Keywords

1 Introduction

HRM plays a wider role in an organisation. The concept of HRM was originated during the 1970 s during the Indian Industrial Revolution and is currently known as “personnel management.” The personnel department takes care of administration, welfare activities, and employee relations. Due to the rapid growth of industrial sectors, the focus shifted from administrative and transactional functions to transformational activity as human-resource management. HRM plays a vital role in the growth and development of an organisation by managing employee issues, performance, and engagement factors in the organisation (Chatterjee 2006).

Personal management was promoted during 1931 based on the recommendations of the Royal Commission Report to appoint a welfare officer to take care of welfare activities for employees at the workplace. During the Industrial Revolution in India in 1948, the Factories Act of India was formed to protect employees in the work environment.

The scope of welfare officers was increased to include taking care of employees and administrative as well as dismissal procedures per the Factories Act guidelines as described by the government. The government of India also promoted human-resource management through establishing leading b-schools in India, such as Xavier School of Management and the Indian Institute of Personnel Management, to help companies overcome management issues. However, a strategic tie-up existed between the company and the institute to resolve issues related to trade unions, employee grievances, introducing modern techniques and principles to implement, and challenges in the work environment. HRM gained important in the organisation by helping to formulate and design a process to improve the efficiency and effectiveness of the organisation (Sucheta 2012).

HRM practices play an important role in improving company performance through formulating the necessary policies and strategies to improve the knowledge, skills, and attitudes of employees in the organisation (Colakoglu et al. 2006; Azmi 2011). The practices mainly focus on enhancing the services offered to customers, decreasing overhead costs, developing an engaged workforce, improving employee performance, controlling attrition, and decreasing stress in the workplace (Nishii et al. 2008).

HRM helps in identifying and bridging the gap between current performance and future organisation requirements through various training programs offered to employees, promoting leadership skills, increasing employee efficacy, and fulfilling employees’ learning needs. HRM also helps in establishing a formal communication channel for better connections and aligning the organisational strategy with HR strategy to achieve better business outcomes (Nkhwangwa 2014).

1.1 Growth of the Indian IT Sector

The IT industry is projected to grow up to $300 billion by the year 2020, and the industry is expecting its growth to be up to 13–16% in the market. The industry mainly focuses on emerging technologies such as mobile computing, cloud computing, social networking, and cloud-based analytic systems. The industries maneuver from various parts of the country and tier 1 cities such as Chennai, Hyderabad, Mumbai, and Bangalore. Due to the increase in infrastructure and the resulting increased costs, companies are focusing on shifting their operations from tier 2 and tier 3 cities such as Mohali, Nagpur, Gurgaon, Mysore, Jaipur, Chandigarh, and Pune (KPMG and NSDC Report 2013).

Indian IT companies play an important role in developing the GDP ratio. In FY 2015–2016, industry is expected to grow to 130$ billion. The domestic sector is projected to grow to 10–12%, and >16,000 companies add 5–8% additional customers ever year, which contributes 10–12% of growth to GDP of the country. Bangalore is known as Silicon Valley of India: The city contributes up to 33% of software exports in India. Mumbai is known as the business capital of India; the majority of Indian companies have their headquarters in Mumbai. Few IT companies have started their operations from Kerala despite trade union issues; for example, TCS has its training center in Trivandrum. The company has invested approximately 10 billion INR and generated 10,000-person seating capacity in Trivandrum training center. The major verticals focused on by the industry are manufacturing, retail, oil and gas, banking, health care, financial services, e-commerce, telecom, and cloud computing, etc. (Taranjit and Batra 2014).

Indian IT companies gain an advantage in terms of outsourcing requirements from multinational companies due to the availability of a skilled workforce, high quality of service offered to customers, and cost-efficiency. India generates 2.3 million degree holders and 3,00,000 post-graduates across every year through 347 institutions all over the country (AON Report 2012).

The information-technology industry created a revolution in the Indian market by offering a cost-efficient communication platform, internet channels, and video-conferencing facilities, etc. The IT industry helps business to operate 24/7 around–the-clock service, which allows business to operate from various places in the country without any hindrance through the help of Internet technology. Internet technology plays an important role in web surfing, online shopping that allows delivering the product to the customer’s doorstep, and staying connected with friends and family through various social-networking platforms, etc. (Kumar 2014).

