COGNITIVE AGING

The previous section offered further insight into the relationship between T-M interruptions and person-environment misfit in the form of mental workload perceptions.

We found that the frequency with which distracters appear reduces the availability of mental resources for current task performance, and that this effect depends on such conditions as attentional inhibition, distracter salience, and experience. However, we still lack a deeper understanding of potential age-related manifestations in a model of

technostress. How effectively do older people inhibit responses to distracters and react to distracter salience compared to younger? Further, since age is a known antecedent to CSE (Marakas, 1998), how much self-efficacy do older people compared to younger have with respect to computer use? Relatedly, how much experience do older people compared to younger have with respect to computer use? In this section we review the literature on cognitive aging, which refers to age-related changes in the allocation of mental resources (Park, 2000), to better understand how adult age relates to these concepts. In the process, we also review IS research that focuses on the concept of age.

2.4.1 T

I

D

T

C

A

Theories of selective attention explain how a subset of information can be effectively processed in the presence of distracting stimuli (Stoltzfus et al., 1993). The Inhibitory Deficit Theory of Cognitive Aging (Hasher & Zacks, 1988; Hasher, Zacks, &

May, 1999) (also referred to as the Theory of Distraction Control; Darowki et al., 2008)

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is a major theoretical approach to cognitive aging (McDowd & Shaw, 2000; Smith, 1996) that focuses on the deliberate control of the contents of working memory.

Working memory is the memory responsible for managing the information required to perform cognitive tasks. It is a temporary storage and processing component in the human brain that keeps the information necessary to complete the task at hand active. In more applied terms, working memory holds a small amount of information that can be “worked on” by cognitive processes (Wickens et al., 2004). It is also referred to as

“current consciousness” (Kausler, 1994, p. 149) or “short-term memory plus controlled attention” (Berti et al., 2004). Consider the example of dialing a telephone number after having looked it up. Once looked up, the number is held in working memory until it is completely dialed (Wickens et al., 2004). However, any distraction, for example someone counting aloud next to the person dialing, may result in slowed or incorrect dialing.

Distractions have this impact on task performance since the capacity of working memory (i.e., the general capacity available for mental work) is very limited, at times to as little as one item (Dumas & Hartman, 2008). As individuals attend to distracting stimuli that call for processing, these distracters enter working memory and reduce the capacity available for current task processing. As a result, mental work on the task at hand becomes slowed and error-prone.

The inhibitory deficit view holds that older adults are differentially vulnerable to the presence of distraction (Darowki et al., 2008; Hasher & Zacks, 1988; Hasher, Zacks,

& May, 1999). Consistent with research on selective attention (Houghton & Tipper,

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1994) the theory assumes an attentional inhibition mechanism that controls the negative impact distracting stimuli and thoughts may have on the processing of information related to the current task. Specifically, this mechanism directly inhibits or down-regulates the processing of distracting stimuli, regardless of whether such processing relates to stimuli in the task environment or to internal thoughts. In other words, distracting information is actively disattended (or disregarded). When an individual’s inhibition functions

effectively, distracting stimuli and thoughts do not enter working memory and hence do not interfere with the processing of current-task information. By contrast, reduced inhibitory effectiveness is associated with less suppression of response tendencies to distracting stimuli; hence, more distracters can enter working memory and can interfere with current task processing. As a result, attention cannot be sustained on the task at hand and task progress becomes slowed and error-prone. The theory indicates that the ability to suppress the processing of distracting stimuli and thoughts declines with age so that older people have less capacity for current task processing than younger when distractions appear ( see Figure 2.10).

Figure 2.10 The Inhibitory Deficit Theory of Cognitive Aging

Frequency of distractions Availability of mental resources for current-task processing Inhibitory

Effectiveness Adult age

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To illustrate, consider the everyday situations of reading the newspaper. The selection of one article involves ignoring other articles as well as photographs and advertisements that appear on the same page. Since their inhibitory mechanism is

impaired, older individuals may face greater difficulty than younger people in sustaining attention on (i.e., allocating resources to) a target story to the exclusion of other available stimuli (Connelly et al., 1991).

