Skip to main content
SpringerLink
Log in

Do older programmers perform as well as young ones? Exploring the intermediate effects of stress and programming experience

  • Original Article
  • Published:
Cognition, Technology & Work Aims and scope Submit manuscript

Abstract

There is a widespread perception that older adults are underperformers when compared with younger adults in tasks that involve intense use of technology, such as computer programming. Building on schema theory, we developed a research model that contradicts this perception. To provide an initial test of the model, we conducted a computer programming experiment involving 140 student participants majoring in technology-related areas with ages ranging from 19 to 54 years. The participants were asked to develop, under some time pressure, a simple software application. The results of our analyses suggest that age was positively associated with programming experience and perceived stress, that programming experience was positively associated with programming performance, and that perceived stress was negatively associated with programming performance. A moderating effect analysis suggests that as programming experience increased, the association between perceived stress and programming performance weakened; going from strongly negative toward neutral. This happened even as age was controlled for. When taken together, these results suggest that the widespread perception that older adults are underperformers is unwarranted. With enough programming experience, older programmers generally perform no better or worse than young ones.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  • Akerstedt T, Gillberg M (1990) Subjective and objective sleepiness in the active individual. Int J Neourosci 52(1–2):29–37

    Article  Google Scholar 

  • Bailey J, Mitchell RB (2006) Industry perceptions of the competencies needed by computer programmers: technical, business, and soft skills. J Comput Inf Syst 47(2):28–33

    Google Scholar 

  • Barnes RF, Raskind M, Gumbrecht G, Halter JB (1982) The effects of age on the plasma catecholamine response to mental stress in man. J Clin Endocrinol Metab 54(1):64–69

    Article  Google Scholar 

  • Bartlett F (1932) Remembering: a study in experimental and social psychology. Cambridge University Press, Cambridge

    Google Scholar 

  • Bartlett F (1958) Thinking: an experimental and social study. Basic Books, New York

    Google Scholar 

  • Beckers JJ, Rikers RM, Schmidt HG (2006) The influence of computer anxiety on experienced computer users while performing complex computer tasks. Comput Hum Behav 22(3):456–466

    Article  Google Scholar 

  • Bera AK, Jarque CM (1981) Efficient tests for normality, homoscedasticity and serial independence of regression residuals: Monte Carlo evidence. Econ Lett 7(4):313–318

    Article  Google Scholar 

  • Bergin S, Reilly R (2005) Programming: factors that influence success. ACM SIGCSE Bull 37(1):411–415

    Article  Google Scholar 

  • Billings AG, Moos RH (1982) Work stress and the stress-buffering roles of work and family resources. J Organ Behav 3(3):215–232

    Article  Google Scholar 

  • Brosnan MJ (1998) The impact of computer anxiety and self-efficacy upon performance. J Comput Assist Learn 14(3):223–234

    Article  Google Scholar 

  • Burgess GA (2005) Introduction to programming: blooming in America. J Comput Sci Coll 21(1):19–28

    MathSciNet  Google Scholar 

  • Byrne P, Lyons G (2001) The effect of student attributes on success in programming. ACM SIGCSE Bull 33(3):49–52

    Article  Google Scholar 

  • Caplan LJ, Schooler C (1990) The effects of analogical training models and age on problem-solving in a new domain. Exp Aging Res 16(3):151–154

    Article  Google Scholar 

  • Catherine BC, Wheeler DD (1994) The Myers-Briggs personality type and its relationship to computer programming. J Res Comput Educ 26(3):358–370

    Article  Google Scholar 

  • Chan DKC, Yang SX, Hamamura T, Sultan S, Xing S, Chatzisarantis NL, Hagger MS (2015) In-lecture learning motivation predicts students’ motivation, intention, and behaviour for after-lecture learning: examining the trans-contextual model across universities from UK, China, and Pakistan. Motiv Emot 39(6):908–925

    Article  Google Scholar 

  • Cohen J (1988) Statistical power analysis for the behavioral sciences. Lawrence Erlbaum, Hillsdale

