Strain Active
2.5 CHAPTER SUMMARY
This chapter reviewed the literature pertaining to individual stress and task performance, selective attention, and cognitive aging. Table 2.6 highlights the key takeaways from the contributing literature that inform model development.
Confidence:
Experience Age-related differences may exist for these constructs Aging Research
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Table 2.6 Key Takeaways for Model Development
Key Takeaway Elaboration References
Stress formation is a transactional process .
Contemporary stress research considers stress formation a transactional process, meaning that stress arises not from a stimulus per se, but from the interaction between a person and the environment.
(Cooper et al., 2001; Hancock & Szalma, 2008; Lazarus, 1999; Ragu-Nathan et al., 2008; Tarafdar et al., 2007) The person-environment fit
perspective is best to examine age-related differences in technostress
The person-environment fit perspective provides a framework for understanding how stress emerges from the interaction between a person and a stressor. As such, it is well-suited to address such individual differences as age in a model of stress. Further, this model incorporates the concepts of coping (e.g., through self-efficacy) and task performance.
(Bandura, 1982; Caplan, 1987; Edwards, 1996; Folkman & Lazarus, 1984;
Lazarus, 1966, 1999; Ozer & Bandura, 1990; Pervin, 1968)
Technology-mediated interruptions indirectly affect stress through their
impact on person-environment fit. (Pervin, 1968; Warburton, 1979)
The frequency of incoming distractions or interruptions reduces person-environment fit.
(Hasher & Zacks, 1988; Lazarus, 1999;
Folkman & Lazarus, 1984; Zacks &
Perceived mental workload is the stressor that ultimately creates stress.
Perceived mental workload refers to the perceived ratio of the mental resources required to perform the current task to the resources available.
Attentional capacity is a major such resource. Mental workload
perceptions constitute a stressor of particular importance in the form of person-environment misfit.
(Endsley, 1995; Hart & Staveland, 1988;
Wickens et al., 2004)
Computer self-efficacy may yield interesting insights regarding age-related differences in technostress
Age is an important and know antecedent to computer self-efficacy, which may serve as a coping mechanism in a theory of technostress. As such, computer self-efficacy may yield interesting insights into age-related differences in the technostress phenomenon.
(Czaja et al., 2006; Lazarus, 1999;
Marakas et al., 1998; Ragu-Nathan et al.
2008)
Since people cannot process all the stimuli that continuously bombard their senses, selective attention is necessary to ensure that the most relevant information is processed and less relevant information is excluded from receiving processing resources.
(Rogers & Fisk, 2001; Strayer & Drews, 2007; Houghton & Tipper, 1994)
Attentional inhibition improves selective attention efficiency.
Attentional inhibition enables people to deliberately suppress distracting information, thereby effectively reducing the quantity of distractions or interruptions that gain access to mental resources.
(Houghton & Tipper, 1994; Hasher &
Zacks, 1988; Zacks & Hasher, 1997;
Darowski et al., 2008) The frequency of
technology-mediated interruptions impacts person-environment fit.
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Table 2.6 (continued) Key Takeaways for Model Development
Key Takeaway Elaboration References
Attentional amplification of salient distractions reduces selective attention
efficiency.
Individuals are more likely to attend to stimuli that are amplified. Hence, distracting stimuli that are amplified through such mechanisms as flashing or stimuli that present a threat are more likely to gain access to mental resources.
(Strayer & Drews, 2007; Tipper &
Houghton, 1994; Wickens et al., 2004)
Experience reduces the attentional requirements of a task.
Experience gradually replaces resource-intense effortful information processing for performaning a task by more efficient automatic processing. Thus, experience reduces the cognitive burden associated with performing a task and frees mental resources.
(Lee et al., 2007; Liu et al., 2004; Rogers
& Fisk, 2001; Sweller, 1988, 1994) Cognitive aging refers to
age-related changes in mental resources.
As individuals grow older, the availability of mental resources used to perform mental tasks becomes subject to change. Generally, older adults have fewer resources available.
