Case Study # 2

Age and technology innovation in the workplace: Does work context matter?

Tracey E. Rizzuto ⇑ Department of Psychology, Louisiana State University, 236 Audubon Hall, Baton Rouge, LA 70803, USA article info Article history:

Available online 4 March 2011 Keywords:

Age Organizational climate Technology Implementation abstract Two workplace trends will become increasingly important in years to come: reliance on information technology (IT) and workforce aging. This study explores the inﬂuence of workplace context on employee reactions to the implementation of a new IT initiative to better understand innovation enhancers and inhibitors. Employees from multiple workplace departments completed a questionnaire that assessed their reactions to the implementation. Age-based differences and contextual inﬂuences were estimated to predict satisfaction with the implementation process. Hierarchical linear models indicate that younger workers reported less satisfaction than older workers—an effect that was more pronounced in relatively young departments. These ﬁndings challenge ageist notions and emphasize the role of context on atti- tudes formation. Multi-institutional and multilevel ﬁeld-setting data are rare making this a unique research contribution.

2011 Elsevier Ltd. All rights reserved. 1. Introduction Two important trends will have an increasing impact on the United States’ (US) workforce in the coming years. One is the grow- ing reliance on workplace information technology (IT) to store, process, and retrieve information (Mosner & Spiezle, 2003). The other is labor participation declines that are expected to intensify as the Baby Boom generation reaches retirement (Toossi, 2009).

At the intersection of these trends are concerns for how best to bal- ance workplace demands for IT innovation and proﬁciency in the context of an aging workforce. Despite the salience of these work- place issues, very little empirical research explores the relation- ships between employee age and attitudes toward IT initiatives, or the contextual inﬂuences that enhance and/or inhibit positive attitudes during the implementation process.

This study provides a naturalistic view of workforce aging and workplace innovation by exploring older workers’ reactions to the implementation of a new IT initiative in order to understand:

(1) their acceptance and satisfaction with the implementation pro- cess, and (2) the extent to which their attitudes are shaped by con- textual inﬂuences within the work environment. This research addresses recent calls in the literature to investigate organizational factors that shape the attitudes of and about older workers (Perry & Finkelstein, 1999; Posthuma & Campion, 2009), and provides in- sights into ways organizations may better support older workers through innovative IT transitions. By identifying factors and inﬂu- ences that foster employee satisfaction with the implementation of important, stressful, and often costly IT initiatives, this researchstands to promote innovative processes that may help organiza- tions be more competitive in the modern marketplace.

This ﬁeld-based study employs a cross-levels multi-institu- tional research design that predicts employee attitudes toward the implementation of a large-scale IT initiative. It examines the direct and moderating inﬂuences of workplace climate attitudes and age characteristics (e.g., average age; age diversity) that are thought to predict implementation satisfaction and age-based dif- ferences in reaction to IT initiatives. In doing so, this paper extends the theoretical understanding of individual adjustment to work- place innovation, and challenges commonly held beliefs about old- er workers’’ orientations toward innovative IT initiatives.

1.1. The intersection of workplace innovation and workforce aging IT can be broadly deﬁned as hardware, software, telecommuni- cations, and ofﬁce equipment that archives, transforms, and adds value to data (Dewett & Jones, 2001). While IT initiatives are increasingly being pursued by organizations to inspire ﬁnancial, motivational, and productivity gains (Mosner & Spiezle, 2003), negative consequences associated with the implementation of these initiatives threaten employee stress and well-being, lead to technology resistance, and contribute to costly initiative failures (Lucas, Swanson, & Zmud, 2007; Rizzuto, Mohammed, & Vance, 2011; Rizzuto & Reeves, 2007). Amidst the push toward IT initia- tives, the global workforce is aging, with nearly half of the US workforce projected to be over the age of 55 by the year 2020 (Toossi, 2009; United Nations, 2002). Research on this demo- graphic group supports the existence of a ‘‘grey digital divide’’ where the gap between the actual and needed IT skill among older adults in the workforce is widening, as is the gap between 0747-5632/$ - see front matter 2011 Elsevier Ltd. All rights reserved.

doi:10.1016/j.chb.2011.01.011 ⇑Tel.: +1 225 578 8924 (ofﬁce); fax: +1 225 578 4125.

E-mail address:[email protected] Computers in Human Behavior 27 (2011) 1612–1620 Contents lists available atScienceDirect Computers in Human Behavior journal homepage: www.elsevier.com/locate/comphumbeh technology use and access between older and younger workers (Friedberg, 2003; Millward, 2003). As a result, the retention of and skill-upgrades for older workers are stafﬁng and economic priorities that emerge at the intersection of workplace innovation and workforce aging (Feyrer, 2007; Friedberg, 2003; Walker, 2007). Research is needed to understand how older workers react to the implementation of innovative IT initiatives, and to identify the contextual factors that enhance or inhibit these workplace transitions.