    MATH  Google Scholar 

  • Cohen S, Kamarck T, Mermelstein R (1983) A global measure of perceived stress. J Health Soc Behav 24(4):385–396

    Article  Google Scholar 

  • Cohen I, Brinkman WP, Neerincx MA (2015) Modelling environmental and cognitive factors to predict performance in a stressful training scenario on a naval ship simulator. Cogn Technol Work 17(4):503–519

    Article  Google Scholar 

  • Cossete P, Audet M (1992) Mapping of an idiosyncratic schema. J Manage Stud 29(3):325–348

    Article  Google Scholar 

  • Czaja SJ (1995) Aging and work performance. Rev Public Pers Adm 15(2):46–61

    Article  Google Scholar 

  • Dermentzi E, Papagiannidis S, Toro CO, Yannopoulou N (2016) Academic engagement: differences between intention to adopt Social Networking Sites and other online technologies. Comput Hum Behav 61(1):321–332

    Article  Google Scholar 

  • Dibiase D, Kidwai K (2010) Wasted on the young? Comparing the performance and attitudes of younger and older US adults in an online class on geographic information. J Geogr High Educ 34(3):299–326

    Article  Google Scholar 

  • Dollinger SMC (1995) Mental rotation performance: age, sex, and visual field differences. Dev Neuropsychol 11(2):215–222

    Article  Google Scholar 

  • Dönmez D, Grote G, Brusoni S (2016) Routine interdependencies as a source of stability and flexibility. A study of agile software development teams. Inf Organ 26(3):63–83

    Article  Google Scholar 

  • Duschl KC, Gramß D, Obermeier M, Vogel-Heuser B (2015) Towards a taxonomy of errors in PLC programming. Cogn Technol Work 17(3):417–430

    Article  Google Scholar 

  • Dyck JL, Smither JAA (1994) Age differences in computer anxiety: the role of computer experience, gender and education. J Educ Comput Res 10(3):239–248

    Article  Google Scholar 

  • Ehremberg ASC, Goodhart GJ (1976) Factor analysis: limitations and alternatives. Marketing Science Institute, Cambridge

    Google Scholar 

  • Elias SM, Smith WL, Barney CE (2012) Age as a moderator of attitude towards technology in the workplace: work motivation and overall job satisfaction. Behav Inf Technol 31(5):453–467

    Article  Google Scholar 

  • Ferguson GA (1981) Statistical analysis in psychology and education. McGraw-Hill, New York

    Google Scholar 

  • Fornell C, Larcker DF (1981) Evaluating structural equation models with unobservable variables and measurement error. J Mark Res 18(1):39–50

    Article  Google Scholar 

  • Gardner H (1985) The mind’s new science. Basic Books, New York

    Google Scholar 

  • Garstka TA, Schmitt MT, Branscombe NR, Hummert ML (2004) How young and older adults differ in their responses to perceived age discrimination. Psychol Aging 19(2):326–335

    Article  Google Scholar 

  • Geisser S (1974) A predictive approach to the random effects model. Biometrika 61(1):101–107

    Article  MathSciNet  MATH  Google Scholar 

  • Gel YR, Gastwirth JL (2008) A robust modification of the Jarque-Bera test of normality. Econ Lett 99(1):30–32

    Article  MathSciNet  MATH  Google Scholar 

  • Gilroy FD, Desai HB (1986) Computer anxiety: sex, race and age. Int J Man Mach Stud 25(6):711–719

    Article  Google Scholar 

  • Gioia DA, Manz CC (1985) Linking cognition and behavior: a script processing interpretation of vicarious learning. Acad Manag Rev 10(3):527–539

    Article  Google Scholar 

  • Gnambs T (2015) What makes a computer wiz? Linking personality traits and programming aptitude. J Res Pers 58(3):31–34

    Article  Google Scholar 

  • González A, Ramírez MP, Viadel V (2012) Attitudes of the elderly toward information and communications technologies. Educ Gerontol 38(9):585–594

