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An online tool to identify white-collar worker profiles in relation to their ICT skills and mental strain

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Abstract

The introduction of Internet and Information and communication Technologies (ICT) in offices is a global phenomenon that transformed white-collar worker job demands. Although there are several studies of e-skills and mental workload for central countries, there is a lack of similar studies for the Latin American context. An online snowball sampled (n = 352) survey was developed and validated by the authors (internal consistency = 0.7). We characterized ICT worker profiles from e-skills and these dimensions: attitudes toward, resources usage and technology dependency. Mental Strain was assessed with raw task load index (RTLX) and correlated with the proposed profiles by means of paired T-tests and Mann–Whitney Tests. The sample was characterized by 7.2% of non visual display terminal users and 92.8% of visual display terminal, ICT skilled users. Of the latter, 30.7% were ICT practitioners, 30.4% were ICT Users and 27.2% were E-Business Users. Non VDT users’ mental strain was statistically meaningful smaller than VDT–ICT skilled users’ mental workload. No statistical differences were found in RTLX results when comparing ICT skilled user profiles. Non VDT users can be identified from ICT skilled Users by their lower ITC Dependency and minor use of ICT resources. There were no differences in those dimensions among ICT skilled Profiles. Attitude toward these technologies was a distinct factor for ICT Users in relation to ICT Practitioners and ITC Business Users. The application of this tool in peripheral and central countries would allow a complete ergonomical characterization of white-collar workers within the Information Society.

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References

  • Alm H, Nilsson L (1994) Changes in driver behaviour as a function of handsfree mobile phones-A simulator study”. Accid Anal Prev 26:441–451

    Article  Google Scholar 

  • Biernacki P, Waldorf D (1981) Snowball sampling: problems and techniques of chain referral sampling. Soc Methods Res 10:141–163

    Google Scholar 

  • Byers J, Bittner A et al (1989) Traditional and raw task load index (TLX) correlations: are paired comparisons necessary? Adv Ind Ergonomics Safety I:481–485

    Google Scholar 

  • European E- skills forum (2004) E-Skills for Europe: Towards 2010 and Beyond Synthesis Report. OCDE

  • Frinking E, Ligtvoet A et al (2005) The supply and demand of e-skills in Europe, RAND Europe:143

  • Hart S, Staveland L (1988) Development of NASA-TLX (Task Load Index): results of empirical and theoretical research. Human Mental Workload. Hancock PA, Meshkati N. Amsterdam, North-Holland, pp 139–183

  • Hemström Ö (2001) Working conditions, the work environment and health. Scand J Public Health 29(58):167–184

    Article  Google Scholar 

  • Hill S, Iavecchia H et al (1992) Comparison of four subjective workload rating scales. Hum Factors 34(4):429–439

    Google Scholar 

  • ISO (1991) Ergonomic principles related to mental workload—general terms and definitions. ISO. 10 075

  • Kramer AF (1991) Physiological metrics of mental workload: a review of recent progress. Multiple-task performance D. L. Damos. Taylor & Francis, London, pp 279–328

  • Lakhani KR, Wolf RG (2005) Why hackers do what they do: understanding motivation and effort in free/open source software projects. Perspectives on Free and Open Source Software (2005). MITPress, Massachusetts, pp 3–23

  • Ledesma R, Ibañez GM et al (2002) Análisis de consistencia interna mediante Alfa de Cronbach: un programa basado en gráficos dinámicos. Psico-USF 7(2):143–152

    Google Scholar 

  • Lemaître G (2002) Measures of skill from labour force studies—an assessment, OECD, Secretariat Working Document

  • Lindbeck A, Snower DJ (1996) Reorganization of firms and labor market inequality. Research Institute of Industrial Economics Stockholm, Research Institute of Industrial Economics 13

  • Meijman TF, O’Hanlon JF (1984) Workload. An introduction to psychological theories and measurement methods. Handbook of Work and Organizational Psychology. PJD

  • Millar J (2001) Skills and employment research—conceptual framework and methodology, STAR

  • MTAS (1997) Disposiciones mínimas de seguridad y salud relativas al trabajo con equipos que incluyen PVD. Madrid, Ministerio de Trabajo y Asuntos Sociales, 488/1997

  • Muñoz G, Martinez G (2006) La carga mental de trabajo como factor de riesgo de estrés en trabajadores de la industria electrónica. Revista Latinoamericana de Psicología 38(2):259–270

    Google Scholar 

  • O’Donnell R, Eggemeier F (1986) Workload assessment methodology. Handbook of perception and human performance. Boff KR, Kaufman L, Thomas JP. Wiley, New York, II

  • Pilat D, Lee FC (2001) Productivity growth in ICT-producing and ICT-using industries: a source of growth differentials in the OECD? Paris, STI: 4

  • Recarte MÁ, Pérez E et al (2008) Mental workload and visual impairment: differences between pupil, blink, and subjective rating. Span J Psychol 11(2):374–385

    Google Scholar 

  • Reid G, Nygren T (1988) The subjective workload assessment technique: a scaling procedure for measuring mental workload. Human Mental Workload. Hancock PA, Meshkati N. North-Holland, Elsevier

  • Rubio S, Díaz E et al (2004) Evaluation of subjective mental workload: a comparison of SWAT, NASA-TLX, and workload profile methods. Appl Psychol Int Rev 53(1):61–86

    Article  Google Scholar 

  • Sawin DA, Scerbo MW (1995) Effects of instruction type and boredom proneness in vigilance: implications for boredom and workload. Hum Factors 37:752–765

    Article  Google Scholar 

  • Teddlie C, Yu F (2007) Mixed methods sampling. J Mix Methods Res 1(1):77–100

    Article  Google Scholar 

  • Van Welsum D, Vickery G (2005) New perspectives on ICT skills and employment. Paris, OECD: 34

  • Wästlund E (2007) Experimental studies of human-computer interaction: working memory and mental workload in complex cognition. Department of Psychology, Sweden, Gothenburg University, PhD

  • Wierwille WW, Casali JG (1983) A validated rating scale for global mental workload measurement application. The Human Factors Society 27th Annual Meeting Human Factors Society

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Acknowledgments

Jose L. Cortegoso and Mirta Ison are acknowledged for their contributions in this study.

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Correspondence to Roberto G. Rodriguez.

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Rodriguez, R.G., Pattini, A. An online tool to identify white-collar worker profiles in relation to their ICT skills and mental strain. Cogn Tech Work 13, 81–91 (2011). https://doi.org/10.1007/s10111-010-0156-1

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  • DOI: https://doi.org/10.1007/s10111-010-0156-1

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