An Experimental Investigation of Turnover Intentions Among New Entrants in IT

An Experimental Investigation of Turnover Intentions Among New Entrants in IT Ritu Agarwal University of Maryland Thomas W. Ferratt University of Dayton Prabuddha De Purdue University

Abstract Although much research has focused on factors driving the turnover behaviors of information technology (IT) professionals once they are in an employment relationship, little is known about their ex ante intentions to stay when embarking on a new employment relationship. The present study investigates the effects of individual and situational factors on the turnover intentions of new entrants into the IT workforce. Arguably, the IT revolution of the past decade, both at its peak as well as its decline, has changed the expectations and values of this population of workers considerably. Adopting an interactionist perspective, we examine the interaction between individual preferences for organizational risk and variety, and the level of entrepreneurial risk inherent in the business model of a given employer together with the amount of variety that IT work with that employer offers. The posited relationships are tested in a policy capturing experiment with graduating information systems majors as subjects. The results provide support for the interactionist perspective, while at the same time questioning its basic premise. ACM Categories: K.6, K.6.1, K.7, K.7.1, K.7.2 Keywords: Turnover Intentions, IT Professionals, Situational Risk, Situational Variety, Experimental Approach, New Entrants Introduction Scholars have argued that IT human capital represents a strategic resource for firms, and has the ability to bestow competitive advantage (Wade & Hulland, 2004; Bharadwaj, 2000). Thus, the turnover and retention of IT professionals persists as an important managerial concern (Luftman, 2005; Luftman & McLean, 2004; Hsu et al., 2003). To this end, IS and organizational behavior researchers have examined a range of individual level factors as explanatory variables for intention to stay and turnover among IS and other populations of employees, including career anchors (Hsu et al., 2003), perceived ease of movement, job dissatisfaction, and organizational commitment (e.g., Igbaria & Greenhaus, 1992; March & Simon, 1958; Mobley et al., 1979; Hom & Griffeth, 1995). Researchers have also suggested that deliberate managerial actions, including IT organizations’ human resource management (HRM) practices, such as compensation and training (Agarwal & Ferratt, 1999; 2001; 2002; Slaughter & Ang, 2002), and internal work characteristics such as job design (Thatcher et al., 2002), are important determinants of intention to

The DATA BASE for Advances in Information Systems 8 Volume 38, Number 1, February 2007

stay and turnover. Finally, IS scholars have examined the effects of discrepancies between individual needs and what the organization supplies as determinants of turnover intention (Jiang & Klein, 2002).

In spite of the significant body of research focused on understanding turnover among IT professionals, however, there is a notable gap in this literature. Extant research examines the factors that influence IT professionals’ staying or leaving behavior after they are already in the employment relationship. This research, thus, provides limited insight into IT professionals’ ex ante intentions to stay when they are entering a new employment relationship. High costs associated with recruitment and socialization suggest that it is important for organizations to retain new hires for a reasonable length of time so as to recover their investment (Griffeth et al., 2000). Thus, it is critical to be able to isolate factors that predict how long the individual will stay with a particular organization at the stage of organizational entry (Cable & Judge, 1996).

Human resource researchers and practitioners have long been intrigued by the question of how individuals embarking upon a career make employment choices, also referred to as the “attraction process” (Schneider, 1987). The decision about where to accept the first employment offer is a significant one: early work experiences and the socialization that occurs often have a profound impact on later stages of an individual’s work life and have been shown to be a proximal cause of multiple significant outcomes such as turnover, satisfaction, and performance (Holton & Russel, 1999). In general, the choice of the first job is fraught with uncertainty since individuals typically have little or no work experience to help shape their expectations about employers and workplace conditions in a concrete fashion. They are inundated with information and advice about prospective employers from a variety of sources. Friends, family, and others in the social circle exert overt and covert pressure to accept employment with organizations that provide certain inducements, such as rewards, status, or security. Career placement centers and counselors at academic institutions provide advice on appropriate places of employment, and indeed, on careers to pursue. And recruiters compete for the applicants’ attention, particularly in a tight labor market, by painting a vivid picture of the benefits and rewards of employment in their organization. Against this backdrop, early career entrants are challenged with synthesizing all this information and making an informed choice about whom to work for in their first job.

Research in organizational behavior suggests that both individual and situational factors play a role in organizational entry decisions. Drawing upon the

nature and context of the IT profession and the upheaval it has witnessed, in this paper we posit that risk and variety are two important explanatory variables for the length of time an IT professional would choose to stay with a new employer. Arguably, the IT revolution of the past decade, both at its peak as well as its decline, has changed the expectations and values of this population of workers considerably. During the mid 90s, Y2K compliance concerns created a strong demand for IT workers with specific skills that declined after the year 2000. The years 1996-1999, riding upon the Internet revolution, were characterized by a severe shortage of IT workers with skills in the new technologies and a supply-demand asymmetry that was unprecedented. This resulted in a “careerist” mindset among IT workers (Agarwal & Ferratt, 1999; Feldman & Weitz, 1991), where opportunities for financial gain caused individuals to change employers frequently. Since the turn of the century and the slowdown in economic activity, there is an ostensible oversupply of high-tech workers, although projections of occupational growth suggest that any oversupply is likely to be temporary (United States Department of Labor, 2004). Thus, this is an interesting occupational group to study because of the turmoil it is experiencing.

We pose the broad question: “How do individual and situational factors interact in explaining a new IT workforce entrant’s turnover intention?” We examine two “matched” individual and situational factors that are likely to be important for this set of high-tech workers: for the organization, the two factors are the level of entrepreneurial risk inherent in the business model of the employer and the amount of variety that an IT job with the organization offers. From the individual perspective, these two factors are reflected in a new IT professional’s preferences for risk and variety. These matched individual and situational factors are consistent with the needs-supplies perspective in models of person-organization fit (Kristof, 1996). Risk and variety preferences represent individual needs, while organizational risk and variety represent what the organization supplies to meet those needs.

We report the results of an experiment with new entrants into the IT workforce where the level of risk and variety in a given employment context were manipulated. Such a research design allows us to gain further insights into an important organizational concern, the effect that its practices are likely to have on turnover. We use four hypothetical work settings characterized by a parsimonious set of factors and investigate their effects on future employees while controlling for all other situational factors. As such, because all other organizational factors are held constant for each individual while risk and variety are


varied, the design of this study provides a powerful basis for examining the ability of the organization to affect turnover intentions via its positioning related to risk and variety. Inspection of the moderating effects of individual preferences provides an understanding of the role of selection (of IT professionals with high or low preferences for risk and variety) and how organizations can expect their selection processes to interact with their “retention” practices (related to organizational risk and variety). From the perspective of the IT professional, the study provides a basis for understanding the effects of their personal preferences (for risk and variety) on their intentions to stay with an employer, potentially offering guidance for the job search and choice process. Theoretical Background and Research Hypotheses Individual and Situational Influences in the Workplace The influence of individual and situational factors on individuals’ work attitudes and behaviors continues to be the subject of much discussion among scholars in organizational behavior. Among the individual factors examined in prior research, both dispositions such as personality variables and other more malleable characteristics such as job attitudes have been studied. While dispositions are relatively stable characteristics of individuals (Fridhandler, 1986), situational factors are the wide range of influences that characterize the individual’s work context and environment. Early influential work by Staw and Ross (1985) demonstrating that job satisfaction remained considerably stable across time and across work situations provided initial support for the relative primacy of dispositional influences. Based on this work, scholars questioned the efficacy of managerial interventions such as supervision and training or situational factors inherent in the work environment in promoting job satisfaction and other affective and behavioral work outcomes.

