Exploring the Relationship Between Age and Training on Levels of Trust and for Technology Adoption in the Irish Air Navigation Service Provision Sector

Theoretical Framework The air traffic management (ATM) sector has undergone substantial growth globally, with an expected continuation of air space growth (Sears and Jacko, 2009). The effective management of this growth is... [ view full abstract ]

Theoretical Framework

The air traffic management (ATM) sector has undergone substantial growth globally, with an expected continuation of air space growth (Sears and Jacko, 2009). The effective management of this growth is important to ensure trust in technology by Air Traffic Control Operatives (ATCO’s), as their job functions will be impacted due to technology innovations (Lee and Kirik, 2013).

Trust and confidence in automation have been posited as necessary when introducing new technology (Lee and Kirik, 2013). Technology confidence is increased through years in service. Consequently, age is one of the independent variables used in this research. Research suggests that age inversely relates to training, therefore, it will impact technology adoption, first through trust (Uden et al., 2013) and secondly through skillset management (Sistare et al., 2015). In an organizational context, skillset management is vital to ensure economic benefits (Sistare et al., 2015).

Advances in technology have changed job roles, in accordance with changing business structures (Brynjolfsson and McAfee, 2014). This has occurred within the air navigation service provision (ANSP) sector via a new satellite-based system that is currently being introduced, which will potentially change the job role of ATCO’s (Eolas, 2012; Iridium, 2012). Salvendy (2012) and Dillingham (2010) propose that it is important to understand how age and organizational training coexist regarding technology adoption in this industry sector. This research, therefore, explores the relationship between age and training on technology adoption in Air Traffic Management Navigation (ATM).

Research Importance and Key Contribution

This study allows potential internal training gaps to be highlighted by employees who rely heavily on automation to conduct their job function. One objective of this research is to statistically evaluate ATCO training needs, with a particular focus on the relationship between age and training on technology.

The key contribution of this study is the identification of a clear statistical relationship between age and technology adoption, concurring with existing research by Dzinolet et al., (2010). A strong statistical relationship between age and systems overview knowledge was also found. This finding concurred with Cummings et al. (2010), who suggests that increased emphasis is placed on systems overview knowledge in safety critical industries over any other industry. The results also showed a statistical relationship between age and job related performance, highlighting an area that needs increased study, as it is an area that encompasses knowledge management, skillset management and age to ensure competitive advantage (Ahmed and Shepherd, 2010). Research has suggested that where there is trust and confidence in technology, performance increases (Giddens, 2013; Yamamoto, 2013).

Interestingly, no statistical relationship was found between training and technology adoption, indicating that training needs are being met, enabling ATCO’s to carry out their current job function. The descriptive results on training strongly indicated that tacit knowledge and learning styles are areas that impact technology acceptance as age increases.


Research Method and Analysis

This study statistically examines four areas highlighted in existing research as variables that may be barriers to technology adoption within organizations, namely, age versus technology adoption (Turner et al., 2007), training and technology adoption (Hedge and Borman, 2013), age and systems overview knowledge (Uden et al., 2013), age and job related performance (Giddens, 2013; Yamamoto, 2013). A positivist, quantitative research methodology is used to assess the interactions between variables. Hypotheses were developed and analyzed using both descriptive and inferential statistics. The inferential statistics included a one-way ANOVA and two-way ANOVA, which were calculated using IBM’s SPSS package. Data was collected through the use of a questionnaire.

The hypotheses results indicated a statistical relationship between age and technology adoption concurring with existing research that proffers that age contributes to technology adoption in organizations (Morris et al., 2005). The descriptive results regarding age also concur with existing research that suggests that older people have an increased willingness to try to use and accept technology (Turner et al., 2007).

The hypothesis exploring training and technology adoption failed in this research indicating that training does not have any statistical relationship to technology adoption, which contradicts existing literature (Lewis et al., 2003). In contrast, the descriptive results of training indicate definite areas that impede technology adoption, namely, time allocated to cover training material. This concurs with Hedge and Borman (2013), who posit that older adults require longer periods to cover the training material.

Age had a very strong statistical relationship with systems overview knowledge. Systems overview knowledge is considered to be a key component to technology trust vital for technology adoption (Uden et al., 2013). The results suggest that as age increases, systems knowledge decreases inferring training and human factors elements that require increased organizational input (Dillingham, 2010).

There is an implied need for informed training to ensure continued productivity, therefore, trust in technology along with adequate systems overview knowledge are important to aid individual job performance (Giddens, 2013). The hypotheses indicated a statistical relationship between age and job related performance where peak performance was shown to be at the 31-40 age group, dropping significantly thereafter. This seems to contradict the concept that tacit knowledge increases with age thereby increasing job related performance (Polani, 1996). There is an implied job degradation issue highlighted with this result concurring with existing research on technology advancement and its affect on skillset management (Frey and Osborne, 2013). In particular, it has been proffered the current speed of system changes may impact basic skillsets and in human-automation teams (Stanten et al., 2014).


Research Implication

This research highlights technology issues that employee responses suggest require additional support and understanding. One implication of this research is the recognition that job related performance within this industry may peak at a certain age, then dramatically decline. This is noteworthy, as the results are from an employee, rather than employer, perspective.

Additionally, while training and job related performance are interrelated, in this study, training was not considered a barrier to technology adoption, but, age was considered an issue to job related performance. This indicates that it is not necessarily training that is an issue but lack of tailored training to suit learning styles, warranting further research.


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Trust and confidence in Technology, age and Technology, training and technology, age and systems overview knowledge, age and job related performance. [ view full abstract ]

Trust and confidence in Technology, age and Technology, training and technology, age and systems overview knowledge, age and job related performance.