The Inhibitory Deficit Theory of Selective Attention suggests that - compared to younger individuals - older peoples' inhibitory mechanism is less effective, thereby enabling more distracting stimuli to gain access to mental resources and interfere with current task processing. As a result, a single train of thought cannot be maintained, and progress on the current task becomes slowed and error-prone.
(Darowski et al., 2008; Hasher & Zacks, 1999; Hasher et al., 1991; Zacks &
Hasher, 1997)
Compared to younger individuals, older people are more likely to attend to salient stimuli, thereby enabling more distracting information to gain access to mental resources and interfere with current task processing. As a result, a single line of thought cannot be maintained, and progress on the current taks becomes slowed and error-prone.
(Fisk et al., 2009; Lorenzo-Lopez et al., 2008; Pratt & Bellomo, 1999; Whiting et al., 2007)
Older compared to younger people have lower levels of computer experience .
As a result of their lower computer experience, older compared to younger adults may need more cognitive resources to operate the computer, e.g. to move the mouse across the screen. Hence, they may have fewer resources to spare for T-M interruptions.
(Panek 1997; Sharit & Czaja, 1999)
Older compared to younger people have lower levels of computer self-efficacy .
As a result of their lower computer self-efficacy, older people may feel
more threatened in stressful situations that involve a computer. (Czaja et al., 2006; Marakas et al., 1998)
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Our review finds that stress arises from the interaction between a person and the environment rather than from a stimulus per se (Hancock & Szalma, 2008; Lazarus, 1999). Consistent with this understanding, the person-environment fit perspective (Pervin, 1968; French et al., 1982) constitutes the most appropriate theoretical
perspective for studying such individual differences as age in a model of technostress.
The essence of P-E Fit is captured by the construct ‘perceived mental workload’
(Kaldenberg & Becker, 1992), which increases in correspondence with cognitive demands and is the stressor that ultimately generates stress. We also find that T-M interruptions may affect stress indirectly through their impact on person-environment fit and that it is the frequency of incoming interruptions that directly impacts such fit (since the P-E Fit perspective, consistent with the inhibitory deficit theory, conceptualizes task demands in terms of environmental demands per unit time, Warburton, 1979). Further, computer-self efficacy is an underexplored construct in the technostress literature that may yield interesting insights into age-related differences. More specifically, the
construct relates to the phenomenon of coping and is a direct causal consequence of age.
The selective attention literature indicates that attentional inhibition and distracter salience serve to reduce and increase vulnerability to distraction, respectively (Houghton
& Tipper, 1994). In so doing, these mechanisms influence the effect of interruptions on P-E Fit in opposite directions. Our review further finds that experience can reduce the mental resource requirements of a task (Rogers & Fisk, 2001), and that – in the context of technostress – computer experience is negatively related to computer anxiety (Beckers &
Schmidt, 2003).
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Research on cognitive aging indicates that attentional inhibition is weaker in older adults, implying that older individuals are more vulnerable to distractions than younger people (Hasher & Zacks, 1988; Zacks & Hasher, 1997). As a result, older adults have fewer mental resources available for performing the current task than younger individuals when distractions appear. Regarding attentional amplification, the literature shows
substantial ambiguity as to whether age differences exist (e.g., Christ et al., 2008; Pratt &
Bellomo, 1999). Our review further shows that older people possess lower levels of computer experience (e.g., Czaja & Sharit, 1998; Fisk et al. 2009) and computer self-efficacy (e.g., Czaja et al., 2006; Marakas et al., 1998). Moreover, we find that–although IS research recognizes the importance of studying age-related differences in technology use–age-focused IS research is largely atheoretical and single-sided with respect to the concept of age. Hence, we point to a gap in the literature as regards the theory-based examination of age-related differences in technostress.
As illustrated in Figure 2.12, prior literature has primarily focused on examining technostress, aging, and selective attention in isolation. Some studies have looked at the intersection of two such areas. For example, Hasher and Zacks (1988) investigated the connection between selective attention and aging, and Ragu-Nathan et al. (2008) looked at the intersection between technostress and aging. However, no research to date has examined the point at which all three research areas intersect, although this point yields the greatest potential for explaining age-related differences in technostress.