The inhibitory system serves two major complementary functions, which are access of extraneous information to focal attention and deletion from attention (Hasher et al., 1999). The access function suppresses the processing of distracting information when the distraction first occurs. The deletion function serves to quickly remove attended-to but rejected information from attention. Since both these mechanisms are compromised in older adults, distracting information is more likely to gain access to focal attention and to continuously disrupt cognitive processing. In effect, older people are differentially

“bothered” by distraction (Zacks & Hasher, 1997, p. 275).

The inhibitory deficit theory has received ample empirical support (e.g., Carlson et al., 1995; Connelly et al., 1991; Connelly & Hasher, 1993; Darowski et al., 2008;

Hasher et al., 1999; Kane et al., 1994; Kim et al., 2007; May et al., 1999; Stoltzfus et al., 1993). Two early studies conducted by Hasher, Zacks, and colleagues were particularly influential (Zacks & Hasher, 1997). Connelly et al. (1991) conducted two experiments, one with 48 and another with 64 participants. In either experiment, half the subjects were younger (i.e., a mean age of approx. 19 years) and half were older (i.e., a mean age of

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approx. 69 years). In experiment one, analysis of variance revealed that both younger and older adults’ task performance was reduced by the presence of distracters, and that older people were undoubtedly more affected. In experiment two, the same analytical

technique showed that the costs of distraction in terms of performance reductions on the current task were particularly large for older people when the distracter had a meaningful association with the current task. In summary, the study evidenced that older people are more susceptible to distraction than younger adults, especially in the case of meaningful distracting stimuli.

In building on the Connelly et al. paper, another study by Hasher, Zacks, and colleagues (Carlson et al., 1995) showed that when distracters appear in predictable locations, older peoples’ inhibition improves markedly and becomes almost as effective as younger adults’ inhibition. Further, predictable locations offset the relatedness effect found by Connelly et al., implying that older individuals are not particularly vulnerable to distractions that bear a meaningful relation to the current task when the distracters appear in fixed locations. These results were obtained through analysis of variance on data from three experiments, two with 64 and another with 32 participants. In all experiments, half the subjects were younger (i.e., a range of approx. 17-24 years) and half were older (i.e., a range of approx. 62-75 years). The authors replicated and extended the findings from prior studies, thereby indicating that older people are more vulnerable to distracting stimuli than younger adults.

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2.4.2 A

A

A

Recent research investigating the relationship between attentional capture and aging has indicated that “older adults tend to be more affected by salient events such as flashing, high-intensity lights as well as stimuli that appear to pose an immediate threat”

(Fisk et al., 2009, p. 22). Hence, object salience may affect older people differentially.

Consistent with this notion, some empirical studies indicate that age-related differences in attentional capture may exist.

A comprehensive literature search across more than 40 databases including four Psychology databases revealed two empirical studies indicating that older people may be more susceptible to attentional capture. Pratt and Bellomo (1999) conducted two

experiments with 16 younger and 16 older participants. The younger subjects ranged in age from 18 to 26 years, the older from 62 to 82 years. Analysis of variance showed age-related differences in attentional capture across both experiments, at least when older individuals’ attention was set for appearing targets. Similarly, Whiting et al. (2007) found that older people compared to younger individuals experience larger attentional capture effects. The authors conducted an experiment with twenty-four younger and twenty-four older individuals. The younger participants ranged in age from 19 to 23 years, the older from 60 to 80 years. Analysis of variance revealed age-related differences in attentional capture when only stimulus-driven information was available.

However, there is substantial conflict in the literature. The visual search/attention literature documents that introducing new motion to a previously existing (i.e., old)

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object captures attention equally well for younger and older adults (Christ et al., 2008).

Similarly, Kramer et al. (1999) found age-equivalence of attentional capture for new objects that appear in the visual field. To complicate matters further, one study (Pratt &

Bellomo, 1999) found that older individuals compared to younger adults exhibit larger capture effects for new objects but not for color distracters, at least when capture effects are measured by response time. Hence, some aspects of salience may show age

differences, while others do not. Yet, another study (Colcombe et al., 2003) observed age-equivalence for both color and new object distracters. Accordingly, the literature is unclear as regards age differences in attentional capture.

