The future of the ageing workforce in engineering: relics or resources?

Australian Journal of Multi-Disciplinary Engineering ISSN: 1448-8388 (Print) 2204-2180 (Online) Journal homepage: https://www.tandfonline.com/loi/tmul20 The future of the ageing workforce in engineering: relics or resources? Michelle L. Oppert & Valerie O’Keeffe To cite this article: Michelle L. Oppert & Valerie O’Keeffe (2019) The future of the ageing workforce in engineering: relics or resources?, Australian Journal of Multi-Disciplinary Engineering, 15:1, 100-111, DOI: 10.1080/14488388.2019.1666621 To link to this article: https://doi.org/10.1080/14488388.2019.1666621 © 2019 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. Published online: 19 Sep 2019. Submit your article to this journal Article views: 687 View related articles View Crossmark data Full Terms & Conditions of access and use can be found at https://www.tandfonline.com/action/journalInformation?journalCode=tmul20 AUSTRALIAN JOURNAL OF MULTI-DISCIPLINARY ENGINEERING 2019, VOL. 15, NO. 1, 100–111 https://doi.org/10.1080/14488388.2019.1666621 ARTICLE The future of the ageing workforce in engineering: relics or resources? Michelle L. Oppert and Valerie O’Keeffe School of Engineering, Centre for Workplace Excellence, University of South Australia, South Australia, Australia ABSTRACT ARTICLE HISTORY Retaining older workers in productive employment is forecast to be a major issue as rapid changes, such as digitalisation and artificial intelligence, will transform how many roles are performed. Two such issues faced by older workers are normal age-related cognitive decline that affects reasoning and problem solving, and workplace stereotyping based on their age. Qualified engineers (n=25, range 24-77 years) participated in a non-verbal multiple-choice abstract reasoning test to assess problem solving ability, then individually interviewed on their perceptions of retaining older engineers in the workplace. The study finds all engineers scored similarly, however, the task revealed that older engineers faced with the same novel problem take significantly longer to solve than their younger counterparts. This finding is countered with evidence that younger engineers rely on older engineers' experience and knowledge for training and mentoring. This study highlights the benefits and resources that older engineers bring to the workplace. Received 10 June 2019 Accepted 3 September 2019 KEYWORDS Ageing workforce; engineers; experience; fluid intelligence; future of work; industry 4.0; knowledge; mentoring; mixed-methods; problem solving; stereotyping; thematic analysis 1. Introduction The ageing population is no longer any surprise, not least to policy and decision makers, as this momentous achievement comes with unprecedented global challenges (Bloom et al. 2015). As humanity experiences this demographic shift, the impact is still to be fully understood. By the year 2050, it is anticipated that 33% (over 2 billion individuals) of the global population will be over the age of 65 (World Health Organization 2015) with life expectancy correspondingly increasing. The consequences of losing older workers through forced retirement, exclusion from workplace practices and professional development, or through failure to invest in the expertise and knowledge of those workers may result in missed opportunities to tap the organisational benefits and potential of an ageing workforce. The issues of the ageing workforce are multifaceted and include, for example, normal age-related cognitive decline. Normal cognitive ageing exists with a low probability of disease and disease-related disabilities, and includes the presence of high cognitive, functional capacity and engagement with life (Rowe and Kahn 1997). The anticipated deterioration of cognitive processes, or normal age-related cognitive decline, in older age, typically around 50 years, mostly affects fluid intelligence, which involves reasoning and problem solving when clear solutions are not available (Shakeel and Goghari 2017). It is widely proposed that a decline in this domain impacts an individual’s ability to solve problems (American Psychological Association 2017; Fisher et al. 2017). CONTACT Michelle L. Oppert Evidence suggests that protective factors, such as years of education, may reduce the impact of cognitive decline (Clare et al. 2017; Kaplan et al. 2009; Opdebeeck, Martyr, and Clare 2016). However, longitudinal studies have demonstrated that fluid intelligence and fluid reasoning generally show a continuous linear decline during early adulthood, that possibly extends into later life (Christensen 2001; Der et al. 2009) impacting individuals’ abilities to solve problems. This concept of age-related decline in cognitive abilities feeds into a secondary problem relating to perceptions about older people; where they are stereotyped based on age. Fiske (1998) describes stereotypes as expectations or preconceived ideas that define members from a different group. The perception of being a burden is exacerbated by associated costs of pensions and health care (Harper 2014). Often, older workers are considered a burden as they approach retirement, due to the competing family obligations likely to occur through requests for greater flexibility in work arrangements to care for ageing spouses or parents (see Lima et al. 2008). Bias towards older workers is common given the inevitable physical and cognitive decline occurring with age irrespective of health status. Technology can mitigate physical decline and enhance many routine tasks, particularly through interventions that address the intersections between technology and ageing (see Calzavara et al. 2019). For industry to remain competitive, workers must continue to be cognitively healthy in order to remain useful