1.2 Statement of the Problem

HR plays an important role in engaging the work force because employee engagement will result in improved employee productivity. An engaged work force will have a better connections, increased level of motivation, be able to accept more responsibilities at work, be energised, and aspire toward better work performance. Higher engagement levels will have a direct impact on company growth and its performance. Engaged employees focus on attaining organisational goals, formulating high-performance teams, building relationships between employees and the employer, and creating a better work environment (Priyadarshni 2016).

Motivation is a key factor that engages the work force to overcome challenges and creates confidence that helps in building strong relationship between employees and the employer (Katzell and Thompson 1990). By encouraging employees to be part of the decision-making process will help improve their performance and commitment toward the job and also acts as a retention tool in terms of retaining the best talent in the organisation (Haizlip 2008).

HR experts promote the change-management process through creating transparent communication channels, promoting a learning culture in the work environment, and facilitating change that improves the work climate (Agarwal 2008). The performance of an employee can be improved through offering employees a wide range of training opportunities within or outside of the organisation that will result in improving performance toward the current job (Blanchard and Witts 2009).

HR plays a crucial role in bridging the gap between personal and professional life through work–life balance initiative by formulating the necessary policies and strategies through various engagements that promote work–life balance initiatives such as flexible working hours, flexible working week, and extending the work-from-home policy, which also helps in managing both personal and professional life. All of these result in reducing the stress factors in the work environment and help in building confidence and integrity toward the work (Ellen et al. 2002). The HR manager should create policies and strategies that improve employee performance by maximum utilisation of available resources, investments in training individual capabilities, rewarding and encouraging a high-performance workforce through promoting rewards and recognition programs, and managing attrition levels through offering a wide range of career-development opportunities within the organisation (Vishva Prasad 2015).

By analysing various literature, the researchers determined that the issues faced with reference to job satisfaction, level of motivation, engagement levels, and work as well as personal life is completely different for IT industries compared with other industries in the country. HR experts face various issues due to factors such as employee satisfaction and motivation levels, engaging employees, and promoting work–life balance, all of which have a potential direct impact on stress levels in the workplace, employee-attrition levels, and employee attitudes toward the job. HR experts place an important role on engaging a talented workforce. By reading various literature reviews, it was identified that no study has previously been performed on the human-resource practices and challenges confronted by HR experts in an Indian IT firm. This motivated the researchers to conduct such a study.

2 Research Objectives

The objectives of the study were as follows:

  • Identify the various human-resource practices and challenges confronted by HR experts in an Indian IT firm.

  • Determine the relationship between gender and level of satisfaction toward the current job.

  • Determine the association between years of experience and response toward employee engagement within the organisation.

  • Determine the association between respondents’ demographic profile and the work–life balance offered by the company.

  • Predict the level of motivation given the respondents’ demographic profile.

3 Literature Review

3.1 Employee Participation in the Decision-Making Process

HR plays a crucial role in developing employee participation in the decision-making process. The process helps managers identify and recognise the contribution of their employees at their work. Participation results in better decisions, generating a positive attitude among the workforce, and increasing the level of employee satisfaction with the job (Lawrence et al. 2014).

Employee participation leads to improved interpersonal skills and serves as a knowledge sharing forum. This practice results in implementing better decisions within the organisation (Davenport and Volpel 2001).

Employee involvement gains an important role in the industry, which results in implementing decisions more quickly, gaining financial and technology benefits, improving individual efficiency, building relationships between employees and the employer, and decreasing stress levels in the work environment, all of which have a impact on attrition levels at the organisation and gaining a competitive advantage in the industry (Shivangee Singh et al. 2011).

3.2 Creating a Motivational Workplace

HR professionals play a major role in creating a motivational work environment through offering incentive programs, fulfilling the short-term needs of the workers, and rewarding a talented workforce when organisational objectives are met. An increased motivation factor at work will result in increased employee productivity. A motivated workforce will express and deliver positive energy and attitude toward their work. The workforce is motivated through monetary and non-monetary terms such as rewards and recognition programs, cash incentives, paid leaves, travel allowance, bonus, cash-reward programs, etc. Recognising employees for their work will help to create a positive work environment and a better work climate. HR managers can help formulate a better work culture and work climate, which will help in developing the productivity level of workforce in the organisation (Vinay 2014).

Motivation plays a crucial factor during difficult times in terms of meeting challenging goals, generating a talented and engaged workforce at all levels, promoting confidence at work, increasing employee commitment, and increasing employee performance and motivational levels, all of which have a focus on realistic goals, which have a direct impact on employee productivity levels. This helps in obtaining the goals and objectives for the organisation (Fishbach et al. 2006).

