Job stress: Understanding the psychological risk factors of technocrat millennials from the socio-demographic perspective

Abstract

BACKGROUND:

The Information Technology and Information Technology Enabled Services (IT and ITES) industry has been the backbone of the Indian economy. The sector is characterized by long working hours, strict deadlines, night shift, constant usage of computers, etc. Hence, the industry and nature of the job are influencing the psychological risk factors of the employees.

PURPOSE:

The purpose of this paper is to explore the psychological risk factors (job stressors) of technocrat millennials and critically analyze them from the socio-demographic perspective.

METHODS:

A single cross-sectional study with snowball sampling was collected from 300 technocrat millennials in Ahmedabad city (India). Job stressors are examined as psychological risk factors. MANOVA and independent-sample t-test has been put to use for establishing the relationship between psychological risk factors and socio-demographic variables.

RESULTS:

The study highlighted that the experience, current position, size of family, number of children, gender, and family type had an impact on psychological risk factors of technocrat millennials.

CONCLUSIONS:

The study contributes to the literature on the psychological risk factors and its association with demographic variables, and specifically in Ahmedabad city (India). Income did not have an impact on psychological risk factor; whereas education was the only demographic variable affecting the responsibility for persons. The experience influenced the role overload, role ambiguity, poor peer relations, and intrinsic impoverishment. The study explained each socio-demographic variable’s impact on 12 psychological risk factors separately. The result of the paper will give insight to HR managers in the recruitment and selection of employees.

Keywords

Information technology & information technology enabled services industry stressors Indian context descriptive research

1 Introduction

The past two decades have witnessed an immense contribution of information technology (IT) sector in the Indian economy. Information technology is a mainstream industry as well as it is an integral part of every industry. The success of the IT sector is due to its dynamic and young workforce. Due to status and high salary associated with this sector, youth are more attracted to this sector for the job [1]. As the IT sector is the growing sector of the developing nation, struggling with some issues in the present scenario. High technology industries like IT & information technology-enabled services (ITES) have a unique workforce and culture that differentiate them from other types of work environments. The organizational culture often embodies the antithesis of interpersonal and intrapersonal competencies [2, 3]. The work environment in the industry is identified by factors like long working hours, deadlines, monotonous type of job, sitting on the computer for long hours, erratic sleep patterns and eating habits, etc. Stressors related to the content of the task are examined as psychological risk factors. Psychological risk factors at work are those factors which emerged due to working condition and situation at the workplace. It adversely impacts on health [4, 5]. Several studies have indicated that increased working hours, workload and less organizational support are major psychological factors [6]. The study explored that high-stress levels, monotonous nature of the job, demand-supply disparity, lack of career growth options, loss of identity, a complete change of biological cycle, etc. have been identified as reasons for attrition in ITES sector [7]. The movement of work to low-cost countries like China has imposed a significant threat to the Indian IT industry. To face this threat and to survive it, the companies have to ensure excellent results at competitive prices. Long working hours have been one of the major problems among IT employees. The employees working in the IT industry have to sit for long working hours. The structure of the industry and working pattern of the industry led to the long working hours and employees are bound to complete an assignment in a day. This causes problems in their life personally as well as physiologically [8].

Employees working in the industry have to use the computer as well as laptop persistently. Hence, they are named as technocrats as they are experts in using technology. Constant usage of laptop leads to particular health issues among employees. The base of the IT industry is the technology which needs continuous updating. IT employees need to continually be part of a work culture where they need to work in odd working shifts, critical time deadlines, monotonous types of jobs, etc. [9]. Individuals in this industry are identified as eccentric, preferring to work alone, and lacking in social life or consciousness, while at the same time revered as creative, prolific, hard workers [10]. The factors of the IT work environment like stress, the pressure to meet deadlines, sitting on the computer for long hours, coupled with erratic sleep patterns and unhealthy food harm the health of IT and ITES employees. The innate nature of the work environment of the IT industry harms the health of IT employees. The most common problems are eye and musculoskeletal ailments, while lifestyle-related disorders such as blood pressure, high blood sugar levels, and obesity follow [11].