Since the publication of Staw and Ross’ work, researchers have offered some criticisms of the research and continued to examine the individual-situational issue from multiple perspectives. For instance, Gerhart (1987) raised questions about a potential age bias in Staw and Ross’ study, and observed that their assertions about the lack of influence of job-redesign programs were not grounded in empirical evidence. Newton and Keenan (1991) noted the existence of a methodological problem with Staw and Ross’ work in that they used correlational analysis to test for stability in job satisfaction. Newton and Keenan’s analysis of the

stability of job attitudes and affect among young professional engineers led them to conclude that individual and situational influences both matter in work reactions. Several other studies also support the simultaneous existence of situational and individual effects (Griffin, 2001; Glynn, 1998; Steel & Rentsch, 1997; Banks & Henry, 1993). For example, Steel & Rentsch examined job satisfaction across a ten-year time period and showed that after accounting for the effects of attitudinal stability, job characteristics explained significant variance in outcomes. In their study, the success of job design interventions interacted with the personal characteristics of individuals.

Today, one perspective on the debate whether it is the person or the situation that determines behavior is the interactionist view of organizational behavior, as articulated in Schneider’s (1987) influential attraction-selection-attrition (ASA) framework. In this perspective, organizational behavior is viewed as being influenced by the characteristics of both the individual and the organization. Scholars in the interactionist tradition essentially reject the idea of using either individual or organizational factors in isolation to predict work attitudes and behavior and exhort researchers to pay attention to a richer set of factors that can influence these outcomes. Examples of interactionist research may be found in the extensive literature on person-organization (P-O) fit, which argues and presents empirical evidence for the fact that employment attitudes and behaviors are influenced by the congruence between individual characteristics and organizational environments (e.g., Kristof, 1996; Kristof-Brown et al., 2005).

In the IT literature, Agarwal and Ferratt (2000) proposed an extension to Rousseau’s (1995) theory of psychological contracts and argued that staying/leaving behavior would be influenced by an IT professional’s career motives, conceptualized as consisting of a preferred employment duration, career anchor, and career stage. Implicit in their arguments was the assertion that these three individual characteristics would change over the life time of an IT professional. In later work, Agarwal et al. (2001; 2002) suggested that preferred employment duration with a specific organization is jointly determined by career anchor, life stage, and competencies. They further argued that the type of organization would moderate the effects of these individual characteristics on preferred employment duration with that organization. Thus, in their conceptualization, individual preferences for length of stay with an employer are driven by both individual and situational factors.

Although there is now a growing corpus of work devoted to understanding the effects of individual and


situational variables on job-related attitudes and behaviors, there are also many unanswered questions (Kristof-Brown et al., 2005) and opportunities for further work. First is the crucial issue of the number of characteristics of persons and situations that serve as explanations for behavior. From a theoretical perspective, fewer characteristics are useful because they provide a more parsimonious explanation. From a managerial perspective, fewer characteristics should be easier and less costly to both evaluate when screening potential recruits for employment and to influence in the organizational environment. Much prior work has focused on profiles of individuals and organizations that involve a large number of variables. For example, the Organizational Culture Profile (OCP) developed by O’Reilly et al. (1991) has 54 value statements that yield eight factors for individual values and seven factors for organizational values. With a goal of parsimony, one unanswered question we address is whether a smaller number of important individual and organizational characteristics explain an individual outcome. Specifically, we focus on two such fit characteristics: risk and variety.

The second issue concerns itself with the characteristics of specific occupational groups and the work environments that typify the organizations they are employed in. Holland’s (1985) influential typology of occupations was developed for the purpose of classifying vocational interests and preferences. In this typology, both people and environments are characterized as Realistic, Investigative, Artistic, Social, Enterprising, and Conventional (RIASEC). In performing a meta-analysis of this theory, Barrick et al. (2003) note that the essence of RIASEC is that an “employee’s satisfaction with a job, as well as propensity to leave that job, depend on the degree to which the individual’s personality matches his or her occupational environment” (p. 46). However, the RIASEC model specifies occupational types (and associated work environments) at a high level of generality, representing distal variables in the prediction of actual work behaviors and attitudes. Moreover, the typology was constructed to be applicable to workers of all types, independent of their specific occupational status. Thus, a second issue we address here is the identification of factors (both individual and organizational) that are likely to be more salient to the specific population of IT workers. Conceptual Model The conceptual model underlying this research is shown in Figure 1. As indicated there, we argue that the effects of situational risk on turnover intention are moderated by individual preferences for risk.

Similarly, the effects of the variety offered by the employer (or the situation) are moderated by individual preferences for variety. The existence and importance of preferences in work-related attitudes and behaviors is underscored by Dawis (1991): he notes that traits influence values that, in turn, influence preferences. Preferences are viewed as being more proximal to choices among alternatives than values (Dawis, 1991; Judge & Cable, 1997) and represent the needs and wants that individuals are trying to satisfy via an employment relationship. To illustrate, an example of a personality trait is openness to experience, that would be reflected in work values that assign importance to broad-ranging work experiences. These work values then yield the individual characteristic of variety preference. We further note that in examining these effects, it is important to control for an individual’s propensity to stay. Each of the constructs and relationships is elaborated upon below. IT Workers and Situational Influences Considerable prior research has examined the motivations and management of high-tech workers in the information technology field (e.g., Cougar & Zawacki, 1980; Igbaria et al., 1991; Saxenian, 1994; Agarwal & Ferratt, 1999). Implicit in this research is the recognition of IT organizations and workers as important economic forces and interesting occupational groups worthy of investigation. Of particular note for this study are findings that IT workers possess a high growth-need strength (Cougar & Zawacki, 1980) and seek variety and technical challenge in job assignments (Hamblen, 2002). Further evidence for the importance of studying this group of workers separately can be found in surveys of the key issues confronting IT management, where attracting, developing, and retaining IT professionals consistently emerges as a critical management concern (Luftman, 2005; Luftman & McLean, 2004; Brancheau et al., 1996). In the past decade, this occupational group has witnessed a significant upheaval in at least two related areas relevant to employment. First, increasing organizational dependence on information technology fueled by the Internet revolution resulted in a major supply-demand asymmetry during the second half of the previous decade that still persists for certain technical competencies. Second, the business climate has been characterized by considerable IT-based entrepreneurial activity and innovation, driven largely by the capabilities offered by new information technologies.