Motivational factors will improve employees’’ confidence level, generate a talented workforce in the organisation, and motivate employees to take on challenging tasks that help in obtaining the company objectives, etc. (Koo and Fishbach 2008).

3.3 Leadership Engagement

Developing leadership skills will help create a talented workforce within the company. Leaders play an important role in guiding and motivating employees to get on the right path and setting clear, realistic goals for the company (Griffin and Parker 2010). Leadership skills focus on task-oriented processes, generates bonding between employee and the employer, enables a clear vision for the company, focuses on persuasive directions, and creates a strong communication channel for employees. There is a positive and strong relationship between employee performance and leadership skills. The employees with fewer motivation skills will have a lower degree of organisational performance (Hackman and Wageman 2005).

Leadership skills help to create a motivated workforce, improve employee performance, and creates employee growth factors. The leadership style will have a strong focus on improving the organisation’s performance and achieving the desired business results (Fry 2003). Leadership factors promote individual performance and organisational growth through leadership engagement by identifying and developing future leaders in the organisation (Van Emmerik et al. 2010),

Leadership plays a major role in formulating and incorporating the necessary strategies to improve the skills required of employees. It also focuses on improving the knowledge, behavior, attitudes, interpersonal skills, motivational spirit, communication, focus, and customer-centric behavior of employees and also adds more value to business functions (Christman and McClellan 2012).

3.4 Retention Strategies

Retention is a key factor that controls attrition levels in the industry. An effective retention tool will result in obtaining better business outcomes. A committed workforce will help in increasing performance levels; improving employee work culture, attitude, and skills; and decreasing attrition rates. Improved work performance will result in a lower absenteeism rate, increased employee motivation, better connections with peers and subordinates, and a better and healthy work environment (Mita et al. 2014).

HR managers promote career-development engagements in order to retain talented resources in the organisation, prevent employees leaving the organisation due to improper communication, developing proper retention policies, and facilitation a proper mechanism to address grievances between the employer and supervisor. The employer should focus on developing a mechanism to address grievances, creating a stress-free work environment, and promoting a better work culture and climate, etc. (Bagga 2013).

HR should create a policies and necessary strategies to attract and retain talented employees by offering a wide range of learning opportunities, promoting self-development engagements, creating an improved feedback-based mechanism, facilitate open and transparent communication, organise coaching and mentoring programs, promote succession-planning programs to help employees climb the “corporate ladder,” and have a plan to retain employees in the organisation (Kaliprasad 2006).

3.5 Work–Personal Life Balance

HR plays an important role in creating a balance between personal and work life through creating wide range of initiatives offered within the company. This will also result in decreasing employee turnover in the organisation and acts as a retention tool for the employer in order to gain a competitive advantage in the industry (Karatepe 2013).

Work–life balance can be obtained through generating a positive workforce and creating a positive work environment, improving job satisfaction, and increasing employees’ commitment levels to stay in the organisation. Negative factors result in increased absenteeism, increased labour turnover, negative feeling toward work, etc. Formulating various policies related to flexible working hours, flexible working week, better recruitment practices, and learning initiatives will help in managing better the work culture and manage WLB among employees (Deery 2008).

HR managers should also focus on meeting the needs and requirements of employees that help create a balance between personal and professional life, which result in creating a better work climate, a transparent communication channel, and better training programs, which serve as the best retention tool for the organisation (Ans De Vos and Annelies Meganck 2009).

3.6 Workforce Planning

Workforce planning results in maximum utilisation of resources and identifying the right resources at the right places at right times. It is a process of identifying the gap between the existing resources and future requirements of an organisation. The planning process improves employees’ performance through organising various training programs and learning initiatives within workplace and anticipating cost benefits with the maximum utilisation of resources with the minimum available resources (Deepa et al. 2013).

Workforce planning determines the following:

  • Identifying the number of employees

  • Retain a talented workforce

  • Managing the existing available resources

  • Developing and motivating employees to next level within the organisation

Manpower planning determines the present and future human requirements to meet the objectives of the organisation. The process creates a linkage between HRM practices and strategic requirements of the organisation. It is a process of identifying the company’s demands and evaluating the available resources to meet those demands (Reilly 2003). The process determines the retention strategies and talent branding, decreases absenteeism, promotes job rotation, increases employee job satisfaction and motivation levels (Maina and Kwasira 2015).

Workforce planning plays an important role in the organisation because proper planning will result in achieving the company’s goals and projecting the long-terms growth for the organisation. It requires the organisation to focus on the required competences to analyse the future needs of the company (Ghazala and Habib 2012). The process helps in identifying the necessary requirements at the recruitment stage and determines the workforce needs to the employer (Gupta 2008).