    Article  Google Scholar 

  • Haenlein M, Kaplan AM (2004) A beginner’s guide to partial least squares analysis. Underst Stat 3(4):283–297

    Article  Google Scholar 

  • Hagan D, Markham S (2000) Does it help to have some programming experience before beginning a computing degree program? ACM SIGCSE Bull 32(3):25–28

    Article  Google Scholar 

  • Hair JF, Black WC, Babin BJ, Anderson RE (2009) Multivariate data analysis. Prentice Hall, Upper Saddle River

    Google Scholar 

  • Hannah L (2014) The rise of the modern firm. Bus Hist 56(5):845–846

    Article  Google Scholar 

  • Hasan B (2003) The influence of specific computer experiences on computer self-efficacy beliefs. Comput Hum Behav 19(4):443–450

    Article  Google Scholar 

  • Hetherington EM, Blechman EA (2014) Stress, coping, and resiliency in children and families. Psychology Press, New York

    Google Scholar 

  • Huang LK (2015) Exploring factors affecting top management support of IT implementation: a stakeholder perspective in hospital. J Inf Technol Manag 26(1):31–45

    Google Scholar 

  • Jaradat MIRM, Faqih KM (2014) Investigating the moderating effects of gender and self-Efficacy in the context of mobile payment adoption: a developing country perspective. Int J Bus Manag 9(11):147

    Article  Google Scholar 

  • Jarque CM, Bera AK (1980) Efficient tests for normality, homoscedasticity and serial independence of regression residuals. Econ Lett 6(3):255–259

    Article  MathSciNet  Google Scholar 

  • John RR (2014) The computer boys take over: computers, programmers, and the politics of technical expertise. Bus Hist 56(5):846–847

    Article  Google Scholar 

  • Johnson KM (2015) Non-technical skills for IT professionals in the landscape of Social Media. Am J Bus Manag 4(3):102–122

    Article  Google Scholar 

  • Khan IA, Brinkman WP, Hierons RM (2011) Do moods affect programmers’ debug performance? Cogn Technol Work 13(4):245–258

    Article  Google Scholar 

  • Kline RB (1998) Principles and practice of structural equation modeling. The Guilford Press, New York

    MATH  Google Scholar 

  • Kock N (2014) Advanced mediating effects tests, multi-group analyses, and measurement model assessments in PLS-based SEM. Int J e-Collab 10(3):1–13

    Google Scholar 

  • Kock N (2015a) A note on how to conduct a factor-based PLS-SEM analysis. Int J e-Collab 11(3):1–9

    MathSciNet  Google Scholar 

  • Kock N (2015b) WarpPLS 5.0 user manual. ScriptWarp Systems, Laredo

    Google Scholar 

  • Kock N (2015c) Common method bias in PLS-SEM: a full collinearity assessment approach. Int J e-Collab 11(4):1–10

    MathSciNet  Google Scholar 

  • Kock N (2016) Non-normality propagation among latent variables and indicators in PLS-SEM simulations. J Mod Appl Stat Methods 15(1):299–315

    Article  Google Scholar 

  • Kock N, Chatelain-Jardón R (2016) Surprise-enhanced and technology-mediated learning: a two-country study. Cogn Technol Work 18(1):105–119

    Article  Google Scholar 

  • Kock N, Lynn GS (2012) Lateral collinearity and misleading results in variance-based SEM: an illustration and recommendations. J Assoc Inf Syst 13(7):546–580

    Google Scholar 

  • Kock N, Mayfield M (2015) PLS-based SEM algorithms: the good neighbor assumption, collinearity, and nonlinearity. Inf Manag Bus Rev 7(2):113–130

    Google Scholar 

  • Kock N, Sexton S (2017) Variation sharing: a novel numeric solution to the path bias underestimation problem of PLS-based SEM. Int J Strateg Decis Sci 8(4):46–68

    Article  Google Scholar 

  • Kraft P (2012) Programmers and managers: the routinization of computer programming in the United States. Springer, New York

