چارچوب تعدیل انتقال بازنشستگی آینده گرا

This theoretical paper presents a person–environment fit framework that extends the Minnesota Theory of Work Adjustment to retirement transition and adjustment. The proposed Retirement Transition and Adjustment Framework (RTAF) also accommodates dynamic intra-individual and environment change over time, configural combinations of variables, and an ecological perspective to psychological-level decisions. The RTAF permits the collection of frequently sampled longitudinal person and environment data, allows for a detailed analysis of change and responsiveness, and can accommodate external influences from the family team, and social, economic and political policies. The paper concludes by using the RTAF to illustrate the types of psychological measurement and analysis opportunities likely to emerge in light of developments in the web, virtual reality, personal informatics and computing and information technology generally.

In this background section three themes are introduced, namely time, pattern or configural analysis, and broader ecological influences, before outlining the RTAF. 1.1. Time In any longitudinal research, time is the third dimension, although few studies specifically build this into theorizing, other than to establish cause–effect relationships. For time to become a serious focus of study requires more than a few snap shots of data. The difficulty in collecting, analyzing and interpreting good real-time data probably accounts for the comparative neglect of time in much psychological research. To date, existing theoretical and philosophical frameworks have not provided sufficient motivation and justification for traditional methodologists to find the solutions to these problems. This has created a vacuum that has been filled readily by qualitative research (Cohen, 2006). The stories that emerge from qualitative research highlight the inadequacy with which current traditional, measurement-oriented methodological approaches capture complex meaning that locates individuals in a particular time and ecological context (Bergman and Magnusson, 1997 and Weiss and Rupp, 2011). Within the vocational and career psychology literature, qualitative methods have become increasingly popular as researchers and practitioners sense that large-scale empirical studies often miss the meaning that is associated with the intense intra-individual differences and activities involved in transitioning across all stages of a career. Most recently, Savickas (2005) and Savickas, Nota, Rossier, Dauwalder, Duarte, Guichard and van Vianen (2009) have outlined a career constructionist approach in their “life-designing” model, which extends Super's (1990) notion of implementing a self-concept by arguing that the self-concept (self-knowledge and identity) is socially constructed through interaction and discourse. Their methodology involves prompting the client to tell stories, from which the life themes can be extracted. Through a story methodology, individuals can communicate their lifelong career self-construction. Quantitative methods are yet to be developed that will permit traditional testing of the life-designing approach. Also, it could be argued that not all self-knowledge is socially constructed. As individuals interact with the physical world their self assessment of motor and other skills evolve, and this too is important to self-knowledge. There have been attempts to develop quantitative approaches to overcome the limitations of the “slice in time” measurement that has become a tradition in psychology. Examples include experience sampling and diary studies, often with electronic prompts (Csikszentmihalyi and Larson, 1987 and Minbashian, Wood and Beckmann, 2010), life history analysis (Dunkel & Decker, 2010), the use of large-scale existing longitudinal databases (Lubinski et al., 1996 and Wang et al., 2008), simulation tasks for decision making (Goodman & Wood, 2004), and real-time monitoring of physiological measurements and virtual reality interaction (Stoermer, Mager, Roessier, Mueller-Spahn, & Bullinger, 2000). Although important, and in some instances innovative, the theories being tested have emerged from a more limited methodological and measurement era, and hence, may lack the sophistication needed to capture the complexity of dense time-related data. Retirement research provides a good example of the consequences of not integrating time in research. Past theories of retirement have tended to build on the connotation of the word “retirement”, implying it is a “point in time” event, rather than a process or a phase. Prediction of retirement age has become the focus of much research (Bidewell, Griffin, & Hesketh, 2006), in part because of the need for a single time-point dependent variable, and also because such data are needed for policy development in retirement, financial and human resource planning (Petkoska & Earl, 2009). However, as most nations no longer have compulsory retirement ages, and many are extending the age at which access to public pension schemes may be possible, the retirement transition process must be considered over a much longer period, most likely from 50 through to well over 70 years of age. For some, retirement is a hard step change, from one state to another; but for most, this is not so. Often, even where it does involve “retiring” from the primary occupation and employment, this may be merely a step to one of many different forms of income generation and voluntary work (Griffin and Hesketh, 2008 and Wang et al., 2008). More recently, we have been investigating another time-related construct, namely the notion of subjective life expectancy or longevity (Hesketh & Griffin, 2007), which is also important in retirement decisions. While actuarial estimates of life expectancy are used by financial planners when giving retirement advice, the role of self-assessments of life expectancy in individual decision making is rarely considered. Self-estimates of likely age at death predict actual mortality (Fry and Debats, 2006 and Siegel et al., 2003) and although research is sparse, factors thought to influence self-estimates include both demographic variables such as parents' age at death, income, health, gender and education (Fulba and Busch, 2005, O'Brien et al., 2005 and Wardle and Steptoe, 2003) and personality variables such