Above mentioned problems due to stress are having a different response from the different socio-demographic variables. E.g. Das et al. [12] pointed out that female brickfield worker faced more stress than male brickfield worker. Uziel et al. [13] Argued that income and workload were contributing to stress. Socio-demographic factors such as age, gender, marital status, academic qualifications, and experience influence the stress caused among sports personnel [14]. Shukla and Srivastava [15] also proved that the response to stress depends on factors such as gender, age, marital status, education, annual income, and work experience. Hence, the current study was aimed to understand how the demographic factors are affecting the psychological risk factors of technocrats. The response for the research was collected from millennials who born between 1980 to 1996 year because, in India digital revolution happened in this era specifically after liberalization, privatization, and globalization (LPG) policy in the year 1991. There are lots of challenges in front of the IT & ITES industry to maintain and retain efficient employees and to understand their stress level. It is important to understand employees’ socio-demographic variables such as income, education, experience, current position, marital status, size of family, number of children, professional of spouse, gender and family type impacting their stress level.

2 Past Studies and hypothesis development

The researcher has carried out a literature review of job stress and socio-economic variables.

2.1 Income

Devi [16] aimed at identifying the degree of life stress and role stress experienced by professional women. A total sample of 180 women professionals belonging to six occupations was chosen for the study. The results revealed that the lower the income, the higher the stress experienced, i.e. stress decreases with an increase in income. In contrary to that, the employees with higher salary experience six times more role stress as compared to those who are earning a relatively lesser salary. The study further revealed more role stress among banking employees who draw a monthly salary of more than 20,000 and further concluded that the higher salaried banking employees are six times more susceptible to role stress as compared to those who are earning a relatively lesser salary [17]. Also, Sharma and Kaur [18] identified significant differences concerning the monthly income of life insurance employees for job stressors, namely, the difference in perceptions among staff, job pressure, lack of advancement opportunities; and health effects of stress, namely, lack of confidence and concentration, lack of positivity and disturbed mind. The study further revealed that the respondents receiving monthly income above 50,000 experienced more job stress than those earning below 50,000. Besides, DeTienne et al. [19] disclosed a higher level of moral stress among younger and lower-income employees, while a lower level of stress among employees at a top position. In contrast, Stacciarini et al. [20] investigated no significant relationship in job stress and income and other demographic variables.

H1.1 Income has a significant impact on psychological risk factors (job stress)

2.2 Education

Chand et al. [21] examined the correlates of and burnout among 100 faculty members from two universities. He found that higher education can combat stress and burn out related problems among the faculty members. Annamalai et al. [22] conducted a study on job stress among ITES employees and revealed that respondents with higher degree experiencing high stress because of career and achievement. In contrast, Sharma [23] explained that education found to be insignificant in predicting stress among army personnel. Other studies also proved that there was no significant difference among educational groups and job stress [24 –26].

H1.2 Education level has a significant impact on psychological risk factors (job stress)

2.3 Experience

Bhatia [27] studied on job stress and burnout among industrial employees. A sample consisted of 100 employees belonging to the supervisor and below the supervisor level. Their experience/length of service varied from 2–6 and 7–12 years. Industrial employees with a rank of supervisor and below supervisor having higher experience felt more job stress. Devi [16] aimed at identifying the degree of life stress and role stress experienced by professional women. The results revealed that more the number of years of serving higher life and role stress.

