Digital technologies playing a crucial role by transforming various business functions, including human resource management. As the global economy advances toward Industry 4.0, the emphasis merely improving manpower, reducing production cost to ensure business sustainability, making eco-friendly business regulations with greener business model. Computer intelligence as a valuable measure for facilitating Green HRM initiatives by enhancing key HR processes such as applying job online, onboarding process, induction programs, interview process and even the performance evaluation system while minimizing environmental impact. By integrating AI into HR policies, procedures, and strategies, organizations can significantly improve the efficiency of Green HRM. Examining moderating role of work engagement with connection over AI applications towards sustainable HR regulations. Empirical research method is used to collected the responses via the google form with 440 participants. Measurement illustrating was designed PLS-SEM model, incorporating both path analysis and Confirmatory Factor Analysis (CFA). Have developed the survey form with 2 sections that consisting with respondent profile with characteristics and another section test the modules consisting with the organizational readiness to adapt the culture, pro-environmental behaviour of the employee and the green HRM practices involvement to achieve the organization sustainability. With this employee engagement and the job satisfaction worked as a mediator between these factors. The results show the organization and the employees have the personal interest and intention to develop the sustainable environment for our greater future. Eco-friendly business development and the business solution model are boosting the employee personal intention
Digital word is acculturating by the way of energy efficient business solution an operations that harmful to the nature and our promise future. Even we are sufficiently aware about the business strategies that helpful for the nature friendly and improving the sustainability goals to active the low-carbon business model to boost the environmental performance. Modern organizations are leveraging HR initiatives to promote the development of sustainable products and services that benefit both society and the planet. This strategic alignment of environmental objectives with workforce management for achieving sustainable business outcomes is referred to as sustainable HRM with a green focus.(Sci, n.d.)
Artificial Intelligence in Organizations
Artificial intelligence plays an important role to improve the productivity by reducing the manpower involvement to reducing the energy emission in use of AI processes. Existing scholarly work has to be understood and applied properly to get the desired results. Digital computer intelligence and data driven solutions assisting in the way of accurate speed over the human intelligence knowledge of the job holder and the organization as a whole by applying new or creative ideas in the business. (Tahira, 2021). The organization should follow the ethical considerations of artificial intelligence to ensure its proper and safe use of it. The merging of artificial intelligence and business organizations makes a successful collaborative approach among machines and human beings. (Kannan, 2024)
Green IT Business to enhance business sustainability
The development of Green IT was solely concerned with cost reduction and computational efficiency. However, as the demands for IT infrastructure in nearly every industry continue to rise, so does the demand for computing power, which has resulted in increased amount of carbon production that causes the harmful gases that really affect the human health. Green IT business is the greatest solution the initiates taken by the corporate firms and its solutions were individual concentrate words the sustainability development. Because technology is changing so quickly, creating new environmentally friendly IT products takes effort and changes the business into greener business in the way of reduce, recycle and reuse.(THE FUTURE OF GREEN IT WHITEPAPER, n.d.-a)
CSR contribution towards Sustainable business transition
In response to growing environmental and financial pressures, business firms are increasingly expected to enhance societal well-being while minimizing their environmental footprint, all without compromising the effective and productive achievement of their strategic objectives. To promising the sustainable future employers insisting employees to involve in social activities such as corporate training related to eco-friendly business practices. (Calvin et al., 2023). The workforce is considered a vital asset in any enterprise, playing a key role in establishing a competitive advantage. Strengthening employee capabilities through skill advancement and strategic talent management contributes significantly to improved workforce output (Alvi, 2021).