as optimism (Mirowsky, 1999). Our own preliminary data (Griffin, Hesketh, & Loh, 2010) from the first phase of a longitudinal study associated with the Australian 45 and Up Study (Banks et al., 2008) found that when controlling for the demographic variables of gender, age, income, education, and marital status, parental longevity (average age of parents at death or current age), higher self-ratings of good health, fewer health conditions, and greater optimism emerged as the strongest predictors of subjective life expectancy. Hesketh and Griffin (2007) highlighted the importance of subjective longevity by showing that it was a stronger predictor of preferred retirement age than current income, expected retirement income, or self-reported health. Those who expected to live longer, planned to retire later. We think that self-estimates of longevity establish “mental” blocks of time within which individuals apportion work, transitioning and retirement, as well as considerations of how to distribute their finances and activities, taking into account likely health trajectories. At the broadest level, self-estimates of life expectancy provide individuals with their own unique time frame or context for retirement transition and adjustment. More recently, van Solinge and Henkens (2010) examined a variable they constructed from an existing longitudinal study that was similar to life expectancy. Their data suggested that longevity predicted later preferred retirement, but not actual retirement, but more research is needed. It is difficult to be precise in estimating the retirement transition period, or how long one will live, as is indicated by extensive rounding in such estimates. As such, we believe that there is a need for innovative graphic measures to obtain such estimates, and ideas are offered later in the article. 1.2. Pattern or configural analysis The recent focus on adjustment trajectories in the retirement literature is an attempt to incorporate changing patterns over time. Examples can be found in a number of studies (Kim and Moen, 2002, Pinquart and Schindler, 2007, Wang, 2007 and Wells et al., 2005, November) that make use of large-scale longitudinal data sets where retirement is only one of many variables measured. It is apparent from these studies that retirees do not follow uniform adjustment trajectories. Wang (2007) in the USA and Pinquart and Schindler (2007) in Germany, found that some retirees maintained well-being over their retirement transition, others improved after leaving the workforce, while there were also individuals whose well-being declined initially, but was followed by gradual improvement. As such, one could develop clusters depending on whether well-being is maintained, declines, remains stable, or declines initially before stabilizing. Such an approach using patterns of change is a start, but the typical focus on a single dependent or outcome variable (usually well-being or satisfaction), as the basis for classifying trajectories is limiting; other variables may not have been available in the historical data sets. The proposed Retirement Transition Adjustment Framework (RTAF), outlined below, offers a more comprehensive approach, while also allowing for the influence of family and societal factors in a broader ecological context. Bergman and Magnusson (1997) have highlighted the importance of constructs being able to speak appropriately across ecological levels. 1.3. Ecological influences One of the earlier career theories (Blau, Gustad, Jessor, Parnes, & Wilcock, 1956) provided a framework that showed the varied ecological influences on decisions. In the Blau et al. (1956) framework, individual and institutional decision making combine to result in an offer of a position being made and accepted. Influences on individual decision making include educational, family, and personal factors while influences on job opportunities include factors such as the labor market factors, the economy, and societal context. Magnusson and Stattin, 2006 and Bergman and Magnusson, 1997 make reference to the ways in which people interact with information that comes from several different ecological levels, and the RTAF framework outlined in this paper permits an analysis of this. Family is one of the ecological influences important in retirement research, although it is seldom examined systematically. Team decision making has received increasing attention in the organisational psychology literature in the past decades (Mesmer-Magnus & DeChurch, 2009), and we suggest that ideas from this research might be applied to the family ‘team’. Family input in career decisions has always been recognised as important. Many years ago Mann (1972) outlined a simple balance sheet decision approach that incorporates ways in which psychologically important personal decisions can account for the impact on and influence from “significant others”, such as a partner and family. We would argue that this is particularly needed in retirement transition decisions, and outline ways in which RTAF can accommodate family influences. The remainder of this article will focus on describing the RTAF, which adapts the Minnesota Theory of Work Adjustment (Dawis, 2005, Dawis and Lofquist, 1984, Hesketh, 1985 and Hesketh and Dawis, 1991) to retirement, while also dealing with time, configural or pattern constructs, and the ways in which different ecological levels and disciplinary perspectives can be included. The discussion lays the basis for building on the individual differences approach to counseling psychology (Dawis, 1992) with ideas for new measurement approaches that we include in the final speculative section of the paper.

It is our view that theories are needed that are able to accommodate the complexity outlined in this article, but also have a simplicity that permits a replication of the structure through time, across individuals in family groups, and as an interface with the social, economic, and policy level issues that impact on the event decisions. The Retirement Adjustment Transition Framework has such potential. Developments will need deep computing, visualization, and statistical analysis skills, as well as deep psychological theorizing, although it is possible that the varied competencies can be obtained through teamwork. These are challenges for the next generation of researchers and thinkers.