An Empirical Analysis of Telecommuters: Their Level of Satisfaction,Commitment and Loyalty

Abstract

The present study measures the influence of telecommuting on job satisfaction by taking job autonomy, schedule flexibility and work control, work–life balance and reduced job stress as outcome variables of telecommuting. The role of job satisfaction has also been investigated in determining organizational commitment and turnover intention. Confirmatory factor analysis was done to test the fitness of the data to the model along with ensuring the convergent and discriminant validity of the data. For testing the proposed hypotheses, the structural equations modelling technique was used. Results from the study confirm the role of telecommuting in enhancing the overall job satisfaction of employees which in turn improves their level of organizational commitment and reduces turnover intention. The findings of the study contribute empirically to the literature on voluntary part-time telecommuting and provide implications for the proper adoption of a part-time telecommuting arrangement post-COVID-19 and the using of telecommuting as a talent retention strategy.

Keywords

Job autonomy job satisfaction organizational commitment telecommuting turnover intention work–life balance

Introduction

Telecommuting is a work arrangement where an employee enjoys the liberty of scheduling work according to their time and to a place of their comfort. It is an arrangement that relates work with the activity done and not with the place (Kurland & Egan, 1999). Recently, after the COVID-19 outbreak, most organizations across the globe have switched to telecommuting. Many articles based on the concept have come up in the popular press as well as academic literature (Bellmann & Hübler, 2020; Collins et al., 2020; Green et al., 2020; Trougakos et al., 2020; Wong et al., 2020). Most of the studies focusing on remote working in the COVID-19 pandemic, study telecommuting as a mandatory full-time phenomenon (Jamal et al., 2021). However, prior to this pandemic, telecommuting was offered mostly as a voluntary part-time arrangement to employees. Advocates of telecommuting have also recommended part-time telecommuting for reaping the advantages of this arrangement without having to deal with its negative effects (Golden & Eddleston, 2020; Van Steenbergen et al., 2018). Generally, telecommuting offered before the pandemic was part-time telecommuting since employees usually worked from home only for a few days in a week and/or month and this sort of work arrangement was voluntary in the sense that workers themselves willingly opted for it (Jamal et al., 2021). Full-time telecommuting was made mandatory by most organizations only when the COVID-19 pandemic broke out. It may be referred to as full-time as under this arrangement, employees were working all days from home and it was mandatory in the sense that it was rolled out by the organizations due to government regulations and concerns around COVID-19 and employees did not opt for it (Jamal et al., 2021). The present study focuses on telecommuting as a part-time phenomenon which is going to be a norm post the pandemic.

According to a US-based public–private association dedicated to the promotion of telecommuting, the International Telework Association and Council (ITAC), technological development in recent years has led to rapid growth in the adoption of telecommuting. Advanced technology increased the rate of telecommuting adoption by ensuring data security through the encryption of organizational information and fast and easy transfer of data through high-speed internet (Ansong & Boateng, 2018). The COVID-19 outbreak made telecommuting seem normal to the extent that even the strongest proponents of telecommuting would not have expected. Even institutions such as the Reserve Bank of India and the Supreme Court of India which could have never been envisioned as working remotely, had to adopt this measure.

Even before the COVID-19 pandemic, telecommuting has attracted practitioners and researchers across disciplines because of the benefits it provides. Some of the reported benefits of telecommuting are greater flexibility, increase in job satisfaction, increased performance, higher productivity, better work–life balance (WLB), increased organizational commitment, decreased absenteeism, lower employee turnover and cost-saving not only for the organization but also for the employees (Delanoeije et al., 2019; Golden & Eddleston, 2020; Golden & Gajendran, 2019; Jamal et al., 2021; Jamal & Khan, 2021; Kwon et al., 2019; Nakrošienė et al., 2019; Van der Lippe & Lippényi, 2019). Most of the studies on telecommuting have focused on only one of its positive outcomes such as job satisfaction or productivity (Feng & Savani, 2020; Giovanis, 2018; Golden & Gajendran, 2019; Van der Lippe & Lippényi, 2019). Very few researchers have empirically studied multiple positive and negative outcomes of telecommuting and gave suggestions for the proper implementation and management of the telecommuting arrangement.