    Google Scholar 

  • Lohmöller J-B (1989) Latent variable path modeling with partial least squares. Physica, Heidelberg

    Book  MATH  Google Scholar 

  • Lord RG, Maher KJ (1990) Alternative information-processing models and their implications for theory, research, and practice. Acad Manag Rev 15(1):9–28

    Article  Google Scholar 

  • Magsamen-Conrad K, Upadhyaya S, Joa CY, Dowd J (2015) Bridging the divide: using UTAUT to predict multigenerational tablet adoption practices. Comput Hum Behav 50(3):186–196

    Article  Google Scholar 

  • Maier C, Laumer S, Weinert C, Weitzel T (2015) The effects of technostress and switching stress on discontinued use of social networking services: a study of Facebook use. Inf Syst J 25(3):275–308

    Article  Google Scholar 

  • Martin MA (2007) Bootstrap hypothesis testing for some common statistical problems: a critical evaluation of size and power properties. Comput Stat Data Anal 51(12):6321–6342

    Article  MathSciNet  MATH  Google Scholar 

  • Morrell W, Park DC, Mayhorn CB, Kelley CLR (2000) Effects of age and instructions on teaching older adults to use Eldercomm, an electronic bulletin board system. Educ Gerontol 26(3):221–235

    Article  Google Scholar 

  • Neumark D (2003) Age discrimination legislation in the United States. Contemp Econ Policy 21(3):297–317

    Article  Google Scholar 

  • Neumark D (2009) The Age Discrimination in Employment Act and the challenge of population aging. Res Aging 31(1):41–68

    Article  Google Scholar 

  • Nunnally JC, Bernstein IH (1994) Psychometric theory. McGraw-Hill, New York

    Google Scholar 

  • Nunnaly J (1978) Psychometric theory. McGraw Hill, New York

    Google Scholar 

  • Ogasawara H (1999) Standard errors for the direct oblimin solution with Kaiser’s normalization. Jpn J Psychol 70(4):333–338

    Article  Google Scholar 

  • Oh SY, Bailenson J, Weisz E, Zaki J (2016) Virtually old: embodied perspective taking and the reduction of ageism under threat. Comput Hum Behav 60(3):398–410

    Article  Google Scholar 

  • Paxton P, Curran PJ, Bollen KA, Kirby J, Chen F (2001) Monte Carlo experiments: design and implementation. Struct Equ Model 8(2):287–312

    Article  Google Scholar 

  • Perry EL, Simpson PA, NicDomhnaill OM, Siegel DM (2003) Is there a technology age gap? Associations among age, skills, and employment outcomes. Int J Sel Assess 11(2):141–149

    Article  Google Scholar 

  • Potosky D (2002) A field study of computer efficacy beliefs as an outcome of training: the role of computer playfulness, computer knowledge, and performance during training. Comput Hum Behav 18(3):241–255

    Article  Google Scholar 

  • Ramalingam V, Wiedenbeck S (1998) Development and validation of scores on a computer programming self-efficacy scale and group analyses of novice programmer self-efficacy. J Educ Comput Res 19(4):367–381

    Article  Google Scholar 

  • Robert C, Casella G (2013) Monte Carlo statistical methods. Springer, New York

    MATH  Google Scholar 

  • Rosenthal R, Rosnow RL (2007) Essentials of behavioral research: methods and data analysis. McGraw Hill, Boston

    Google Scholar 

  • Rubio MA, Romero-Zaliz R, Mañoso C, Angel P (2015) Closing the gender gap in an introductory programming course. Comput Educ 82(2):409–420

    Article  Google Scholar 

  • Rumelhart DE (1978) Schemata: the building blocks of cognition. Center for Human Information Processing, University of California, San Diego, San Diego

    Google Scholar 

  • Schumacker RE, Lomax RG (2004) A beginner’s guide to structural equation modeling. Lawrence Erlbaum, Mahwah