Figure 1. Conceptual Model of Individual and Situational Influences

With burgeoning demand, organizations struggled to recruit and retain IT workers, and IT wages were driven up as a result. IT turnover rates as high as 20% were reported in some industries, further exacerbating organizational costs associated with maintaining the IT human capital base (Agarwal & Ferratt, 1999). Given the challenges associated with managing this occupational group, it is useful to attempt to characterize organizations in terms of aspects that are likely to influence the decision of IT workers to remain in the employment relationship for a greater length of time. As explained below, we argue that risk and variety represent two important situational characteristics with matching individual preferences that are relevant to IT workers’ turnover intentions. However, as noted by researchers in human capital theory, labor economics, and organizational behavior (e.g., Lazear, 1995; Topel & Ward, 1992; Rousseau, 1990), there may be differences in individuals’ propensities to stay in a job. We, therefore, include such a propensity as a control variable for turnover intention. Controlling for Propensity to Stay One important individual difference variable that serves as a predictor of job mobility is the inherent propensity to be a “stayer” or a “mover” (Topel & Ward, 1992). While movers exhibit considerable job mobility, stayers are characterized by relatively long tenure with specific employers. Thus, in a similar organizational situation, stayers are likely to exhibit lower turnover intentions than movers. The notion that

individuals possess distinct propensities to stay with an employer is also reflected in Rousseau’s (1995) characterization of “careerists.” Careerists regard their current employment situation as merely a stepping stone to better opportunities. In a study of the orientations of recent graduates toward their first job, Rousseau (1990) found that certain individuals were seeking to move quickly through employment situations in search of advancement. These careerists expected to stay with an organization for less than three years on average. At the other extreme, individuals low on careerism expected to stay for five or more years.

The difference between propensity to stay found in the economics and organizational behavior literatures is informative. This construct from the economics literature is an individual difference inferred from behavioral observation. By contrast, it is based on cognitions in the organizational behavior literature. Behavioral observation indicates the validity of the construct.

Since we seek to control for this variable prior to behavior, we use a cognitively-based definition of the construct. Individuals asked to think generally about how long they would stay with an employer need an anchor to describe that employer. We make the assumption that this cognitive expectation or propensity to stay is anchored in an employer that the individual would prefer. Another anchor that could be used is any employer; however, the uncertainties associated with this abstract employer would most likely be much greater than those associated with one

Preferred Risk

Turnover Intention

Situational Risk

Situational Variety

Preferred Variety

Propensity To

Stay


for which the individual would prefer to work, leading to a construct whose measurement would be inherently less reliable. In our model, propensity to stay refers to the individual’s expected employment duration with an employer for which the individual would prefer to work, i.e., an ideal employer.

It is important to note that our conceptualization of propensity to stay clarifies the distinction between preferred employment duration as discussed in Agarwal and Ferratt (2000) and preferred employment duration with a specific organization in Agarwal et al. (2001; 2002). Propensity to stay is a relatively stable individual difference variable that reflects inherent staying tendencies with an ideal employer. We recognize that an individual’s ideal employer may change over time, but we would expect that change to evolve gradually over time, e.g., as the individual moves through life and career stages. In effect, then, propensity to stay is an individual’s preferred employment duration within the context of an ideal employer. This conceptualization, which is similar to that in Agarwal and Ferratt (2000), is distinct from preferred employment duration with a specific organization, which is based on the interaction between the individual and specific situational variables (see Agarwal et al., 2001; 2002). If a specific organization would be identical to an individual’s ideal employer, then preferred employment duration with that specific organization would be identical to the individual’s propensity to stay. Situational Risk Organizations have different levels of employment risk and commensurate returns associated with them. Prior research has tended to focus more on the “returns” aspect of organizations, by considering attributes such as pay and reward systems. However, we suggest that the returns associated with a particular employment situation are likely to be evaluated in the context of the risks that the employer embodies. It is a truism to assert that advances in technology are changing organizations. In particular, web-based technologies led to the emergence of young entrepreneurial ventures in the shape of dotcom organizations during the latter half of the 1990s. This entrepreneurial activity was unprecedented in volume: in 1999 alone, 96 Internet companies went public, accounting for a total funding of $6.7 billion (Tice, 2000). Some of the attractions of dotcom organizations for IT professionals are the excitement and the potentially large financial gains,

which may come with considerable risk (Cher, 2000). Indeed, the much-publicized failures of a number of entrepreneurial ventures exposed the risk inherent in their business models.

At the same time, traditional brick and mortar organizations are also transforming themselves by focusing on core competencies and shedding functions that are peripheral to their main mission. The ostensible reason for this shift is that a focus on core competencies should improve the efficiency and effectiveness of the organization, thereby increasing returns to key stakeholders. Many of these transformations are IT-enabled. Increasing reliance on IT increases the vulnerability of the organization to a scarce supply of IT professionals or short-staying IT professionals when the organization requires firm-specific competencies, thereby amplifying business risk. Thus, while IT-enabled business innovation offers an opportunity for reward, these rewards are not without risk. Theories of choice assert individuals will evaluate risk/reward tradeoffs in their decision-making processes (Kahneman et al., 1982). These theories also argue that the level at which risk is traded off with reward differs across individuals.

All other things being equal, situational risk is likely to be positively related to turnover intention. For example, between two individuals in situations with the same reward, the one in the situation with the higher risk is likely to have the higher turnover intention. However, there is considerable evidence to suggest that individuals vary in their risk-taking propensities (e.g., Dulebohn, 2002). To the extent that an IT worker’s preference for risk and reward is consonant with that offered by the organization – as suggested by considerable prior research (e.g., O’Reilly et al., 1991; Cable & Judge, 1996) – this should result in a lower turnover intention. The underlying causal mechanism is one of greater job satisfaction and organizational commitment (Hom & Griffeth, 1995): when workers view their preferences as congruent with the situation, they develop stronger affective commitment (Meyer et al., 2002) and are less likely to exhibit withdrawal behaviors such as turnover. These arguments suggest the following hypothesis:

H1a: Situational risk has a positive relationship with turnover intention.

H1b: Preferred risk moderates the relationship between situational risk and turnover intention; the relationship becomes less positive as preferred risk increases.


Situational Variety While evaluating employment opportunities, a second dimension that IT professionals are likely to consider is the range of technologies and experiences that are potentially available in an organization, i.e., the variety of opportunities available with a given employer. Agarwal and Ferratt’s (1999) field research across a large number of organizations underscored the importance of interesting work as an important predictor of intentions to stay. Indeed, surveys of “Best places to work” in information systems (Brandel, 2006) consistently point to the nature of the work (e.g., leading-edge technologies, interesting projects) as a significant driver of IT professionals’ attitudes and work behaviors.

Organizations exhibit significant diversity in the types of information technologies they utilize and in the complexity and variety of their business processes. Some firms choose to standardize technology platforms and use a small number of technologies to support all activities, while others run applications on multiple platforms. Some organizations are characterized as laggards or traditional IT shops in that their information technology has not been refreshed for several years, whereas others prefer to stay closer to the leading edge and are constantly updating their technology base. Likewise, the extent of infusion of information technology across different processes within a business can vary considerably, as can the complexity of a firm’s business processes. Some organizations may choose to utilize IT purely in a utility mode, e.g., for automating back-office operations, while for others IT is a source of competitive advantage and permeates every business process within the company (Applegate et al., 2003). Organizations can have a single strategic mission and offer a limited range of products and services, or they may be diversified with a broad range of market offerings. Finally, there is considerable variance in the types of job rotation and other development practices adopted by IT organizations (Agarwal & Ferratt, 1999).