Net Zero Transmission to reduce GHG Emissions
Accenture has pledged to achieve net-zero emissions by 2025. To reach this goal, the company is transitioning to 100% renewable electricity across its facilities and is actively engaging suppliers to reduce their emissions. Additionally, Accenture is investing large scale investment towards greenery to offset any remaining emissions. “By 2025, Accenture aims to reuse or recycle 100% of its electronic waste, including computers and servers, as well as all office furniture”. The company has also eliminated single-use plastics in its facilities by adopting reusable and plastic-free alternatives. (THE FUTURE OF GREEN IT WHITEPAPER, n.d.-b)
Empirical studies
Green hiring and selection
(Ahmad, 2015) examines the role sustainable business practices and regulations by enhancing companies’ production development in developing nations. Study highlights findings based on increasing importance of eco-friendly practices endeavour to aim recycle solution goals while addressing the unique challenges faced by developing economies. The s authors define sustainable economics eco-friendly business regulation focus on business bundles HRM practices like hiring, screening, selecting, inducting, evaluating by sustainable way of solutions. They argue that effective GHRM practices can lead to improved green s models by embedded business solutions to develop the green nation.(Jamal et al., 2021)
H1: Green Recruitment and selection positively influence GHRM practices
Worker’s development module
Furthermore, (He et al., 2024) his findings suggest important implications for both policy and practice, indicating that organizations should focus on integrating GHRMP with initiatives that inspire employees' passion for environmental sustainability. This alignment is essential for achieving tangible and long-lasting changes in environmental performance. Overall, the paper underscores the importance of harmonizing organizational practices with employee motivations to drive effective environmental change.(He et al., 2024).
H2: Green Training and development positively influence GHRM practices
Employee Performance management system
(Khan, 2022) interprets that sustainable business practices that directly have effect over on job-holders attitude and intention in way a positive affirmation within India's IT industry. The scholarly work explain that workers performance and evaluation greatly rely individual contribution toward the society and the company’s business growth that incorporate the economic and business transition to construct the better nation and their development. Hence the bundled business hr regulation and policies significantly influence employees' behaviour, fostering both job-related and voluntary eco-friendly actions. Top of Form
H3: Green Performance management system positively influences GHRM practices
Remuneration and Recognition System
Reward and Compensation Strategy refer to provide the financial benefits to the workers and designed the programs to improve and contributing them to facilitate ecological sustainability initiatives (Mandago, 2018). The key aspects of green compensation and rewards include: skill-based rewards, Personal attrition rewards, recognition for earth-friendly findings beneficial programs for adopting green and sustainable behaviours (Ahmad, 2015). Previous research has shown that offering monetary incentives for engaging in environmental responsibilities enhances employee commitment to sustainability programs. Exiting reviews demonstrated the effectiveness about sustainable advantages compensation in industries with high pollution levels, a context similar to the present study.
H4: Green compensation and reward management system positively GHRM practices
Environmental performance growth
GHRM methods are based on three pillars: facilitating green opportunities, motivating green personnel, and developing green capacity. Earth-friendly business innovations and policies that measures focus on include recruiting, talent management system, human capital talent retention, and compensation benefits. Sustainable business practices contributing the world environment particularly develop the company’s goal, mission and statement to achieve the promotable future. Tasks contained eco-friendly solutions assist companies by adopting environmental action by environmental objectives. (Correia et al., 2024)
H5: GHRM practices have significant influence on sustainable development
Mediating role of employee engagement
Environmentally conscious HRM practices drives eco-driven motivation, employee contentment, and long-term organizational effectiveness has not previously been explored within the corporate software industries. This study was undertaken to address this break in the previous literature reviews. Specifically, the research investigates how sustainable HR practices affect green bundled regulations and policies those are hiring and screening, selecting the applicant, training methods, and evaluating the job holder to improve their intention towards organizational commitment and work fulfilment in shaping sustainable outcomes. To analyse the link over green HRM bundled practices with the mediation analysis job satisfaction and employee perception and awareness to connect the organization sustainability.(Abdelhamied et al., 2023)
H5: GHRM practices have affirmative impact on sustainable development
Organization readiness to adopt the culture
By equipping sustainable business policies of corporate culture improvement GHRM played important role to enhance the growth. By integrating organizational learning, performance reviews, job roles, training, hiring, collaboration, selection, and organizational culture to environmental challenges, organizations can achieve environmental strategies and goals. GHRM grabs attention from practitioners and academicians all around the world . Such cultural alignment plays successful execution of GHRM to achieve long-term firm’s environment performance. (Mustafa et al., 2023).