in the work michelle.oppert@mymail.unisa.edu.au © 2019 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/ by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way. AUSTRALIAN JOURNAL OF MULTI-DISCIPLINARY ENGINEERING environment. Although a degree of cognitive decline, in areas such as reasoning, memory and processing speed is normal in older age (Harada, Natelson Love, and Triebel 2013), it is the reduction in these abilities that contributes to society’s perception that older workers are an encumbrance. A recent review revealed positive stereotypes including the belief that older workers maintain a strong work ethic, reliability, and are attributed with experience and knowledge from past life experiences (Harris et al. 2018). Nonetheless, negative stereotypes persist and include perceptions that older workers are less competent, particularly when faced with new technologies, experience reduced work capacity, are resistant to change, and unwilling or unable to adapt to new technologies (Harris et al. 2018; Ng and Feldman 2012). Some older workers experience the potential threat of being stereotyped. This threat is exacerbated by younger managers and workgroups, manual occupations, and high-performance practices in the workplace, and can result in negative impacts, such as lower workplace engagement, for older workers later on (Kulik, Perera, and Cregan 2016). Factors such as a cognitive decline in problem solving, either real or perceived by others, can reduce the choice older individuals have to remain in the workforce. The Fourth Industrial Revolution, known as Industry 4.0, is purported to be a catalyst for rapid change in the world, with the potential to profoundly influence the lives and behaviours of individuals and organisations (Morrar, Armann, and Mousa 2017). Industry 4.0 is described as a paradigm whereby different kinds of physical devices are all connected to a network or internet, so they interact (Kamble, Gunasekaran, and Gawankar 2018). The literature highlights that these interconnections occur in real time and result in optimised and self-organised intercompany value systems, with the continuous interaction and exchange of information between machines, between humans, and between humans and machines (Bahrin et al. 2016; Wan et al. 2016). While the potential for grand-scale change is yet to be realised, digital infrastructure has been evolving in engineering practice and inter-company collaboration during the past 20 years, stimulating adaptation and transformation (Lobo and Whyte 2017). Engineers continue to evolve their problem solving abilities through applying tacit knowledge, acquired through collectively engaging across teams, companies and projects, to make sense of digital data and representations. Through interrogating, reconciling and integrating this emerging knowledge with static digital information, engineers iteratively transform drawings and data into real physical structures built with tolerances for flexibility (Whyte 2013; Whyte, Lindkvist, and Jaradat 2016). Contemporary engineering practice recognises the importance of retaining problem solving ability, though it is amplified in the rhetoric 101 of current Industry 4.0 literature, for instance: ‘Employees will be called on to combine digital literacy with essential human skills such as communication and problem solving’ (Deloitte Access Economics 2017, 10). Arguably, this is already happening in engineering practice. Despite claims of Industry 4.0 technological and artificial intelligence advancements, firms are increasingly relying on critical thinking, emotional judgement, and problem solving skills in their staff to understand, analyse, and interpret what technology is reporting to the users (Deloitte Access Economics 2017). Research exploring competencies required for Industry 4.0 highlights a number of skills involved in engineering practice such as technical, communication, creativity, compliance, and problem solving skills (Hecklau et al. 2016; Benešová and Tupa 2017). The pressure to address the impact of normal, agerelated cognitive decline on problem solving is increasing substantially with the emergence of Industry 4.0. On the one hand, across all professions, as automation and artificial intelligence take on more of the routine (i.e. analytical, algorithmic) cognitive work, there is a shift in focus regarding the kind of work that humans will likely perform. There is wide agreement that creativity and problem solving will form the core of human cognitive work in the future (World Economic Forum 2019), with the engineering profession no exception. On the other hand, as the age profile of the working population increases, not least in engineering, the focus of human labour on creativity and problem solving must include the abilities not only of younger workers, but also of older engineers. Horenstein (2002) explains, albeit unwittingly, why the focus of engineers will shift to creativity and problem solving in an environment characterised by the impact of Industry 4.0. Speaking about the difference between analysis and design, Horenstein (2002, 23) states, ‘if only one answer exists, and finding it merely involves putting together the pieces of the puzzle, then the activity is probably analysis.’ This is the kind of work ideally suited to artificial intelligence in the future Industry 4.0 work environment. Goldstein (2011) makes a similar point, arguing that well-defined problems usually have a correct answer, and if certain procedures are applied, the solution will become apparent, much like the technical problem solving described by Trevelyan (2014). Pretz, Naples, and Sternberg (2003) talked about ill-defined problems, which are more frequent in everyday life and typically lack a single, correct answer. This case represents the kind of problem solving that is impacted through normal agerelated cognitive decline. Cropley (2015) emphasised that not only does engineering need a focus on problem solving (in the routine, technical sense), but it must also frequently be creative, particularly in 102 M. L. OPPERT AND V. O’KEEFFE a world where rapid change creates new problems that can only be solved by new solutions. As long as some proportion of engineering work involves illdefined problems, stemming from rapid change, and requiring wholly new solutions, then engineering will remain a relatively well-protected career, as the skills and expertise used are required in developing most goods, products and services (Kaspura 2017). Engineering will remain person-centric, with little replacement by computerisation possible (see Frey and Osborne 2017). Authors such as Trevelyan (2014) argue strongly that technical problem solving comprises only a small component of the engineering role, where engineers efficiently reuse and adapt designs from previous projects as frequently as possible to save time and reduce uncertainty, often in the form of incremental innovation (Cropley and Oppert 2018). However, problem solving itself, both technical and non-technical, remains an integral component of what many engineers do. While engineers might not explicitly identify the following factors with problem solving, much of their work is a combination of negotiation, communication, shaping client expectations, foresight, experience, careful planning and the implementation of effective organisational methods (Trevelyan 2010). In other words, the work of engineers will remain a combination of more routine technical problem solving (in which humans may be replaced by artificial intelligence) and non-routine, ill-defined, uncertain inter and intrapersonal problem solving. The literature suggests, overall, that fluid intelligence declines as age increases, so how will this impact workforces requiring high knowledge capabilities, particularly in roles such as engineering where problem solving is an important component of the work performed? If engineering is largely a cognitive enterprise, and the workforce is ageing, a problem begins to emerge. The authors of the current study advocate ‘big picture’ analysis to identify and begin to understand complex issues such as the ageing working population. Therefore, the current study aimed to apply a mixed-methods approach using a valid and reliable measure to examine whether age-related decline in fluid intelligence and problem solving ability is detrimental to the practice of older engineers. Drawing on the lived experience of engineers themselves to reveal their own perceptions about older engineers is the rich, meaningful data that holds real implications and importance to those in this field. The current study qualitatively explores perceptions about ageing engineers in the workplace from engineering professionals to highlight potential benefits of hiring or retaining older engineers in the engineering workplace. There is much research on negative stereotypes; the authors wished to highlight the positive. Recognising the stereotypes held of older people in the workplace from the literature, specifically their perceived decreased cognitive ability to efficiently solve problems, the authors considered the benefit of also testing the theory that fluid intelligence decreases with age by using a quantitative measure to examine the extent to which engineers’ fluid intelligence is impacted by normal age-related cognitive decline. Hypothesis: It is proposed that a career requiring frequent and complex problem solving, such as engineering, will mitigate normal age-related cognitive decline in a problem solving task. Research Question: From the perspective of engineers, what are the benefits and impact of older engineers in the engineering workplace? 2. Method 2.1. Participants Twenty-five engineers (8 females, 17 males) holding recognised engineering qualifications with at least 1 year of experience in the engineering workforce were recruited from the South Australian general population. The participants were recruited over a seven-month period. The average age of participants was 48.36 years (range 24–77). Participant qualifications ranged from Diploma to PhD with the majority of participants’ highest qualification a bachelor’s degree (n = 17). Within the participants, four specialisations were identified: civil (n = 3), chemical (n = 6), electrical and electronic (n = 11) and mechanical (n = 5). Of the 25 engineers who participated, all those 55 years and over had been employed in an engineering role for 30 or more years. The mean years of work experience as an engineer for the sample were 16.33 years. Ethics approval was obtained from the University’s Human Research Ethics Committee. Participants received written information about the research and signed informed consent was obtained prior to data collection. Participants were interviewed and tested individually by the primary author. The study utilised a mixed-methods approach in which participants were invited to complete a simple demographic questionnaire, two openended interview questions recorded and transcribed verbatim, and a problem solving task. 2.2. Materials and procedure 2.2.1. Demographic questionnaire The demographic questionnaire collected information on age, education, engineering specialisation, and work experience. AUSTRALIAN JOURNAL OF MULTI-DISCIPLINARY ENGINEERING 2.3. Perceptions The open-ended interview questions were phrased, ‘From your perspective, what are the benefits of hiring or retaining older engineers in the workplace?’ and ‘How do you think retaining older workers in the engineering workforce impacts younger engineers?’ 