Elsevier

Computers in Human Behavior

Volume 34, May 2014, Pages 273-283
Computers in Human Behavior

Neurophysiological correlates of cognitive absorption in an enactive training context

https://doi.org/10.1016/j.chb.2014.02.011Get rights and content

Highlights

  • Neurophysiological variables doubled the explained variance.

  • We find that cognitive absorption is positively related to a more relaxed, less vigilant state.

  • Perceived control appears to have the strongest effect on training outcomes.

Abstract

Various aspects of intrinsic motivation have long been theorized as key determinants of learning achievement. The present research seeks deeper insights into these intrinsically motivating mechanisms by investigating neurophysiological correlates of cognitive absorption in the context of enactive learning, specifically simulation-based training on the use of enterprise resource planning (ERP) software. An experiment was conducted in which 36 student trainees used ERP software to make decisions while running a simulated company. Consistent with flow theory, skill, difficulty, and their interaction significantly influenced cognitive absorption (R2 = .16). Five neurophysiological measures were captured for each trainee: EEG alpha, EEG beta, electrodermal activity (EDA), heart rate, and heart rate variability. Each of the five neurophysiological measures explained significant unique variance in cognitive absorption over and above skill, difficulty, and their interaction, and collectively more than doubled the explained variance to R2 = .34. Overall, cognitive absorption was positively related to a more relaxed, less vigilant state. Cognitive absorption was significantly related to the training outcome. These findings provide new insights into the psychological states that are conducive to experiencing cognitive absorption during enactive training.

Introduction

End-user training has long been recognized as a key factor in the acceptance and effective use of information technology (IT) (Compeau and Higgins, 1995, Nelson and Cheney, 1987). The objective of end-user training is to produce a skilled user who is motivated to apply this newly acquired knowledge in order to perform a job-related task (Gupta, Bostrom, & Huber, 2010). Moreover, organizations are investing significant resources in end-user training. According to the US Bureau of Labor Statistics, every year over 170 Billion US$ is spent on employee training and development; the American Society for Training and Development (ASTD, 2011) estimates that IT training accounts for 10% (on average) of all formal learning hours over the past ten years. Research shows that poor or insufficient training results in limited acceptance of the technology, which prevents organizations from fully realizing the benefits from these significant new investments (Compeau and Higgins, 1995, Nelson and Cheney, 1987). For example, undertrained end-users could cost five to eight times more to support than a well-trained worker (Fiering & Kirwin, 2006).

Among the various techniques used to train IT users, researchers call for more enactive methods (De Freitas and Jarvis, 2007, Derouin et al., 2005, Hays, 2005, Hedberg, 2003, Kirkle et al., 2005, Mayo et al., 2006). Enactive learning is based on social cognitive theory (Bandura, 1986) and is a form of observational learning that involves learning as a consequence of one’s interaction with and feedback from the environment. Gupta et al. (2010) provides evidence that combining enacting learning with vicarious learning (learning by observing others) leads to better training outcomes compared to vicarious learning alone. Realistic simulations provide a training context that generates relevant feedback in response to learner actions. More research is needed to properly understand such enactive learning in IT training (Martocchio and Webster, 1992, Sein et al., 1999). Computer-based simulation games have been demonstrated to be more effective than alternative forms of training for teaching work-related knowledge and skills (Sitzmann, 2011). Moreover, computer-based simulation games more readily incorporate enactive learning (as opposed to learners simply observing others). Consequently, the advantages of computer-based simulation training are theorized to result primarily from intrinsic motivation experienced by trainees when they actively engage in learning the training material (Sitzmann, 2011). Léger et al. (2012) report that learning is perceived to occur moreso during the enactive period (such as during a simulation of an IT training) as compared to other more direct instruction periods (such as a more formal presentation). While the literature provides evidence of the effects of affective and cognitive states on training outcomes, further investigation is needed on the impact of such psychological states on training effectiveness in enactive learning contexts (Gupta et al., 2010).