Recently, Indian workplaces have witnessed demographic changes. Women engagement in the workforce and the IT sector per se has increased and now there are more families with both working partners. For instance, Wipro HCL Tech, Infosys and HCL hired 60,000 women from college campuses in 2021 (The Economic Times, 2021). A career service for women in India, AVTAR I-WIN reported that, in addition to other flexible work arrangements, telecommuting has perhaps been the most useful for women employees in India (Lila & Anjaneyulu, 2013). Also, Gen X and the Millennials are replacing Baby Boomers at work. These new generation workers are mostly dual-career couples and have a higher preference for WLB (Berman et al., 2019). For improved WLB, they seek employee-friendly work arrangements that give them greater autonomy and control over their work. Organizations in India lagged in providing facilities to employees that could help minimize work–family conflict. The availability of a telecommuting arrangement could provide expected flexibility to employees and it may help in reducing the work–family conflict. A survey showed that 70% of employees opt for working from home for at least one day per week (Browne, 2018). Another survey conducted by Dell showed that the company was expecting 50% of its workforce to opt for working from home at least for a few days per week by 2020 (Sahadi, 2016). Hence, it is the need of the hour to assess job satisfaction among telecommuters and it is equally important to acknowledge if there exists any relationship between job satisfaction and organizational commitment on the part of the employees.

Segments of the study are presented in the following manner. A short review of literature is provided for theory building and hypothesis formulation. This is followed by the development of the research model and the explanation of the research methodology applied. The next section focuses on data analysis which leads us to the results. The findings are discussed in the next section after which recommendations for practice are made. Limitations of this study and suggestions for future research are provided in the subsequent section and lastly, the study closes with a short conclusion.

Theoretical Background and Hypotheses Development

Job Satisfaction

As per the telecommuting arrangement, the employees have the liberty to do their job from the comfort of their homes away from the central office (Jamal et al., 2021). When an organization rolls out remote work to its employees, it is an indication that the organization is considerate about the needs of the workforce and is ready to alter the work environment in order to cater to the needs of its employees. When employees telecommute, they save time on the time-consuming commute and also money that would have been spent on fuel, tickets, cafeteria food and even formal clothing (Jamal & Khan, 2021). Personality traits such as the need for autonomy and the tendency to seek order also moderate the relationship between telecommuting and job satisfaction. Employees with higher needs for autonomy and a lesser tendency to seek order get more satisfaction from telecommuting (O’Neill et al., 2009). Also, when an organization offers a telecommuting arrangement, the employees perceive the organization as one making efforts to create a fit between employees and their jobs; this is one of the aspects of positive work role adjustment (Baltes et al., 1999). In light of the Organization Support Theory by Eisenberger et al. (1986), rolling out telecommuting means that the organization is concerned and facilitates employees to achieve a better WLB. Also, according to the Signalling Theory by Spence (1973), it sends a signal about employee-friendly policies of the organization (Onken-Menke et al., 2018; Thompson et al., 2015). Thus, telecommuters enjoy improved job satisfaction on account of the availability of greater autonomy, schedule flexibility and control, WLB and reduced job stress (Nakrošienė et al., 2019; Perry et al., 2018).

Job Autonomy

Greater autonomy given to employees is inherent in the very meaning of telecommuting. Telecommuting enables employees to work according to their productivity cycles by providing discretion regarding where, when and how they carry out their tasks (Gajendran et al., 2015; Nakrošienė et al., 2019; Pyöriä, 2013). Additionally, telecommuters have control over things like the lighting, the layout, ventilation, breaks, clothing, music and decoration that increase the perception of autonomy for an individual (Elsbach, 2003). Increased flexibility provided by the telecommuting arrangement has been found to increase job satisfaction among telecommuters (Allen et al., 2015; Fonner & Roloff, 2010; Golden, 2009; Perry et al., 2018). Researchers have also claimed that telecommuters maintain an improved WLB due to the autonomy provided by telecommuting which further leads to increased job satisfaction (Gajendran & Harrison, 2007; Golden, 2009; Gregg, 2013). Building upon these arguments, the authors hypothesize that:

H ₁: Job autonomy positively influences job satisfaction among telecommuters.

Schedule Flexibility and Control

All individuals desire greater control over their work schedules (Greenberger & Strasser, 1986). Work time flexibility or control over the allocation of time to work brings about beneficial employee outcomes (Sarbu, 2018). Telecommuting is often rolled out to individuals to grant them increased control over their schedules. Olson and Primps (1984) suggested that telecommuting increases schedule flexibility and control for the employees who are working on complex jobs and those that require autonomy. Hence, it is expected that the remote working arrangement increases the sense of flexibility and control for an individual. A meta-analysis by Baltes et al. (1999) found that along with other beneficial outcomes, schedule flexibility positively affects job satisfaction. Nakrošienė et al. (2019) also found that telecommuters are satisfied with their work because of the work scheduling flexibility available to them. Hence, we hypothesize that:

H ₂: Schedule flexibility and control positively influence job satisfaction among telecommuters.