Employee age has been examined as a predictor of technology perceptions, acceptance, and use, as well as demonstrated its abil- ity to shape the socio-cognitive processes and values that charac- terize workplaces (e.g.,Cleveland, Shore, & Murphy, 1997; Morris, Venkatesh, & Ackerman, 2005; Ostroff, Kinicki, & Tamkins, 2003). While much of this research reports high levels of anxiety and discomfort with technology among older adults (e.g.,Turner, Turner, & Van De Walle, 2007), it also suggests a willingness among older workers to adopt new IT innovations. For example, employee age has been shown to interact with normative workplace atti- tudes to produce greater IT acceptance and use among older adults when the technology is endorsed by others in the work environ- ment (Morris & Venkatesh, 2000; Morris et al., 2005). However, lit- tle is known about older workers’’ reactions to IT initiatives, whether technology-related discomforts lead them to react more negatively to implementation, whether these reactions are unique to their relative age cohort, common across organizations, or inﬂu- enced by their work environments.

1.2. Satisfaction with the implementation of innovative IT initiatives The implementation of large-scale IT initiatives has been de- scribed as complex, difﬁcult to manage, and as a process that inev- itably introduces ‘‘...hassle factors [that] can render even the most enthusiastic technophile frustrated and annoyed’’ (Klein & Knight, 2005, p. 244). An organization’s ability to satisfactorily manage common implementation pitfalls (e.g., training, participation, etc.) and evoke positive end-user reactions is an indication of IT initiative success (Rizzuto & Reeves, 2007; Sokol, 1994). Employees who respond positively to the implementation of IT initiatives en- dorse attitudes and demonstrate behaviors that promote work- place innovation. Speciﬁcally, implementation satisfaction predicts IT system use, support for and compliance with organiza- tional change initiatives, and is associated with lower levels of per- ceived work stress and fewer incidents of work slowdowns, sabotage, and resistance during IT transitions (Klein & Ralls, 1995; Morris & Venkatesh, 2000; Wanberg & Banas, 2000).

These outcomes make implementation satisfaction an impor- tant reaction to assess during a technology transition, particularly with respect to employee age since it sheds light on older workers ‘‘reactions to challenges that result from the ‘‘grey digital divide.’’ For example, while younger professionals develop IT skills in school-settings and through personal use outside of the work, employees over ﬁfty are less likely to have educational or work- force training experiences that build IT knowledge and skills that are highly sought in today’s labor market (Friedberg, 2003). As a re- sult, older workers face greater demands to learn new skills and technology-mediated work processes when new IT initiatives are implemented. Therefore, affective reactions toward an implemen- tation may serve as a litmus test for projecting IT initiative success in an aging labor market.

1.3. Employee age and implementation satisfaction A number of biological and psychosocial challenges emerge alongside new IT initiatives and are thought to make implementa- tion less satisfying for older workers. First, from a biologicalperspective, losses in visual and auditory capacities over the life- span contribute greatly to physical fatigue, stress, and eye strain among older computer users (Forteza & Prieto, 1990). Further, de- clines in complex cognition and processing speed are thought to make learning, retaining, and recalling new knowledge more difﬁ- cult at older ages (Minton & Schneider, 1980). These biological fac- tors, combined with the greater-than-average need for IT training among older workers (Friedberg, 2003), are thought to make IT implementation more burdensome and less satisfying for older workers.

From a psychosocial perspective, aging and innovation trends create the potential for ageist stereotypes to become more preva- lent in the workplace (Posthuma & Campion, 2009; Walker, 2007) and may have a bearing on older workers ‘‘reactions to IT initiatives. For example, there is a persistent stereotype that older adults are more resistant to change and to IT innovation, with ‘‘cor- rect age’’ perceptions commonly classifying older adults as unwill- ing to work with IT and/or ill-suited for computing-related occupations (Posthuma & Campion, 2009). Although is very little empirical evidence to support these assumptions, ageist beliefs are common, espoused by people of all ages (Garstka, Hummert, & Branscombe, 2005; Garstka, Schmitt, Branscombe, & Hummert, 2004), and particularly threatening to older workers who already face disproportionate employment hardships (Slack & Jensen, 2008). Stereotype threat research suggests that older adults who are faced with ageist beliefs often internalize and act in concert with them (O’Brien & Hummert, 2006). As a result, ageist stereo- types pertaining to technology may undermine the conﬁdence old- er workers have in their abilities to acquire new IT skills, and diminish their satisfaction with the implementation process. Given these considerations, the relationship between employee age and implementation satisfaction merits attention.

1.4. Workplace climate and employee implementation satisfaction According toNelson’s (1990)theory of individual adjustment to technology, an individual’s chronological age is not the only factor that predicts attitudes during the implementation of a new IT ini- tiative. Workplace characteristics, shared attitudes and beliefs form collective structures, climates, and cultures that operate within work environments, and are thought to inﬂuence imple- mentation satisfaction (Nelson, 1990).Rogers’ (2003)theory of innovation diffusion provides one explanation for how these mul- tilevel contextual inﬂuences affect the development of employee attitudes during workplace IT transitions. The theory asserts that individuals within social systems communicate their perceptions of and experiences with innovation to other members of the collec- tive. ‘‘These meanings and perceptions then inﬂuence the way in which individuals behave within the organization through their attitudes, norms, and perceptions of behavior-outcome contingen- cies’’ (Hofmann & Stetzer, 1996, p. 314). The resulting normative (i.e., workplace climate) attitude about an IT innovation can rein- force the adoption of similar attitudes by employees in the work environment.

