The Effects of Change Management in General Civil Aviation Authority Essay

For the current research project, the longitudinal study seems like an appropriate design. Longitudinal studies are mainly used to assess the processes that take considerable amounts of time to have an effect. They measure certain variables through multiple surveying. A longitudinal study is one of the best designs to use when change is under research. The present study is aimed at implementing change management. Revealing possible benefits and drawbacks of this management model in the paperless environment project in General Civil Aviation Authority (GCAA) could take time. Change often has short-term and long-term effects, so there is a need to conduct several assessments in the course of the study (Bryman & Bell 2015). Longitudinal study design will allow tracking all changes that happen after the implementation of the chosen intervention. The project will focus on the three main areas: employee’s printing behaviors, automation of business processes and introducing new tools and technologies.

In all those areas longitudinal study will allow to thoroughly assess the results of the planned interventions. Printing behaviors may change with time. There is also a risk that after the implementation of change management techniques the positive result will be observed, but after a certain time, the effect of the intervention may be minimized due to lack of belief in the chosen course of change. Longitudinal research design could help address this problem. Business process automation is not a trivial task and requires plenty of preparations and adjustments (Cichocki et al. 2012). Along the way, there can be some hindrances that can undermine the implementation process. The chosen study design will allow timely noticing and recording those obstructions. The same issues may occur with the implementation of new tools and technologies. It may be hard to assess the effects of change at once so multiple inquiries could help gather all-around information about the intervention. Therefore, longitudinal study design seems to be best suited for the tasks of the current research project.

Experimental study design cannot be used for the current research. It involves a researcher being able to control the process of implementation and manipulate the variables in order to conduct a scientifically viable experiment (Montgomery 2017). In the environment chosen for the current study, it is deemed hard for a researcher to control anything, as it requires certain authority. This design is better suited for laboratory setting rather than for a budgetary institution. A cross-sectional design could be used for the current study, but there is also a reason for not implementing it. Similarly to the longitudinal design, it uses multiple assessment occasions but unlike it, does not necessarily imply surveying the same people. In the current research project, it is viable to get the feedback from the same sample more than once (Christensen, Johnson & Turner 2013).

A case study allows to have an in-depth research of an event or organization, but it also does not seem to be a suitable choice for the current study. It provides a static research experience while the present project focuses more on the changes and needs to assess short-term and long-term effects. A comparative study also appears to be inappropriate because the research is organization-specific and it may be hard to find a matching material for comparison.

There is a need to use a quantitative approach for the current study to best determine the correlation between the intervention and the effects it produces. Quantitative methods are mostly used when there is a need to establish a connection between certain concepts, events or interventions. Applicable to the current study, they can be used to ensure the sample is affected by the change. It also provides an opportunity to gather data from a larger group of participants. A bigger sample benefits the research due to the better validity (Ceptureanu 2015). When preoccupations are concerned, the quantitative approach focuses on measurement, causality, generalization, and replication. Without measurement, there seems to be no point in the research as there will be no information on whether the implemented adjustments change anything in the way the organization or its employees operate. Causality helps establish the theoretical basis of the study. Generalization may be useful for other researchers when they want to focus on similar topics. Replication allows obtaining one more proof that the current research results are viable. Qualitative methods will be used to assess the participants’ opinions on whether positive changes took place in the organization.

Qualitative data will allow building theories and researching the peculiarities of change management in a budgetary institution setting. Qualitative methods provide an opportunity to obtain a well-rounded view of the issues connected to the organization’s performance. As to the main preoccupations relative to this study, they include data-oriented theory and context priority. It appears to be meaningless to predict how change management works in the chosen setting without real data. In assessing the responses gathered from the sample, there will be a need to base the observations on the understanding of the organization’s and employees’ personal backgrounds. There is a certain degree of subjectivity in the interpretation of qualitative data, as the researcher can sometimes be biased. Quantitative data offers a more reliable source of information regarding the outcomes of change management implementation (Myers 2013). The first evaluation will use quantitative tools only, while both qualitative and quantitative will be utilized in the second. The reason for such an approach is that an in-depth reflection provided by an interview, for example, seems to be suitable after a considerable time has passed since the intervention. Change assessment through survey, however, appears to provide valuable data on performance even in the short run.