    MATH  Google Scholar 

  • Sorensen LJ, Stanton NA (2015) Exploring compatible and incompatible transactions in teams. Cogn Technol Work 17(3):367–380

    Article  Google Scholar 

  • Soror AA, Hammer BI, Steelman ZR, Davis FD, Limayem MM (2015) Good habits gone bad: explaining negative consequences associated with the use of mobile phones from a dual-systems perspective. Inf Syst J 25(4):403–427

    Article  Google Scholar 

  • Stone M (1974) Cross-validatory choice and assessment of statistical predictions. J Roy Stat Soc B 36(1):111–147

    MathSciNet  MATH  Google Scholar 

  • Thompson B (2004) Exploratory and confirmatory factor analysis: understanding concepts and applications. American Psychological Association, Washington

    Book  Google Scholar 

  • Tse D, Langston RF, Kakeyama M, Bethus I, Spooner PA, Wood ER, Witter MP, Morris RG (2007) Schemas and memory consolidation. Science 316(5821):76–82

    Article  Google Scholar 

  • Vauclair CM, Lima ML, Abrams D, Swift HJ, Bratt C (2016) What do older people think that others think of them, and does it matter? The role of meta-perceptions and social norms in the prediction of perceived age discrimination. Psychol Aging 31(7):699

    Article  Google Scholar 

  • Whitbourne SK (2012) The aging body: physiological changes and psychological consequences. Springer, New York

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Division of International Business and Technology Studies, Texas A&M International University, 5201 University Boulevard, Laredo, TX, 78041, USA

    Ned Kock

  2. Management Information Systems Department, University of Oklahoma, Adams Hall Room 305, 307 West Brooks, Norman, OK, 73019-4007, USA

    Murad Moqbel

  3. Division of International Business and Technology Studies, Texas A&M International University, 5201 University Boulevard, Laredo, TX, 78045, USA

    Yusun Jung & Thant Syn

Authors
  1. Ned Kock
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Murad Moqbel
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yusun Jung
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Thant Syn
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ned Kock.

Appendices

Appendix 1: Experimental task description

1.1 Purpose

The director of the PhD program at a school of business needs to make decisions on whether or not to admit a doctoral program applicant in a timely manner. The director of the PhD program will save time and be more productive if he or she has a stand-alone application at his desktop to make such a decision.

1.2 Algorithms

For an applicant to be eligible for admission, he/she must satisfy one of the following sets of conditions:

  • Have a GMAT greater than or equal to (≥) 600, a GPA greater than or equal to (≥) 3.5, WE greater than or equal to (≥) 0, and RecLtr greater than or equal to (≥) 80%.

  • Have a GMAT greater than or equal to (≥) 500, GPA greater than or equal to (≥) 3.8, WE greater than or equal to (≥) 1, and RecLtr greater than or equal to (≥) 90%.

1.3 Legend

  • GMAT: Graduate Management Aptitude Test.

  • GPA: Grade Point Average.

  • WE: Work Experience related to the major.

  • RecLtr: Composite rating of the student based on a structured recommendation letter.

1.4 Notes

  • The application should allow the director of the PhD program to reset all values on the screen to blank so that another calculation can be performed.

  • The decision should be run based on the term “Decide,” so please include a working button for this. The reset of the values should be designated by the term “Reset,” so please include a working button for this term as well. The decision output, once all information is entered into the program and the “Decide” button is clicked should be either, “Admit” or “Not Admit.”

Appendix 2: Measurement instrument

The questions and question-statements below were used for data collection, in addition to demographic questions. The questions on perceived stress were answered on a Likert-type scale going from 1 to 7. Programming performance was measured based on a rubric with five dimensions, whereby three researchers independently scored the quality of the software applications developed by the participants.

2.1 Perceived stress

Stress1: I felt stressed while completing this task.

Stress2: I felt nervous while completing this task.

Stress3: This task made me feel stressed.

Stress4: Completing this task was stressful.

2.2 Programming performance (based on rubric)

Perf1: Completeness of the software application.

Perf2: Correctness of the software application.