Research that establishes positive associations between accep- tance of workplace initiatives and job satisfaction (Judge, Thoresen, Pucik, & Welbourne, 1999) and the application ofRoger’s (2003) diffusion of innovation theory suggests that workplaces comprised of employees who are accepting of an IT initiative would be char- acterized by climate attitudes that are also supportive of the initia- tive. These workplace climates would then, in turn, directly promote positive implementation attitudes (Nelson, 1990; Rogers, 2003). In essence, accepting attitudes toward IT initiatives are likely to generalize to other aspects of the transition, and facilitate a positive relationship between workplace climate attitudes and individual-level employee implementation satisfaction. T.E. Rizzuto / Computers in Human Behavior 27 (2011) 1612–16201613 1.5. Age context and employee implementation satisfaction The normative age characteristics of a work environment are also expected to impact employee adjustment to new IT initiatives (Nelson, 1990). In this study, ‘‘age context’’ is a department-level characteristic that represents the average age (mean age) and age diversity (standard deviation of ages) of employees within a given work context. Age context measures are relatively stable over time and are correlated with a variety of work outcomes that include job attitudes and organizational development efforts (Cleveland & Shore, 1992; Cleveland et al., 1997). Since individuals within age cohorts share a number of historical inﬂuences (i.e., introduction of the Internet, household computers, educational technologies, etc.), they are also likely to share common work attitudes, values, and experiences that differ from other age cohorts. For instance, gi- ven typical differences in their historical experiences using tech- nology, on balance employees in departments with ‘‘younger’’ age contexts (i.e., lower mean age) are characteristically more IT skilled and experienced than employees in ‘‘older’’ departments (i.e., higher mean age). For this reason, employees in departments with younger age contexts are likely to express different attitudes toward the implementation of new IT initiatives than employees in departments with older age contexts.

An important consideration, however, is that two departments with the same mean age may have drastically different composi- tions due to age variability. For this reason, age diversity is also potentially an important variable. Age homogeneity (uniformity) may strengthen age cohort effects by building consensus and cohe- sion around a majority view that is shared by others of the same age. In contrast, age heterogeneity (diversity) may dampen cohort effects since age diverse departments are likely to reﬂect a multi- tude of IT attitudes and experiences that vary across age groups.

1.6. Potential moderating effects of contextual workplace inﬂuences on employee age and implementation satisfaction Previous research indicates that, compared to their younger counterparts, the IT attitudes of older adults are more strongly inﬂuenced by normative social inﬂuences (Morris & Venkatesh, 2000). Therefore, older workers’ attitudes toward IT implementa- tion are also likely to be inﬂuenced by contextual workplace inﬂu- ences by being more persuaded by climate attitudes toward the IT initiative, and more likely to adopt these attitudes for themselves than younger workers. In addition, older workers also stand to ben- eﬁt from the informal IT training opportunities that have greater chances of occurring in departments with younger age contexts where members, on average, have greater IT competencies to share. Therefore, it is important to explore the possibility of cross-level interaction effects associated with contextual work- place inﬂuences (climate attitude and age context).

1.7. Research questions Given the research outlined above, this study addresses the fol- lowing research questions (RQ): (1) What is the relationship be- tween employee age and employee IT implementation satisfaction; (2) What is the relationship between workplace cli- mate and employee IT implementation satisfaction; (3) What is the relationship between workplace age context and employee IT implementation satisfaction; and (4) Do workplace climate and age context inﬂuence the relationship between employee age and employee IT implementation satisfaction?

In order to address these questions, two conditions must be sat- isﬁed. First, consensus with regard to IT initiative attitudes must be established among employees in each department. Agreement implies a shared assessment of the psychological value of the ITinitiative (i.e., a shared climate attitude) among department employees and suggests that their individual attitudes can be char- acterized as an aggregated whole (James, 1982; Klein, Conn, Smith, & Sorra, 2001; Klein, Conn, & Sorra, 2001). It is expected that with- in-unit department-level homogeneity will be demonstrated in the assessment of these attitudes. But in addition, between-units, departments must also systematically vary in their assessments of the IT initiative. Implementation strategies can differ across departments – some proactively manage IT initiatives while others passively acquiesce.

According toRogers’ (2003)theory of innovation diffusion, var- ious innovator typologies can be used to classify reactions to inno- vation that range from resistance (i.e., ‘‘laggard’’ reactions) to enthusiastic acceptance and adoption (i.e., ‘‘innovator’’ reactions).

It is expected that department identities will be established (e.g., ‘‘innovator departments,’’ ‘‘laggard departments’’), with members becoming increasingly responsive to the group-centered motives regarding the IT initiative. By employees sharing IT experiences, in- group dynamics may encourage the development of a uniﬁed departmental identity and shared attitude toward the IT initiative, while outgroup dynamics might create differences across departments.