Work–Life Balance

Telecommuting blurs the boundary between office and home and thus telecommuters have more flexibility in dealing with work and non-work demands. Hence, telecommuters enjoy improved WLB (Raghuram & Wiesenfeld, 2004; Sarbu, 2018). Based on the boundary theory, critics of telecommuting claim that it increases work–family conflict due to the very same reason that it fades the boundary between one’s office and home, thus allowing the spilling of one role into another (Eddleston & Mulki, 2017; Giovanis, 2018). Critics also claim that working from home is subject to interruptions from family members and thus, telecommuting serves no purpose in reducing interruptions and boosting concentration (Lee & Hong, 2011). However, telecommuters have the flexibility to schedule their work in such a way that they face minimal interruptions from the family, thus mitigating the negative effects. They are also able to cater to the needs of both home and office. Some researchers have recommended having a separate space for working within a home as this would help in cutting down on interruptions by family members (Baruch, 2001; Rothbard et al., 2005).

Additionally, time saved on commuting is generally spent with the family and thus helps in having an improved WLB. In their meta-analysis, Gajendran and Harrison (2007) found the mediating role of WLB on the relationship between telecommuting and job satisfaction. Ford et al., (2007) and Michel and Hargis (2008) found that WLB reduces work interference from family and increases family and job satisfaction. In their studies, Behson (2002) and Thompson et al. (2004) found a positive relation between WLB and job satisfaction. Thus, it is hypothesized that:

H ₃: WLB positively influences job satisfaction among telecommuters.

Reduced Job Stress

The daily commute to office can be stressful in itself. Not having to commute to the place of work reduces the stress related to travelling as well as getting ready for office (Hartig et al., 2007; Recarte & Nunes, 2003). The relationship between telecommuting and work-related stress was found to be mediated partially by increased autonomy implying that employees are less stressed when they are given more autonomy (Perry et al., 2018; Sardeshmukh et al., 2012). Also, researchers have discussed the stress related to having to report to the office at a particular time. Employees feel stressed about reaching the office on time as they fear being late could tarnish their reputation in office (Pierce & Newstrom, 1980). Not having to commute for work helps save time which instead can be utilized to engage in social or recreational activities. Engaging in such activities will help individuals mitigate job related stress (Konradt et al., 2003). Fonner and Roloff (2010) also reported that job satisfaction is high for telecommuters because they are subjected to fewer interruptions which would otherwise stress them out. Telecommuters are supposed to be increasingly satisfied with their work since their job provides them such opportunities. Based on the boost in WLB provided by telecommuting, it is hypothesized that:

H ₄: Reduced job stress positively influences job satisfaction among telecommuters.

Organizational Commitment and Turnover Intentions

Telecommuters are thought to be more committed to their organization (Felstead & Henseke, 2017; Onken-Menke et al., 2018). Pierce & Newstrom (1980, 1982) reported that employees who have schedule flexibility are more committed than those with a set working schedule, implying that employees who have an opportunity to work remotely have more organizational commitment. Chow and Chew (2006) did further research and found that employees who have flexible working hours are more committed to the organization as compared to those who work fixed hours. However, a survey of 463 technical and professional workers by Eaton (2003) showed that no relationship exists between telecommuting and organizational commitment. But it is possible that telecommuting can lead to a decline in organizational commitment because an employee might feel isolated and may feel the need for social inclusion (Wang & Walumbwa, 2007). However, Staples (1996) suggested that organizational commitment can be increased by removing the feeling of isolation through better communication and other effective management practices.

Lewis et al. (2001) and Stavrou (2005) empirically found that the flexibility provided by working remotely positively impacts employee turnover intentions. Turnover intention refers to an individual’s willingness to quit an organization consciously and deliberately (Tett & Meyer, 1993). McNall et al. (2009) found that employees are more unlikely to quit an organization that provides remote working opportunities. Feldman and Gainey (1997) suggested that telecommuting is sought by employees to maintain better WLB and employees are thus attracted to organizations that offer an opportunity to telecommute. They are more likely to quit organizations that do not provide better WLB. When an organization provides a telecommuting option, the psychological contract between the organization and employee strengthens (Scandura & Lankau, 1997). Employees perceive that the organization is concerned about the well-being of its employees. This results in lower turnover intention and higher psychological organizational commitment (Rhoades & Eisenberger, 2002).

Individuals with high WLB are committed to their organization and are less inclined to look for other jobs and quit an organization (Behson, 2002; Thompson et al., 2004). Researchers consider turnover intention and organizational commitment to be positive outcomes of telecommuting by claiming that telecommuting leads to a decrease in turnover intention while it boosts the organizational commitment of employees (Glass & Finley, 2002; Kwon et al., 2019; Maxwell et al., 2007; Schmidt & Duenas, 2002). A recent meta-analysis of 68 studies by Onken-Menke et al. (2018) also supported the argument that telecommuting increases organizational commitment and reduces turnover intention. On the basis of the above arguments, it is hypothesized that:

H ₅: Job satisfaction positively influences organizational commitment among telecommuters.

H ₆: Job satisfaction reduces turnover intention among telecommuters.