2.3.1. Problem solving The Raven’s Standard Progressive Matrices (SPM) (Raven 2000) is a non-verbal, multiple choice test involving abstract reasoning assessed via 60 items within a set time period (60 min for the current study) that increase in difficulty (ChamorroPremuzic and Furnham 2010). The SPM test comprises a series of diagrams in the form of an abstract pattern in a matrix of cells and the participant is requested to identify the missing segment from an option of cells that would complete the matrix pattern (Raven 2000; Brouwers, Van de Vijver, and Van Hemert 2009). The SPM is an intelligence test focusing on fluid intelligence through inductive reasoning (see Carroll 1993: Waschl et al. 2016). The SPM is considered to have construct validity and be a good measure of fluid intelligence with sound psychometric properties, easy administration and scoring (Raven and Raven 2003). 2.3.2. Participant grouping Participants were divided into three groups: Age Group 1 (18–34 years, n = 7), Age Group 2 (35–54 years, n = 10), and Age Group 3 (55+ years, n = 8). The rationale for grouping participants by age is based on a combination of factors. First, engineering education provides graduates with knowledge, but much skill acquisition is learned on the job. Trevelyan (2014) reports that for most people it takes a minimum of 10 years to achieve an expert level of performance, which places many graduates in their 30s. Second, data extracted from the Australian Bureau of Statistics (ABS 2017) and the Australian Census Longitudinal Database (Australian Bureau of Statistics 2016) by Kaspura (2017) provided information about older engineers and revealed that of those who entered retirement at 55 years of age in 2006, a total of 26% were actively engaged in employment again by 2011. This is likely due to preservation age, which is the age at which an Australian individual born before 1960 can access their superannuation fund, which was 55 years (SuperGuide 2019). 103 thematic analysis is to identify and explore salient themes and how they relate to each research question examining benefits of older engineers in the workplace, and the perceived impacts on younger engineers. The interviews were transcribed and then analysed in multiple cycles with rounds of coding in NVivo 12 software (QSR, 2019). Braun and Clarke (2006, 2013) analytic method of thematic analysis was used and includes the following steps: (a) transcription; (b) reading and familiarisation with data; (c) coding across the entire data set; (d) identifying themes; (e) reviewing themes by producing a thematic map; (f) defining and naming themes; and (g) finalising analysis. The analysis of interview data was further examined by an additional experienced academic and confirmed to be salient, thus ensuring consistency of results. 3.2. Statistical analysis Complete data were available for all participants (no exclusions). No participants reported prior knowledge or attempts of the SPM. Data were analysed using a one-way multivariate analysis of variance (MANOVA) to investigate the independent variable of age on the dependent variables of total time and total score on the SPM problem solving task, using SPSS statistical software (IBM 2017). 4. Results 4.1. Qualitative results Thematic analysis identified two salient themes describing perceived primary benefits to hiring or retaining older workers in the engineering workplace. These are (a) experience and knowledge; and (b) mentorship. 4.2. Experience and knowledge Twenty-two of the participants featured in 43 statements on how a benefit of hiring and retaining older engineers in the workplace is seen as positively impacting both younger engineers and their firms by providing experience and knowledge. See Table 1. 4.3. Mentorship 3. Analysis 3.1. Qualitative analysis Thematic analysis was used to identify the primary themes in the text-based data. The purpose of Thirteen participants contributed to 20 statements on how hiring or retaining older engineers impacts younger engineers by providing mentorship (including bidirectional mentoring) and is viewed as a benefit. See Table 2. M. L. OPPERT AND V. O’KEEFFE 5. Discussion The Standard Progressive Matrices (SPM) revealed that while all engineers scored similarly on the SPM there was a significant difference in time taken by engineers in Age Group 3 (55+) compared to the two younger age groups. Older engineers took an average of 12 min longer than their younger counterparts. This indicates that engineers over the age of 55 experience normal cognitive decline despite their years of engagement in problem solving. However, of Group 3, five of the eight engineers scored above norms (between 75th and 94th percentile) and one of the eight scored superior (above the 95th percentile) with the remaining two scoring as average (between 25th and 74th percentile). The percentile rankings No of participants No of statements Illustrative statement A one-way between-groups multivariate analysis of variance (MANOVA) was performed to investigate the effect of engineer’s age on a test of intelligence, the Standard Progressive Matrices (SPM). Two dependent variables were assessed: total score and total time to complete the SPM. The independent variable was age. Engineers were separated into three age groups: Group 1 (18–34), Group 2 (35–54), and Group 3 (55+). Preliminary assumption testing revealed that data were normally distributed as assessed by Shapiro Wilk test (p = > .05). There were negative linear relationships, as assessed by scatterplot; there was no multicollinearity as assessed by Pearson correlation (r = −.459, p = .021); and homogeneity of variance-covariates matrices, as assessed by Box’s test of equality covariance matrices (p = .911). Engineers in all three age groups scored similarly in the SPM Score Total (M = 55.86, SD = 2.54; M = 54.90, SD = 2.42 and M = 52.50, SD = 3.42, respectively). See Table 3 and Figure 1. There was a statistically significant difference between the age groups on the combined dependent variables (SPM Score Total and SPM Total Time), F (4, 44) = 3.28, p < .022; Pillai’s Trace = .45; partial eta squared = .244. See Table 4. Follow up univariate ANOVAs showed that only SPM time, F (2, 22) = 6.62, p = < .05; partial eta squared = .38, and not SPM Total score, F (2, 22) = 2.93, p = > .05; partial eta squared = .210, was statistically significantly different between the age groups. Tukey post hoc tests showed that for SPM Total Time scores, engineers in the 55+ Age Group had significantly higher mean scores than engineers in the 18–34 Age Group (p = < .006) and the 34 − 54 Age Group (p = < .037). There was no significant difference between the 18–34 Age Group and 35–54 Age Group (p = .833). See Table 5 and Figure 2. Table 1. Experience and knowledge identified as primary benefit to retaining or hiring older engineers. 4.4. Problem solving ability Age Group 1 Age Group 2 Age Group 3 (18–34) (35–54) (55+) 6 10 6 14 21 8 P3C: I think engineering is one of those places where you keep P15O: That experience and expertise. From all levels, not just the technical but also P19S: I think they should keep the expertise and the older people for longer because of the tacit knowledge, the business and looking after projects and managing people and all sorts of experience – it is the corporate knowledge that because of the experience they have. things they just get a different perspective on things. you have to retain and pass on. P12L: I think older people can bring an extreme amount of P16P: We have people here that have been here for 35 years, like from the P11K: It’s just experience, I guess, being able to call expertise and experience to the engineering workplace. start, second or third employee in the company and they’ve got this wealth of on experience. knowledge – it would be disastrous if we lose that. 104 No of participants No of statements Illustrative statement Age Group 1 Age Group 2 Age Group 3 (18–34) (35–54) (55+) 4 4 5 6 6 8 P13M: If you happen to be a young person who snaps up a job and P1A: Younger engineers might feel like they’re not been given a go P9I: Older people, you don’t want a lot of them, you want lots of you enter the working environment that has a lot of senior staff if they keep the older ones, but I think provided an older person younger people because they’re the ones who are going to work there, that is a great learning opportunity to be paired with retains his or her cognitive abilities and is flexible enough to fast and do all the busy things but a few [older] people who them and hear their stories and just watch the way they go keep learning and modifying their work routines to new hold the reigns and bring them back, that’s what you want. through their day-to-day tasks. practices I don’t . . . I don’t see that it’s a problem because at the same time they can mentor the younger ones. P14N: So, young people should work with senior[s] to get that – it P24X: Younger people mentoring other people? Absolutely, yeah, P11K: The presence of older ones in the workplace enables a sort depends on the senior – if you have a really patient, nice particularly in technology. So, a lot of the times the younger of mentoring and transfer of knowledge of skills to take place. engineer [he] can tell you why he is doing this, then you will be generation have been taught the newer techniques and the quicker, you will adapt to this thinking and know what he is ways to do things or a different tool of how to do things and meaning. that can be something that they can bring back up through the organisation. Table 2. Mentorship identified as a benefit of retaining or hiring older engineers. AUSTRALIAN JOURNAL OF MULTI-DISCIPLINARY ENGINEERING 105 (Raven, Raven, and Court 2004) indicate that compared to general population norms, the older engineer typically scores above average for their age, suggesting either a selection bias to problem solving work, a ‘survivor’ effect in which the best engineers remain in the profession at older ages; or a protective effect of performing such work over long periods. Though these findings require further investigation, implications for recruitment and retention practices should be considered in Human Resources Management (HRM) protocols. The focus should be on evaluating how individuals compare to norms on standardised tests, rather than basing employment decisions on stereotypes and entrenched subjective opinions. Group 2 (35–54 years) performed highest in both total time and total score. The authors propose some plausible explanations for this difference; that it takes approximately a decade of work to be considered an expert engineer (Trevelyan 2014) and that engineers in the middle-aged group have a higher degree of confidence and domain knowledge obtained from their years of experience whereas, conversely, younger engineers are still learning and are potentially more cautious than their mature counterparts. Either way, further exploration into this phenomenon is needed. Accordingly, due to positive stereotypes of older workers (Harris et al. 2018), the authors reasonably anticipated that experience and knowledge would be a recognised benefit of hiring and retaining older engineers in the workplace, and the statements of the participants provided compelling acknowledgement that this is of importance to their industry. There were few mentions of negative stereotypes or perceptions of older engineers and no salient themes were found that provided evidence that engineers perceive their older colleagues as poor problem solvers or poor at adapting to new technology. Most statements espousing the