4 Model of Modern HR Practices

5 Research Methodology

5.1 Type of Research

The purpose of analysing the descriptive research is to study a phenomenon that occurs at particular place and time. Basis on the said factor, descriptive research was executed in this study.

5.2 Sample

The sample size defined as:

$$n = \left( {z\sigma /d} \right)2.$$

where n-sample size of the population.

z :

Value at a complete level of confidence.

σ :

Standard deviation of the population.

d :

Difference between the population mean and sample mean.

It is challenging to determine the standard deviation and population mean; hence, the researchers used a systematic sampling method, and the survey was conducted with HR experts at an Indian IT firm. The company operates from various parts of India such as Mysore, Mangalore, Trivandrum, Jaipur, Chandigarh, Gurgaon, Bhubaneswar, Hyderabad, Pune, and Mumbai with 450 HR experts working from various development centers in India. In the Bangalore development center, approximately 150 h experts are employed in the firm. Hence, the researchers approached 75 HR experts, who were ready to respond for the primary data collection through questionnaire and considered as sample size for this study. This is the main limitation of the study.

5.3 Research Instrument

This study was purely empirical in nature; hence, a questionnaire was used as a sole instrument for data collection. The researchers framed a well-structured questionnaire consisting of three categories. The first category deals with the demographic profile of the HR experts; the second category deals with human-resource practices; and the third category contains various issues and challenges confronted by HR experts in the IT company. The researchers performs a validity test for the variables in the questionnaire in order to measure the human-resource practices and challenges encountered by HR experts (43 items with a Likert-type scale in the questionnaire) and obtained α = 0.7. The questions were used for the validity test including respondents’ responses toward their “level of satisfaction,” “Work–life balance,” “employee-engagement initiatives,” “motivation levels,” and “career engagements,” etc.

5.4 Sources of Data

Primary source of data and information collected through questionnaires and the secondary source of data were journals, publications, books, and the handbook of the IT firm.

5.5 Data-Analysis Procedures

The primary information collected through questionnaires and their responses were keyed in and analysed using SPSS (Statistical Package for Social Sciences) for analysis. Pearson’s Chi-Square was used to identify whether two random variables were independent in nature. Hence, the researchers were able to determine the significant level of association between years of experience and response toward employee engagement at the organisation. Correlation analysis was used to identify the degree of relationship between the dependent and independent variables. Hence, the researchers were able to determine the relationship between gender and level of satisfaction toward the current job.

Discriminant analysis identified the association between one dependent variable (dichotomous questionnaire) and many independent variables. Hence, discriminant analysis revealed the significant association between work–life balance engagements with gender, age, annual income, educational qualification, and years of experience in the organisation. Multiple regression was helpful to find out the important independent variables among all other independent variables to predict the dependent variable. Therefore, multiple regression analysis was performed to determine the HR experts’ level of motivation toward the work with gender, age, educational qualification, annual income, and years of experience. The investigators determined that employee number of years of experience is a more important independent variable than other variables in predicting the level of employee motivation factors toward their work faced by HR experts.

6 Findings and Data Analysis

6.1 Correlation Analysis

Relationship between gender and level of satisfaction toward the current job.

The Pearson’s correlation value (Table 1) was 0.049, which revealed that there is a positive correlation between gender and level of satisfaction toward the current job. Most of the female respondents are satisfied with their job toward the current role compared with male respondents.

Table 1 Correlation value
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6.2 Chi-Square

Hypothesis no. 1: There is a significant association between years of experience and response regarding employee engagement at the organisation.

The Chi-square presents a significance level of 0.469 at the 95% confidence level (Table 2). This number is greater than the hypothetical value of 0.05. Hence, hypothesis no. 1 is accepted: There is a significant association between years of experience and response toward employee engagement at the organisation.

Table 2 Chi-square
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Cramer’s V is 0.469 (Table 3), which reveals that there is a moderate association between years of experience and response regarding employee engagement at the organisation.

Table 3 Symmetric measures
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The asymmetric-lambda value is 0.000 (Table 4), which reveals that there is a 0% error reduction in predicting responses regarding years of experience and those regarding employee engagement at the organisation. If the lambda value increases then dependent variable output could be predictable. Hence, the researchers could predict responses regarding years of experience and as well as those regarding employee engagement at the organisation. Here the researchers cannot determine participants’ response regarding years of experience nor those regarding employee engagement at the organisation.