Research Methodology

Primarily, this research attempts to measure the role of telecommuting in determining the job satisfaction of the employees working in the IT sector in India. Further, an attempt has also been made to check the impact of job satisfaction on their organizational commitment and turnover intention. To achieve these research objectives, job autonomy, schedule flexibility and control, WLB, and reduced job stress were included in the study as determinants of telecommuting. Cross-sectional data were taken from the employees of IT sector companies in the National Capital Region (NCR) of India during December 2019 and January 2020 by employing the snowball sampling technique (Biron & van Veldhoven, 2016). Before the COVID-19 outbreak, telecommuting was prevalent to some extent in the IT and IT-enabled sectors. The nature of job of knowledge and information workers employed in the IT sector allows them to work from home for a few days per week and thus the target population for the study was from the IT sector engaged in part-time telecommuting. Data was collected through the physical distribution of the questionnaire and also through email on the Google form. Questionnaires were sent to only those employees who worked from home for a few days in a week and/or a month. As one of the authors has been an employee in an IT company in New Delhi, connections with former colleagues enabled us to employ the snowball sampling technique for data collection. About 250 questionnaires were physically administered while about 470 emails were sent using Google forms to employees in IT companies in the NCR of India. A sample of 328 was achieved as the final sample size for the study from offline and online modes of data collection. Demographical information of the sample is provided in Table 1.

Table 1.

Demographic Profile of the Respondents (N = 328).

Demographic Variable	Frequency	(%)
Age
Less than 25 years	73	22.26
25–39 years	168	51.22
40 years and above	87	26.52
Gender
Male	193	58.84
Female	135	41.16
Marital status
Single	198	60.36
Married	122	37.20
Other	8	2.44
Children
Yes	100	30.49
No	228	69.51
Designation
Managerial	132	40.24
Non-managerial	196	59.76
Years with the organization
Less than 3 years	133	40.55
3–5 years	98	29.88
More than 5 years	97	29.57

Questionnaire Development

This study has used seven variables: job autonomy, schedule flexibility and control, WLB, reduced job stress, job satisfaction, organizational commitment and turnover intention. A seven-point Likert type scale ranging between 1 (strongly agree) and 7 (strongly disagree) was used for the development of the data collection instrument. Published and validated scales were adopted to measure all the variables while designing the questionnaire for the study. The items of measurement were first assessed for their face and content validity by taking the opinion of experts from academia and the industry. The first section of the questionnaire was dedicated to items related to the measurement of variables. It comprised 28 items while the second part of the questionnaire was reserved for measuring the demographic profile of the respondents such as age, gender, marital status, designation, the extent of telecommuting in a week and month, and so on. The extent of telecommuting was assessed on a nominal scale by asking individuals how many days they telecommuted in a week. Additionally, in conformity with previous research and to add more robustness, employees were also asked to state how many days they telecommuted in a month (Golden & Eddleston, 2020; Golden & Veiga, 2005). A list of questionnaire items with their source of adoption has been given below in Table 2.

Table 2.

Items of the Questionnaire and Source of Adoption.

Construct Name with Items of Measurement
Job autonomy (Source: Langfred (2000))
I have control over the pace of work.
I have authority in determining the tasks to be performed.
My job has minimal written rules and procedures pertaining to the job.
I have the authority in determining the rules and procedures for my own work.
Schedule flexibility and control (Source: Morgeson and Humphrey (2006))
The job allows me to make my own decisions about how to schedule my work.
The job allows me to decide on the order in which things are done on the job.
The job allows me to plan how I do my work.
Work–life balance (WLB) (Source: Geurts et al. (2005) and Stephens et al. (1997))
After a pleasant working day/working week, I feel more in the mood to engage in activities with my spouse/family/ friends.
I am better able to keep appointments at home because my job requires this as well.
I manage my time at home more efficiently as a result of the way I work.
I have greater self-confidence at work because I have my home life well organised.
Reduced job stress (Source: Staples et al. (1999))
I feel less tense while working remotely.
If I had a different job, my health would probably worsen.
I face fewer job-related problems when working remotely.
I never feel fidgety or nervous as a result of my job.
Job satisfaction (Source: Weiss et al. (1967))
I am satisfied with my pay.
I am satisfied with the amount of work I do.
I am satisfied that my job provides for steady employment.
I am satisfied with the chances for advancement on this job.
I am satisfied with the feeling of accomplishment I get from the job.
Organizational commitment (Source: Porter et al. (1974))
I feel loyal to my organization.
I find that my values and the organization’s values are very similar.
I am proud to tell others that I am part of this organization.
I really care about the fate of my current organization.
Turnover intention (Source: Bothma and Roodt (2013))
I would not consider leaving my current job.
I am satisfied the way my job fulfils my personal needs.
I am less likely to accept another job offered to me at the same compensation level.
I look forward to another day at work at my current organization.