benefits of older engineers’ experience and knowledge were from those in Age Group 2 (35–54). This group is in the position to form this view as they bridge the gap between being an early career engineer and a senior engineer; since they have both acquired a level of expertise and knowledge, and have also provided it. The experience and knowledge of older engineers enables progression towards sharing their acquired resources largely their valuable non-technical skills. As opposed to technical skills specific to engineering, non-technical skills are necessary for success in many workplace settings and include professional, interpersonal and personal skills (Larsen et al. 2017). Flin and O’Connor (2017) describe non-technical skills as cognitive and social skills that complement workers’ technical skills, and include awareness of the work environment, decision making, communication, teamwork, leadership and managing stress. Older M. L. OPPERT AND V. O’KEEFFE 106 Table 3. Descriptive statistics for SPM total time and SPM total score with norm percentiles. SPM Total Time SPM Score Total Age Groups 18–34 35–54 55+ Total 18–34 35–54 55+ Total Mean 34.09 31.08 48.60 37.53 55.86 54.90 52.50 54.40 SD 12.60 10.56 8.45 12.80 2.54 2.42 3.42 3.02 Average (25th N 7 10 8 25 7 10 8 25 – 74th) Above Average (75th 4 6 3 13 (52%)* – 94th) 2 3 4 9 (36%)* Superior (95th+) 1 1 1 3 (12%)* SPM Norm Percentiles Note. SPM Total Time maximum time of 60 min, SPM Total Score maximum score of 60, Norm Percentiles from SPM (Raven, Raven, and Court 2004) raw score percentiles, * = the total amount of participants that fall into each percentile band including sample percentage. Total SPM Score 60 50 40 30 20 10 0 18-34 35-54 55+ Age Group in Years Figure 1. Mean total scores for each age group on Raven’s standard progressive matrices. engineers have the capacity to impart their acquired non-technical skills through the implicit role of mentorship, as exemplified in the interviews. Mentoring relationships are valuable in relieving stress in newer engineers, by building their confidence and skills (San Miguel and Kim 2015) and providing a foundation for learning. Hayes (1998) described mentorship as a voluntary, intense, committed, extended, dynamic, supportive relationship between two people, one experienced and the other less experienced; built on mutuality, where the mentor acts as guide, role model, teacher, and sponsor. Traditional definitions of mentorship describe it as one-on-one leadership involving coaching, counselling, supporting and sponsoring of a junior employee by a more senior employee (Kalliath et al. 2014), although, this is now debatable. Mentoring is increasingly occurring in the reverse (or reciprocal, bi-directional mentorship in the engineering discipline) with the influx of older people changing careers, or returning to work, and the presence of five generations in the workplace (Nyanjom, Harper, and Mackenzie 2019). Despite limited recent formal studies on mentoring in the engineering profession, it is understood to be an unquestionable component of the engineering role for all engineers. Traditionally, engineers are thought to be hired for their technical skills, promoted for their management skills and ultimately dismissed for their people management skills (Locurio and Mitvalsky 2001). In Industry 4.0 successful engineers will continue to require competencies involving problem solving and communication skills, teamwork and collaboration, technical competence, innovation, ethics and professionalism (Deloitte Access Economics 2017; Silva and Table 4. Multivariate testsa of SPM total score, SPM total time and age group. Effect Intercept Age Group Pillai’s Trace Value .998 .448 F 5475.643b 3.175 Hypothesis df 2 4 Error df 21.000 44.000 Sig. .000 .022 Partial Eta Squared .998 .224 Noncent. Parameter 10,951.287 12.700 Observed Powerc 1.000 .779 Note. a = design, b = exact statistic, c = computed using alpha = .05. Pillai’s Trace is used, as the Age Groups did not have equal participants. Table 5. Multiple comparisons with post hocs between SPM total time, SPM score total and age groups. Dependent Variable SPM Total Time (I) Age Groups 18–34 35–54 55+ SPM Score Total 18–34 35–54 55+ (J) Age Groups 35–54 55+ 18–34 55+ 18-–34 35–54 35–54 55+ 18–34 55+ 18–34 35–54 Mean Difference (I- J) 3.0137 −14.5118 −3.0137 −17.5255 14.5118 17.5255 .96 3.36 −.96 2.40 −3.36 −2.40 Std. Error 5.20900 5.47055 5.20900 5.01384 5.47055 5.01384 1.385 1.454 1.385 1.333 1.454 1.333 Tukey HSD 95% Confidence Interval Note. The error is Mean Squared (Error) = 7.898 and the mean difference* is significant at the .05 level. Sig. .833 .037* .833 .006* .037* .006* .771 .076 .771 .193 .076 .193 Lower Bound −10.07 −28.25 −16.09 −30.12 .76 4.93 −2.52 −.30 −4.44 −.95 −7.01 −5.75 Upper Bound 16.09 −.76 10.07 −4.93 28.25 30.12 4.44 7.01 2.52 5.75 .30 .95 AUSTRALIAN JOURNAL OF MULTI-DISCIPLINARY ENGINEERING Time in Minutes 60 50 40 30 20 10 0 18-34 35-54 55+ Age Group in Years Figure 2. Mean total time taken to complete Raven’s standard progressive matrices for each age group. Yarlagadda 2014). Where new technologies become apparent through scientific discovery, research and development, engineers are required to find new possibilities and new markets to exploit these possibilities (Cropley 2015). New problems present the need for new solutions and engineers need to level-up their problem solving to find new and creative solutions. While formal knowledge in these domains can be learned during undergraduate or professional training, the integration of skills to develop practical proficiency occurs in the workplace. Older engineers typically have acquired wisdom and competence – hallmarks of expertise and tacit knowledge, derived through application of skills to realworld, complex