Each of these environments represents a very different set of work opportunities and experiences for an IT worker. For instance, in a large, global organization with varied IT-related activities, the breadth and depth of experiences available to an IT professional are likely to be considerably greater than those available in a smaller, narrowly focused organization. Staying in the former type of organization provides potential opportunities to acquire a diversity of IT skills through a broad range of experiences. Or it could be argued that IT professionals in smaller organizations are required to perform a variety of different tasks because of the IT

human capital constraints the organization faces. Thus, “situations” encountered by IT workers exhibit considerable variance in the level of variety offered.

We would expect variety to be negatively associated with turnover intention in that higher levels of variety will likely imbue the IT job with greater significance and challenge; the associated intrinsic rewards should lead to lower turnover intention. However, the notion that individuals vary in their variety-seeking behaviors is echoed in a significant body of consumer behavior research (e.g., Ratner & Kahn, 2002; Inman, 2001; Trivedi, 1999). Given that innate preferences for variety vary, these preferences are likely to moderate the effects of variety on turnover intention. Theoretically, the mechanism causing this interaction is similar to that discussed above for risk, and reflected in the notion of P-O fit (e.g., Kristof, 1996). More specifically, a match between preferences for variety and opportunities for variety should result in more positive outcomes. Thus, we propose the following hypotheses:

H2a: Situational variety has a negative relationship with turnover intention.

H2b: Preferred variety moderates the relationship between situational variety and turnover intention; the relationship becomes more negative as preferred variety increases.

Methodology Sample Information systems majors about to graduate and accept employment constitute the subject pool. This choice of subjects limits the population to new college graduate entrants to the IT workforce. This population, however, is important for those organizations that renew their IT workforce through college recruiting. In addition, this population makes this study relevant for the job search and choice decisions made by a large number of individuals about to enter the IT profession. Voluntary participants consist of 63 senior management information systems (MIS) majors at two U.S. universities (35 at a private midwestern university and 28 at a public eastern university). Characteristics of the sample, including demographics and the various measures described below, are shown in Table 1. Of the 63 subjects, 43 (68%) report some work experience in IT, with 39 indicating more than one year. We conclude that the subjects are not naïve about work environments and can make well-formed judgments about different employment situations.


Private Midwestern University Sample

Public Eastern University Sample

Combined Samples

Variables

Mean S.D. N Mean S.D. N Mean S.D. N Demographics++ Gender (0=woman, 1=man)* .74 .44 35 .44 .51 27 .61 .49 62 Ethnicity See below+ 35 See below# 28 See below+# 63 Years until Retirement 40.2 11.5 35 39.9 7.2 21 40.1 10.0 56

Individual Measures Propensity to Stay 4.1 1.2 35 4.5 1.2 28 4.3 1.2 63 (Number of years to stay) 5.9 5.6 35 6.3 5.5 27 6.1 5.5 62 Preferred Risk 2.4 1.1 35 2.7 1.5 28 2.5 1.3 63 Preferred Variety 5.5 1.3 35 5.3 1.5 28 5.4 1.4 63

Situational Measures by Scenario Scenario: Low Risk, Low Variety

Turnover Intention 4.4 1.0 34 4.4 1.3 28 4.4 1.1 62 (Number of years to stay) 3.5 1.8 34 4.1 3.2 28 3.8 2.5 62

Scenario: Low Risk, High Variety Turnover Intention 3.4 0.9 35 3.0 1.3 27 3.2 1.1 62 (Number of years to stay) 6.2 4.1 35 9.3 11.1 26 7.6 7.9 61

Scenario: High Risk, Low Variety Turnover Intention 5.4 1.0 35 5.1 1.3 28 5.3 1.1 63 (Number of years to stay) 1.8 1.0 35 2.6 2.4 28 2.2 1.8 63

Scenario: High Risk, High Variety Turnover Intention 5.0 1.1 34 4.8 1.1 28 4.9 1.1 62 (Number of years to stay) 2.8 1.9 34 3.5 2.3 28 3.1 2.1 62

* p < .05; (Means of the two samples are not significantly different except as noted with *) ++ 43 subjects (68%) report some prior work experience in IT. Of these, 39 report at least one year. + 86% (30) White, 6% (2) Asian, 3% (1) Hispanic, 3% (1) Black/African American, 3% (1) Other # 32% (9) White, 54% (15) Asian, 7% (2) Hispanic, 4% (1) Black/African American, 4% (1) Other

Table 1. Sample Characteristics Measures

Propensity to Stay. The measure for propensity to stay is taken from Agarwal et al. (2002), but is anchored to the context of an ideal employer. Participants are instructed: “Think about the type of organization for which you would prefer to work.” They answer four questions related to how long they are likely to stay with this organization. Three questions are on a seven-point Likert scale, where 1 is strongly disagree and 7 is strongly agree:

• My preference would be to stay with this organization for a long period of time.

• I would most likely quit this organization within a year.

• I would most likely quit this organization within three years.

The second and third questions are reverse scored. The fourth question is the following:

• What is the length of time you would prefer to stay with this organization at this stage of your career? _________ years

• This question is used with the following demographic question to obtain the fourth item in the measure:

• Approximately how many years do you have until retirement? _________ years

The fourth item is the ratio of these last two questions (constrained to a maximum of 1), converted to a seven-point scale (by multiplying by six and adding 1). Propensity to stay is the mean of these four items.

Turnover Intention. For each of the scenarios in the experiment (see Procedures below), participants are instructed in Section 1 of the scenario to assume that they have just been hired by an organization with the risk and variety profile described in the scenario (see Appendix A for a sample scenario). They are then instructed as follows: “Think about the organization that has just hired you (see Section 1 above) in


comparison with the type of organization for which you would prefer to work. Assume that this organization (i.e., the one that has just hired you) is similar to your preferred type of organization on all characteristics not included in the profile described above. Given all those characteristics and the ones described in the above profile for the organization that has just hired you, respond to each of the following statements by circling a number that best represents your degree of agreement or disagreement with each statement.” The same four questions used to measure propensity to stay are used to ask respondents how long they are likely to stay with the organization described in the profile. The demographic on years until retirement is used similarly to obtain the fourth item. The mean of the four items, scored in the same manner as propensity to stay, is reverse scored to obtain the measure of turnover intention. Each participant has a measure of turnover intention for each of the four specific organizational risk and variety profiles.

Preferred Risk and Variety. The individual measures of preferred risk and variety use the same instructions as those used for propensity to stay: “Think about the type of organization for which you would prefer to work.” Preferred risk is measured by the mean of the following three items (reverse scored) on a seven-point Likert scale, where 1 is strongly disagree and 7 is strongly agree:

• This organization provides stable, guaranteed work and income.

• A job with this organization would be fairly secure.

• The risks associated with this organization are low.

Preferred variety is measured by the mean of the following three items on a seven-point Likert scale, where 1 is strongly disagree and 7 is strongly agree:

• I could obtain a wide variety of experience with IT in this organization.