Data Screening

Prior to the application of any statistical technique, the data were first screened and cleaned to make it appropriate and suitable for statistical tools. Of the physically administered questionnaires, 156 were received as completely filled. Of the total emails sent, 196 responses were received. This added up to a total of 352 responses. After creating a combined dataset using offline and online responses, the data were processed for screening and cleaning. In the data cleaning process, 17 questionnaires were observed to be filled without being engaged and thus they were removed from the dataset while eight responses were identified as having missing values. The missing values were imputed using the median replacement method (Cohen et al., 2014; Kline, 2015) since the data had been taken on a Likert type scale and the sample size could not be reduced.

To check for the outliers in the data, the authors used the Cook’s distance method. It was found that seven responses had Cook’s distance statistics above threshold limit of 1 and thus they were deleted from the dataset (Stevens, 2012). The study was left with a final sample size of 328 respondents of which 203 were found to telecommute for less than 2 days a week while 91 employees reported being engaged in telecommuting between 2 and 3 days a week and 34 employees were found to be telecommuting for more than 3 days per week. As far as the extent of telecommuting in a month is concerned, 220 employees were found telecommuting for at least eight days a month, while 69 and 39 employees reported being engaged in telecommuting for 8–12 days and more than 12 days in a month, respectively. The scale for measuring the intensity of telecommuting was based on previous research that classified less than two days of telecommuting in a week as low-intensity telecommuting, 2–3 days as medium-intensity telecommuting and more than 3 days as high-intensity telecommuting (Golden & Veiga, 2005; Lila & Anjaneyulu, 2013). For the present study, only days per week has been used and roughly 62%, 28% and 10% of individuals qualified as low, medium and high-intensity telecommuters, respectively. Kline (2015) suggested having at least 10 responses for every observed variable to be used in the measurement model of the study. The present study has used 28 observed variables for model measurement and SEM, thus necessitating a minimum sample of 280 responses. The available final sample size of 328 respondents is justified for applying confirmatory factor analysis (CFA) and SEM using AMOS.

In order to fulfil the normality assumption of the dataset to apply CFA and SEM using AMOS, skewness and kurtosis were computed (Kline, 2015). Table 6 reveals that the statistics for skewness and kurtosis were within the suggested limit of –1 and +1 in compliance with the normality assumption for the dataset.

Furthermore, to ensure the study is not affected by any common method variance, remedial measures recommended by Podsakoff and Organ (1986) were taken into consideration right from the inception of this study, that is, development of a data collection instrument. Due care was taken regarding the face and content validity of the items incorporated into questionnaires for every respective construct. It was ensured that the language used for the indicators is simple and free from unnecessary jargon and ambiguity. Double-barrelled statements were not included and the conceptuality of the questionnaire items was also confirmed on the basis of expert opinion. Also, a brief amount of information regarding each construct was provided before the items that measure the construct in order to create psychological separation in the respondents’ minds (Podsakoff & Organ, 1986). Once the soundness of the content and face validity of the questionnaire were verified, the common method variance for constructs was also checked using Harman’s one-factor test (Podsakoff & Organ, 1986). Since the questionnaire used in this research comprised 28 items, it was ensured that one single factor does not account for the majority of variance, that is, 50% from Harman’s one-factor test. Results from Table 3 demonstrate that 41.638% variance could be extracted from all 28 items under one single factor thus ensuring that this research is not affected by any common method variance.

Table 3.

Harman’s One-Factor Test.

Component	Extraction Sums of Squared Loadings
Component	Total	% of Variance	Cumulative %
1	11.659	41.638	41.638

Note: Extraction method: principal component analysis; rotation method: Varimax.

Post the data screening and preparation process, the dataset was processed for CFA to confirm its validity, reliability and model fitness (Anderson & Gerbing, 1988). Thereafter, hypothesis testing was done by employing structural equations modelling using the AMOS version 20.0.

Results

Measurement Model: Fit Indices, Reliability and Validity

CFA was applied for the measurement of the model. This study has seven constructs with 28 observed variables hence it was ineluctable to ensure the convergence of the observed variables with their respective latent constructs. CFA was applied as the discriminant validity of the constructs was also to be ensured. It was found that all observed variables showed loadings with their respective latent constructs above the recommended threshold of 0.70 (Bagozzi & Yi, 1988; Hair et al., 1998) thus forming an average loading of not less than 0.70 while the data was also found to be a good fit to the model witnessing the model fit indices given as follows; CMIN/df = 2.004, GFI = 0.892, TLI = 0.915, CFI = 0.932, RMSEA = 0.611 (see Table 4).

Table 4.

CFA Model Fit Indices.

