To examine the role of automation in providing real-time feedback and career development opportunities

252 https://researchtrendsjournal.com Online at: https://researchtrendsjournal.com ISSN No: 2584-282X Indexed Journal Peer Reviewed Journal INTERNATIONAL JOURNAL OF TRENDS IN EMERGING RESEARCH AND DEVELOPMENT Volume 2; Issue 6; 2024; Page No. 252-256 Received: 01-08-2024 Accepted: 05-10-2024 To examine the role of automation in providing real-time feedback and career development opportunities 1Shah Richa Veer Kumar, 2Prof. T Ravi and 3Prof. Bhaskar Nalla 1Research Scholar, P.K. University, Shivpuri, Madhya Pradesh, India 2, 3Professor, P.K. University, Shivpuri, Madhya Pradesh, India DOI: https://doi.org/10.5281/zenodo.17132103 Corresponding Author: Shah Richa Veer Kumar Abstract In order to evaluate employee experience (CE, PH, FWA, TE), the EX-framework developed by Morgan (2017) was modified. The research is quantitative and draws from both exploratory and descriptive methods. Measurement, structure, and hypothesis are all assessed using the SEM model. Employee experience has a substantial and well-supported effect on employee engagement. A weak and unsubstantiated correlation bridges the gap between work experience and dedication to the company. Based on the results of this study, EX and OC fully mediate employee engagement. Keywords: Employee, Automation, Employee Engagement, Technologies, Workers 1. Introduction A new age marked by a transformative influence has begun with the fast spread of automation technologies, which has led to the incorporation of AI, robots, and complex digital technologies into business operations. This shift in automation has far-reaching consequences for the dynamics of employee engagement and, therefore, for the execution of company operations. The consequences of automation on employee engagement must be thoroughly investigated since more and more companies are using it to boost productivity, save money, and stay ahead of the competition. The success of an organisation, its workers' happiness on the job, and the workforce as a whole are all dependent on employee engagement. Historically, the word "automation" has been associated with worries about job loss and a potential decline in workplace quality of life. From a higher vantage point, we may see that technology is having an increasingly difficult effect on employee engagement. The several parts that make up the shift to automation and how it affects employee engagement are explored in this comprehensive study. It thinks about the challenges and possibilities that come with this paradigm change. To start, advancements in automation technologies: Furthermore, the introductory section should lay out the historical background of automation technologies, starting with the first mechanization processes and ending with the most recent AI and ML systems. To fully grasp the many ways in which automation interacts with human tasks in the workplace, one must have an awareness of this evolution. The justification for using automation: Focusing on factors like increased productivity, lower costs, and greater competitiveness, investigate why organisations adopt automation. The introduction lays the groundwork for comprehending the organizational imperatives propelling the integration of automated systems by highlighting these reasons. Engaged employees are those who: Your detailed explanation of employee engagement would be much appreciated; in particular, the part it plays in creating a motivated, contented, and productive staff. Incorporating the psychological, cognitive, and behavioural components that make up the idea of engagement would make this description more thorough. The impact on professional duties and expertise: Look at International Journal of Trends in Emerging Research and Development https://researchtrendsjournal.com 253 https://researchtrendsjournal.com how the rise of automation is changing people's roles and the skills they need for the job. The need for flexibility, inventiveness, and higher-order cognitive abilities, as well as the prospect that common occupations may be superseded by novel ones, must be acknowledged. The opinions and outlooks of the employees come in at number five: A variety of emotions, from fear and worry to acceptance and encouragement, should be recorded when asking workers about their feelings towards automation. Think about factors like the perceived stability of your position, how it will influence your happiness on the job, and the opportunities for you to develop your skills. 