Perf3: Extent to which the software application met the requirements.

Perf4: Ease of use of interface.

Perf5: Programming code clarity.

Appendix 3: Programming performance scoring rubric

Below is the rubric we used to score programming performance. Three researchers independently scored the quality of the software applications developed by the participants, from which average scores were calculated for each of the five dimensions. Each individual dimension’s score was then included as an indicator, with respect to the programming performance variable, as part of the measurement model in our structural equation modeling analysis.

 

0–25

25–50

50–75

75–100

Completeness

The assignment was incomplete or completed without regard to instructions

The assignment was only partially completed per instructions

The assignment was moderately completed per instructions

The assignment was fully completed per instructions

Correctness

The program did not perform per instructions

The program performed only partially per instructions

The program performed moderately per instructions

The program performed fully per instructions

Requirements met

Adheres to less than 70% of standard

Adheres to between 70 and 80% of standard

Adheres to between 80 and 90% of standard

Adheres to between 90 and 100% of standard

Ease of use

Required user to reread before understood

Required user to reread to confirm understood

Reread was not required to confirm understood

Immediately understood

Code clarity

Code is unclear or too specific to stated purpose to be revised

Code is enough to revise

Code is clear and modular enough to ease revision

Code is clear and general enough to simplify revision

Appendix 4: Coefficients for measurement instrument validation

Loadings, weights, cross-loadings, cross-weights, and indicator effect sizes are summarized in Table 6. Loadings, shown in bold, are from a structure matrix and thus unrotated; cross-loadings, shown in italics, are from a pattern matrix and thus oblique-rotated (Ehremberg and Goodhart 1976; Thompson 2004). This combination of structure and pattern matrices’ loadings allows for easy identification of possible validity problems, while at the same time obviating the need for a potentially distorting normalization procedure (Ferguson 1981; Kock 2015b; Ogasawara 1999).

Table 6 Combined loadings and cross-loadings, weights, and effect sizes
Full size table

R-squared, adjusted R-squared, composite reliability, Cronbach’s alpha, average variance extracted, and Q-squared coefficients are listed in Table 7. Composite reliability and Cronbach’s alpha coefficients are reliability measures (Fornell and Larcker 1981; Nunnaly 1978; Nunnally and Bernstein 1994). Average variances extracted are sometimes used for convergent validity assessment, in addition to loadings (Fornell and Larcker 1981). Q-squared coefficients are used, together with R-squared coefficients, for predictive validity assessment (Geisser 1974; Kock 2015b; Stone 1974).

Table 7 Latent variable coefficients
Full size table

Latent variable correlations and square roots of average variances extracted are listed in Table 8. These coefficients are used for discriminant validity assessment; that is, to assess whether measures associated with each latent variable are not confused by respondents with measures associated with other latent variables (Fornell and Larcker 1981; Kock 2014; Schumacker and Lomax 2004).

Table 8 Latent variable correlations and square roots of average variances extracted
Full size table

Table 9 shows variance inflation factors (Hair et al. 2009) from a full collinearity test. In a full collinearity test, variance inflation factors are calculated for all of the variables in the model (Kock and Lynn 2012). This allows for the assessment of whole-model collinearity in the presence of variables measured through single indicators. Full collinearity variance inflation factors can also be used in common method bias tests (Kock 2015c).

Table 9 Full collinearity variance inflation factors
Full size table

The measurement model assessment results summarized in the tables above suggest that the measurement instrument presents acceptable convergent validity, discriminant validity, and reliability. These also suggest that the measurement instrument presents acceptable predictive validity. Finally, these results above suggest that the measurement instrument is free from model-wide collinearity and that common method variance does not have a significant biasing effect in the analysis.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kock, N., Moqbel, M., Jung, Y. et al. Do older programmers perform as well as young ones? Exploring the intermediate effects of stress and programming experience. Cogn Tech Work 20, 489–504 (2018). https://doi.org/10.1007/s10111-018-0479-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10111-018-0479-x

Keywords

Search

Navigation