H6: GHRM Practices affirmatively impact Organization readiness to adopt the culture
H7: Organization readiness to adopt the culture directly mediate the relationship on sustainable development
Mediating role of employee engagement towards sustainable development
Worker’s commitment to involve as crucial factor as intermediator, facilitating value transformation of HRM practices into measurable performance outcomes. A key aspect of employee engagement, known as employee voice, is particularly significant within sustainable HRM. Research suggests that encouraging employee participation and feedback strengthens affirmative relationship over of sustainable policies on both engagement and production improvement. This highlights the importance of engagement in knowledge sharing as a mechanism through which sustainable HRM practices enhance environmental performance.
H8: “Employee Engagement mediates the relationship GHRM practices towards Sustainable development”
Moderating role as Employee Pro-environment behaviours of AI infusion on Sustainable development
The existing body of research on GHRM is predominantly rooted in Western contexts, leaving a notable research gap concerning its application and relevance within the rapidly growing economies of Asia an area critical to global environmental progress. Exploring how different management strategies across diverse regions can advance GHRM objectives and enhance ecological competitiveness. Furthermore, an organization’s contribution to society is increasingly evaluated based on its ethical conduct, initiatives in corporate social responsibility, capacity to generate employment, and the overall strength of its public status and companies image transition and growth. (Renwick, Redman, & Maguire, 2013).
H9: Employee Pro-environment behaviours of AI infusion Sustainable development moderate the relationship GHRM towards Sustainable development
H10: Employee Pro-environment behaviours of AI infusion Sustainable development moderate the relationship GHRM towards organizational readiness to adopt the culture
AMO Framework towards organization sustainability
Existing scholarly work exhibits extensive overview about how green HRM practices positively influencing the organization sustainability to achieve ability motivation theory factor. Based on the ability skills employee personal development factors and motivation factors to improve the theory perspectives influencing organization sustainability dimension’s The review highlights how sustainable HR regulations play a role in fostering environmental sustainability with EM objectives. Using the AMO model existing studies deviation of business policies that enhance employees' capabilities that driven with employee personal intention and intrusive factors to improve dimensions. (Renwick, Redman, & Maguire, 2013).
Objectives
c)To observe how artificial intelligence to succeed GHRM by achieving firm’s green culture
d)To integrate AMO theory align with GHRM followed by the Corporate Industries
Research methodology
Sample Description
Data analysed through non probability sampling, especially convenience sampling validate representation across key demographic groups such as age, gender, educational background, age of the organization, sector split, firm’s size. Questionnaire was administered to gather data, focusing on constructs relevant to GHRM and sustainability, values shown in (Table 1.1) All responses were screened for completeness and accuracy, and 440 valid responses were retained for SEM analysis. The sample size met the recommended threshold for Partial Least Squares SEM (PLS-SEM), allowing for reliable estimation of the model's parameters. Values illustrated in table 1.2
Measurement Model
The collected responses were thoroughly examined, and both the measurement and structural models were developed using SmartPLS-4. Demographic variables were analysed using SPSS version 26. Frequency analysis and reliability tests were also conducted in SPSS, yielding acceptable threshold values. The dataset was partialized into two sections(i) demographic information, (ii) independent, dependent, moderating, and mediating variables. Statistical tests, including Cronbach's alpha, discriminant validity, and convergent validity, were performed, and the results confirmed acceptable levels of reliability and validity for the measurement model. “Composite reliability” and “Cronbach alpha” values were tested and illustration defined with table 1.3
Instrument design
The questionnaire with GHRM variables consisting bundled GHRM including “hiring and selection”, “training”, “performance evaluation” and “welfare relation management system”. Developed the questionnaire of organization sustainability to test over the green HRM practices the additional part was moderated and mediated study for the SEM model. Final survey form based on the model was created to assess our organization's readiness to adapt the culture to implement the green HRM practices. “Five-point liker scale” ranging from (1) “strongly disagree” to (5) “strongly agree” to validate the 70 sample questions.