2. Method 2.1. The study context: multi-agency SAP Procurement implementation This study assesses the implementation of an innovative IT ini- tiative: an SAP Procurement system for purchasing and requisi- tions within departmental units across multiple government organizations (i.e., agencies) in one northeastern US state. The implementation of this system provides an ideal context for this study for three reasons. First, the work process changes imposed by the IT initiative were deemed novel and substantial by most (95%) users who cited processing speed and capacity for inter- agency communication as innovative advantages (Sawyer, Hin- nant, & Rizzuto, 2006). Second, the research context allows for the examination of data at multiple levels of analysis.Eveland and Rogers (1980)contend that implementation behaviors are not uniformly displayed throughout organizations and, therefore, should be analyzed at the unit level with four or more persons rep- resenting at least two different status levels, and with work orga- nized by output and workﬂow technology (Dewar & Hage, 1978).

This sample is divided into numerous similarly structured depart- mental units comprised of procurement agents and directors. Fi- nally, the study’s context provides a notable representation of mid-to-late career employees. Half of the sample is over the age of 40, the demarcation for a protected group ascribed by theAge Discrimination in Employment Act (1967)(EEOC, n.d.); and 11% are over the age of 56. In sum, the broad IT implementation, levels of analysis, and age demographic make this an ideal research con- text for studying older workers’ reactions to the implementation of IT initiatives.

2.2. Sample The sample for this questionnaire study included 286 purchas- ing agents and directors (response rate 38%) from 25 departments (response rate 61%) across 18 government agencies in a northeast- ern US state as they underwent the statewide implementation of an SAP Procurement IT initiative, an enterprise resource system for purchasing. The average job tenure for the sample was 15 years.

Most respondents (55%) were procurement agents responsible for basic purchasing in departments; the rest were procurement 1614T.E. Rizzuto / Computers in Human Behavior 27 (2011) 1612–1620 directors with greater purchasing authority. No signiﬁcant differ- ences emerged between the two groups with regard to any of the study variables, including employee age, technology experi- ence, technology acceptance, or implementation satisfaction.

Therefore, the groups were analyzed as a single sample that was comprised of mostly female (82%) and Caucasian (93%) respon- dents, which is demographically representative of government procurement agents in this state (pay grade GS-05) (USOfﬁce of Personnel Management, 2004). Also representative, the sample’s age distribution was slightly negatively skewed: 25 years and younger (3%), 26–35 years (10%), 36–45 years (31%), 46–55 years (45%), and 56 years and older (11%). In keeping with the literature (Dewar & Hage, 1978), each department was represented by at least 4 purchasing agents (or more than one-ﬁfth of its procure- ment staff). On average, departments were represented by nine procurement agents (or 42% of its procurement staff). Departments with fewer than four participating agents (or 21% of its purchasing staff) were omitted from the sample.

2.3. Procedure All data for this project were collected within 18 months of SAP Procurement being implemented. Once institutional review board approval was granted, a government procurement listserv (the pri- mary mode of inter-agency communication) was used to invite all state procurement agents and directors to participate in a conﬁ- dential web-based questionnaire. A paper questionnaire was avail- able upon request, but none were requested. The instrument contained 18 scales, took approximately 15 min to complete, and was electronically administered devoid of participant names and email addresses. Scales with missing data were excluded from analysis, but the questionnaire responses were retained in the data set. Participation was voluntary and no compensation was pro- vided to the procurement agents. Modal themes from the data analysis were provided to participating departments.

2.4. Measures All variables except employee age and technology experience were measured using a ﬁve-point Likert scale ranging from 1 (‘‘Strongly Disagree’’) to 5 (‘‘Strong Agree’’) with high scores repre- senting a strong association with the characteristic or attitude being measured. Mean scale scores were calculated. Scale descrip- tives are presented inTable 1.

2.4.1. Employee age and age context The employee age variable was based on self-report question- naire data provided by procurement agents, and was solicited using a categorical response format to increase the response ratefor this key research variable (25 years and younger; 26–35 years; 36–45 years; 46–55 years; and 56 years and older). These data were used to calculate two age context variables. Age Context Mean was calculated as the statistical average of the employee age for procurement agents in each department, while age diver- sity within the work units was calculated based on the standard deviation (SD) to create the variable Age Context SD. Larger values represent greater age diversity, and smaller values represent great- er age uniformity.

2.4.2. Technology experience Given the study’s computer-based medium for data collection, level of technology experience represents a potential confound.

Therefore, technology experience was explored as a control vari- able and measured as the self-reported response to the question, ‘‘How would you describe your experience using information tech- nologies prior to the implementation of SAP Procurement?’’ The re- sponse options were anchored using the following 5-point Likert scale: 1 (‘‘No prior experience’’); 2 (‘‘Exposed to such systems, but never used them before’’); 3 (‘‘A little experience/Have used them before’’); 4 (‘‘Moderate experience’’); or 5 (‘‘A lot of experience’’).

2.4.3. Implementation satisfaction Implementation satisfaction indicates the degree to which an individual expresses satisfaction with the implementation of a new IT initiative. A narrow conceptualization is adopted for this measure to focus speciﬁcally on employees’ feelings about this par- ticular SAP Procurement initiative. The items were derived from Sokol’s (1994)Difﬁcult Designs framework which identiﬁes the ﬁve most commonly cited IT implementation challenges (training, management, participation, rewards, and feedback) (Rizzuto & Reeves, 2007). The degree to which an employee indicates agree- ment with each of the ﬁve aspects reﬂects his/her level of satisfac- tion with the implementation (e.g., ‘‘I am satisﬁed with...’’...the training opportunities made available;...the level of managerial support;... ...the rewards put in place;...the degree to which I was involved and informed about the SAP procurement implemen- tation process). This ﬁve-item scale exhibits an internal consis- tency reliability of a= .88. A principle components factor analysis conﬁrmed a single factor that explains 68% of the construct’s vari- ance, with factor loadings ranging from .70 to .84.