The present research focuses on the relationships between neurophysiological measures and the cognitive and affective states related to effective enactive IT training. The purpose of the paper is to determine the relationships, if any, that exist between neurophysiological measures and cognitive absorption (CA). Specifically, the paper focuses on the relationships between individuals’ cognitive absorption (and its dimensions) and EEG (electroencephalography), EKG (electrocardiography), EDA (electrodermal activity) and HR (Heart rate). Understanding these relationships could further enhance end-user and IT training as well as user acceptance of technology and systems. Moreover, the results of such studies not only contribute to the current call for research on end-user training, but also point to the importance of considering neurophysiological factors along with trainees’ experience and task difficulty when developing effective IT training. Enactive training programs can perhaps be designed to induce neurophysiological states that will result in more efficient and better use of the technology. Such insights could open up new frontiers for advancing the development of more efficient, effective, and enjoyable training environments; and hence, enhanced user acceptance and efficient use of technology. Given the continuing high failure rate of new information systems implementations, much of which can be directly attributed to inadequate training practices, the quest for such insights warrants urgent attention.

Section snippets

Cognitive absorption

Based on flow theory (Csikszentmihalyi, 1990), cognitive absorption is conceptualized as a state of deep involvement with IT (Agarwal & Karahanna, 2000). Research suggests that cognitive absorption significantly affects trainees’ behavioral intention to use the target information system both directly and through its indirect effects via perceived usefulness and perceived ease of use (Agarwal and Karahanna, 2000, Saadé and Bahli, 2005, Scott and Walczak, 2009, Shang et al., 2005). Stated

Neurophysiological correlates of cognitive absorption

Hanin (2000) proposes a framework to capture the psychological and physiological states of the flow state for an individual task. Their model suggests that the overall psychophysiological disposition of an individual is also an important factor in being able to achieve a state of flow. In this framework, the psychological dimension refers to the affective, cognitive, and motivational components; while the physiological dimensions consist of behavioral and bodily-somatic factors. According to

Experimental protocol

An experimental approach was used to test these hypotheses. Thirty-six (36) right-handed male and female subjects took part in a two-hour simulation-based training session on an ERP system. The experiment was approved by the Institutional Review Boards (IRB) of the institutions involved in the study. The IRB reviews research protocols and procedures to ensure the appropriateness of the study. All subjects were undergraduate students from an AACSB accredited institution, in the United States,

Results

This research presents evidence of links between specific neurophysiological markers of controlled and automatic processing, and user trainees’ perceptions of cognitive absorption. EEG (electroencephalography), EDR (electrodermal activity) and HR (Heart rate) data were gathered using the Procomp Infinity encoder from Thought Technology. Descriptive statistics and correlations are presented in Table 2.

Table 3 compares the effects of two different models on the comprehensive cognitive absorption

Discussion and concluding comments

The objective of this research was to seek deeper insights into intrinsic motivating mechanisms, key determinants of learning achievement. By investigating neurophysiological correlates of CA in the context of enactive learning (specifically simulation-based training on the use of enterprise resource planning (ERP) software) our objective was to generate new insights into the psychological states that are conducive to experiencing this flow related state. In effect, our objective was to

References (99)

  • W. Klimesch et al.

    EEG alpha oscillations: The inhibition–timing hypothesis

    Brain Research Reviews

    (2007)
  • G.G. Knyazev et al.

    The default mode network and EEG alpha oscillations: An independent component analysis

    Brain Research Reviews

    (2011)
  • R. Mandryk et al.

    A fuzzy physiological approach for continuously modeling emotion during interaction with play technologies

    International Journal of Human–Computer Studies

    (2007)
  • A. Ortiz De Guinea et al.

    Measure for measure: A two study multi-trait multi-method investigation of construct validity in IS research

    Computers in Human Behavior

    (2013)
  • A.H. Roscoe

    Assessing pilot workload: Why measure heart rate, HRV and respiration?

    Biological Psychology

    (1992)
  • R. Saadé et al.

    The impact of cognitive absorption on perceived usefulness and perceived ease of use in on-line learning: An extension of the technology acceptance model

    Information & Management

    (2005)
  • J. Scott et al.

    Cognitive engagement with a multimedia ERP training tool: Assessing computer self-efficacy and technology acceptance

    Information & Management

    (2009)
  • R. Shang et al.