• I could move among many different types of jobs in IT in this organization.

• I could develop many different skills related to a career in IT in this organization.

Situational (i.e., Organizational) Risk and Variety. Each scenario of the experiment consists of an organization with a specified risk and variety profile. The profile is presented via semantic differential type scales, as in Agarwal et al. (2002). For an example, see Appendix A which presents a low risk, high variety profile. The first five items in the profile – financial stability, guaranteed income, job security,

guaranteed work, and overall financial and employment risk – represent risk items. Organizational risk has generally been measured as some type of variance in return on assets and return on equity (e.g., Miller & Reuer, 1996) and, to the best of our knowledge, there are no scale-based measures of organizational risk in the extant literature. Therefore we based the operational measure of risk on considerable work in labor economics that identifies compensation and employment as the two major sources of risk that employees face (Turner, 2001; Berloffa & Simmons, 2003).

The last five items – technologies involved in work, skills developed, opportunity to move among different IT jobs internally, experience gained by working on projects, and organization size – represent variety. Given the lack of existing scales for measuring organizational variety, we constructed these items (also used in Agarwal et al., 2002) based on the conceptual definition of variety as the opportunities available in an organization for a range of different work experiences and associated skill development. A low risk (or variety) profile has an X placed on the 7-point scale for each risk item at a value of 2. A high risk (or variety) profile has an X placed at 6. Procedures This study is based on an experiment with individuals preparing for entry into an IT career. The specific employment context was controlled by experimental manipulation of the two situational factors in Figure 1. Specifically, situational (i.e., organizational) risk and variety were manipulated by randomly presenting each participant with scenarios describing organizations with specific risk and variety profiles as described below. Participants were instructed to assume that they had just been hired by the organization in a scenario. Participants answered turnover intention questions focused on how long they would expect to be employed with an organization having the profile of characteristics identified in a scenario. This experimental design allows us to have various combinations of high and low organizational characteristics for hypothesis testing. Such a within-subjects design that permits the assessment of the relative attractiveness of different organizational environments is typically referred to as a policy capturing study (Judge & Bretz, 1992; Bretz & Judge, 1994).

The data for this study are obtained at two points in time. In the initial time period (called the baseline), participants answer questions on individual factors and demographics. Relative to Figure 1, they answer questions on their propensity to stay with an organization for which they would prefer to work; they


also answer questions on their preferred risk and variety.

At a later time (approximately 3 weeks after the baseline measurement), they are given four scenarios corresponding to four types of organizations that have one of the following profiles for situational risk and variety: (1) low risk, low variety, (2) low risk, high variety, (3) high risk, low variety, and (4) high risk, high variety. In all other respects, the organizations in each scenario are similar to their preferred employer. The scenarios and related questions are presented in random sequence, one right after another, in a single sitting.

For the last of the four scenarios, participants answer questions about the risk and variety that they perceive as being associated with the organization presented in that scenario to allow a manipulation check for risk and variety. If the manipulation is working as expected, subject responses to the risk and variety they perceive in the scenario should match the risk and variety assigned in the scenario description. The questions used are the same as those used for measuring preferred risk and variety, but the referent organization is the one in the scenario rather than the ideal employer. These questions were not used after each scenario to limit the amount of time required for the experiment and to limit participant fatigue and monotony. Since the scenarios are in random sequence, the manipulation check covers the range of situations. Statistical Analysis

Prior to the analyses for testing hypotheses, various scale analyses are conducted to ascertain

psychometric properties. Cronbach alpha reliability coefficients for the scales used in this study are all greater than .70 as shown in Table 2. These coefficients indicate that all scales have adequate internal consistency. Moreover, the situational risk and variety scales are significantly correlated with turnover intentions in a manner consistent with theoretical expectations (negative for situational variety and positive for risk), attesting to the predictive validity of the measures. Factor analysis of the items comprising the measures completed by participants – propensity to stay, preferred risk, preferred variety, and turnover intention – shows that these expected factors cleanly emerge with eigenvalues greater than 1 and appropriately high item loadings on the a priori factors for each measure (all greater than .60) and no high cross loadings (none greater than .40). Even though the same items are used to measure propensity to stay and turnover intention, the referents (i.e., preferred organization vs. organization with a specific risk and variety profile) are different. Indeed, the factor analyses demonstrate that these and the other scales have discriminant and convergent validity (See Appendix B).

A series of linear mixed effects models are used to test the hypotheses. Such models are particularly suited to the analysis of data that involves repeated measurements for different treatments on the same subject, as is the case in this experiment (McCulloch & Searle, 2001). This modeling technique specifically accounts for correlations between the repeated measures. In all models, individuals with nested risk by variety treatment sequences are specified as random effects and all other variables included in the model are specified as fixed effects.

Correlations

Mean

Std.

Deviation Turnover Intention

Propensity to Stay

Preferred Risk

Preferred Variety

Situational Risk

Situational Variety

Turnover Intention2 4.5 1.5 0.853 Propensity to Stay4 4.3 1.2 -.16 0.73

Preferred Risk4 2.5 1.3 .11 -.36** 0.84 Preferred Variety4 5.4 1.4 -.04 .43** -.47** 0.87

Situational Risk5 4.0 2.0 .47** .06 .14 -.22 --Situational Variety5 4.0 2.0 -.25* .20 -.16 .06 -.02 --Notes: * p ≤ .05; ** p ≤ .01; *** p ≤ .001 1. N = 63 for all means, standard deviations, and correlations except for those involving turnover intention, where N = 62. 2. Measured for each scenario. The data reported here are based on the second scenario presented to each participant. Since

scenarios were presented in a random sequence, all situational risk and variety conditions are represented in the data presented here.

3. Coefficient of reliability (Cronbach’s alpha) reported on the diagonal. 4. Measured at the baseline. 5. Experimentally assigned.

Table 2. Scale Analyses1


In the first stage of analysis, propensity to stay is used in the model as a control variable. For hypothesis 1, situational variety, preferred variety, and their interaction are also used as control variables in the first stage. Situational risk is added to the model in the second stage of analysis to assess its relationship with turnover intention after controlling for the non-risk related variables in Figure 1. Hypothesis 1a is tested by examining the change in information criteria between the estimated models from the first and second stages. If the model with situational risk fits the data better than the model without it, the information criteria should decrease in value from stage one to stage two. In addition, the sign of the situational risk coefficient is examined to determine if it is consistent with the hypothesis. For example, given that situational risk is a categorical variable (i.e., 2 = low risk and 6 = high risk), one category will serve as the base condition. If the high risk situation is the base condition against which low risk is compared in the statistical analysis, the low risk situation coefficient must be significant and negative to be consistent with the hypothesis, i.e., to show that lower situational risk is associated with lower turnover intention.