2.4.4. Information technology (IT) acceptance Two IT acceptance variables were constructed for this study:

individual-level IT acceptance and department-level climate attitude toward IT acceptance. Both were adapted fromKlein, Conn, & Smith,et al., 2001; Klein, Conn, & Sorra, 2001) six-item technology acceptance scale that assesses the degree to which Table 1 Scale, correlation, reliability, and descriptive analyses.

VariablesNMean SD 1 2 3 4 5 6 7 Department-level variables 1. Climate attitude 28 2.51 0.34 (.89) 2. Age Context Mean 28 3.51 0.27 .15 * – 3. Age Context SD 28 .89 .21 .02 .34 ** – Individual-level variables 4. Information technology (IT) acceptance 286 2.51 0.88 .38 ** .10 .03 (.88) 5. Implementation satisfaction 286 3.26 0.90 .18 ** .12* .08 .42 ** (.88) 6. Employee age 286 3.51 0.92 .12 .30 ** .11 .14 * .13* – 7. Technology experience 286 3.42 1.50 .16 ** .12 .09 .05 .08 .08 – 8. Time lag 286 0.92 0.90 .56 ** .04 .06 .23 ** .07 .02 .01 Notes: Internal consistency reliability is presented in bold on the diagonal. *p< .05.**p< .01.T.E. Rizzuto / Computers in Human Behavior 27 (2011) 1612–16201615 procurement agents were accepting of a new IT initiative. All items were revised to indicate the proper referent of interest: individual referent (‘‘I think...’’) and department referent (‘‘People in my workplace think...’’). For the latter, scale items were aggregated by the mean at the department-level to represent the climate atti- tude held by procurement agents and directors within each depart- ment (climate attitude toward IT acceptance). The individual-level IT acceptance scale exhibited an internal consistency reliability of a= .87, while the reliability for the department-level climate atti- tude toward IT acceptance scale was a= .89. To support the aggre- gation of individual-level data to the department-level, estimations were calculated forr wg, the degree to which scale rat- ings within groups are interchangeable (James, Demaree, & Wolf, 1984); and intraclass correlations (ICC), an estimation of homoge- neity within (ICC1) and across departments (ICC2). All indices met sufﬁcient standards to justify a department-level effect with the r wg value of 0.93 rising above .70 (George & Bettenhausen, 1990), a signiﬁcant ICC1 value of 0.11 (F= 1.80,p< .01, g2 = .61), and an ICC2 value of 0.80 (e.g.,Brett & Atwater, 2001).

3. Results 3.1. Preliminary analyses 3.1.1. Time lag After gaining access to SAP Procurement, not all employees use the system right away. Therefore, time lag is estimated as the dura- tion of time between when employees ﬁrst report using the SAP Procurement system and the administration of this study’s ques- tionnaire. Since time lag varied across system users, each individ- ual-level variable was analyzed to detect potential differences.

Across time lag intervals, no signiﬁcant differences were found for implementation satisfaction (F(3257) = 1.32,p> .05,f= .12) or employee age (F(3, 250) = .10,p> .05,f= .03). Although an anal- ysis of variance (ANOVA) detected signiﬁcant differences for indi- vidual IT acceptance (F(3255) = 5.58,p< .01,f= .23), a Tukey Student–Newman–Keuls post hoc analysis failed to reveal signiﬁ- cant differences over time. To ensure that time lag effects were negligible, analyses that involved individual IT acceptance were tested twice, ﬁrst using time lag as a control variable and then removing it from the model. Since the pattern of results remained the same with and without the time lag control, the ﬁndings pre- sented below omit time lag to maintain a more parsimonious mod- el and to preserve degrees of freedom.

3.1.2. Factor analysis Because individual-level IT acceptance and implementation sat- isfaction are correlated, a conﬁrmatory factor analysis was con- ducted to establish divergence between these constructs. The AMOS 4 statistical package estimated factor loadings as well as the absolute (chi-square;Bollen, 1989) and relative ﬁt indices of two proposed models. A non-signiﬁcant chi-square indicates good model ﬁt; however, this estimate is sensitive to sample size. The comparative ﬁt index (CFI;Bentler, 1990) and root mean squared error of approximation (RMSEA;Browne & Cudeck, 1993) are less sensitive to sampling characteristics and take degrees of freedom into account. CFI values close to .95 and a RMSEA values close to .06 are considered indicative of good model ﬁt (Hu & Bentler, 1999).