    Extrinsic versus intrinsic motivations for consumers to shop on-line

    Information & Management

    (2005)
  • G.J. van Boxtel et al.

    A novel self-guided approach to alpha activity training

    International Journal of Psychophysiology

    (2012)
  • R. Agarwal et al.

    Time flies when you’re having fun: Cognitive absorption and beliefs about information technology usage

    MIS Quarterly

    (2000)
  • ASTD (2011). State of the industry 2011: Annual review of workplace learning and development data (pp. 64): American...
  • A. Bandura

    Social foundations of thought and action: A social cognitive theory

    (1986)
  • H. Barki et al.

    Linking IT implementation and acceptance via the construct of psychological ownership of information technology

    Journal of Information Technology

    (2008)
  • C. Berka et al.

    EEG correlates of task engagement and mental workload in vigilance, learning, and memory tasks

    Aviation, Space, and Environmental Medicine

    (2007)
  • D. Besthorn et al.

    Using variance as a tonic SCR parameters

    Journal of Psychophysiology

    (1989)
  • Boer, L. C., & Veltman, J. A. (1997). From workload assessment to system improvement. In Paper presented at the The...
  • W. Boucsein

    Electrodermal activity

    (1992)
  • M. Bradley et al.

    Affective picture processing

  • J.T. Cacioppo et al.

    Handbook of Psychophysiology

    (2007)
  • S.L. Christenson et al.

    Promoting successful school completion: Critical conceptual and methodological guidelines

    School Psychology Quarterly

    (2001)
  • D.R. Compeau et al.

    Computer self-efficacy: Development of a measure and initial test

    MIS Quarterly

    (1995)
  • T.P. Cronan et al.

    Decision making in an integrated business process context: Learning using an ERP simulation game

    Decision Sciences Journal of Innovative Education

    (2011)
  • T.P. Cronan et al.

    Comparing objective measures and perceptions of cognitive learning in an ERP simulation game

    Simulation & Gaming

    (2012)
  • R. Crott et al.

    Mapping the QLQ-C30 quality of life cancer questionnaire to EQ-5D patient preferences

    The European Journal of Health Economics

    (2010)
  • M. Csikszentmihalyi

    Flow: The psychology of optimal experience

    (1990)
  • R.J. Davidson et al.

    Asymmetrical brain electrical activity discriminates between psychometrically-matched verbal and spatial cognitive tasks

    Psychophysiology

    (1990)
  • M.E. Dawson et al.

    The electrodermal system

  • S. De Freitas et al.

    Serious games-engaging training solutions: A research and development project for supporting training needs

    British Journal of Educational Technology

    (2007)
  • Ö. de Manzano et al.

    The psychophysiology of flow during piano playing

    Emotion

    (2010)
  • M. De Rivecourt et al.

    Cardiovascular and eye activity measures as indices for momentary changes in mental effort during simulated flight

    Ergonomics

    (2008)
  • E.L. Deci et al.

    Characteristics of the rewarder and intrinsic motivation of the rewardee

    Journal of Personality and Social Psychology

    (1981)
  • E. Demerouti

    Job characteristics, flow, and performance: The moderating role of conscientiousness

    Journal of Occupational Health Psychology

    (2006)
  • R.E. Derouin et al.

    E-learning in organizations

    Journal of Management

    (2005)
  • A. Dimoka et al.

    NeuroIS: The potential of cognitive neuroscience for information systems research

    Information Systems Research

    (2011)
  • Drachen, A., Yannakakis, G., Nacke, L., & Pedersen, A. (2009). Correlation between heart rate, electrodermal activity...
  • A.J. Elliot et al.

    A subtle threat cue, heart rate variability, and cognitive performance

    Psychophysiology

    (2011)
  • G. Ellis et al.

    Measurement and analysis issues with explanation of variance in daily experience using the flow model

    Journal of Leisure Research

    (1994)
  • Fiering, L., & Kirwin, B. (2006). Untrained users cost more to support than trained users (pp. 8):...
  • J.C. Grannis

    Task engagement and the consistency of pedagogical controls: An ecological study of different structured classroom settings

    Curriculum Inquiry

    (1978)
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