Model	CMIN/DF	GFI	TLI	CFI	RMSEA
Study model	2.004	0.892	0.915	0.932	0.611
Recommended value	Acceptable 1–4	≥0.90	≥.90	≥0.90	<0.07
	Wheaton et al. (1977)	Shevlin and Miles (1998)	Hu and Bentler (1999)	Hu and Bentler (1999)	MacCallum et al. (1996)

Fornell and Larcker (1981) and Hair et al. (1998) advised that the squared value of the average loading of the observed variables for each construct should be above 0.50, that is, above the average variance explained (AVE) for ensuring enough convergence of the observed variables with their respective latent constructs. The results from CFA revealed that the average loading for each latent construct is above the recommended threshold of 0.70 (see Table 5) and that their squared values (AVE) are greater than the threshold of 0.50, thus confirming the convergent validity of latent constructs. Furthermore, for testifying the reliability of the constructs, Cronbach’s alpha and composite reliability (CR) were measured (see Table 5) and the statistics for each latent construct were found well above the advocated threshold of 0.70 (Bagozzi & Yi, 1988; Hair et al., 1998).

Table 5.

CFA Loadings, Cronbach’s Alpha, CR and AVE.

Variable Name	No. of Items	Avg CFA Loading	Alpha (α)	CR	AVE
Job autonomy	4	0.765	0.800	0.815	0.585
Schedule flexibility and control	3	0.844	0.881	0.894	0.712
Work–life balance	4	0.754	0.771	0.792	0.568
Reduced job stress	4	0.752	0.775	0.788	0.565
Job satisfaction	5	0.743	0.869	0.871	0.552
Organizational commitment	4	0.869	0.922	0.934	0.755
Turnover intention	4	0.796	0.785	0.804	0.633

For fulfilling the assumption of discriminant validity of the latent constructs, the squared root of AVEs, that is, the average factor loading for each construct was compared with their correlations with other latent constructs. For confirming enough divergence, Chin et al. (1997) suggested that the squared root value of AVE of any latent construct should be greater than its relationship with other latent constructs. The results from Table 6 unveil that the squared root value of AVE (shown in bold) for each construct is greater than its correlation with other latent constructs thus fulfilling the assumption of discriminant validity.

Correlation among the variables provides the primary support for hypothesis testing since a reasonable amount of correlation among outcome variables and their predictors leads to better causal effects. From Table 6, the highest correlation of 0.717 is found between job satisfaction and organizational commitment while the lowest correlation of 0.382 can be seen between reduced job stress and turnover intention. Regarding descriptive statistics of the latent constructs, the mean, standard deviation, skewness and kurtosis were also computed using SPSS version 20.0. From Table 6, the highest mean value of 4.980 was found for organizational commitment while the lowest mean value of 4.424 was found for turnover intention. Standard deviations for all seven constructs were found ranging between 1.306 and 1.588. Skewness and kurtosis were witnessed within the range of –1 and +1 for all seven constructs thus confirming the normality of data (Kline, 1998).

Table 6.

Correlations, Divergent Validity and Descriptive Statistics.

Variable Name	1	2	3	4	5	6	7
Job autonomy	(0.765)
Schedule flexibility and control	0.605**	(0.844)
Work–life balance	0.642**	0.550**	(0.754)
Reduced job stress	0.562**	0.424**	0.575**	(0.752)
Job satisfaction	0.495**	0.583**	0.599**	0.571**	(0.743)
Organizational commitment	0.387**	0.460**	0.540**	0.405**	0.717**	(0.869)
Turnover intention	0.390**	0.501**	0.577**	0.382**	0.682**	0.640**	(0.796)
Mean	4.688	4.846	4.779	4.585	4.486	4.980	4.424
Standard deviation	1.306	1.577	1.347	1.338	1.404	1.588	1.371
Skewness	–0.773	–0.676	–0.524	–0.350	–0.600	–0.743	–0.155
Kurtosis	0.601	0.067	0.038	–0.453	0.304	0.091	–0.086

Notes: Squared root of AVE has been shown in bold on diagonals and it should be greater than off-diagonal values for divergent validity.

**Correlations are significant at .01 level.

Hypotheses Testing

The authors have proposed six hypotheses in accordance with the conceptual framework of the study. Three regression models have been tested using structural equations modelling. The first model tested four hypotheses: H1, H2, H3 and H4 that examined the impact of job autonomy, schedule flexibility and control, WLB and reduced job stress on job satisfaction while the second and third models testified the impact of job satisfaction on organizational commitment and turnover intention, respectively (see Figure 1). The results from Table 7 conclude that the first model explained a variance of 50.60% (R² = 0.506) with the predictability of job autonomy, schedule flexibility and control, WLB and reduced job stress. Hypotheses H2, H3 and H4 were found supported with standardized estimates of 0.337, 0.276 and 0.301, respectively, at a significance level of 1% (p < .01) while hypotheses H1 was found unsupported as job autonomy could show an impact of only 5.50% (B = 0.055) on outcome variable, that is, job satisfaction. Hypotheses H5 and H6 were also found supported with significant coefficients at 0.682 and 0.717, respectively. Job satisfaction was found to be a strong predictor of organizational commitment and turnover intention with the prediction strength of 68.20% and 71.70%, respectively. R², that is, the explanatory power for model two and model three were found at 0.515 and 0.465, respectively.