Data analysis and results
Direct effect -------------------------à
Indirect effect - -- - - - - - -- - - - -- >
Fig 1: Green HRM Practices Hypothetical model
Fig 1.2: Theoretical framework
|
Employee engagement (Mediator)
|
|
Employee Pro-environment behaviours of AI infusion (Moderator) |
|
AI driven HR regulations 1.Hiring 2.Trianing 3. Performance evaluation 4. Reward beneficial |
|
Sustainable development (DV) |
|
Organizational Readiness to adopt the culture (Mediator)
|
Table 1.1 Descriptive analysis
|
|
Gender |
Age |
Education |
Work Exp |
Size of the Organization |
Sector |
Designation |
|
|
N |
Valid |
440 |
440 |
440 |
440 |
440 |
440 |
440 |
|
Mean |
1.47 |
2.07 |
2.54 |
3.52 |
4.23 |
2.08 |
2.26 |
|
|
Median |
1.00 |
2.00 |
3.00 |
4.00 |
5.00 |
2.00 |
2.00 |
|
|
Std. Deviation |
.500 |
.823 |
.579 |
.959 |
1.133 |
.910 |
.666 |
|
|
Skewness |
.119 |
.258 |
.078 |
-.680 |
-1.466 |
.088 |
-.301 |
|
|
Std. Error of Skewness |
.116 |
.116 |
.116 |
.116 |
.116 |
.116 |
.116 |
|
Table 1.2: Profile of the sample
|
|
Frequency |
Percent |
Valid Percent |
Cumulative Percent |
|
|
Gender |
Male |
233 |
53.0 |
53.0 |
53.0 |
|
Female |
207 |
47.0 |
47.0 |
100.0 |
|
|
Total |
440 |
100.0 |
100.0 |
|
|
|
Age |
< 25 - 30yrs |
118 |
26.8 |
26.8 |
26.8 |
|
31- 40yrs |
188 |
42.7 |
42.7 |
69.5 |
|
|
41 - 50yrs |
118 |
26.8 |
26.8 |
96.4 |
|
|
> 50yrs |
16 |
3.6 |
3.6 |
100.0 |
|
|
Total |
440 |
100.0 |
100.0 |
|
|
|
Education |
Intermediate/diploma |
6 |
1.4 |
1.4 |
1.4 |
|
Graduation (B.com,B.Tech,BBA,BCA,Others) |
202 |
45.9 |
45.9 |
47.3 |
|
|
Post-Graduation (MBA,MCA,M.Tech,M.Com,Others) |
219 |
49.8 |
49.8 |
97.0 |
|
|
Above Post Graduation |
13 |
3.0 |
3.0 |
100.0 |
|
|
Total |
440 |
100.0 |
100.0 |
|
|
|
Work Exp |
Below 2 Years |
18 |
4.1 |
4.1 |
4.1 |
|
2-5Years |
41 |
9.3 |
9.3 |
13.4 |
|
|
5-8 Years |
126 |
28.6 |
28.6 |
42.0 |
|
|
8-10Years |
203 |
46.1 |
46.1 |
88.2 |
|
|
Above 10 Years |
52 |
11.8 |
11.8 |
100.0 |
|
|
Total |
440 |
100.0 |
100.0 |
|
|
|
Sector |
IT(Information technology) |
151 |
34.3 |
34.3 |
34.3 |
|
IT IS/ITES |
115 |
26.1 |
26.1 |
60.5 |
|
|
BPS/BPO |
160 |
36.4 |
36.4 |
96.8 |
|
|
Other Services |
14 |
3.2 |
3.2 |
100.0 |
|
|
Total |
440 |
100.0 |
100.0 |
|
|
|
Designation |
Top level/Administrative level |
54 |
12.3 |
12.3 |
12.3 |
|
Middle level/ Executive |
219 |
49.8 |
49.8 |
62.0 |
|
|
Low level/ First line managers |
166 |
37.7 |
37.7 |
99.8 |
|
|
Others |
1 |
.2 |
.2 |
100.0 |
|
|
Total |
440 |
100.0 |
100.0 |
|
|
Table 1.3 Correlation results
|
Variables |
Empeng |
Green HRM Practices |
ORAC |
Proenv |
