Purpose of the Research and Research Questions
This study lies in the field of human resource management since it examines the main obstacles and incentives for the automation of manufacturing in the defense sector. The purpose of this work is the determination of relations and the influence of factors affecting defense companies in the UAE in the production process. This study helps to identify weaknesses in human resource management, which will help companies optimize and accelerate the implementation of automated technologies and improve company productivity.
A review of defense companies’ data demonstrates that there is a gap in knowledge about the process of automation of production in this area since the interaction of various factors is not defined or partially considered. Therefore, the main research question is the determination of factors exerting the acceptance of automation in manufacturing and the degree of their influence on this process. For this purpose, similar scholarly sources will be investigated, and a survey of employees will be conducted to determine empirical evidence of the research hypotheses.
Theoretical and Practical Contributions
In this study, a new variable of resistance that is not taken into account by other researchers aimed at introducing innovations into work processes in various fields will be added. Consequently, the theoretical contribution of the work will be the development of a new research model, which allows scientists to study in detail the issue of adopting new mechanisms or tools that increase productivity. The improved research model can be applied to analyze the processes of adoption of automation, electronic systems, and technological innovations in any field of activity, for example, medicine, banking, delivery services, or the public sector. In addition, this research will supplement the theoretical knowledge base in human resource management in such a specific area as the defense industry.
Moreover, the results of this research can be applied by defense companies to analyze their working conditions and direct efforts toward problematic factors of automatic adoption. Hence, defense companies will be able to use this research to develop methods for influencing the perceptions of new technologies by employees. This understanding will accelerate and improve the process of automation adoption in manufacturing and increase the productivity of companies.
Expected Managerial Outcomes
The results of the research can be used by managers at various levels and kinds of organizations in the defense industry. Human resource specialists will be able to create training and programs that will increase employee satisfaction with the automation of manufacturing and influence their perception by focusing on problematic factors. In addition, the data can be used to expand training programs directly for using new technologies in the process of adapting them to the workflow.
Theoretical Framework
In this study, several sociological and managerial theories are used that make it possible to define the factors that influence the admission of automated technologies. The primary scientific source, which gives a general idea of the impact of various aspects of technology perception by a person, is a study of the introduction of Internet banking in the UAE by Al-Jabri and Sohail (2012). The authors justify the need for the exploration of factors such as relative advantage, complexity, compatibility, trialability, observability, and perceived risk. According to Al-Jabri and Sohail (2012), perceiving technologies as difficult to learn can scare people away from their use, as well as the perceived risks that their applications can bring. However, the relative advantages compared to conventional methods, the ability to observe technologies and try them before adoption are positive incentives for their acceptance.
These factors are also suitable for the research of automation in the defense industry as workers perceive technology in the way suggested by Al-Jabri and Sohail. However, it is worth replacing the perceived risk with a factor of resistance to innovation. This need is also confirmed by the work of Goodwin-Sak, McClain-Mpofu, Wieck, Zimmerman, and Merritt (2018), in which resistance to new technologies and automation is described as a key factor in the success of the company. The authors highlight several reasons for resistance, among them the fear of losing a job due to automation, the lack of necessary skills for managing technologies, ease of their use, and perceived threats (Goodwin-Sak et al., 2018). At the same time, perceived risks are just one of the aspects that go into the parameters of workers’ resistance to innovation or change. Therefore, this factor covers a broader range of variables that may affect the perception of automation, including perceived risks, which makes it more appropriate for this study.
Research Model
This study will use-dependent and independent modeling variables, as well as control demographic data to test hypotheses. A dependent variable is the adoption of automation in manufacturing since it is the result that defense companies need to get for increasing their productivity. Independent variables are relative advantage, complexity, compatibility, trialability, observability, and resistance. Managers and employees change their attitude to new technologies in production, depending on the perception of these factors, which affects their performance of duties and the company’s productivity. The control demographic data are gender, age, position, education, and experience with automated production technologies. These features can also affect the employee’s perception of factors affecting the adoption of automation.
These variables and the research model will help verify and analyze the following hypotheses:
- H1. Relative advantage will have a positive effect on automation adoption.
- H2. Complexity will have a negative effect on automation adoption.
- H3. The compatibility will have a positive effect on automation adoption.
- H4. Trialability will have a positive effect on automation adoption.
- H5. Observability will have a positive effect on automation adoption.
- H6. Resistance will have a negative effect on automation adoption.
Selection of Research Methodologies and Their Appropriateness
The primary research method in this paper will be quantitative analysis. The development of questions for a survey will consist of two parts, which include questionnaire items, their pilot check, and revision. The first part of the questionnaire will contain general questions about the demographic data of respondents, precisely gender, age, position, education, and experience with automated production technologies. Six categories of items follow with such independent variables as relative advantage, complexity, compatibility, trialability, observability, and resistance, respectively. After compiling the questionnaire, a pilot test will be conducted for a small sample of 20-30 respondents to determine the wording inaccuracies and their correction.
The main method of data analysis will be a multiple regression model, which will determine the effect of factors on automation adoption. In addition, the Bartlett Test of Sphericity and Kaiser-Meyer-Olkin (KMO) Measure of Sampling Adequacy to validate the use of factor analysis will be used for demographic data (Al-Jabri & Sohail, 2012). All data will be processed using the statistical tool SPSS v25 package.
This research method is the most convenient and suitable for the topic and objectives of the study since it gives accurate data on the relationship and correlation of various factors and the adoption of automation. In addition, demographic data are also taken into account, which provides more precise ideas about perceived factors depending on the position and status of an employee. The quantitative research method also is the most convenient way to determine the main problem that points to the attitude of workers to technologies, since it allows researchers to obtain accurate data and save time on analysis. Therefore, this method is suitable for the topic and purpose of the study.
Data Collection and Survey Instrument Development
The method of data collection will be a survey of employees and managers in the defense industry through such a tool as a self-report questionnaire. The study will include 55 UAE defense industry companies, and it will be conducted by sending surveys to managers by e-mail, who will provide them to their subordinates. The sample will be random, but among employees who are related to manufacturing and willing to participate.
Questions will have a range of answers from “strongly agree” to “strongly disagree,” except for demographic data, where respondents choose the option from the proposed list. For such factors as advantage, complexity, compatibility, trialability, observability, questions from Al-Jabri and Sohail (2012) will be used; however, they will be adapted to the specifics of the defense industry. Questions for variable resistance will be developed for this study independently by the researcher to choose the most suitable ones for the topic and purpose of the research.
References
Al-Jabri, I., & Sohail, M. (2012). Mobile banking adoption: Application of diffusion of innovation theory. Journal of Electronic Commerce Research 13(4), 349-391.
Goodwin-Sak, C., McClain-Mpofu, C., Wieck, M., Zimmerman, H., & Merritt, S. (2018). Courageous cultures embrace automation: A grounded theory investigation to determine individual willingness to adopt automation in the workplace. In Proceedings of the Ninth International Conference on Engaged Management Scholarship (pp. 1-24). Web.