Table 4 Directional measures
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6.3 Discriminant Analysis

Hypothesis no. 2: There is a significant association between gender, age, educational qualification, annual income, and years of experience with work–life balance at the company (Tables 5, 6, 7 and 8).

Table 5 Wilks’ lamba
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Table 6 Standardised canonical discriminant-function coefficients
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Table 7 Canonical discriminant-function coefficients
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Table 8 Functions at group centroids
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The classification matrix (Table 9) indicates that the discriminant functions were able to classify 69.3% of the 75 objects correctly. The Wilks’ lambda value (Table 3) is 0.852. The value falls between 0 and 1, which indicates the better discriminating power of the variables selected. The probability value of the F-test determined that the discrimination between the two groups is extremely significant. Because p < 0.107, it indicates the F-test would be significant at a confidence level up to (1 − 0.045) × 100 or 95.5%. Hence, hypothesis no. 2 is accepted.

Table 9 Classification results
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The standardised coefficient (Table 4) reveals that gender is the best predictor with a coefficient of 0.757 followed by age (coefficient = 0.564), annual income (coefficient = 0.491), educational qualification (coefficient = –0.520, and years of experience (coefficient –0.979.6). The means of the canonical variables (Table 6) shares the new means for the transformed group centroids. Hence, the new mean for group 1 (non-work–life balance) is –0.257, and the new mean for group 2 (work–life balance) is 0.660. This means that the midpoint of these two is 0. This is clear when the two means are plotted on a straight line and their midpoints are located as shown below.

Chart 1

−0.257

0

0.660

Mean for group 1 (no work‒life balance)

 

Mean for group 2 (work‒life balance)

If the discriminant scores of a respondent falls left of the mid points, this infers that the respondents rated positively the work–life balance offered in the company; if a respondent’s score is falls right of the midpoint, the respondent rated that no work–life balance is offered by the company. Therefore, any positive (> 0) value of the discriminant score will lead to classification the career-development opportunities are offered within the company, and any negative (< 0) value of the discriminant score will lead to classification there is no work–life balance in the organisation.

Unstandardised discriminant function (Table 7) is

$$\begin{aligned} Y & = - 0. 5 6 6+ 1. 5 5 9 \, \left( {\text{gender}} \right) + 0. 6 9 8 \, \left( {\text{age}} \right) - 0. 80 1 \, \left( {\text{educational qualification}} \right) \\ & \quad + 0. 4 4 3 \, \left( {\text{annual income}} \right) - 1. 1 6 9 \, \left( {\text{years of experience}} \right). \\ \end{aligned}$$

Y gives discriminant scores of any person based on the categories of gender, age, educational qualification, annual income, and years of experience.

For example, a female HR professional in the age group 40 years who completed her post-graduate degree and who currently has experience >12 years falls under the category of annual income >7 lakhs provided that there is work–life balance in the organisation. This can be proven by the following equation.

The researches coded in SPSS were as follows: A female HR professional respondent (coded in SPSS as 2), falls under age group of 40 yrs (4), education qualification as post-graduate (3), annual income >7 Lakhs (4), with >12 years of experience (4). By calculating these values into the above said discriminant function, the discriminant score Y is as follows:

$$\begin{aligned} {\text{Y}} & = - 0. 5 6 6+ 1. 5 5 9 \, \left( {\text{gender}} \right) + 0. 6 9 8 \, \left( {\text{age}} \right) \\ & \quad - 0. 80 1 { }\left( {\text{educational qualification}} \right) + 0. 4 4 3 \, \left( {\text{annual income}} \right) - 1. 1 6 9 \, \left( {\text{years of experience}} \right). \\ {\text{Y}} & = - 0. 5 6 6 + 1. 5 5 9 \, \left( 2\right) + 0. 6 9 8\, \left( 4\right) - 0. 80 1 \, \left( 3\right) + 0. 4 4 3 \, \left( 4\right) - 1. 1 6 9\, \left( 4\right). \\ {\text{Y}} & = - 0. 5 6 6 { } + { 3}. 1 1 8+ 2. 7 9 2- 2. 40 3 + 1. 7 7 2- 4. 6 7 6. \\ {\text{Y}} & = 0.0 3 7. \\ \end{aligned}$$

From chart 1, the preceding Y-value of 0.037 shows the decision rule of the discriminant score left of the midpoint 0, which leads to a classification of work–life balance available in the organisation.

6.4 Regression Analysis

Factors identifying the relationship between level of motivation at work and the respondents are age, gender, qualification gained, annual income, and years of experience through regression analysis (Tables 10, 11 and 12).