2.4.3 C

E

, C

S

-E

,

A

Some researchers argue that since older people have less working memory capacity than younger, any such aspect as experience, which enhances the amount of working memory available for the current task, should benefit them more (e.g., Van Gerven et al., 2000). Tomporowski (2003) examined this proposition empirically by applying cognitive load theory, which indicates that such conditions that overload

working memory capacity can lead to performance declines. While he found that practice reduces both younger and older individuals’ ratings of perceived mental workload, he failed to detect age differences. Other empirical evidence on age effects in the domain of experience is lacking. However, in the context of ICTs, it is well known that older people have less experience with computers than younger people (e.g., Fisk et al. 2009; Mead et al., 2000; Rogers et al., 1996; Rousseau et al., 1998; Sharit & Czaja, 1999). In fact, many older adults have very limited or even no computer experience (Czaja & Sharit, 1998).

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To illustrate, Czaja and Sharit (1998), using chi-square difference tests, found a highly significant difference in computer experience across age groups. More

specifically, 54% of their older subjects, compared to 17% of their younger participants, had no prior experience with computers. In total, 110 individuals participated in Czaja and Sharit’s experiment, in which older subjects were defined as those between 60 and 75 years of age, and younger subjects were defined as those between 20 and 39 years. This finding supports the idea that older adults have less experience with computers and may, therefore, require more mental resources to accomplish computer-based tasks⁴,

potentially resulting in more stress and lower task performance.

4 This conclusion is unlikely to be an artifact of our time; good reasons exist to believe that differences in computer experience between younger and older people will continue to exist in the future. In Section 3.4.3 we advance specific arguments for this belief on the bases of continuing educational differences between younger and older people and differences in the ease of ICT use, two major drivers of computer experience.

Additionally, consistent with Benbasat & Zmud’s (1999) argument that the nature of ICTs continuously evolves and changes at a rapid rate, we suggest here that technology will continue to evolve at a pace at least as high as the one observed over the last decades. More specifically, consistent with Moore’s law (established in 1958), the speed and complexity of computers has risen exponentially over the last decades since transistors in integrated circuits have continuously become smaller in size. More importantly, the pace of this development has become faster – not slower – over time (Pacholski, 2006). While at the beginning of the 20^th century the speed of computer calculations doubled every three years, this process was shortened to two years in the middle of that century. Today, this process takes only eighteen months (Pacholski, 2006). Thus, the pace of the computer evolution has become faster over the last century, not slower, and there is little evidence suggesting that this trend will change. In fact, since technological advances have become so regular, many observers expect them to continue “indefinitely” (Borsuk &

Coffey, 2003). However, some argue that this trend cannot continue indefinitely since transistors can only become so small in size and would reach atomic dimensions already by 2050 (Borsuk & Coffey, 2003).

Still, this may not reduce the pace of the computing evolution since new developments are on the horizon that may outperform regular transistors. For example, silicon processors have recently been joined in laboratory settings with neural cells so that information can flow directly from biological tissue to the silicon (Pacholski, 2006). As another example, IPv6, which replaces IPv4, supports four times as many bits for an internet protocol address, vastly expanding the number of devices and users on the internet. Hence, there is reason to believe that technology will continue to evolve at a pace at least as high as the one observed over the last decades, if not higher.

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Similar to computer experience, computer self-efficacy (CSE) may be limited in older adults since “in cultures that revere youth and negatively stereotype the elderly, age becomes a salient dimension for self-evaluation” (Bandura, 1986, p. 418). Further, normal age-related changes in cognitive abilities threaten older individuals' beliefs in their ability to deal with the variety of cognitive demands involved in computer work (Reed et al., 2005). Consistent with this notion, recent studies predicted and found that older adults have lower CSE than younger people. For example, Marakas et al. (1998) indicated that age is an important and known antecedent to CSE, such that older

compared to younger individuals have lower self-efficacy with regard to computer use.

Likewise, Laguna and Babcock (2000) reported that older adults have lower CSE than younger individuals. In a sample of 144 individuals ranging in age from 18 to 86 years, they found overwhelming evidence for a relationship between age and CSE. The relationship between the two concepts was strongly negative with a correlation coefficient of almost -0.50. Another study (Czaja et al., 2006) came to similar

conclusions. Czaja et al.’s sample consisted of 1,204 adults ranging in age from 18 to 91 years with a mean age of 47 years. Like Laguna and Babcock before them, they found overwhelming support for a negative relationship between age and CSE.