Preferred risk and the interaction of preferred risk and situational risk are added to the model in the third stage of analysis. Hypothesis 1b is tested by examining the change in information criteria between the estimated models from the second and third stages. If the model with the moderating effects of individual characteristics fit the data better than the model without moderation, the information criteria should decrease in value from stage two to stage three. In addition, the sign of the coefficient for the interaction of preferred and situational risk is examined to determine if it is consistent with the hypothesis. Continuing with the example from above, the coefficient in that case must be significant and

positive to be consistent with the hypothesis, i.e., to show that for lower risk situations turnover intention increases as preferred risk increases. A similar analytic approach is used to test hypotheses 2a and 2b. Results Manipulation Check

For the experimental scenario presented fourth to each participant (see Procedures), the absolute difference between the assigned and perceived values of both risk and variety (which are both on 7-point scales) is not significantly different from 1.0. Since the assigned values are either 2 (to represent low risk or low variety) or 6 (to represent high risk or high variety), perceived values within 1.0 of the assigned values indicate that the perceived values of risk and variety are consistent with the assigned values. Thus, the experimental procedure demonstrates internal validity.

Hypothesis Tests

H1a asserts that situational risk has a positive relationship with turnover intention. As shown in Table 3, after controlling for the effects of propensity to stay (plus the other variables in the model not related to hypothesis 1), the introduction of situational risk yields a decrease in the information criteria (e.g., AIC decreases from 828.1 to 756.5). The F statistic for situational risk is 89.1 with a significance of .000. Given that the high risk situation is the base condition against which low risk is compared in the statistical analysis, the low risk situation coefficient must be significant and negative to be consistent with the hypothesis.

Hypothesis 1

Model Controls Controls + Situational Risk

Controls + Situational and Preferred Risk and their Interaction

-2 Restricted Log Likelihood 824.1 752.5 750.7

Akaike’s Information Criterion (AIC) 828.1 756.5 754.7

Bozdogan’s Criterion (CAIC) 837.0 765.4 763.6

Hypothesis 2

Model Controls Controls + Situational Variety

Controls + Situational and Preferred Variety and their Interaction

-2 Restricted Log Likelihood 774.1 747.1 750.7

Akaike’s Information Criterion (AIC) 778.1 751.1 754.7

Bozdogan’s Criterion (CAIC) 787.1 760.0 763.6

Table 3. Information Criteria for Hypothesis Tests


Its value is -1.315 with a standard error of .139, a t-value of -9.441, degrees of freedom of approximately 180, and significance of .000 (see Appendix C). Thus, H1a is supported, i.e., individuals who perceive the organization to be high risk have greater turnover intentions.

H1b asserts that the positive relationship between situational risk and turnover intention becomes less positive as preferred risk increases. In other words, for higher risk situations individuals with higher preferred risk should have lower turnover intention than individuals with lower preferred risk; for lower risk situations, individuals with higher preferred risk should have higher turnover intentions. The addition of preferred risk as a moderator yields a further decrease in the information criteria (e.g., AIC decreases from 756.5 to 754.7 in Table 3). The F statistic for the interaction of preferred and situational risk is 8.1 with a significance of .005. As above, the high risk situation is the base condition against which low risk is compared in the statistical analysis; thus, the coefficient for the interaction between preferred and situational risk must be significant and positive to be consistent with the hypothesis. The interaction coefficient is .296 with a standard error of .104, a t-value of 2.853, and significance of .005 (see Appendix C). These findings indicate that H1b is supported. The interaction between preferred and situational risk is graphically illustrated in Figure 2.

Moderating Effect of Preferred Risk

3.0

4.0

5.0

6.0

7.0

Lo Hi

Organizational Risk

Turn

over

Inte

ntio

n

High PreferredRiskModeratePreferred RiskLow PreferredRisk

Figure 2. Moderating Effect of Preferred Risk

H2a asserts that situational variety has a negative relationship with turnover intention. As shown in Table 3, after controlling for the effects of propensity to stay (plus the other variables in the model not related to hypothesis 2), the introduction of situational variety yields a decrease in the information criteria (e.g., AIC decreases from 778.1 to 751.1). The F statistic for

situational variety is 31.5 with a significance of .000. Given that the high variety situation is the base condition against which low variety is compared in the statistical analysis, the low variety situation coefficient must be significant and positive to be consistent with the hypothesis, i.e., to show that lower situational variety is associated with higher turnover intention. Its value is .768 with a standard error of .137, a t-value of 5.611, and significance of .000 (see Appendix C). Thus, H2a is supported.

H2b posits that the negative relationship between situational variety and turnover intention becomes more negative as preferred variety increases. In other words, for higher variety situations, individuals with higher preferred variety should have lower turnover intention than individuals with lower preferred variety; for lower variety situations, individuals with higher preferred variety should have higher turnover intentions. The addition of preferred variety as a moderator does not yield a further decrease in the information criteria (e.g., AIC increases from 751.1 to 754.7 in Table 3). The F statistic for the interaction of preferred and situational variety is 1.822 with a significance of .179. Thus, H2b is not supported; however, the sign of the coefficient is in the direction hypothesized. As above, the high variety situation is the base condition against which low variety is compared in the statistical analysis; thus, the coefficient for the interaction between preferred and situational variety must be significant and positive to be consistent with the hypothesis, i.e., to show that for lower variety situations turnover intention increases as preferred variety increases. The interaction coefficient is .135 (see Appendix C). Figure 3 provides a visual plot of the interaction of situational and preferred variety.

Moderating Effect of Preferred Variety

3.0

4.0

5.0

6.0

Lo Hi

Organizational Variety

Turn

over

Inte

ntio

n

High PreferredVariety

ModeratePreferredVarietyLow PreferredVariety

Figure 3. Moderating Effect of Preferred Variety


Limitations The results from this study must be interpreted in light of four limitations that future studies could be designed to address. First, the sample is not a large random sample of new entrants to the IT workforce. Nevertheless, the results are strong enough with this sample to suggest that studies with additional samples would be appropriate to assess how generalizable the results are. Second, the scenarios represent a broad range of potential employers, but the distribution of actual employers with the range of risk and variety represented in the experimental situations is unknown. To understand how generalizable our results are to actual employers of IT professionals, future studies could include assessments of the risk and variety of actual employers. Of course, as with any study based on simulated situations, field studies are needed to support the generalizability of the results to real situations. Third, although we experimentally controlled the situational data, the data for the individual variables – propensity stay, preferred risk, preferred variety, and turnover intention – were collected from a single source. We did collect data for propensity to stay, preferred risk, and preferred variety after the survey and with alternative measures. Similar results were obtained, indicating that the results are fairly robust with regard to instruments. Finally, this study focused on understanding turnover intentions. Turnover intentions do not necessarily lead to actual turnover. It would be informative to study how the variables studied in this research on new IT workforce entrants are related to actual turnover. Discussion and Conclusion Our goal in this paper is to understand the role of a parsimonious set of two individual and situational characteristics expected to be important to IT professionals, viz., risk and variety, on the turnover intentions of IT workers who are entering the IT workforce. By using an experimental research design, we are able to examine the interactions between these factors across a range of organizational situations. We find that organizational risk interacts with individual preferences for risk in explaining turnover intentions, while organizational variety exhibits a main effect on turnover intentions with no interaction with individual preferences for variety. Thus, the former result supports the interactionist perspective, while the latter questions its basic premise. The findings yield several implications for practice and research that are elaborated upon below.