SD |
Proenv x Empeng |
Proenv x Green HRM Practices |
|
Empeng |
1.000 |
0.884 |
0.833 |
0.550 |
0.552 |
0.546 |
0.526 |
|
Green HRM Practices |
0.884 |
1.000 |
0.898 |
0.612 |
0.613 |
0.492 |
0.500 |
|
ORAC |
0.833 |
0.898 |
1.000 |
0.628 |
0.628 |
0.515 |
0.527 |
|
Proenv |
0.550 |
0.612 |
0.628 |
1.000 |
0.998 |
0.310 |
0.263 |
|
SD |
0.552 |
0.613 |
0.628 |
0.998 |
1.000 |
0.312 |
0.267 |
|
Proenv x Empeng |
0.546 |
0.492 |
0.515 |
0.310 |
0.312 |
1.000 |
0.837 |
|
Proenv x Green HRM Practices |
0.526 |
0.500 |
0.527 |
0.263 |
0.267 |
0.837 |
1.000 |
Validity of Discriminants
Discriminant validity measurement model by first examining the shared AVE of the latent constructs. Correlation values show the constructs to ascertain the SEM model deviation with threshold values. Final results shows that discriminant validity meet positive correlations between the modules. As suggested, Table 1.3.1 shows that the constructs with values between 0.27 and 0.86 accounted for remaining factors implied with positive limits.
Table 1.3.1 Discriminant and Validity
|
Variables |
“Cronbach's alpha” |
“Composite reliability” (rho_a) |
“Composite reliability” (rho_c) |
“Average variance extracted” (AVE) |
|
Empeng |
0.861 |
0.880 |
0.893 |
0.518 |
|
Green HRM Practices |
0.856 |
0.950 |
0.869 |
0.362 |
|
ORAC |
0.850 |
0.885 |
0.886 |
0.513 |
|
SD |
0.271 |
0.995 |
0.435 |
0.254 |
Fig 1.4 Structural equation model- SEM model for Green HRM
Table 1.4 Descriptive statistics results
|
Variables |
Mean |
Median |
Observed min |
Observed max |
Standard deviation |
Excess kurtosis |
Skewness |
Number of observations used |
|
Empeng |
0.000 |
-0.161 |
-1.887 |
2.072 |
1.000 |
-0.487 |
0.537 |
450.000 |
|
Green HRM Practices |
0.000 |
-0.246 |
-1.732 |
2.221 |
1.000 |
-0.709 |
0.525 |
450.000 |
|
ORAC |
0.000 |
-0.288 |
-1.757 |
1.960 |
1.000 |
-0.821 |
0.520 |
450.000 |
|
Proenv |
0.000 |
0.096 |
-1.348 |
1.558 |
1.000 |
-1.320 |
0.048 |
450.000 |
|
SD |
0.000 |
0.073 |
-1.499 |
1.653 |
1.000 |
-1.314 |
0.043 |
450.000 |
Fig 1.5 Structural equation model- SEM model Measurement model
Path co-efficient values
Table 1.5 displays the path analysis conducted as part of this research. The table illustration shows with a path coefficient of “B = 0.197” and “p-value of 0.05”, with threshold values. Findings reveal that GHRM practices have affirmative relationship over organization readiness to adapt the culture and pro-environmental behaviour association with sustainable development” (B = 0.675, p = 0.05)”. GHRM practices have significant link over organization sustainability.