problems (Maslen 2014). In doing this, they draw on knowledge from multiple sources with the goal of developing solutions. The expert knowledge of older engineers is located in practices, which are refined over time and qualitatively differ from the largely technical and theoretical knowledge that young graduate engineers bring to industry. It is through transference of tacit knowledge, situated in real-world practice that mentoring can support competency development in less experienced engineers. Successful mentoring in engineering involves partnering in a creative and thoughtful process to maximise personal and professional potential (Silva and Yarlagadda 2014; Trevelyan 2014) and aims to develop and bring to the fore resources already possessed in the new engineer. Older engineers through their longstanding experience ‘on the job’ provide valuable support to younger engineers in ‘learning from the everyday’ by gaining assistance on tasks, feedback on ideas, and through transmission of stories about successes and failures in engineering practice (Maslen 2014). These are the foundations for building culture and socialisation of the new engineer into a professional identity. In principle, mentoring creates opportunities for younger and older engineers, the organisation and the profession, with potential benefits for all. However, in practice, many postgraduate engineers experience an ad hoc apprenticeship style introduction to the profession with little formalised instruction. Newer engineers are constrained in their opportunities 107 to learn from more senior engineers (and vice versa) due to organisational hierarchies, rigid divisions of labour, time pressures, under-recognition of the role of mentoring, and older engineers’ willingness and confidence to actively engage in mentoring (Davis, Vinson, and Stevens 2017). Despite these impediments in the structure and organisation of engineering work, newer engineers are successful in seeking out older, senior engineers who recognise the importance of coaching and mentoring. Each party recognises the value of the relationship in facilitating the next generation (and updating the current generation) in integrating their technical skills with practice to promote proficiency and professional identity. Engineering work in the age of Industry 4.0 must adapt to the more fluid nature of contemporary work and develop mechanisms that facilitate informal coaching and mentoring as part of the fabric of all engineers’ roles. Arguably, senior engineers have more to offer grounded in their experience, and engineering organisations can benefit from harnessing the resources of senior engineers by promoting their value across generational boundaries. In the dynamically changing workplace, intergenerational mentorship can facilitate an effective form of knowledge transfer. The organisational transfer of knowledge (also known as Knowledge Management) is topical in current Human Resource Management (HRM) literature and highlights the benefits of employers creating the right culture, environment and support systems to retain and motivate retirement-eligible individuals to remain in the workplace and proceed to transfer their knowledge to their younger colleagues (Calo 2008). Creating a collaborative, human-friendly work/life environment will require a broader approach than organisations are currently taking (Deloitte Insights 2017) and the authors believe that tapping into the benefits and potential of the ageing workforce is one strategy. In fact, within the Industry 4.0 environment, the demand for soft skills is likely to increase. Industry 4.0 requires societies to address gaps in skills and competencies. The World Economic Forum (2019) has released a call for action to strengthen both public and private businesses’ roles in reshaping the education and training systems for the future of work, including new skills, reskilling, and upskilling the current workforce. A HRM strategy for more flexible work design could be considered by engineering workplaces, in collaboration with the workforce, to both adapt to the changes initiated by Industry 4.0 and concurrently capitalise on the experience of older engineers. Possibilities include developing part-time work, shortterm contracts, innovative approaches to leave arrangements, and new roles that recognise the fluid nature of contemporary workplaces, like internal consultants. Such a role could cover multiple projects and functions, with the focus of mentoring newer engineers in applied practice and translating wisdom into corporate knowledge. 108 M. L. OPPERT AND V. O’KEEFFE 5.1. Strengths and limitations Given the diversity of the participants, the results are generalisable to engineering firms, specifically in Australia, but can be extended to other western engineering sectors, particularly with the globalisation of Industry 4.0. While the qualitative sample size provides compelling data, due to the small quantitative sample size, findings must be interpreted with caution. Considering a sample size of 10–12 participants is reported as being ample in qualitative research as long as data saturation is achieved (Guest, Bunce, and Johnson 2006), the sample size of 25 participants can be considered a strength of this project, however, quantitatively 25 participants lack statistical power. The quantitative results obtained on the SPM would be best interpreted as a pilot study and the authors encourage further research into this phenomenon in ageing professionals. The SPM quantitative results indicate that older engineers retain high levels of problem solving skills. This finding may reflect the possibility of a ‘survivor’ effect in which the participating engineers were more likely to have remained in the workforce due to the retention of these skills, while