Keywords

Knowledge Based Technology, Strategic Management, Banks, Technology, Information.

Introduction

Knowledge and its applications are the essences of businesses in today's advanced era for obtaining competitive advantage and are defined as a new strategic approach to innovation and a possible ingredient for expanding market share. Understanding the knowledge management process as part of the banking industry will reveal how it affects banks performance. In a growing country like India, knowledge management is also revealing evidence of competition and increased performance; however, whether the knowledge management approach is used in Indian banks has yet to be confirmed (Blackler, 1995; Cabrera & Cabrera, 2002; Shahid, 2014; Parise et al., 2006; Jain & Jeppe Jeppesen, 2013; Goswami, 2009).

For the banks, this is a decade of retirement, and those who work there are already seeing the effects of the loss of skilled hands in their day-to-day operations. While junior level recruitment would take place, there would be a virtual void at the medium and senior levels for some time. Middle management's absence might harm banks' decision-making systems, as this group of officers was responsible for translating top management's strategy into actionable plans. Some of the largest banks are also failing as a result of prolonged leadership voids. High attrition rates are being witnessed by all banks, especially those in the private sector, generating constrained resources. The problem is only going to get worse once the banks that have since been registered about to be authorized start recruiting. As a result, bridging resource gaps and regulating workforce turnover are key problems for which banks must be prepared (Amit & Schoemaker, 1993; Barney, 1991; Bhatt, 2001; Evans, 2012; Shukla, 2015).

Banks in the public and private sectors have different perspectives and work cultures. Public-sector banks have a work culture founded on the principle of socio-economic responsibility, with profitability coming in second. Private sector banks, on the other hand, strive for profitability. Because these distinctions between the sectors are critical parameters in defining the banking sector's HRM culture, it's crucial to investigate whether they might affect job satisfaction (Aldaibat & Irtaimeh, 2012; Chivu & Popescu, 2008; Conner & Prahalad, 1996).

It is critical for Indian banks' performance and long-term growth to develop a pool of long-term endeavors by determining whether they are content with their jobs. Their happiness would have an impact on their performance and commitment, which in turn would have an impact on the banks' growth and profitability (Rasoulinezhad, 2011; Hall, 1992; Ibraimi, 2019). In the Indian baking industry, there are two critical measures to measure the growing importance of human resources: a) Per-employee profit b) Per-employee cost. In response to this need, this study examines the key processes and technologies of knowledge management as a means of Strategic Approaches in private and public sector banks, to provide the bankers and strategists with a greater understanding of its implications (Carmeli & Tishler, 2004; Cuganesan, 2005; Youndt, 2004).

Objective of the Research

With the advent of the globalized economy, the Indian banking system is being forced to confront a challenge that is concentrated on an issue that is particularly important for public sector banks, namely the use of technology in banking. All PSBs are now on the CBS platform and have gained the ability to provide banking services anywhere. Customers also may perform basic banking transactions via their phone, according to a few companies. However, this is only skimming the surface, since the technology may be used to achieve substantially greater results. PSBs must be able to employ knowledge to build data warehouses and then do data mining and analytics on them. The goal should be to use data to inform decisions at all levels, including product customization, business model development, and delivery channel development, among other things. Through the internet and mobile banking channels, PSBs must be able to pitch appropriate products to their clients. Traditional businesses are gradually moving online, and e-commerce is the preferred method of payment for millennials (Davenport & Klahr, 1998; Davenport & Prusak, 1997; Davenport et al., 1998).

The research's major goal is to picture banks' perceptions by taking into consideration strategic initiatives conducted through Knowledge Management during the Liberalization, Privatization, and Globalization (LPG) era. The goal of the study is to discover strategic human resource management approaches that are compatible with knowledge-based technology. The study also aims to study the effect of knowledge-based technology (Knowledge Management) on employee performance and efficiency in banks, as well as whether Knowledge Management can meet the needs of the technical and professional workforce in terms of gaining business effectiveness and efficiency even while generating profit (Schuler & Jackson, 1987).

Research Design

The current research project is part of analytical research. The primary goal of the study is to analyze the effect and influence of Knowledge-Based Technology (Knowledge Management) on performance management in India's governmental and industrial sector banks. The goal of this study is to look at how various knowledge-based technology have been used as tools for Strategic Human Resource Management in a globalized economy by a group of Indian banks.