Table 1.5 Path Co-Efficient
|
“Original sample” (O) |
“Sample mean” (M) |
“Standard deviation” (STDEV) |
T statistics (|O/STDEV|) |
P values |
|
|
Empeng -> SD |
-0.102 |
-0.099 |
0.045 |
2.245 |
0.025 |
|
GCMS -> GHRMPractices |
0.182 |
0.181 |
0.066 |
2.762 |
0.006 |
|
GHRMPractices -> Empeng |
0.773 |
0.773 |
0.020 |
38.913 |
0.039 |
|
GHRMPractices -> ORAC |
0.780 |
0.780 |
0.019 |
40.208 |
0.047 |
|
GHRMPractices -> SD |
-0.152 |
-0.151 |
0.035 |
4.349 |
0.019 |
|
GPMS -> GHRMPractices |
0.257 |
0.261 |
0.037 |
6.926 |
0.029 |
|
GRS -> GHRMPractices |
0.290 |
0.291 |
0.034 |
8.428 |
0.037 |
|
GTD -> GHRMPractices |
0.366 |
0.365 |
0.038 |
9.498 |
0.045 |
|
ORAC -> SD |
0.019 |
0.024 |
0.045 |
0.430 |
0.667 |
|
Proenv -> SD |
1.013 |
1.007 |
0.032 |
31.722 |
0.054 |
|
Proenv x Empeng -> SD |
0.071 |
0.073 |
0.027 |
2.615 |
0.025 |
|
Proenv x GHRMPractices -> SD |
0.006 |
0.005 |
0.022 |
0.268 |
0.789 |
Fig 1.6 Boot strap Model
Hypothesis Testing
To understand the relationship between the GHRM practices with organization sustainability with the moderator and mediator regard to the examination of measurement model the hypothesized model makes use of a structural model to emphasize how dependent the relationship is on one another. An internal modelling analysis of the positive correlation among GHRM practices with sustainable development. Path co-efficient values with t-values are provided by the structural model in PLS., the values are illustrated in table 1.7.
Confirmatory Factor Analysis
“Confirmatory Factor Analysis” (CFA) is recognized as a fundamental component of the Structural Equation Modelling (SEM) approach theoretical constructs through careful evaluation of the relationships between GHRM practices over sustainable development. To measure the CFA before that EFA was initiated and finalized after checked the reliability analysis, communality check and the cross loadings. In the cross-loadings the statement analysed to meet the threshold values. EFA was finalized and checked based on the rotated component analysis. PLS-SEM model was derived out and the model fit checked (Table 1.6)
Table 1.6: Discriminant and validity
|
Variables |
Heterotrait-monotrait ratio (HTMT) |
|
Green HRM Practices <-> Empeng |
0.957 |
|
ORAC <-> Empeng |
0.958 |
|
ORAC <-> Green HRM Practices |
0.971 |
|
SD <-> Empeng |
0.710 |
|
SD <-> Green HRM Practices |
0.765 |
|
SD <-> ORAC |
0.739 |
Table 1.7: Inner model valuation and Hypothesis testing
AVE values- Mean, Median, standard deviation
|
Variables |
Original sample (O) |
Sample mean (M) |
Standard deviation (STDEV) |
T statistics (|O/STDEV|) |
P values |
|
GCMS -> Empeng |
0.141 |
0.139 |
0.050 |
2.834 |
0.025 |
|
GCMS -> ORAC |
0.142 |
0.141 |
0.050 |
2.847 |
0.006 |
|
GCMS -> SD |
-0.039 |
-0.038 |
0.015 |
2.621 |
0.039 |
|
GHRMPractices -> SD |
-0.063 |
-0.058 |
0.037 |
1.729 |
0.047 |
|
GPMS -> Empeng |
0.199 |
0.202 |
0.029 |
6.800 |
0.019 |
|
GPMS -> ORAC |
0.201 |
0.204 |
0.029 |
6.910 |
0.029 |
|
GPMS -> SD |
-0.056 |
-0.055 |
0.012 |
4.775 |
0.037 |
|
GRS -> Empeng |
0.224 |
0.225 |
0.027 |
8.169 |
0.045 |
|
GRS -> ORAC |
0.226 |
0.227 |
0.028 |
7.976 |
0.667 |
|
GRS -> SD |
-0.063 |
-0.061 |
0.012 |
5.109 |
0.054 |
|
GTD -> Empeng |
0.283 |
0.282 |
0.031 |
9.109 |
0.025 |
|
GTD -> ORAC |
0.285 |
0.285 |
0.031 |
9.157 |
0.789 |
|
GTD -> SD |
-0.079 |
-0.077 |
0.015 |
5.143 |
0.025 |
Interaction effect analysis
Moderation analysis within the framework of Structural Equation Modelling (SEM) is employed to investigate investigated among GHRM Practices to mediate the link with sustainable development. To test the bootstrapping values the moderation analysis were been checked by the R2 square values. Table 1.9 illustrate the values to f2 value is 0.017; thus, we conclude that sustainability development has 0.073 a high value between GHRM and an employee’s sustainability development while pro-environmental behaviour. Additionally, employee engagement as mediator, organization readiness to adopt the culture on the GHRM practices with work engagement have 0.010 limit p values with the 0.010 value (see Figure 1.9).