For IT managers, our conceptual model (Figure 1) helps direct their attention toward practices affecting perceptions of the situation (risk and variety) and our empirical findings reinforce the importance of individual and situational factors. It also helps direct them to think of practices affecting the types of individuals their organization recruits, selects, and retains since individual characteristics moderate the effect of the situation on turnover intention, particularly when considering risk preferences (H1b). Finally, our results suggest that the effects of organizational practices, whether directed at affecting perceptions of the situation or affecting the types of IT professionals the organization employs, will affect turnover intention through the interaction of the situation and individual characteristics, particularly when considering risk.

For prospective employers of IT professionals, our results have important implications for how they position themselves to potential new hires. Individuals form assessments of organizational characteristics at the stage of organizational entry by processing and synthesizing information from multiple sources. Organizations reveal information about themselves through a variety of mechanisms, both purposely and inadvertently. For instance, they may develop promotional materials that are distributed to potential applicants. For college recruiting, organizational representatives often host information sessions on campus. As discussed extensively in the realistic job preview literature (e.g., Phillips, 1998), the more accurate this information is, the greater is the likelihood of achieving organizational effectiveness through outcomes such as job satisfaction, organizational commitment, and job performance. A wide variety of organizational data is also accessible to the discerning job seeker through, for example, publicly available corporate documents such as annual reports and press coverage of significant organizational events. Another important source of information over which organizations have less control is current and former employees; often the testimonials and experiences of these workers offer new applicants a richer and more contextualized view of organizational life. Together, these multiple sources of information help shape the applicant’s opinion about specific characteristics of the organization. The implication of this study for prospective employers of IT professionals then is that risk and variety are powerful, parsimonious characteristics of the employment context, and realistic pictures of these situational characteristics should be emphasized through such channels.

Several areas for future research arise from this study. Six such areas are discussed next. First, even though risk and variety are important predictors of


turnover intentions, additional potentially powerful fit characteristics could be identified and investigated. Some of the characteristics in O’Reilly et al.’s (1991) OCP might arguably be correlated with risk and variety. For instance, items related to innovation, experimenting, risk-taking, security, and stability all load on the individual factor of “innovation” and are similar to the notion of risk described here. Likewise, items tapping into opportunities and professional growth, which loaded on different factors in the OCP (aggressiveness and emphasis on rewards, respectively), are reflective of our notion of variety. Although the explanatory power of the OCP is limited, the other values measured in the OCP may suggest additional potentially relevant fit characteristics.

Second, there are several ways in which the experiment described here can be extended and enriched. For instance, we assumed that risk and variety are important individual and situational characteristics and, with a goal of parsimony, included only these two factors. An alternative approach, in the spirit of the P-O fit literature (Kristof, 1996), would be to elicit the importance of different job and organization characteristics from individuals prior to presenting the organizational scenarios. Such a strategy could help alleviate the potential for social desirability bias in the responses (e.g., a preference for variety is generally considered good) and also provide insights into additional factors that should be included in the model. Further, we chose risk and variety as two factors that drive the turnover intentions of new entrants. It may well be the case that for individuals who are already in an employment situation, factors other than risk and variety are more salient to determining their turnover intentions. This possibility can be investigated in future research.

A third avenue for future research is to examine the antecedents of individual preferences. Much research asserts that individual preferences for situational factors are driven by their personal characteristics (Judge & Cable, 1997; Kristof, 1996; Kristof-Brown, 2000). In other words, what an individual seeks in an employment relationship is a function of some attributes that characterize the job seeker. Therefore, in light of our findings regarding risk, it would be fruitful to examine what the drivers of risk preferences are. Given our focus on IT workers, one important driver of risk preferences may be the competency bundle the IT worker perceives himself or herself as possessing, characterized in terms of the value of the knowledge, skills, and abilities (KSA) possessed and the performance level at which they are exhibited. Competencies are one instantiation of the general KSA construct prevalent in the job choice and employment behavior literature.

The need to include competencies in a model of IT employment behavior is evident when one considers the rapid pace of technological change in IT, along with variations in demand, supply, and market value of various competencies. On the one hand, technical skills decay rapidly as technologies change, and the new competence-destroying technologies often have limited lives, suggesting that the competencies of IT professionals erode rather than increase over time. On the other hand, IT organizations often need IT professionals with managerial and business skills that arguably increase in value with experience, in general, and perhaps even more with firm-specific experience. In addition, the organization’s need for IT professionals with specific competencies may be high for a limited period of time, which may coincide with a labor market that has a particularly acute undersupply of those competencies. Furthermore, IT workers may choose to develop a variety of different competencies. For instance, they may focus on generic skills or on firm-specific skills (Becker, 1983). Generic skills are those that can be traded in the open labor market and that are equally valuable to all employers. By contrast, firm-specific skills, which include deep knowledge of the firm’s context, social networks and relationships developed within the firm, and an understanding of firm culture, may be of limited value to competing employers.

Fourth, our study was conducted in the 2001-2002 time frame after the dot-com bubble was already in decline and there was a purported excess supply of IT labor in the market. We anchored our measurement of preferred risk and variety on the characteristics of an “ideal” employer. It is possible that the profile of such an ideal employer may be affected by the labor market situation and other environmental factors. Future research could theorize about and test the effects of such factors on IT professionals’ ideal employers.

It would also be interesting to examine if risk and variety are important in explaining turnover intention in IT professionals other than new entrants or in occupational groups that are relatively more stable and less entrepreneurial than the IT profession, such as retail banking. Our sample is limited to new entrants to the IT profession, and our hypothesis that the person-situation interaction on the dimensions of risk and variety would be significantly associated with turnover intention is predicated on idiosyncratic characteristics of the IT profession. Confirming these relationships with established IT professionals, e.g., those in the later career stages studied by Hsu et al. (2003), and examining them with other professions would be interesting directions for future research.


Finally, the finding that the individual characteristic of preferred risk moderates the effects of situational risk but that the individual characteristic of preferred variety does not moderate the effects of situational variety suggests that some situational characteristics dominate individual characteristics while others do not. Since these findings both confirm and question the basic premise of the interactionist perspective, it would be helpful to understand better the situational or individual characteristics that yield such results. Future research in this area could extend prior work on strong situations and individual differences that matter (Bowen & Ostroff, 2004; Mischel, 1977; 1997).

In conclusion, this study makes substantive and methodological contributions to the interactionist perspective on understanding turnover intentions of IT professionals, particularly new entrants who have been neglected in prior research on turnover intentions. We have argued that risk and variety are parsimonious, important situational and individual characteristics predicting turnover intentions among these new entrants to the high-tech profession and found that these two characteristics have strong explanatory power in a simulated environment. In addition, our experimental scenarios provide an example of a methodological approach for examining individual-situational interactions across a broad range of organizational settings. Finally, our scales for the fit characteristics of risk and variety provide a measurement foundation upon which future research can build. References Agarwal, R., De, P., and Ferratt, T.W. (2001). “How

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About the Authors Ritu Agarwal is Professor and Robert H. Smith Dean’s Chair of Information Systems at the Smith School of Business, University of Maryland. She is also the Director of the Center for Health Information and Decision Systems. Professor Agarwal is currently a Senior Editor at Information Systems Research. She has published in MISQ, ISR, Management Science, IEEE Transactions, JMIS, and other journals.