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Figure 2.12 Illustrative Studies in the Contexts of Technostress, Selective Attention, and Aging
Furthermore, we find that research examining the role of age in technostress is largely inconclusive and nascent. Similarly, precious little work has examined the roles of T-M interruptions, computer experience, and computer self-efficacy in the context of technostress. Not surprisingly, recent research has called for such investigations.
Figure 2.13 illustrates how our literature review frames a model of T-M
interruptions, ageing, stress, and task performance. It shows that stress research provides the core model connecting ICTs, stress, and task performance, and that age and computer self-efficacy are important constructs to examine within the context of such technological
Technostress Selective Attention
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stressors as T-M interruptions. More specifically, the P-E Fit perspective combined with the literature on T-M interruptions indicates that the frequency with which T-M
interruptions appear increases perceptions of person-environment misfit in the form of mental workload (the P-E Fit perspective conceptualizes resource demands in terms of environmental demands per unit time or frequency). Perceived mental workload, in turn, gives rise to the experience of stress, which subsequently diminishes performance on computer-based tasks. Further, since age is an important antecedent to CSE, CSE may yield interesting insights regarding age-differences in the context of technostress.
Selective attention theory provides additional constructs that can further explain the link connecting ICTs to individual stress and performance on computer-based tasks.
These constructs include the inhibitory effectiveness of individuals, the salience of T-M interruptions, and computer experience. While the inhibitory mechanism may enable people to actively disregard appearing T-M interruptions, thereby reducing the number of such interruptions that can increase perceptions of mental workload, the salience of T-M interruptions may have the opposite effect. As T-M interruptions become relatively salient by appearing in an intrusive color with dynamism and sound effects, they may become more likely to capture individuals’ attention and give rise to perceptions of mental workload. Further, under the condition of high levels of computer experience, T-M interruptions can potentially draw mental resources away from computer-based tasks without increasing perceptions of mental workload. However, older people may benefit less than younger individuals from these mechanisms’ moderating impacts on
technostress.
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The literature on cognitive aging suggests that the inhibitory effectiveness of individuals, the salience of T-M interruptions, the level of computer experience, and the level of computer self-efficacy may substantially vary with age, thereby affecting older compared to younger people differently. More specifically, the Inhibitory Deficit Theory of Cognitive Aging, the literature on stimulus-driven attentional capture, and the
literature on experience indicate that T-M interruptions may be more likely to give rise to perceptions of mental workload in older compared to younger adults. Further, since age is an important antecedent to CSE such that older adults have significantly lower levels of CSE than younger individuals, older people may benefit less from CSE’s potential to mitigate the consequences of T-M interruptions on individual stress.
These four points through which age may impact technostress tie into the 4 C’s introduced earlier. The Inhibitory Deficit Theory of Cognitive Aging, which is entirely consistent with the literature on selective attention, leads us to coin the term
Concentration, which implies that older compared to younger adults may experience more trouble concentrating on the task at hand when T-M interruptions appear. Similarly, the literature on stimulus-driven attentional capture suggests that older individuals may be more susceptible to high levels of salience of these interruptions, an aspect of age that we refer to as Capture. Further, as indicated by the literature on the attentional
implications of experience along with research on computer experience, older adults may incur higher resource-related Costs from attending to T-M interruptions. Finally, as suggested by Self-Efficacy Theory and research on CSE, older adults may be less likely
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to cope effectively with the additional mental workload arising from T-M interruptions, an aspect of age that we refer to as Confidence.
In the next chapter, we integrate all these facets of individuals’ interactions with ICTs into a single articulated model of technostress that lies at the intersection of technostress, aging, and selective attention.
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Figure 2.13 Illustration of how our literature review informs model development.
The role of Age in Technostress
Stress Research Selective Attention Aging Research
Transactional
Experience Age-related differences may exist for these constructs Attentional
Legend: Dashed arrows connect research topics we reviewed to the constructs they provide for our research model.
Age-focused IS Research
Needed
Core Model Age-Related Manifestations
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