H1.3 Experience has a significant impact on psychological risk factors (job stress)

2.4 Occupation and position

Pandey et al. [28] studied the female personnel working in railway, bank, and teaching institutions. A sample of 96 females, 16 subjects in each professional area were considered for the study. The study identified that respondents among all the three dimensions, clerks of bank and railway employees experienced more work stress as compared to teachers. Aminabhavi et al. [29] revealed that managers experience significantly higher job stress than clerks. The fact was that managers have a greater responsibility for this position than the clerks. Kaur et al. [30] attempted to make a study on job stress and burn out among women police. The sample comprised of 80 women police and age ranges between 25–45. The results concluded that police work was the most stressful occupation in which with the increase in job stress, the level of the burnout also increases. DeTienne et al. [19] disclosed a lower level of stress among employees at a higher position. In contrast, Gaur et al. [31] examined the relationship between work-related stressors and adaptation pattern among women professionals. A sample of 120 women professionals (30 teachers, 30 doctors, 30 bank officers, and 30 bureaucrats) participated in the study. It showed that the four professionals groups had shared an almost similar level of stress except in the categories of career development and stressors specific to working women.

H1.4 Current position has a significant impact on psychological risk factors (job stress)

2.5 Marital status, Number of children & Profession of a spouse

Bahadoran et al. [32] revealed that there was no significant relationship between marital status and job stress. On the contrary, there was a significant relationship between several children and age among healthcare professionals. Sumangala et al. [33] studied the influence of marital status on job stress among employees of IT companies. Results revealed that there is a significant difference among married and unmarried IT employees on the subscales- unreasonable group and political pressure, responsibility for persons, under participation, and powerlessness. However, there is no significant difference in other subscales and total job stress. It was also examined that married employees had comparatively high stress on the subscales- ‘unreasonable group and political pressure’ and ‘responsibility for persons’ and unmarried employees had comparatively more scores on subscales- ‘under participation’ and ‘powerlessness’. Various researches done on married working women proved that married working women experience more stress than unmarried working women. Nagaraju et al. [34] studied the influence of marital status among female respondents of the insurance sector. The results revealed that marital status has a high impact on job stress and non- working married women are better adjusting in their married life than working married women. Along with this, they do not feel depression and stress in their married life as compared to working married women. Several researchers [35 –37] supported that marital status affected the psychological risk factors, whereas the study by Aftab et al. [38] revealed that marital status did not affect the job stress of the respondents. Crossfield et al. [39] found a strong positive association between women’s work stressors and the anxiety and depression reported by their male partners, it was found only modest crossover from men’s work stressors to women. Elloy et al. [40] studied 121 lawyers and accountants in Australia. The study analyzed the levels of stress, work family conflict, and overload among dual career and single career couples. The results confirm that dual career couples experience higher levels of stress, work family conflict, and overload than single career couples.

H1.5 Marital status has a significant impact on psychological risk factors (job stress)

H1.6 Size of family has a significant impact on psychological risk factors (job stress)

H1.7 Number of children has a significant impact on psychological risk factors (job stress)

H1.8 Profession of spouse has a significant impact on psychological risk factors (job stress)

2.6 Family structure

Vashishtha et al. [41] observed that social support from the family, coworkers, supervisors, and other people could minimize stress among the employees. Pandey et al. [28] studied the female personnel working in railway, bank, and teaching institutions. The study identified that respondents belonging to the nuclear family had expressed more interpersonal work stress. Patil et al. [42] studied stress among homemakers and working women and revealed that women from nuclear families experience significantly more stress than joint family women. Working women from nuclear families undergo a substantially higher level of stress than working women from the joint family. There are no significant differences between homemaker women who belong to a joint or nuclear family. Sabre et al. [43] revealed that there was a significant difference in marital adjustment among women of nuclear and joint families. The women belonging to nuclear showed higher levels of marital adjustment as compared to women of joint families.