Table 7.

Standardized Regression Weights (Structural Equations Modelling).

Dependent Variable		Independent Variable	Std Estimate
Standardized direct effects
Job satisfaction	←	Job autonomy	0.055^NS
Job satisfaction	←	Schedule flexibility and control	0.337***
Job satisfaction	←	Work–life balance	0.276***
Job satisfaction	←	Reduced job stress	0.301***
Organizational commitment	←	Job satisfaction	0.682***
Turnover intention	←	Job satisfaction	0.717***

Note: ***Paths have been found significant at 1% level of significance, that is, p < .01.

Discussion

Telecommuting is a relatively new phenomenon in India, and most of the academic studies and articles in the popular press have focused only on the productivity and performance of telecommuting employees (Sekhar & Patwardhan, 2021). The findings of these studies conclude that telecommuting, up to an extent, improves the WLB of the employees, thus leading to better job satisfaction, which in turn results in improved job performance (Jamal et al., 2021). The authors have endeavoured to assess the role of telecommuting on employee job satisfaction after which the role of job satisfaction has been testified in determining employee commitment to the organization and their turnover intention. The outcome variables of telecommuting for this study were as follows: job autonomy, schedule flexibility and control, WLB and reduced job stress.

As per our proposed conceptual model (see Figure 2), the outcome variables of telecommuting, viz. job autonomy, schedule flexibility and control, WLB and reduced job stress would positively influence employee job satisfaction. Results from the analysis (see Table 7) unveil that except for job autonomy, schedule flexibility and control, WLB and reduced job stress positively influence the job satisfaction of employees. Job autonomy has not shown a significant impact on job satisfaction which contradicts the findings of earlier research (Allen et al., 2015; Fonner & Roloff, 2010) wherein job autonomy has been reported to be a significant predictor of job satisfaction. Recent studies conducted on Indian employees post-COVID-19 have also found job autonomy to have a positive impact on job satisfaction (Jamal et al., 2021). One plausible reason for this finding in the present study may be the extra responsibility that comes with autonomy since there is no direct supervision and the employees are basically on their own. There is also a possibility that employees who have spatial flexibility might hoard most of the work or leave incomplete comparatively complex tasks for telecommuting days and this might counter any gain in job satisfaction when employees telecommute.

Another outcome variable of telecommuting is schedule flexibility and control. Since employees are allowed to schedule and control their work as per their convenience, creating some space between their professional and personal lives becomes necessary. A better balance between family and work leads to a greater level of satisfaction. In this study, schedule flexibility and control as a construct, has emerged as the strongest predictor of job satisfaction henceforth concluding that, as an outcome of remote working, the liberty to schedule their work timings as per their own convenience brings about a better WLB which eventually leads to greater job satisfaction. This is very much in line with the suggestion that telecommuters have leverage over their work demands (White et al., 2003) because of the schedule flexibility and control which allows them to decide when, how and under what circumstances they perform their tasks (Kossek & Thompson, 2016). A recent study of Indian employees working from home during COVID-19 also supports the present findings; the study found that the availability of schedule flexibility and control enhances the WLB of employees (Jamal et al., 2021).

Further, the results show a direct positive relationship between WLB and job satisfaction and this conforms with previous research since telecommuting has primarily been propounded as a measure to enhance WLB (Eddleston & Mulki, 2017; Sarbu, 2018). It is argued by boundary theorists that making shifts between work and home is not always easy and it becomes difficult for employees to maintain a good WLB due to the fading of the boundary between the two roles resulting in the spilling of one role into another (Eddleston & Mulki, 2017; Hislop et al., 2015). For instance, McNall et al. (2009) attributed improved job satisfaction to better WLB due to an increase in time spent with the family. The present study concludes that there is a significant positive relationship between the employees’ WLB and their job satisfaction which is supported by recent research as well (Jamal et al., 2021; Jamal et al., 2021). Felstead and Henseke (2017), in their study, also found that WLB is positively related to overall job satisfaction but it is subject to the extent of the difficulty and complexity of the job. Employees engaged in high complexity jobs tend to have a better WLB while working remotely, thereby leading to a greater level of overall job satisfaction. Similarly, a strong correlation can be seen between WLB and reduced job stress. With better WLB, job stress tends to reduce, thus enhancing work productivity (Eddleston & Mulki, 2017; Giovanis, 2018). Data from the present research also support the same and the study concludes that telecommuting reduces job stress and that, in turn, enhances the level of satisfaction among telecommuters (Golden & Gajendran, 2019).