Table 10 Model summary
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Table 11 Analysis of variance
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Table 12 Coefficients
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In the output of the regression model, the value of B gives all the coefficients of the model, which are as follows:

$$\begin{aligned} {\text{Y}} & = 2. 2 9 3+ 0. 3 3 7 \,\left( {\text{gender}} \right) - 0.0 2 9 \, \left( {\text{age}} \right) - 0. 1 1 8 \, \left( {\text{education qualification}} \right) \\ & \quad + 0. 1 8 8 \, \left( {\text{annual income}} \right) - 0. 3 60 \, \left( {\text{years of experience}} \right). \\ \end{aligned}$$

From the above equation, it can be inferred that the female employees are highly motivated toward the job and the best predictor variable is gender with a higher coefficient of 0.337. The factors of age, educational qualification, and years of experience have a negative coefficient, and those of gender and annual income have a positive coefficient, which shows a statistically significant t-value of 0.898.

The p-level is observed to be 0.248, indicating that the model is statistically significant. The R 2 value is 0.090. The Student t-test for significance of an individual dependent variable indicates that at the significance level of 0.05 (confidence level of 95%), only gender is statistically significant in the model. Experienced female HR experts are highly motivated toward their job compared with other factors such as age, years of experience, annual income, and education qualification.

6.4.1 Forward Regression

Given an output of forward regression, the regression ends up with one of five independent variables remaining in the regression model. The variable “years of experience” is statistically significant at the 95% confidence level. The F-test of the model is also highly significant, and the R 2 value is 0.053 (Tables 13, 14, 15 and 16).

$$Y = 2.844{-}0.306\;({\text{years of experience}}).$$
Table 13 Model summary
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Table 14 Analysis of variance
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Table 15 Coefficients
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Table 16 Excluded variables
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6.4.2 Backward Regression Analysis

The results of the backward regression analysis show that only “years of experience” remains in the model to predict the effectiveness level of motivation at work compared with other independent variables. The independent variables are statistically significant at the 95% confidence level. The F-test of the model is also highly significant, and the R 2 value is 0.053 (Tables 17, 18, 19 and 20).

$$Y = 2.844 - 0.306\;({\text{years of experience}}).$$
Table 17 Model summary
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Table 18 Analysis of variance
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Table 19 Coefficients
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Table 20 Excluded variables
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7 Findings and Conclusion

Based on the research and survey conducted in a group of HR experts, there is a positive correlation between gender and level of satisfaction with the current job. Most of the female respondents are satisfied with their job regarding their current role compared with male respondents. This finding aligns with the findings of Ogundele (2005), but it contradicts the findings of Kim (2005). From the analysis, it is proven that there is a significant association between years of experience and response regarding employee engagement at the organisation. This finding aligns with the findings of Sagayarani (2015) and those of Fatma and Shyqyri (2015), but contradicts with the findings of Elizabeth and Dennis (2014). From discriminant analysis, the researchers determined that there is a significant association between work–life balance initiatives organised by the company and gender, age, annual income, educational qualification, and years of experience. Of many independent variable, gender acts as important independent variable with higher a coefficient value, which predicts work–life balance engagements in the company compared with other independent variables. This result aligns with the findings of Amy et Al. (2014) but contradicts with the findings of Karishma and Harvinder (2015). Multiple regression analysis reveals that independent variables—such as age, gender, education qualification, annual income, and years of experience—are statistically significant with level of motivation at work. The study found that gender is the best predictor and gain importance in level of motivation at work. This finding aligns with the findings of Fapohunda (2014), but it contradicts with the findings of Rashmi et al. (2010).

8 Implications of the Study

HR experts act as a business-driven HR by incorporating necessary policies and practices with reference to onboarding and induction, manpower planning–induction process, and feedback-based system. The study also identifies and encourages a learning culture, leadership engagements, career advancements, employee-engagement drives, increasing the level of satisfaction, and work–life balance initiatives, etc. HR experts should focusing on and decrease attrition levels through a wide range of rewards and recognition programs, counseling, mentoring programs, creation of a positive and friendly work environment, promoting employment involvement, participation, and generating and retaining talented workforce in the organisation. This research helps to determine human-resource practice and the challenges confronted by HR experts in an Indian IT firm. The study is limited to an IT firm in Bangalore, India, but the results can be extrapolated to IT firms and other industries in India and abroad to get a better understanding of human-resource practices and the challenges faced by HR experts.