H1.9 Type of family has a significant impact on psychological risk factors (job stress)

2.7 Gender

Barkat et al. [44] studied the effect of gender on organizational role stress. The sample consisted of 50 managers, 25 male and 25 female of SBI. Results indicated that females showed a lower degree of role stress than their male counterparts. Pradhan et al. [45] studied the effect of gender on stress and burnout in doctors. They considered the experience of work and family stress as intra-psychic variables. The sample consisted of 50 employed doctor couples. The result indicated no gender difference in the experience of burn out, but female doctors experience significantly more stress. Triveni et al. [46] conducted a study to know the gender difference in job stress of professional and non- professionals. The sample consisted of 300 professionals (doctors, lawyers, and teachers) and 100 non-professionals. The result revealed that women professionals experienced significantly higher job stress than men due to under participation. In contrast, Aminabhavi et al. [29] conducted a study on the nationalized and non-nationalized bank employees. The sample consisted of 78 bank employees of 39 nationalized and 39 non nationalized banks. The result revealed that male and female bank employees do not differ significantly in their job stress.

3 Research methodology

3.1 Sample and procedure

The study has adopted a descriptive single cross-sectional research design. The study was aimed to understand the impact of demographic variables on psychological risk factors of IT & ITES employees. The researcher has administered a structured questionnaire for the study. The present study has adopted the non-probability snowball sampling. Out of 579 questionnaires, 327 filled in questionnaires were received, out of which 300 usable sample responses were considered for the study. The remaining responses were not considered due to incomplete or missing data. The demographic profile showcase that 50 per cent were male and 50 per cent were female. 53 per cent of the respondents were from joint families, 56 per cent respondents were unmarried, and 76 per cent respondents did not have a child. 57 per cent of respondents had family members, i.e. up to four members in the family, and 37 per cent of the respondents were earning a maximum of 25,000 per month. The majority of the respondents, i.e. 53 per cent respondents, were at the junior position, and around 70 per cent were having below 5 years of experience. The demographic profile of the respondents has been presented in Supplementary Table 1. Data were analyzed using SPSS 20 and Microsoft excel. MANOVA and independent t-test were tested to demonstrate the hypothesis

3.2 Measure

The current study has used the Interval scale; i.e. a 5-point Likert scale, ranging from Strongly Disagree [1] to Strongly Agree [5]. The questionnaire was developed based on the Job stress Inventory developed by Srivastava et al. [47]. The 12 factors of job stress included; role overload, role ambiguity, role conflict, unreasonable group and political pressure, under-participation, unprofitability, powerlessness, poor peer relations, strenuous working conditions, low status, intrinsic impoverishment, and responsibility for persons. The demographic factors such as income, education, experience, and current position, and marital status, size of family, number of children, the profession of spouse, gender, and family type were used as independent factors.

4 Results

One-way multivariate analysis has been used to check the relationship between psychological risk factors and demographic variables. Here, the researcher has considered socio-demographics as independent variables and psychological risk factors, i.e. role overload, role ambiguity, role conflict, unreasonable group and political pressure, under-participation, unprofitability, powerlessness, poor peer relations, strenuous working conditions, low status, intrinsic impoverishment and responsibility for persons as dependent variables (Summary of the results is given in Table 1)

Table 1
Summary of the Relationship between Psychological Risk Factors (job stressors) and Demographic Variables

Demographic PRF Income Education Experience Current position Marital status Size of family No. of children Profession of spouse Gender Family type