Thomas W. Ferratt is Sherman-Standard Register Professor of MIS at the University of Dayton. His work appears in ISR, MISQ, JMIS, Academy of Management Journal, and others. Besides being an

SE here, he is an AE at ISR. He served as Chair of the Special Interest Group on Computer Personnel Research (SIGCPR) before its merger with ACM SIGMIS.

Prabuddha De (Ph.D., Carnegie-Mellon University) is Accenture Professor of IT at Purdue University. He has published in Management Science, Operations Research, ISR, MISQ, and other journals. He serves or earlier served on the Editorial Boards of Management Science, ISR, JMIS, and DSS, among others. He was Doctoral Consortium Co-Chair for ICIS’2006, Program Chair for ICIS’1999, and Workshop Co-Chair for WITS’1994.

Appendix A: Organization Profile Section 1. Assume you have just taken a job with an organization that has the following profile. Think about this organization in comparison with the type of organization for which you would prefer to work. Assume that the organization described in the profile below is similar to your preferred type of organization on all characteristics not included in the profile described below. Each X represents where the organization that has just hired you lies on a 7-point linear scale. The two extremes of each 7-point scale are described, more or less, by the words at the opposite ends of the scale. Thus, each X is closer to the words most descriptive of the organization that just hired you.

Profile

Organization is financially stable |------X------|-------|-------|-------|-------| Organization value may vary widely over time

Income, benefits, and retirement are guaranteed |------X------|-------|-------|-------|-------|

Personal financial gains or losses vary considerably with organization’s performance

Job security is high |------X------|-------|-------|-------|-------| Future employment depends on organization’s performance

Work is guaranteed |------X------|-------|-------|-------|-------| Work load is driven by market demand

Financial and employment risks associated with this organization are low

|------X------|-------|-------|-------|-------| Financial and employment risks associated with this organization are high

Work involves a few selected technologies |-------|-------|-------|-------|------X------| Work involves a wide variety of

technologies

High skill levels are developed in a limited number of areas |-------|-------|-------|-------|------X------| Many different skills are developed

Opportunity for IT job movement internally is limited by the size of organization

|-------|-------|-------|-------|------X------| Opportunity to move among many different IT jobs internally is high

IT experience is gained by working on similar projects over time |-------|-------|-------|-------|------X------| IT experience is gained by working on a

variety of different projects over time

Organization is small |-------|-------|-------|-------|------X------| Organization is large

* This appendix shows a portion of the instrument used during the experiment.


Appendix B: Rotated Component Matrix for Items Measuring Individual Characteristicsa

Component

1 2 3 4

Turnover intention-quit 3 years-specific-reverseb .896

Turnover intention-quit 1 year-specific-reverseb .851

Turnover intention-stay long time-specificb .855

Turnover intention-years/yrs_until_retire-specific-7pointsb .712

Preferred variety-move among IT jobs-baseline .855

Preferred variety-experience with IT-baseline .834

Preferred variety-develop many different skills-baseline .817

Preferred risk-risks low-baseline-reverse .877

Preferred risk-secure job-baseline-reverse .850

Preferred risk-stable, guaranteed work, income-baseline-reverse .788

Propensity to stay-long time-baseline .768

Propensity to stay-quit 3 years-baseline-reverse .686

Propensity to stay-quit 1 year-baseline-reverse .636

Propensity to stay-years/yrs_until_retire-baseline-7points .630

a Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization.

Loadings < .40 not shown. These results were obtained with 54 observations for the second experimental situation. Substantially similar results were obtained for separate factor analysis of the observations on turnover intention in each experimental situation and the baseline observations on the other measures.

b Turnover intention items are scored in the same direction as the propensity to stay items for this analysis.


Appendix C: Estimates of Fixed Effects a

H1a Test

Parameter Estimate Std. Error df t Sig. Intercept 5.709 .451 147.056 12.666 .000

[Situational Variety=2.00] .039 .567 179.455 .069 .945

[Situational Variety=6.00] 0(a) 0 . . .

[Situational Risk=2.00] -1.315 .139 179.695 -9.441 .000

[Situational Risk=6.00] 0b 0 . . .

Preferred Variety -.008 .082 138.459 -.096 .923

Propensity to stay -.225 .071 58.110 -3.157 .003

Preferred Variety*([Situational Variety=2.00]) .136 .102 179.392 1.325 .187

Preferred Variety*([Situational Variety=6.00]) 0 b 0 . . .

H1b Test

Parameter Estimate Std. Error df t Sig. Intercept 6.024 .596 104.638 10.104 .000

[Situational Variety=2.00] .041 .556 178.432 .073 .942

[Situational Variety=6.00] 0 b 0 . . .

[Situational Risk=2.00] -2.066 .296 178.558 -6.970 .000

[Situational Risk=6.00] 0 b 0 . . .

Preferred Variety -.003 .086 121.301 -.038 .970

Preferred Risk -.137 .086 128.555 -1.603 .111

Propensity to stay -.222 .073 57.061 -3.063 .003

Preferred Variety*([Situational Variety=2.00]) .135 .100 178.371 1.350 .179

Preferred Variety*([Situational Variety=6.00]) 0 b 0 . . .

Preferred Risk*([Situational Risk=2.00]) .296 .104 178.396 2.853 .005 Notes: a Dependent variable: Turnover intention b This parameter is set to zero because it is redundant.


H2a Test Parameter Estimate Std. Error df t Sig. Intercept 5.962 .419 79.641 14.220 .000[Situational Variety=2.00] .768 .137 179.653 5.611 .000[Situational Variety=6.00] 0 b 0 . . .[Situational Risk=2.00] -2.065 .297 179.547 -6.953 .000[Situational Risk=6.00] 0 b 0 . . .Preferred Risk -.162 .081 143.189 -1.983 .049Propensity to stay -.198 .068 57.964 -2.932 .005Preferred Risk*([Situational Risk=2.00]) .296 .104 179.382 2.846 .005Preferred Risk*([Situational Risk=6.00]) 0 b 0 . . .

H2b Test Parameter Estimate Std. Error df t Sig. Intercept 6.024 .596 104.638 10.104 .000[Situational Variety=2.00] .041 .556 178.432 .073 .942[Situational Variety=6.00] 0 b 0 . . .[Situational Risk=2.00] -2.066 .296 178.558 -6.970 .000[Situational Risk=6.00] 0 b 0 . . .Preferred Variety -.003 .086 121.301 -.038 .970Preferred Risk -.137 .086 128.555 -1.603 .111Propensity to stay -.222 .073 57.061 -3.063 .003Preferred Variety*([Situational Variety=2.00]) .135 .100 178.371 1.350 .179Preferred Variety*([Situational Variety=6.00]) 0 b 0 . . .Preferred Risk*([Situational Risk=2.00]) .296 .104 178.396 2.853 .005Preferred Risk*([Situational Risk=6.00]) 0 b 0 . . .

Notes: a Dependent variable: Turnover intention b This parameter is set to zero because it is redundant.


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An Experimental Investigation of Turnover Intentions Among New Entrants in IT