those with declining skills are likely to have retired earlier. One area worthy of attention is assessing the impact of fluid intelligence on problem solving with other measures or phenomena like cognitive reserve theory (see Clare et al. 2017; Kaplan et al. 2009; Opdebeeck, Martyr, and Clare 2016). The use of the SPM as a non-verbal assessment of intelligence was well suited considering the first language demographics of the participants, where one third of the sample identified English as a second language. The authors wished to examine the perceived benefits of older engineers, not base the interview questions on previous negative stereotypes to avoid inadvertently priming the participants. Future research could explicitly explore both the positive and negative perceptions together. The introduction of a mixed-methods approach to this domain is novel and the authors also encourage further explorations into the dilemma of the normal age-related cognitive decline and workplace facilitation to capitalise on the transferable skills of older workers. 6. Practical implications and conclusion The authors conducted the current study with the aim of first, providing information to determine whether engineers have the capacity to continue problem solving and effectively processing information in their later years. The second aim was to explore the potential for older engineers to contribute to the workplace through the preservation of these primary skills. While there is no doubt that the ageing of the workforce has some negative factors, it should be recognised that there are positive aspects to this demographic change. HRM literature highlights broader advantages of keeping older workers employed, for example, older workers are loyal and 2.4 times more likely to remain in their position compared to younger cohorts (Brooke 2003). In Australia, approximately $7 billion is spent on recruitment and $4 billion on training (Deloitte Access Economics 2017). These resources could be better invested on internal training and adaption to new roles to fill the needs of Industry 4.0 and include effective transfer of knowledge. To do so, effective skills such as communication are required to facilitate this mentorship. While it is likely that older engineers will experience normal age-related cognitive decline in their fluid intelligence, they are proficient at solving a novel problem with the same accuracy of their younger counterparts, albeit it may take longer. The small time lag that may occur on a new problem is mitigated by the immense benefit that an experienced, knowledgeable engineer can bring to the workplace. It is likely that expertise of a senior engineer may reduce problem solving time on known issues, especially those that occur less frequently and have significant consequences. The engineering sector should prepare for the retention of older workers resulting in different types of HRM practices, including access to flexible working arrangements that facilitate different work–life balance options to suit this demographic. Equally, senior engineers are more likely to request such arrangements as incentives to continue working and enabling transitioning to retirement (Gahan et al. 2016). Effective mentorship and knowledge transfer in the engineering workplace is crucial to producing competent engineers. This includes educating new engineers on professional norms and enculturating them within the discipline (Leondardi, Jackson, and Diwan 2009). The best-placed employees to provide these benefits are senior, experienced engineers. Therefore, the authors argue that ageing engineers are not relics whose usefulness has expired. Rather, older engineers embody the practices and philosophies of a profession, acquired over time, and have much wisdom to pass on to new generations. As such they should be considered a valuable resource to any firm that is fortunate enough to retain them. Acknowledgments The authors from the current paper acknowledge assistance and support from Professor David H. Cropley and Professor Maureen Dollard, University of South Australia. For full qualitative data set statements please contact the primary author. Disclosure statement No potential conflict of interest was reported by the authors. AUSTRALIAN JOURNAL OF MULTI-DISCIPLINARY ENGINEERING Funding This work was supported by the Department of Education and Training, Australian Federal Government [RTPDomestic]. Notes on contributors Michelle L. Oppert has Bachelor’s Degree in Psychology, a Diploma in Counselling and is a currently a Doctoral Candidate. She has a keen interest in psychosocial wellbeing in the workplace and the benefits that arise from a positive workplace climate including, but not limited to, problem solving, knowledge, creativity, psychosocial safety and how age impacts these factors. Her research aims to contribute to the body of knowledge on the ageing workforce and will interpret the findings to address what engineers, and ostensibly other professionals, can offer the workplace as they age. Valerie O’Keeffe has a PhD in Psychology, Masters’ Degree in Ergonomics, and Graduate Diplomas in Occupational Health, Social Science and Leadership. She is a human factors and work health and safety specialist who applies human factors, ergonomics and psychology principles to promote worker and organisational health, safety and performance at work. Valerie’s research and practical approach is grounded in experience working in partnership with organisations in diverse industries including healthcare, education, government, manufacturing, construction and transport. Her roles in consultancy, education, policy and research ensure she understands the dynamic environment of today’s workplaces. ORCID Michelle L. 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