Overall PRF No No Yes Yes No Yes Yes No Yes Yes

Role-overload No No Yes No No Yes Yes No No Yes

Role-ambiguity No No Yes No No Yes Yes No No Yes

Role-conflict No No No No No Yes No No No No

Unreasonable group and political pressure No No No No No No No No No No

Under-participation No No No No No No Yes No Yes No

Unprofitability No No No No No No Yes No No No

Powerlessness No No No No No Yes No No Yes Yes

Poor peer relations No No Yes Yes No No No No Yes Yes

Strenuous working conditions No No No No No No No No Yes Yes

Low status No No No Yes No No No No No No

Intrinsic impoverishment No No Yes Yes No No No No No No

Responsibility for persons No Yes No Yes No Yes No No Yes Yes

Demographic PRF	Income	Education	Experience	Current position	Marital status	Size of family	No. of children	Profession of spouse	Gender	Family type
Overall PRF	No	No	Yes	Yes	No	Yes	Yes	No	Yes	Yes
Role-overload	No	No	Yes	No	No	Yes	Yes	No	No	Yes
Role-ambiguity	No	No	Yes	No	No	Yes	Yes	No	No	Yes
Role-conflict	No	No	No	No	No	Yes	No	No	No	No
Unreasonable group and political pressure	No	No	No	No	No	No	No	No	No	No
Under-participation	No	No	No	No	No	No	Yes	No	Yes	No
Unprofitability	No	No	No	No	No	No	Yes	No	No	No
Powerlessness	No	No	No	No	No	Yes	No	No	Yes	Yes
Poor peer relations	No	No	Yes	Yes	No	No	No	No	Yes	Yes
Strenuous working conditions	No	No	No	No	No	No	No	No	Yes	Yes
Low status	No	No	No	Yes	No	No	No	No	No	No
Intrinsic impoverishment	No	No	Yes	Yes	No	No	No	No	No	No
Responsibility for persons	No	Yes	No	Yes	No	Yes	No	No	Yes	Yes

Source: based on primary data analysis Where, Yes = There was an association between PRF and socio-demographic variables. No = There was no association between PRF and socio-demographic variables.

4.1 H1.1 Income has a significant impact on psychological risk factors (job stress)

A one-way Multivariate Analysis of Variance was examined to check the impact of income on psychological risk factors. The result showed that there was no statistically significant impact of income, i.e. (below Rs. 16000, Rs. 16000 to Rs.25, 000, Rs. 25,000 to Rs. 50,000 and above Rs. 50,000) on twelve psychological risk factors (Wilks’λ= 0.897, F (36, 842.792) = 0.877, p = 0.677 > 0.1).

The univariate F-ratio was significant for one dependent variable, i.e. unreasonable group and political pressure (F = 3.322, Sign. = 0.020). But, F-ratio was insignificant for the other factors. In addition to this, the Tukey HSD test was conducted to check the extent of significant pairwise differences among an unreasonable group and political pressure and income. Post-hoc Tukey HSD test revealed a statistically significant difference between the 16,000 to Rs. 25,000 (M = 3.369, S.D. = 0.082) and below Rs. 16,000 (M = 3.063, S.D. = 0.089) and Rs. 25000 to 50,000 (M = 3.00, S.D.=0.108). Henceforth, overall income did not have an impact on psychological risk factors.

4.2 H1.2 Education has a significant impact on psychological risk factors (job stress)

The result revealed that there was no statistically significant impact of education i.e. (diploma, graduation, post-graduation and others) on twelve on twelve psychological risk factors (Wilks’λ= 0.898, F (36, 842.792) = 0.870, p = 0.688 > 0.1)

The univariate F-ratio were significant for role conflict (F = 2.796, Sign. = 0.040) and responsibility for persons (F = 3.437, Sign. = 0.017) But, F –ratio was insignificant for other factors of job stress. In addition to this, the Tukey HSD test was performed to check the extent of significant pair wise differences among role conflict & responsibility for persons and education. Post-hoc Tukey HSD test revealed statistically significant difference between the post-graduation (M = 2.894, S.D. = 0.064) and others (M = 3.625, S.D. = 0.262) for role-conflict and others (M = 3.750, S.D. = 0.301) and diploma (M = 2.545, S.D. = 0.256) and post-graduation (M = 2.985, S.D. = 0.074) for responsibility for persons. Henceforth, overall education was not having an impact on psychological risk factors.

4.3 H1.3 Completed years of service has a significant impact on psychological risk factors (job stress)

The result showed that there was a statistically significant impact of completed years of service, i.e. (below 5 years, 5–10 years, 11–15 years, 16–20 years and above 21 years) on twelve on twelve psychological risk factors (Wilks’λ= 0.759, F (48, 1096.036) = 1.695, p = 0.002 < 0.1) (Supplementary Table 2).