Since the two models are recursive, two separate analyses were conducted. Employing a similar multilevel modeling strategy as Han and Williams (2008), ﬁrst a one-factor model was tested to determine whether a single factor underlies the two scales. Next, a chi-square estimate for a two-factor model was calculated (x 2 (77) = 387.95,p< .01), along with the two relative ﬁt measures(CFI = 0.97; RMSEA = .11). A comparison between these models shows that the one-factor model has poorer ﬁt with the data, as evidenced by a signiﬁcantly larger chi-square (x 2(77) = 677.13, p< .01), larger effect size, larger RMSEA (.17), and smaller CFI (0.93), thus supporting a two-factor model that differentiates IT acceptance and implementation satisfaction. Scale items loaded on the respective factors as predicted with weights that ranged from .64 to .81.

3.1.2.1. RQ 1: exploring the employee age and implementation satisfaction relationship.To control for the potentially confounding inﬂuence of technology experience, a hierarchical linear regression (HLR) model was structured with technology experience in Step 1 and employee age in Step 2. As shown inTable 2, results from this analysis indicate that older workers were more satisﬁed with IT implementation than younger workers (b= .14,p< .05,f 2= .02).

Since previous research revealed curvilinear relationships between employee age and job satisfaction (Rhodes, 1983), a test for linear- ity was conducted and no deviations from linearity were exhibited.

This medium effect (based on the estimatef 2=(R 2/1 R 2);Cohen, 1988) suggests that, independent of technology experience, older workers report greater satisfaction with IT change initiatives than their younger counterparts. This ﬁnding reveals an interesting pat- tern of results that challenges the notion that older workers react negatively to IT initiatives. Providing convergent support for this relationship, older workers similarly report greater individual- level IT acceptance than younger workers (b= .14,p< .05, f 2= .02). Ruling out concerns that reactions to the implementation might be reactions to change, more generally, results from a one- way ANOVA found no signiﬁcant differences in resistance to change (F(4270) = 1.45,p> .05, Oreg, 2003) by employee age sug- gesting that age-based differences in implementation satisfaction are reﬂections of affective reactions to the IT initiative, speciﬁcally.

3.1.2.2. RQ 2 and RQ3: testing the inﬂuences of workplace climate and age context on employee implementation satisfaction.Given that the individuals in this sample are nested within departments, thus vio- lating ordinary least squares regression assumptions of indepen- dent observations, hierarchical linear model (HLM) intercepts and slopes-as-outcomes models were conducted to account for independence issues and to estimate cross-levels effects (Rauden- bush & Bryk, 2002). The data for each serial model is presented in Table 3. The ﬁrst step in HLM is a random one-way ANOVA (Uncon- ditional Means Model) for the individual-level (‘‘Level 1’’) depen- dent variable, implementation satisfaction, to determine whether there is systematic between-department variance in the outcome measure. Calculations revealed that 8% of the variance in imple- mentation satisfaction was accounted for by between-department variance. A chi-square analysis showed signiﬁcant differences from zero (x 2(24) = 41.97,p< .01), suggestive of systematic between- department variance.

Next, for department-level (‘‘Level 2’’) effects to be established there must be signiﬁcant variance in the Level 1 intercepts and in the slopes across departments. To establish this precondition, a random-coefﬁcient regression model was estimated using Table 2 Hierarchical linear regression model estimates in the prediction of implementation satisfaction. b DR2 R2totalF(d.f.) .02 3.24 **(1261) Step 1: Technology experience .07 .01 Step 2: Employee age .14 * .02* * p< .05.**p< .01. 1616T.E. Rizzuto / Computers in Human Behavior 27 (2011) 1612–1620 individual-level employee age to predict implementation satisfac- tion. The Level 1 equation consisted of a null model with the indi- vidual-level predictor (employee age) that allowed determination of whether enough between-department variance existed in the dependent variable to allow for Level 2 analyses. FollowingRau- denbush and Bryk (2002), the Level 1 model is expressed as follows:

Yij¼b ojþb 1jðemployee ageÞþr ij: whereYis the observed outcome,b ojis the intercept, andb 1is the regression slope, andris the individual-and department-speciﬁc residual. The simple effect tested in the model revealed a signiﬁcant pooled Level 1 intercept ( c00) and slope ( c10) between employee age and implementation satisfaction ( c00= 2.81,t(24) = 12.13,p< .01; c10= .12,t(266) = 2.16,p< .05). In addition to establishing the pre- condition of signiﬁcant Level 1 variance, the chi-square statistic for the intercept also indicated remaining systematic variance across units for implementation satisfaction (x 2(24) = 39.52,p< .05). The ﬁnal step in the HLM analysis estimates an intercepts and slopes-as-outcomes model for implementation satisfaction to determine whether the signiﬁcant variance in the intercept term across departments is related to the contextual workplace inﬂu- ences, climate attitude and age context. More speciﬁcally, depart- ment-level effects of climate attitude and age context were modeled on individual-level implementation satisfaction while controlling for individual-level employee age. In these models, the individual-level model remains the same as the random-coef- ﬁcient regression model described above, only the department-le- vel model is now expanded to include Level 2 predictors. The model is expressed as follows: boj¼c00þc01jðclimate attitudeÞ jþc02ðAge Context MeanÞ j þc03ðAge Context SDÞ jþu 0jb1j ¼c10þc11jðAge Context MeanÞ j: where cis the average of the department mean of climate attitude and age context across the sample, and whereuis the unique incre- ment to the intercept associated with each department. Thet-test for the gamma coefﬁcient parameter that is produced is an opera- tional test of these effects (Raudenbush & Bryk, 2002). As presented inTable 3, the Level 2 intercepts showed some signiﬁcant differ- ences in their inﬂuence on implementation satisfaction (climate attitude:

c01= .24,t(21) = 1.23,p= .17; Age Context Mean: c02= .95,t(21) = .42,p< .05; Age Context SD: c03= .41,t (21) = 1.43,p= .36). Although implementation satisfaction did not vary in association with climate attitudes, when controlling AgeContext SD, implementation satisfaction was greater in depart- ments with older age contexts than in departments with younger mean ages, providing support for a direct effect of Age Context Mean. Similar to the individual-level ﬁnding, older departments re- port greater IT implementation satisfaction than younger depart- ments. Once again, this pattern of results challenges common notions about older workers’ reactions to the implementation of IT initiatives. 3.1.2.3. RQ 4: do contextual inﬂuences moderate the employee age and implementation satisfaction relationship?.Finally, Age Context Mean was used to predict the within-department slope, and support for a Level 2 interaction effect was found. Controlling for the effects of climate attitude and Age Context SD, older departments differed from younger departments in the slope strength of the Level 1 rela- tionship between employee age and implementation satisfaction (Intercept c10= .85,t(262) = 2.81,p< .01; Age Context Mean: c11= .22,t(262) = 2.49,p< .05). While older employees and departments with older mean age contexts generally report more implementation satisfaction, Age Context Mean attenuates the Le- vel 1 relationship. Controlling for climate attitudes and Age Con- text SD, the employee age-implementation satisfaction relationship weakens as Age Context Mean increases. As a result, older workers report more satisfaction when the department is Table 3 Hierarchical linear model parameter estimates in prediction of implementation satisfaction.

HLM model Coefﬁcient Standard errort(d.f.) r2 s00 Unconditional means.76 .06 Random-coefﬁcient regression.76 .05 Intercept, c00 2.81 .23 12.13 (24) ** Employee age slope, 10 .12 .06 2.16 (266) * Intercepts and slopes-as-outcomes.75 .05 Model for means Intercept, c00 .57 1.47 .39 (21) Climate, c01 .24 .19 1.23 (21) Age Context Mean, c02 .95 .42 2.26 (21) * Age Context SD, c03 .41 .28 1.43 (21) Model for slopes Intercept, c10 .85 .30 2.81 (262) ** Age Context Mean, c11 .22 .09 2.49 (262) * * p< .05.**p< .01. Fig. 1.The cross-level inﬂuence of Age Context Mean on the employee age and implementation satisfaction relationship. T.E. Rizzuto / Computers in Human Behavior 27 (2011) 1612–16201617 younger (lower Age Context Mean) than when it is older. In con- trast, younger employees report more implementation satisfaction when the age context of the workplace is older than when it is younger. A graph of this cross-level interaction effect is depicted inFig. 1along with a conceptual model presented inFig. 2.

4. Discussion This paper responds to the need for research that provides a better understanding of older workers’ reactions to innovative IT initiatives, and considers the multilevel contextual inﬂuences within the work environment that affect them (e.g.,Posthuma & Campion, 2009; Rupp, Vodanovich, & Crede, 2006; Wanberg & Ba- nas, 2000). It challenges commonly held notions about employee age and attitudes toward IT implementation by investigating theo- retical relationships proposed in the scientiﬁc literature. Attempts to empirically measure cross-level interaction effects between con- textual workplace inﬂuences and human cognitions are relatively rare in the organizational innovation and IT implementation liter- atures (Devaraj, Easley, & Crant, 2008; Lucas et al., 2007). As a re- sult, little is known about older workers’ reactions to IT initiatives or the contextual inﬂuences that may support them through inno- vative transitions—a knowledge gap acknowledged by scientists and practitioners alike (e.g.,Lucas et al., 2007; Posthuma & Cam- pion, 2009; Silzer, Cober, Erickson, & Robinson, 2008). This study advances the theoretical understanding of how older workers react to IT transitions by using multi-institutional data collected in a ﬁeld-setting and by applying robust hierarchical linear modeling (HLM) techniques to parse out theoretically supported cross-level inﬂuences of workplace climate attitudes and age context (e.g., Nelson, 1990; Rogers, 2003). Evidence of the contextual inﬂuence suggests that IT attitudes are not simply a product of an individ- ual’s age or aging, as some ageist stereotypes portray. Rather, they are a juxtaposition of multilevel organizational inﬂuences that may be leveraged to better support older workers during IT transitions and capitalize on the beneﬁts of age diverse work environments (Rizzuto, Cherry, & LeDoux, in press).

The ﬁndings from this study suggest that older workers react more positively to implementation of IT initiatives than their younger counterparts, contradicting conventional beliefs that older adults resist IT innovation. Further, the age characteristics of the workplace are shown to shape employee attitudes toward IT initia- tives, not just of older workers but of younger workers as well. In this study, the attitudes that procurements agents held about the implementation of the new IT initiative were affected directly and in moderation by the mean age of other agents within the workplace, independent of climate attitudes and age diversity.