Lastly, the present study found that increased job satisfaction leads to increased organizational commitment and reduced employee turnover intention. In conformity with existing literature, employees satisfied with their jobs while telecommuting are likely to be more committed to their current organization and less likely to quit (Afshari et al., 2022; Kossek et al., 2014). The findings of the present study with regard to increased organizational commitment and reduced turnover intention could be explained by the Conservation of Resources Theory (Hobfoll, 1989) which states that individuals make efforts to obtain, foster, protect and retain resources. In the present case, autonomy and schedule flexibility and control are the resources available to employees and they will make efforts to retain these resources through increased organizational commitment and reduced turnover intention since not all organizations provide telecommuting.

Implications

A culture of power exists in India. Managers do not want to give up the control they yield over their subordinates. This supervisory style of work is a hindrance to the successful adoption of the telecommuting arrangement (Raghuram, 2014). Greater schedule flexibility and control to employees is often considered a threat to supervisory powers (Chen & Fahr, 2001; Hofstede, 1993). The findings of the current study suggest that allowing employees to have greater control over their schedules has a positive impact on job satisfaction. This added control given to the employees is often considered a privilege and the employees feel that the organization is committed to their well-being. As suggested by the Social Exchange Theory (Blau, 2017), employees reciprocate by working extra hours and exhibiting increased commitment to the organization (De Menezes & Kelliher, 2011; Golden & Eddleston, 2020).

The IT sector is plagued with a high employee turnover rate of 20%–30% since the demand for talent is more than the supply (Raghuram, 2011). Adya (2008) and Ahuja et al. (2007) observed that the turnover rate is higher among Indian women as they quit on account of work and family pressure. In their meta-analysis, Martin and MacDonnell (2012) suggested that younger employees are more likely to stay in an organization that provides an opportunity to work remotely. The findings of this study indicate that giving employees the telecommuting opportunity will increase organizational commitment. Further, there is a scarcity of qualified talent and Indian organizations are failing to attract talent because of competition from multinational giants such as Microsoft, Dell and IBM (Acharya & Mahanty, 2007).

Improved organizational commitment and reduced turnover intention will help retain the required talent pool (Martin & MacDonnell, 2012) and will also save the amount that would be spent on hiring and training new employees (Halpern, 2005). Further, by providing the opportunity to telecommute, organizations can attract new talent and have a competitive edge in the industry (Bharadwaj et al., 2021). Telecommuting has helped organizations in business continuity during adverse situations such as terror attacks, epidemics and natural calamities; the large-scale adoption of telecommuting during the COVID-19 outbreak is one such example. After the COVID-19 outbreak, for the first time, many organizations allowed their employees to telecommute for extended periods. Such organizations have realized the beneficial outcomes of this work arrangement and plan to continue to allow their employees to work from home for a few days a week. As employees start going back to the physical office, the focus will again shift to the voluntary part-time telecommuting arrangement and the present findings could be relevant in the post-COVID-19 period. A clear understanding of the impact that telecommuting voluntarily for a few days a week makes on various employee-related outcomes will prepare organizations for the successful adoption of voluntary part-time telecommuting.

Limitations and Future Research

No study is free of limitations and the present study is no exception. The first limitation of the study might be its sample size which is relatively low for the generalization of the findings for the entire industry. The data collected for this study are limited to only IT sector companies. Similar studies in other sectors may be undertaken. The present study is limited to the data taken from the NCR of India and thus the findings of the present research might not be of relevance in other cultural and geographical contexts. Therefore, more research can be conducted by collecting and analysing data from other IT hubs and cities. The data collected for this study are self-reported and it might not portray the actual perception of telecommuters. The data collected from supervisors might provide a better picture.

The cross-sectional data used in this study depict the present perceptual situations only. Longitudinal data might come up with altogether different perceptions thus furnishing better results. The role of the extent of telecommuting has not been taken into consideration for the present study. Thus, an in-depth study in this area may help determine telecommuters’ job satisfaction. The present study is based on the data collected before the COVID-19 pandemic. A new study along similar lines, focusing on full-time telecommuting, would be helpful in studying organizational commitment and turnover intention in the current times.

Conclusion

Job satisfaction of telecommuters has always been a focal point for researchers. Some researchers have empirically claimed that there is a strong influence of telecommuting on the job satisfaction of telecommuters while some have contradicted these claims. Therefore, in this study, the authors have attempted to resolve this ongoing parley by adding organizational commitment and turnover intention as the outcomes of job satisfaction. Results from the study showed that organizational commitment and turnover intention are positively related to job satisfaction. Telecommuters are inclined to be more committed to the organization and exhibit a lower level of turnover intention when they experience a sense of satisfaction with the present job.

Footnotes

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding

The authors received no financial support for the research, authorship and/or publication of this article.

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