The univariate F-ratio were significant for four dependent variables; role overload (F = 2.175, Sign. = 0.072), role ambiguity (F = 5.653, Sign. = 0.000), poor peer relations (F = 2.396, Sign. = 0.051) and intrinsic impoverishment (F = 3.552, Sign. = 0.008) But, F –ratio was insignificant for other factors of job stress. In addition to this, the Tukey HSD test was performed to check the extent of significant pairwise differences among role overload, role ambiguity, poor peer relations & intrinsic impoverishment for completed years of service. Post-hoc Tukey HSD test revealed statistically significant difference between the below 5 years (M = 3.014, S.D. = 0.051) and 11–15 years (M = 3.435, S.D. = 0.154) & 16–20 years (M = 3.833, S.D.=0.213); 5–10 years (M = 3.143, S.D. = 0.106) & 16–20 years (M = 3.833, S.D.=0.213) for role ambiguity. 5–10 years (M = 2.959, S.D. = 0.107) and 11–15 years (M = 3.435, S.D. = 0.156) & 16–20 years (M = 3.667, S.D. = 0.216) for intrinsic impoverishment. Tukey HSD test does not reveal any statistical pair wise difference for role overload and poor peer relations. Henceforth, overall completed years of service was having an impact on psychological risk factors.

4.4 H1.4 Current position has a significant impact on psychological risk factors (job stress)

The result showed that there was a statistically significant impact of the current position, i.e. (junior, senior, assistant manager, manager, team-leader and others) on twelve on twelve psychological risk factors (Wilks’λ= 0.761, F (60, 1328.957) = 1.332, p = 0.048 < 0.1) (Supplementary Table 3).

The univariate F-ratio were significant for four dependent variables; poor peer relations (F = 2.353, Sign. = 0.041), low status (F = 2.947, Sign. = 0.013), intrinsic impoverishment (F = 3.858, Sign. = 0.002) and responsibility for persons (F = 3.594, Sign. = 0.004) But, F –ratio was insignificant for other factors of job stress. In addition to this, the Tukey HSD test was performed to check the extent of significant pairwise differences among poor peer relations, low status, intrinsic impoverishment & responsibility for persons for completed years of service. Post-hoc Tukey HSD test revealed statistically significant difference between the assistant manager (M = 2.875, S.D. = 0.298) and junior (M = 2.950, S.D. = 0.067) & manager (M = 3.462, S.D. = 0.234) for poor peer relations. Senior (M = 2.989, S.D.=0.078) and manager (M = 3.769, S.D. = 0.206) for intrinsic impoverishment. Junior (M = 2.950, S.D. = 0.067) and team leader (M = 3.50, S.D. = 0.188) & others (M = 3.70, S.D. = 0.266) for responsibility for persons. Henceforth, overall current position was having an impact on psychological risk factors.

4.5 H1.5 Marital status has a significant impact on psychological risk factors (job stress)

In case of marital status, The result showed that there was a statistically significant impact of marital status, i.e. (unmarried, married, widow/divorcee) on twelve on twelve psychological risk factors (Wilks’λ= 0.910, F (24, 572) = 1.15, p = 0.283 > 0.1)

4.6 H1.6 Number of children has a significant impact on psychological risk factors (job stress)

In case of the number of children, the result showed that there was a statistically significant impact of the number of children (i.e. One, Two, Three or more, None) on twelve on twelve psychological risk factors (Wilks’λ= 0.794, F (36, 842.792) = 1.90, p = 0.001 < 0.1) (Supplementary Table 4).

