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TopIntroduction
Jobs become obsolete and new jobs replace them. As Peter Drucker (1998) observed, “the next information revolution” (p. 1) advances with the advent of ever newer technology. Soon it will be almost impossible to keep up with the material produced in the explosion of information. Yet, it is necessary to give it a good try. The issue treated in this article is the need to stay ahead of obsolescence. The obvious solution is to stay current with advancing technology. Doing so will require continuous learning and relearning.
Kim and Park (2020) observed that “Rapid technological development makes skills depreciate faster than in the past while new technologies generate gaps in workers’ skills and call for the acquisition of appropriate skills and lifelong learning” (p. 1). Other scholars echo this sentiment (Cummings et al., 2018; Yamashita et al., 2018; Morris, 2019). Furthermore, Morris (2019) noted that “Self-directed learning is a process in which a learner assumes responsibility to control their learning objectives and means in order to meet their personal goals or the perceived demands of their individual context” (p. 634).
Allen and de Grip (2012) explored two models “…we developed a static as well as a dynamic model to explain the relationships between technological change, skill obsolescence, training and informal learning on the job and labour market exit” (p. 15). They concluded that the dynamic model was the more useful of the two models in relating skill obsolescence to an increase in the risk of employment loss. As early as 1986 Fossum et al. noted that various remediation actions to counteract skill obsolescence had not been empirically examined, yet the phenomenon exists. Downs (1995) noted that traditional employee selection fails to produce individuals with talents that can survive in a constantly changing environment. Such skills should include expansive technical knowledge, management competences, organizational understanding, and learning abilities.
The information explosion is expanding at an exponential rate. See Figure 1 for an illustration of this. Buckminster Fuller was credited with creating the knowledge doubling curve. “He noticed that until 1900 human knowledge doubled approximately every century and by the end of World War II knowledge was doubling every 25 years” (Chamberlain, 2020. para. 2). Schilling (2013) goes on to note that "on average human knowledge is doubling every 13 months” (para, 1). Chamberlain further asserts that
Different types of knowledge have different rates of growth, but it is acknowledged that human knowledge is increasing at an extraordinary rate. Arguably we may have reached a point where relevant knowledge is increasing faster and in greater quantities than we can absorb (2020, para 2).
Figure 1. Information doubling rate
Considering the exponential growth of information, one could prepare for the future by staying ahead of obsolescence through education and training.
TopJob Obsolescence
One result of the exponential growth of knowledge is the ongoing obsolescence of old jobs and careers and the appearance of new jobs and careers. Unlike today, for example, in 1860 North Carolina, 80.03% of jobs consisted of farmers (45.2%), laborers (32.94%), and tradesmen (14.15%) (Walbert, 2008). See Table 1 for more complete information.
Figure 2. Table 1: Occupations in 1860 in North Carolina