2. Literature Review ALDamoe et al., (2012) [1] investigated the role of employee retention as a moderator in the connection between HRM practices and organisational success. The study found that employee retention is a mediator between HRM practices and organisational performance, which is an important conclusion. Findings from the research stress the importance of personnel management in retaining top people and boosting business results. To keep workers around for the long haul, businesses need to implement retention strategies that include things like competitive and fair salaries, opportunities for advancement, autonomy, positive public perception of the company, and financial incentives. Chang et al., (2012) [2] This essay delves into the topic of job turnover choices made by IT professionals in Taiwan and examines the role of career anchors and disruptions. Previous models have included work satisfaction as a key indicator. In addition, the company coordinates the internal anchoring of IT staff with workplace resources. The correlation between contentment in one's job and the desire to look for work elsewhere is sometimes disrupted by factors that do not follow this pattern. Turnover models do not take these disruptions into account on a global scale, which makes planning more difficult. Joāo and Coetzee (2012) [3] investigated what made earlycareer workers stay, what they thought about their future prospects, and how committed they were to the company. The findings indicated that the perceived cost of leaving had an effect on the career mobility and organizational loyalty of older workers. When asked about the importance of professional progression in terms of career mobility and organizational commitment, younger Black workers gave it a high priority. Professional staff workers in the financial industry should be considered for talent retention strategies that prioritize their requirements for advancement opportunities within the company, harmony between work and personal life, practical use of acquired skills and knowledge, competitive pay, and a work environment. Kim (2012) [4] looked at how human resource management affected the intents of state government IT employees to leave. The findings showed that Satisfactory pay and benefits, opportunities for career advancement, and training and development are the most influential aspects of an employee's decision to stay or go from an organisation, policies that support families, and clear and consistent communication from supervisors. Another important element influencing the inclination to leave among female One perk of working in IT is the variety of rules that are good for families. Kumar and Arora (2012) [5] pinpointed the elements that impact employee loyalty in the business process outsourcing (BPO) market. Organizational culture, top-down support, wage parity, and other monetary perks were determined to have a greater relative worth in the research. According to the research, business process outsourcing (BPO) firms may help their workers strike a better work-life balance via the provision of competitive incentives and the maintenance of a pleasant work environment and recognition programs. 3. Research Methodology 3.1 Research Approach Academic studies have shown that finding, hiring, and retaining great personnel is a major challenge for many companies. Meanwhile, workers are proving to be an increasingly significant asset to any business. A descriptive research technique is used to study organizational effectiveness, employee engagement, and organizational commitment based on current literature. 3.2 Research Tool This research uses the survey approach to get data from participants. A self-administered, structured questionnaire was used to gather the main data. Component indicators such as physical environment, CE, FWA, TE, EE, OC, and OE were covered in the second segment. 3.3 Sample Size Estimation and Response Rate A method called "Structural Equation Modelling" (SEM) was used by the researcher to evaluate the suggested model. For both conventional and elliptical theories, the optimal structural equation modeling ratio is 5:1, With 568 responses, a reliability analysis of all seven components was conducted for the final research. The acceptance level of goodness of fit is measured based on the indices such as GOF, adjusted AGFI, CFI, RMSEA, and normed Chisquare. The measurement model indices are shown in Table 1 with values (Hair et al., 2015) [16]. Table 1: Measurement model indices Indices Threshold Value Normed chi-square >1 and <3 GFI >0.90 AGFI >0.90 CFI >0.95 RMSEA <0.08 Source: Research Methodology 4. Data Analysis The final questionnaire and study should be built based on the results of the pilot research. For the final analysis, 568 replies were taken into account 4.1 Descriptive Statistics of Final Study Table 2 displays the descriptive statistics of the questionnaire that were used in the final analysis. International Journal of Trends in Emerging Research and Development https://researchtrendsjournal.com 254 https://researchtrendsjournal.com Table 2: Descriptive statistics of the final study Constructs Mean Standard Deviations Skewness Kurtosis CE1 3.98 .703 -.711 1.241 CE4 4.04 .713 -.557 .662 CE5 4.08 .695 -.831 1.872 PH2 3.79 .686 -1.118 2.492 PH3 3.74 .700 -.808 1.314 PH4 3.99 .565 -.472 1.812 FWA1 4.04 .845 -.960 .929 FWA2 4.22 .662 -.672 .990 FWA3 3.98 .830 -.923 1.138 TE1 2.77 1.148 .267 -.797 TE2 2.76 1.182 .176 -.953 TE3 2.64 1.181 .396 -.802 EE3 2.84 .903 .105 -.793 EE7 3.03 .963 -.140 -.942 EE8 3.01 .964 -.117 -.911 OC4 2.12 .786 .684 .514 OC5 1.98 .789 .813 .786 OC9 2.11 .829 .896 .670 OE5 3.74 .782 -.776 1.049 OE6 3.74 .723 -.748 1.220 OE8 3.83 .734 -.573 1.014 Source: Primary data The data's descriptive statistics are shown in Table 2. The assumption that the data follows a normal distribution may be made using