Data- Collection and Methodology

The goal of this research was to determine the impact of knowledge management on bank performance in a worldwide economy. A survey methodology questionnaire was utilised to obtain responses from 156 banking industry respondents in Madhya Pradesh for this purpose. The collection of both primary and secondary data is necessary to understand the importance and role of Knowledge Management Strategic Business Approaches of Indian Banking Sector in a worldwide market. The research will be based on both primary and secondary data in order to achieve the study's principal goal.

Hypothesis

The following are the hypotheses for the research study:

Null and Alternate Hypothesis

H_o: Knowledge based technology is not required for strategic human resource management in indian bank in a globalized economy.

H₁: Knowledge Based Technology is essentially required for Strategic Human Resource Management in Indian bank in a globalized economy.

H₂ Knowledge acquisition does not have a positive impact on strategic human resource management in banks.

H₃: Knowledge acquisition has a positive impact on strategic human resource management in banks.

H₄: Knowledge conversion does not have a positive impact on strategic human resource management in banks.

H₅: Knowledge conversion has a positive impact on strategic human resource management in banks.

H₆: Knowledge application does not a positive impact on strategic human resource management in banks.

H₇: Knowledge application has a positive impact on strategic human resource management in banks.

H₈: Knowledge protection does not have a positive impact on strategic human resource management in banks.

H₉: Knowledge protection has a positive impact on strategic human resource management in banks

H₁₀: Knowledge based Technology does not have a positive impact on strategic human resource management in banks.

H₁₁: Knowledge based Technology has a positive impact on strategic human resource management in banks.

Tools Adopted for Analysis

Various statistical methods and techniques will be utilized, depending on the needs of the study, to evaluate the main data gathered via interviewing respondents with the use of a questionnaire. The suggested statistical techniques will primarily be correlation, which will be used to establish a relationship between two variables, and regression, which will be used to build a relationship between several variables.

Reliability Analysis of the Scale

The information's reliability is assessed using Chronbac alpha. Lee Cronbach discovered it in 1951 (Cronbach, 1951) to provide a gauge of an investigation's or scale's within-stability; it is expressed as a digit between 0 and 1. To ensure authenticity, inner reliability must be resolved before a test may be used for examination or examination purposes. Furthermore, unwavering performance of the company reveal the degree of estimation error in a test. When the items in a test are connected to one another, the alpha estimation is increased. A high coefficient alpha, on the other hand, does not always suggest an exceptional state of internal consistency (Tables 1-3).

Table 1 Reliability Statistics
Scale	Cronbach's Alpha
Total Scale	0.967
Knowledge Management	0.934
Knowledge Acquisition	0.871
Knowledge Conversion	0.795
Knowledge Application	0.841
Knowledge Protection	0.815
Banks Performance	0.922

Table 2 Respondent Gender Profit
	Frequency	Percent
Male	92	75.0
Female	64	25.0
Total	156	100.0

Table 3 Respondent Experience Profile
Experience In Years	Percent
6-10 Years	25.0
11-15 Years	18.87
16-20 Years	15.86
21-25 Years	24.6
26-30 Years	10.3
36-40 Years	5.4
Total	100.0

This is due to the fact that the length of the test has an impact on alpha. The estimation of alpha is lowered when the test length is excessively short. The value of Alpha can range from 0.7 to 0.95. Alpha is the same as or more prominent than. 9 is excellent, while Alpha is on par with or better than. 8 is a good number, with Alpha being equal to or more significant than. 7 is satisfactory, and Alpha is on par with or better than. 6 is doubtful, Alpha is equal to or more significant than. Alpha is equal to or more prominent than and 5 is poor. The number five is unacceptably high.

Table 2 shows that there are 92 men and 64 women among the 156 respondents. Males account for 75% of the entire sample, while females account for 25%.

Correlation

Pearson's coefficients of association (r) range from –1 to +1. It appears whether there is a positive or negative association (Pallant, 2005). If (r) is less than 0.33, the relationship is considered powerless; if (r) is between 0.34 and 0.66, the relationship is considered mid-quality; and if (r) is between 0.67 and 0.99, the relationship is considered strong. The Pearson's correlation value in Table 4 ranges from medium to high, yet it is within acceptable bounds.