The univariate F-ratio were significant for four dependent variables; role overload (F = 3.73, Sign. = 0.012), role ambiguity (F = 5.78, Sign. = 0.001), under-participation (F = 2.96, Sign. = 0.032) and unprofitability (F = 2.58, Sign. = 0.054) But, F –ratio was insignificant for other factors of job stress. In addition to this, the Tukey HSD test was performed to check the extent of significant pairwise differences among role overload, role ambiguity, under-participation and unprofitability for number of children. Post-hoc Tukey HSD test revealed a statistically significant difference between the three or more children (M = 3.70, S.D. = 0.48) with one child (M = 3.03, S.D. = 0.94) & none (M = 3.462, S.D. = 0.234) for role overload. Two children (M = 3.64, S.D. = 0.87) with one child (M = 3.12, S.D. = 0.88) and none (M = 3.03, S.D. = 0.70) for role ambiguity. Two children (M = 3.46, S.D. = 0.84) with one child (M = 2.97, S.D. = 0.90) and none (M = 3.03, S.D. = 0.76) for under-participation. One child (M = 2.91, S.D. = 1.11) and none (M = 3.39, S.D. = 0.91) for unprofitability. Hence, overall number of children was having significant impact on psychological risk factors.

4.7 H1.7 Size of a family has a significant impact on psychological risk factors (job stress)

The result showed that there was a statistically significant impact of the size of the family, i.e. (up to 4 members, 5 to 7 members and above 7 members) on twelve psychological risk factors (Wilks’λ= 0.888, F (24, 572) = 1.46, p = 0.073 < 0.1) (Supplementary Table 5).

The univariate F-ratio were significant for three dependent variables; role ambiguity (F = 4.88, Sign. = 0.008), powerlessness (F = 4.71, Sign. = 0.010) and responsibility for persons (F = 2.82, Sign. = 0.061) But, F –ratio was insignificant for other factors of job stress. In addition to this, the Tukey HSD test was performed to check the extent of significant pairwise differences among role ambiguity, powerlessness and responsibility for persons in case of size of family. Post-hoc Tukey HSD test revealed statistically significant difference between the up to 4 members (M = 3.00, S.D. = 0.75) with above 7 members (M = 3.42, S.D. = 0.76) for role ambiguity. Above 7 members (M = 3.45, S.D. = 1.12) with up to 4 members (M = 2.91, S.D.=0.87) and 5 to 7 members (M = 3.04, S.D. = 0.90) for powerlessness. Up to 4 members (M = 2.96, S.D. = 0.86) with above 7 members (M = 3.35, S.D. = 1.08) for responsibility for persons. Hence, overall size of family was having significant impact on psychological risk factors.

4.8 H1.8 Profession of spouse has a significant impact on psychological risk factors (job stress)

The result showed that there was no statistically significant impact of the profession of spouse, i.e. (Housewife, Administrative/clerical job, Technical job, Does not apply) on twelve dependent twelve psychological risk factors (Wilks’λ= 0.888, F (36, 842.792) = 0.961, p = 0.536 > 0.1). The univariate F-ratio was non-significant for all the twelve dependent variables. Hence Tukey HSD posthoc test cannot be performed.

4.9 H1.9 Type of a family has a significant impact on psychological risk factors (job stress)

Independent sample t-test was administered to compare the agreement regarding twelve dependent psychological risk factors among joint and nuclear family type.

The result revealed that type of family type was having significant impact on overall stress between Joint (M = 3.19, SD = 0.54) and Nuclear (M = 3.07, SD = 0.50) conditions; t (298) = 1.95, p = 0.052; role-ambiguity between Joint (M = 3.19, SD = 0.77) and Nuclear (M = 3.01, SD = 0.74) conditions; t (298) = 2.08, p = 0.038; family type was having impact on opinion regarding role conflict, between Joint (M = 3.06, SD = 0.73) and Nuclear (M = 2.87, SD = 0.75) conditions; t (298) = 2.29, p = 0.023; family type was having impact on opinion regarding powerlessness between Joint (M = 3.17, SD = 0.90) and Nuclear (M = 2.83, SD = 0.91) conditions; t (298) = 3.24, p = 0.001; family type was having impact on opinion regarding poor peer relations between Joint (M = 3.20, SD = 0.80) and Nuclear (M = 2.96, SD = 0.77) conditions; t (298) = 2.63, p = 0.009; family type was having impact on opinion regarding strenuous working conditions between Joint (M = 3.20, SD = 0.74) and Nuclear (M = 3.05, SD = 0.75) conditions; t (298) = 1.78 p = 0.075; family type was having impact on opinion regarding responsibility for persons between Joint (M = 3.13, SD = 0.82) and Nuclear (M = 2.94, SD = 0.90) conditions; t (298) = 1.845, p = 0.066. Technocrats who are living in joint family were having impact on above six factors of psychological risk (Supplementary Table 6).