The older workers expressed greater IT implementation satisfac- tion when working in departments of lower mean age (younger departments), while younger workers reported greater implemen-tation satisfaction in higher mean age (older) departments. Given the large implementation satisfaction differences reported in high and low age contexts among younger workers, younger adults may be more likely to adopt the implementation attitudes of others around them. Alternatively, the interaction effect might suggest mutual beneﬁts of mixed-age mentorship models during IT imple- mentation such that older employees gain satisfaction from being introduced to IT in environments where other (primarily younger employees) are able to support their need for IT experience and skill. As reported byFinkelstein, Allen, and Rhoton (2003), older workers typically have fewer mentors in the work environment, with mixed-age mentorship relationships often occurring among younger protégés and older mentors. With regard to the develop- ment of IT experience and skill on-the-job, older workers in youn- ger departments may have greater opportunity to receive mentorship by learning new skills and computer-mediated work processes from their younger coworkers. Likewise, younger work- ers may ﬁnd satisfaction in demonstrating and sharing their IT-re- lated expertise with others. Semi-structured interviews with the procurement agents provide preliminary evidence that informal learning and individual participation processes were engaged (e.g., ‘‘We learned most of the transactions after go-live by [devel- oping] our own step-by-step ‘‘cheat sheets’’...we shared these with our own coworkers’’). However, the instrumentality of these processes in building positive IT attitudes is speculative given this study’s data and design limitations.

4.1. Implications for theory and practice This research has important implications for both theory and practice. This study provides empirical support for cross-level con- textual inﬂuences on IT attitudes that previously had only been theorized (e.g.,Nelson, 1990; Ostroff et al., 2003). Future research should explore the underlying mechanisms in these cross-level relationships, giving particular attention to the role of formal and informal IT training opportunities, as well as employee participa- tion in the IT training and implementation process. Not only are the factors among the most frequently reported human (non-tech- nical) obstacles to IT implementation (Rizzuto & Reeves, 2007 ), preliminary ﬁndings from this study suggest they may be a key to understanding the role of employee age in the IT implementa- tion process.

From an applied perspective, organizational planning efforts that occur prior to implementation are commonly suggested to im- prove IT initiative success (Rizzuto & Reeves, 2007). Workplace interventions that foster positive IT attitudes and help to combat ageist stereotypes may help to build momentum for the initiative and ease difﬁculties associated with innovative transitions (Maurer & Rafuse, 2001). Efforts to sensitize employees to the dangers of stereotyping and educate them about the realities of cognitive t = -2.49* Individual-Level Employee Age Individual-Level Implementation Satisfaction t = 2.26* Department-Level Age Context Mean Department-Level Age Context SD Department-Level Climate Attitude toward IT Acceptance Fig. 2.Multilevel predictors of individual-level implementation satisfaction. 1618T.E. Rizzuto / Computers in Human Behavior 27 (2011) 1612–1620 changes over the life course decrease ageist workplace attitudes and behaviors (Braithwaite, 2002; Finkelstein, Burke, & Raju, 1995). By cultivating age-supportive workplaces prior to imple- mentation, organizations may help younger and older workers appreciate the mutual beneﬁts each brings to the work environ- ment, strengthen the self-efﬁcacy of older workers, and retain their valued knowledge and experience during IT transitions.

4.2. Limitations and future directions While this study applies robust modeling techniques to difﬁ- cult-to-acquire multilevel data, it has methodological limitations.

First, this study’s small sample size may have limited its ability to detect effects or suppressed the size of these observed effects (Bryk, Raudenbush, & Congdon, 1994; Raudenbush & Bryk, 2002).

Although this study’s effects were moderate to small, considering the difﬁculty detecting signiﬁcant effects using complex modeling techniques that are applied to ﬁeld-setting data, the cross-level ef- fects revealed in this study are noteworthy and unique, and con- tribute to a rarely explored topic in the applied psychology and organizational science literatures. Researchers should replicate this work using larger multi-organizational samples from different work sectors and industries (e.g., private; non-proﬁt).

Second, this study is also limited by its cross-sectional research design and concerns for method bias. Time can soften and stabilize responses to change (e.g., Unfreeze-Change-Refreeze Change Stages;Lewin, 1951). By capturing an individual’s response to change at a single point in time using one data collection method (i.e., self report), one is limited in understanding the antecedent inﬂuences that precede IT acceptance or the behavioral outcomes associated with IT attitudes. Furthermore, internal measurement validity can be threatened by common method biases that overes- timate relationships (Podsakoff, MacKenzie, Lee, & Podsakoff, 2003). Despite efforts to minimize method bias by controlling for time lag and technology experience, and by offering a paper alter- native to the web-based medium, these ﬁndings should be cau- tiously interpreted and replicated using future studies that employ longitudinal research designs and multiple data collection methods.

5. Conclusions A great deal of attention and resources are devoted to the tech- nical aspects of IT innovation, but little attention is given to the interactions that occur between employees and contextual inﬂu- ences within the workplace that can be critical to IT initiative suc- cess (Devaraj et al., 2008; Klein, Conn, & Smith, et al., 2001; Klein, Conn, & Sorra, 2001). Investigations into these relationships will help us to better understand the organizational challenges and solutions for managing the intersection of workplace IT innovation and workforce aging. The demographic realities of the contempo- rary workforce will make these matters increasingly salient in the coming years.

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