Table 4 Model Summary
Model	R	R Square	Adjusted R Square	Std. Error of the Estimate
	0.611a	0.457	0.505	0.7280

H₁: Knowledge acquisition has a positive impact on startegic human resource management in banks.

The Pearson coefficient correlation r between knowledge acquisition and organisational performance is 0.611, showing that knowledge acquisition and organisational performance have a strong positive association. The R square value is 0.457, indicating that knowledge acquisition accounts for 50.7 percent of the variation in performance of the organization (Table 4). Knowledge acquisition does not account for 49.3% of variation in knowledge acquisition; we can assume that this variation is attributable to other factors that are not being examined.

The table indicates the value of design variables. The value of the constant term is 0.996, as seen in the Table 5. This is unaltered by any variable, while the coefficient of the regression line is 0.523, indicating that 0.523 unit increase in knowledge acquisition will result in 0.523 unit increase in performance of the banks. As a result, hypothesis H-1 has been proven to be true.

Table 5 Coefficients
Model	Unstandardized Coefficients		Standardized Coefficients	T	P
	B	Std. Error	Beta
(Constant)	0.996	0.151		6.54	0
Knowledge Acquisition	0.523	0.043	0.712	16.158	0

H₂: Knowledge conversion has a positive impact on Strategic Human Resource Managementin India.

The Pearson's coefficient association r between knowledge conversion and performance of the organization is 0.563, indicating that there is a somewhat good association between the two. The R² value is 0.329, indicating that knowledge transfer can explain 32.9% of the variation in performance of the banks in State of Madhya Pradesh (Table 6). Knowledge conversion does not account for 67.1 % of the variation in knowledge conversion; we can assume that this variation is caused by other factors that are not being examined.

Table 6 Model Summary
Model	R	R2	Adjusted R2	Std. Error of the Estimate
	0.563a	0.329	0.327	0.83636

Table 7 shows the analysis of variation, which is a metric for how much the model has improved. In the table, the F ratio is 130.46, which is statistically relevant at 0.001. The significance level in this case is 0.000, which is lower than 0.001. As a result, the model is an excellent fit.

Table 8 lists the model parameters in numerical order. The value of the constant term is 1.012, which is unaffected by any variable, however the coefficient of the regression line is 0.66, indicating that a 0.66 unit increase in knowledge conversion will result in a 0.66 unit increase in banks performance. As a result, hypothesis H-2 has been proven and is correct.

Table 7 Anova
Model	Sum of Squares	Df	Mean Square	F	P
Regression	91.255	1	91.255	130.46	0
Residual	177.671	254	0.698
Total	268.928	255

Table 8 Coefficients
	Un-std Coefficients		Standardized	t	p
	B	Std. Error	Coefficients
			Beta
(Constant)	1.012	0.21	0.583	4.821	0
Knowledge Conversion	0.66	0.06		11.422

H₃: Knowledge application has a positive impact on banks performance.

The Pearson's coefficient correlation r between knowledge management and bank performance is 0.62, suggesting that knowledge application and bank performance have a strong positive association (Table 9). The R2 score is 0.514, suggesting that knowledge application can explain 51.8 % of the variation in bank performance.

Table 9 Model Summary
Model	R	R Square	Adjusted R Square	Std. Error
1	0.62	0.514	0.518	0.71544

Table 10 shows the analysis on variance-ratio, which is a measurement of how much the model has enhanced the outcome forecast relative to the model's level of inaccuracy. In the table, the F ratio is 123.74, which is statistically significant at 0.001. The significance threshold in this case is 0.000, which is lower than 0.001. As a result, the model is an excellent fit.

Table 10 Anova
Model	Sum of Squares	Df	Mean Square	F	P
Regression	123.74	1	123.74	271.24	0
Residual	130.041	253	0.521
Total	268.926	254

The table indicates the value of model parameters. The Table 11 depicts that the constant term has a value of 0.572, which is unaffected by any variable, and the coefficient of the regression line has a value of 0.751, demonstrating that one percent change in knowledge application will result in 0.751 unit increase in bank performance. As a result, hypothesis H-3 has been found to be correct.