this. 4.2 Reliability Analysis Reliability Alpha values ranged from 0.936 to 0.759, as shown in Table 3. For further statistical analysis, Nunnally (1978) [17] predicted that "Cronbach's alpha results were above the recommended minimum of 0.7.” Table 3: Reliability analysis Constructs Cronbach’s Alpha Cultural Environments .936 Physical environment .759 Flexible working arrangements .813 Technology Environment .917 Employee Engagement .926 Organizational commitment .886 Organizational effectiveness .897 The researcher had a better grasp of demographic context and data distribution among demographic variables with the use of Table 4. Notably, 49% of the comments are from tier A cities, which include Bangalore, Chennai, Delhi, Mumbai, Kolkata, and Hyderabad. Table 4: Demographics of the Respondents in the Final Study Demographical Factors Description Frequency Percentage Age 21-30 167 29 31-40 219 39 41-50 159 28 Above 51 23 4 Total 568 100 Gender Female 286 50 Male 278 49 Other 4 1 Total 568 100 Marital Status Married 413 73 Other 7 1 Single 148 26 Total 568 100 Educational Qualification Diploma / ITI 40 7 Graduation 267 47 Other 4 1 Post Graduation 257 45 Total 568 100 Income level in Rupees (Monthly) 40,001 -60,000 214 38 60,001 -1, 00,000 171 30 Above 1, 00,001 62 11 Less than 40,000 121 21 Total 568 100 Job Profile Associate 130 23 Executive 100 18 In Leadership Role 46 8 In support team 39 7 Manager 113 20 Other 19 3 Supervisor 121 21 Total 568 100 No of years of Experience Five years and above 155 27 Less than one year 46 8 One to three years 153 27 Three years to five years 214 38 Total 568 100 International Journal of Trends in Emerging Research and Development https://researchtrendsjournal.com 255 https://researchtrendsjournal.com Cities Tier A: Bangalore, Chennai, Delhi, Mumbai, Hyderabad, Kolkata 281 49 Tier B: Agra, Lucknow, Jaipur, Chandigarh, Nagpur, Mysore, Pune 215 38 Tier C: others 72 13 Total 568 100 Source: Primary Data About 39% of the people who filled out the survey were in the 31–40 age bracket. Married status is represented by 73% of the respondents, while 47% have completed some kind of postsecondary education. 4.3 Measurement Model While measurement models quantify latent or composite variables, structural models use route analysis to analyze all conceivable relationships. As stated by Hair et al. (2015) [16], the statistical model's goodness of fit indicates how well it matches a collection of data. Indexes and the measuring methodology are shown in Table 5. Table 5: Measurement model indices Flexible working arrangements FWA1 .801 0.835 0.633 0.166 0.077 FWA2 .807 FWA3 .863 Technology Environment TE1 .886 0.917 0.789 0.192 0.056 TE2 .906 TE3 .900 Employee Engagement EE3 .815 0.895 0.740 0.192 0.092 EE7 .863 EE8 .873 Organizational commitment OC4 .848 0.862 0.675 0.092 0.031 OC5 .834 OC9 .859 Organizational effectiveness OE5 .792 0.835 0.629 0.335 0.115 OE6 .805 OE8 .707 Source: Primary data 4.4 Structural Model Data is tested for discriminative and convergent validity when the measurement model is determined to be adequate using threshold value indices (Hair et al., 2015) [16]. A structural model's execution is simple; Table 6 predicts it and uses model fit indices to quantify it. The "Goodness of Fit Index" according to its definition (GFI). "The Adjusted Goodness of Fit Index" (AGFI) "corrects the GFI as a function of the number of latent variable indicators." Table 6: Structural model indices Indices Threshold Value (Hair et al., 2015) [16]. Present study results Normed chi-square >1 and <3 2.573 GFI >0.90 0.928 AGFI >0.90 0.908 CFI >0.95 0.958 RMSEA <0.08 0.053 Source: Primary data 4.5 Path Analysis "Path Analysis is a kind of predictive modeling that researchers use to look into the connections between study model variables." To measure and examine the correlations between the latent and visible variables, statisticians employ structural equation modeling (SEM). There was a statistically significant positive relationship between EE and the main factors of culture, physical environment, technology environment, and flexible working arrangement. Table 7 shows the direct impacts of employee experience on OC. Table 7: Path coefficients and indirect effects for the mediation model Relationships Total Effects Direct Effects Indirect Effects EX > OC (EE) EX> OE (EE) .194 .753 0.049 .862 .145 -.109 Note: *p<0.001; **p<0.01 Test for Full and Partial mediation 5. Conclusion Personalized experiences, administrative task automation, instant feedback, and data-driven decision-making are just a few ways in which artificial intelligence (AI) may significantly improve employee engagement. For an organization to achieve its goals, it needs a well-rounded plan that addresses leadership, culture, communication, and continuous initiatives. This will create a positive work environment where employees feel valued, motivated, and dedicated to their work. Automation in the workplace has complex and ever-changing implications for workers' psychological well-being. Increased productivity, improved accuracy, and decreased operating expenses are a few of the positives of automation. 6. References 1. 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