Ensuring quality and Reliability

The reliability of the gathered data will be ensured by the research design that implies several assessment sessions. This will allow determining if the trends and concepts that emerged during the first survey are confirmed to be stable. Face and concurrent validity will be established through the use of scholarly sources for designing a proper survey tool and comparing its effectiveness to those previously used in a similar setting. The predictive validity of the data gathered quantitatively will be tested by the qualitative survey conducted in the future. The study will analyze the qualitative and quantitative data after it is collected to hypothesize on the implications of change management in the GCAA setting. The findings will include recommendations on introducing the same practices in similar environments. Convergent validity will probably be tested by measuring the perceived success levels using the Likert scale and assessing the relation of different items from the answers to the concept of successful change management. The subject’s answers will be tested using correlation coefficients. Similarly, to establish discriminant validity for this research, the utilization of the same coefficients appears to be a valid choice (Voorhees et al. 2015). Problems and successes in the establishment of change management according to the calculations should show low correlations. If that is successfully proved, then the study’s results should have the two concepts discriminated.

Explaining Concepts

The first concept that the research will try to explain is changes management. It stands for the actions and reactions a leader together with his or her team should perform to be able to adequately embrace changes in the organization and successfully implement them with beneficial outcomes (Hayes 2014). The concept will be measured through the variable ‘level of success.’ If the level is low, then the errors in the implementation of this concept will dictate the need for examination. If otherwise, the key elements of success should be possible to define. Measuring this change management variable should allow to generalize the results and provide the basis for further research (Van der Voet 2014). The employee involvement concept will also be under analysis. It represents the engagement of the team members in various tasks. The number of hours appears to be the appropriate variable to measure employees’ involvement. It will allow assessing the exact time a team member is involved with a project and making observations about the effect of this on the cost, overall time, and performance of the project. The last concept is process automation. It stands for making machines perform human tasks to save resources. Several variables will be used to assess and explain this concept, including ‘average time saving,’ ‘average cost-effectiveness,’ and ‘impact on performance.’ Such design will help build correlations between the success of the project and the said concept.

Explanation of Applicable Samples

The population selected for the current research consists of senior staff employed at GCAA. Sample-based on the population is narrowed down to project managers. Random sampling is not applicable due to the fact that the selected managers will be asked to apply change management strategies and then evaluate their efforts. Phase two will once again survey the same project team leaders to reflect on the positive and negative sides of the intervention after some time. Therefore, probability sampling seems like a valid option. To prevent possible sampling bias, the research will include team leaders with different levels of experience and a number of team members. Possible non-response bias may occur in the second stage of the sample surveying, as the same participants may not be available. Stratified sampling seems like an adequate technique to use for the tasks set out in the current research.

To assess change management in all its complexity and to uncover solid theoretical and practical implications concerning its applicability in the public sector and GCAA specifically, there is a need to test it on different teams. Other sampling techniques like most of the non-probability sampling types also seem to apply to the research design, as there is a limited population of managers in GCAA that may agree to participate. Given that fact, convenience sampling can be used to form the sample only from those managers who are easily accessible and willing to participate (Bryman & Bell 2015). In this case, there is a risk of not having a sufficient number of people to conduct research. Snowball sampling can also theoretically be used as managers often communicate and if the most senior of them are approached, they could invite others. The problem here is a low probability of this scenario as managers are usually busy and do not have the time for additional tasks.

Reference List

Bryman, A & Bell, E 2015 Business research methods, Oxford University Press, USA.

Ceptureanu, EG 2015, ‘Research regarding change management tools on EU SMEs’, Business Excellence and Management Review, vol. 5, no. 2, pp. 28-32.

Christensen, L, Johnson, B & Turner, L, 2013, Research methods, design, and analysis, Pearson, London.

Cichocki, A, Ansari, H, Rusinkiewicz, M & Woelk, D, 2012, Workflow and process automation: concepts and technology, vol. 432, Springer Science & Business Media, Berlin.

Hayes, J 2014, The theory and practice of change management, Palgrave Macmillan, Basingstoke.

Montgomery, C 2017, Design and analysis of experiments, John Wiley & Sons, New York.

Myers, MD 2013, Qualitative research in business and management, Sage, London.

Van der Voet, J 2014, ‘The effectiveness and specificity of change management in a public organization: transformational leadership and a bureaucratic organizational structure’, European Management Journal, vol. 32, pp. 373–382.

Voorhees, M, Brady, K, Calantone, R & Ramirez, E 2015, ‘Discriminant validity testing in marketing: an analysis, causes for concern, and proposed remedies’, Journal of the Academy of Marketing Science, vol. 44, no. 1, pp. 119-134.