Table 11 Coefficients
Model	Unstandardized Coefficients standardized Coefficient			t	P
	B	Std. Error	Beta
(Constant)	0.572	0.152		4.838	0
Knowledge_Application	0.751	0.045	0.718	16.468	0

H₄: Knowledge protection has a positive impact on banks performance.

Knowledge protection and bank performance have a Pearson's coefficient correlation r of 0.594, indicating a strong positive association across knowledge application and bank performance. The R2 score is 0.45, indicating that knowledge protection can contribute for 33% of the variation in a bank's performance (Table 12).

Table 12 Model Summary
Model	R	R Square	Adjusted R	Std. Error
1	0.594	0.45	0.33	0.72

Table 13 presents data on the study of variance-ratio. It is a measure of how much the model has improved the outcome predictions compared to its level of inaccuracy. As a result, the model is a great fit.

Table 13 Anova
Model	Sum of Squares	Df	Mean	F	P
Regression	130.072	1	130.072	237.934	0
Residual	138.856	255	0.546
Total	268.927	254

The value of model parameters can be seen in the table. The constant term has a value of 0.501 in the Table 14, which is unaltered by any variable, meaning that a positive coefficient of 0.74 in knowledge protection will result in 0.74 unit improvement in bank performance.

Table 14 Coefficients
Model	Un-standardized Coefficients		Standardized Coefficients	T	Sig.
	B	Std. Error	Beta
(Constant)	0.501	0.173		4.693	0.000
Knowledge_Protection	0.74	0.050	0.693	15.423	0.000

As a result, hypothesis H₄ has been validated (Field, 2009:2013).

H₅: Knowledge basedc Technoogy has a positive impact on banks performance.

The Pearson coefficient correlation (r) between knowledge-based technology and bank performance is 0.765, indicating that knowledge-based technology and strategic human resource management have a significant positive association (Table 15).

Table 15 Model Summary
Model	R	R Square	Adjusted R Squar	Std. Error of the Estimate
1	0.765a	0.476	0.566	0.6061

Table 16 shows the results of the analysis of variance-ratio. In the table, the F ratio is 362.234, which is statistically significant at 0.001. Overall significance threshold in this instance is 0.000, which is less than 0.001. As a result, the model is an excellent fit.

Table 16 Anova
Model	Sum of Squares	Df	Mean Square	F	Sig.
Regression	158.082	1	158.082	362.234	0
Residual	110.845	252	0.434
Total	268.926	252

Outcomes of the Study

Knowledge-based technological initiatives, such as knowledge acquisition, conversion, application, and protection, result in strategic enhancements to employee management, high employee satisfaction, resource efficiency, improved performance, and overall strategic human resource management, according to the findings. For practitioners, the research will serve as a roadmap for implementing knowledge management activities within the organisation in order to improve Strategic Human Resource Management and achieve better results. The findings of this study suggest that knowledge acquisition, conversion, application, and protection have a good impact on strategic human resource management. Through enhanced strageic human achievements, knowledge production, transfer, and application are essential for a bank's survival. Banks would suffer from excessive employee turnover if knowledge-based technological efforts are not implemented. Employees apply what they've learned in the classroom to their daily work, resulting in creativity, product and service innovation.

Knowledge-based technology also lowers staff costs by improving operational flow and lowering employee non-productivity. Banks can obtain an advantage in the form of increased employee satisfaction and better work performance by deploying knowledge-based technological initiatives. As a result, banks that have not yet implemented knowledge-based technological efforts can improve their strategic performance by following the lead of other successful benchmarks. Other issues that can affect strategic human resource management must also be identified.

Conclusion

The findings of this study show that knowledge-based technological initiatives help banks function better. So, if banks want to improve their performance, they should strengthen information management exercises among their staff, which will result in an increase in a firm's development capacity as well as its performance. Without knowledge, technical endeavours are always reinventing the wheel, whereas knowledge management ensures that organisations influence employees' strategic use of learning resources so that they may be strategic value creators and market pioneers.

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Received: 18-Jan-2022, Manuscript No. ASMJ-22-10900; Editor assigned: 20-Jan-2022, PreQC No. ASMJ-22-10900(PQ); Reviewed: 27-Jan-2022, QC No. ASMJ-22-10900; Revised: 02-Feb-2022, Manuscript No. ASMJ-22-10900(R); Published: 07-Feb-2022