiers

            • Research-article

            Contributors

            Other Metrics

            Bibliometrics & Citations

            Bibliometrics

            Article Metrics

            • Downloads (Last 12 months)0
            • Downloads (Last 6 weeks)0
            Reflects downloads up to 09 Feb 2025

            Other Metrics

            Citations

            Cited By

            View all
            • (2024)Recall for Good: Flexible Retrospective Mobile In-App Topic Tracking in a Privacy-Friendly Local-First ApproachProceedings of the International Conference on Mobile and Ubiquitous Multimedia10.1145/3701571.3703382(451-453)Online publication date: 1-Dec-2024
            • (2024)Using smart and connected health services to cope with pandemicsInformation and Management10.1016/j.im.2024.10396461:7Online publication date: 1-Nov-2024

            View Options

            View options

            Figures

            Tables

            Media

            Share

            Share

            Share this Publication link

            Share on social media