2.4.4 IS R

C

A

Recent research (Venkatesh et al., 2003, p. 469) indicates that age is a “key demographic variable” for IS research. Age has predominantly been studied to explain for whom certain user perceptions result in individual adoption and sustained use of technology in the workplace. For example, Venkatesh et al. (2003) included age as a

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moderator in their Unified Theory of Acceptance and Use of Technology. They showed that age moderates the links between performance expectancy, effort expectancy, social influence, and facilitating conditions as independent variables, and intention to use a technology as the dependent variable. More specifically, age negatively moderated the link between performance expectancy and intention to use, while it positively moderated the other links.

While IS research has included the concept of age in studies on technology acceptance, few studies have actually focused their attention on age. A comprehensive search across over 40 databases using such keywords as “age,” “old,” and “older”

revealed only three IS studies that deliberately focused their attention on the concept of age. Morris and Venkatesh (2000) examined age differences in technology adoption decisions to help businesses deal with the aging workforce. Using the theory of planned behavior, they developed a model that integrates age with attitude toward a technology, subjective norm, perceived behavioral control, and system use. Morris and Venkatesh (2000) found that adoption decisions were indeed influenced by age, such that older workers were more influenced by subjective norm and perceived behavioral control than younger people, but less by their attitude toward the technology. With their sole reliance on the theory of planned behavior to derive their model, the authors chose not to use a theory of aging to predict age differences and interpret their findings.

Similarly, Morris et al. (2005) looked at three-way interactions between age, gender, and the constructs provided by the theory of planned behavior to predict

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employee decisions about the adoption of new technology. They found that gender differences in technology perceptions were significant among older workers, while such differences were not found among younger workers. The authors concluded that common stereotypes describing technology as a male-oriented area are fading away—particularly among younger workers. Like Morris and Venkatesh before them, the authors’ solely relied on the theory of planned behavior to derive their model. Since the theory of

planned behavior does not address age-related differences at all, Morris et al.’s study was largely atheoretical with regard to age-related differences in technology adoption.

Another study (Lam & Lee, 2006) examined internet adoption by older individuals. Using social cognitive theory, the authors explored the effects of internet self-efficacy and outcome expectations on older peoples’ extent of internet usage. In a sample of almost 1,000 individuals aged 55 and older, they found general support for their model. Both internet self-efficacy and outcome expectations were significant predictors of usage intention for older adults. Since the authors solely relied on social cognitive theory, which makes some assumptions about age, but is not a theory of aging, the study was largely atheoretical with regard to technology adoption by older adults.

To summarize, IS studies that have focused on age and age-related differences have derived their models and interpreted their findings without reliance on theories of aging. As such, IS scholarship is largely atheoretical with regard to its treatment of age.

Further, IS research with an age focus is restricted to the domain of technology adoption, be it in the context of general technology adoption or internet usage. Other domains, such

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as technostress, have yet to be infiltrated. Not surprisingly, recent IS research (Tarafdar et al., 2007) called for an examination of the role of age in technostress. In conclusion, aged-focused IS research needs to be more theoretical with regard to its treatment of the concept of age, and more diversified in terms of the phenomena under investigation (see Table 2.5).

Table 2.5 IS Research with a Focus on the Concept of Age

2.4.5 S

S

Our review of the literature on aging yields interesting insights for age-related manifestations in a model of technostress. Consistent with the literature on selective attention, the Inhibitory Deficit Theory of Cognitive Aging postulates an attentional inhibition mechanism. This mechanism enables people to control attention and sustain the focus of attention on a particular active task despite the presence of distracting stimuli (Hasher & Zacks, 1988; Zacks & Hasher, 1997). The theory holds that attentional

inhibition is impaired in older adults, thereby enabling more distractions to gain access to mental resources. This means that older compared to younger adults experience greater

Source Phenomenon under

Examination Theories used Bearing Theories used have on the Concept of Aging

Atheoretical with regard to the Concept of Aging

Lam & Lee (2006) Technology Adoption

Lam & Lee (2006) Technology Adoption