Table 1.8 Predictive Accuracy
|
Variables |
Original sample (O) |
Sample mean (M) |
Standard deviation (STDEV) |
T statistics (|O/STDEV|) |
P values |
|
Empeng |
0.597 |
0.597 |
0.031 |
19.447 |
0.035 |
|
GHRMPractices |
0.830 |
0.835 |
0.019 |
42.653 |
0.044 |
|
ORAC |
0.608 |
0.608 |
0.030 |
20.081 |
0.050 |
|
SD |
0.820 |
0.820 |
0.015 |
55.850 |
0.046 |
Table 1.9 Hypothesis testing
|
Hypothesis testing |
Variables |
Original sample (O) |
Sample mean (M) |
Standard deviation (STDEV) |
T statistics (|O/STDEV|) |
P values |
Decision |
|
H1 |
GRS -> GHRMPractices |
0.290 |
0.291 |
0.034 |
8.428 |
0.025 |
Supported |
|
H2 |
GTD -> GHRMPractices |
0.366 |
0.365 |
0.038 |
9.498 |
0.006 |
Supported |
|
H3 |
GPMS -> GHRMPractices |
0.257 |
0.261 |
0.037 |
6.926 |
0.039 |
Supported |
|
H4 |
GCMS -> GHRMPractices |
0.182 |
0.181 |
0.066 |
2.762 |
0.047 |
Supported |
|
H5 |
GHRMPractices -> Empeng |
0.773 |
0.773 |
0.020 |
38.913 |
0.019 |
Supported |
|
H6 |
GHRMPractices -> ORAC |
0.780 |
0.780 |
0.019 |
40.208 |
0.029 |
Supported |
|
H7 |
GHRMPractices -> SD |
-0.216 |
-0.210 |
0.034 |
6.353 |
0.037 |
Supported |
|
H8 |
ORAC -> SD |
0.019 |
0.024 |
0.045 |
0.430 |
0.077 |
Not Supported |
|
H9 |
Proenv x GHRMPractices -> SD |
0.006 |
0.005 |
0.022 |
0.268 |
0.667 |
Not Supported |
|
H10 |
Proenv x Empeng -> SD |
0.071 |
0.073 |
0.027 |
2.615 |
0.054 |
Supported |
|
H11 |
Proenv -> SD |
1.013 |
1.007 |
0.032 |
31.722 |
0.025 |
Supported |
|
H12 |
Empeng -> SD |
-0.102 |
-0.099 |
0.045 |
2.245 |
0.089 |
Supported |
Findings
Mediating effect of GHRM over organization readiness to adopt the culture to attain the sustainable development was clearly explained. The empirical evidence suggests that green hrm practices markedly enhances corporate reputation, albeit exerting with significant impact on employee engagement. PLS-SEM utilized to check the hypothesis. For that questionnaire have designed and distributed from to mediate the relationship towards sustainable development. The finding shows sustainable development towards GHRM in the software industries. (Kannan, 2024). Bundled GHRM practices to mediate the link to test the sustainable development. The other factors organization readiness to adapt the culture and pro-environmental behaviours were shows positive values to significant the association.
Limitations of the Study
Information was gathered from software industry IT companies. As a result, the results are restricted to the Tamil Nadu IT industry. Descriptive study design analysing longitudinal data is a crucial first step in future research to rule out other possible factors. It is better to do qualitative research with a cultural perspective when examining the outcomes of GHRM implementation on sustainable development. An essential recommendation for additional research is a study that attempts to investigate this is inclusion of artificial intelligence with additional factors.