4.10 H1.10 Gender has a significant impact on psychological risk factors (job stress)

An independent sample t-test was administered to compare the agreement regarding twelve dependent psychological risk factors among males and females.

The result revealed that gender had a significant impact on overall psychological risk factors between Male (M = 3.20, SD = 0.59) and Female (M = 3.07, SD = 0.45) conditions; t (298) = 2.19, p = 0.029; under-participation between Male (M = 3.18, SD = 0.859) and Female (M = 2.93, SD = 0.738) conditions; t (298) = 2.66, p = 0.008; gender was having impact on opinion regarding powerlessness, between Male (M = 3.12, SD = 0.957) and Female (M = 2.98, SD = 0.838) conditions; t (298) = 3.246, p = 0.001; gender was having impact on opinion regarding poor peer relations between Male (M = 3.19, SD = 0.824) and Female (M = 2.98, SD = 0.741) conditions; t (298) = 2.262 p = 0.009; gender was having impact on opinion regarding strenuous working conditions between Male (M = 3.14, SD = 0.771) and Female (M = 3.11, SD = 0.719) conditions; t (298) = 1.783, p = 0.075 & gender was having impact on opinion regarding responsibility for persons between Male (M = 3.17, SD = 0.880) and Female (M = 2.90, SD = 0.822) conditions; t (298) = 1.845, p = 0.066. It was clear that male technocrats were more impacted by under-participation between, powerlessness, poor peer relations, strenuous working conditions, and responsibility for persons (Supplementary Table 7).

5 Discussion

5.1 Theoretical implications

The results of the study revealed that monthly income did not have an impact on overall psychological risk factors. However, only one factor, unreasonable group, and monthly income affected political pressure. The results were in line with those of DeTienne et al. [19] but contradicted other past studies [16, 18]. The majority of the respondents were beginners and learners. Hence, psychological factors were not impacted by income.

The study revealed that overall psychological risk factors were not impacted by education among IT and ITES employees of Ahmedabad city. In the study majority, i.e. 149 respondents were graduates and remaining 132 respondents were post-graduates. For technical jobs, graduation was preferred. Hence, among IT and ITES employees, there was no impact on education. The study was in line with Sharma48who revealed that the demographic variable education was insignificant in predicting stress among army personnel. Other studies also proved that there was no significant difference among educational groups and job stress [14, 26]. The result revealed that both completed years of service and current position had an impact on overall psychological risk factors. In the case completed years of service, role overload, role ambiguity, poor peer relations, and intrinsic impoverishment were having the impact. In the case of a current position, poor peer relations, low status, intrinsic impoverishment, and responsibility for persons affected. 136 respondents who had completed less than 5 years in the job and 93 junior position employees had moderate job stress. The results were in contradiction with the majority of past studies where experience had an impact on factors of job stress [48, 49]. In terms of this study, the results were in contradiction with the earlier studies [16 , 29].

The results revealed that the marital status and profession of a spouse did not have an impact on psychological risk factors among IT and ITES employees of Ahmedabad city in India. The majority of the respondents, i.e. 169, were unmarried and experienced moderate stress levels as they were beginners in the industry. The results were in line with the past studies; Aftab and Khatoon1 revealed that marital status did not affect the job stress of the respondents. Bunker et al13 opined that the size of family, type of family, and the number of children had an impact on psychological risk factors. In the case of the size of family, role ambiguity, role conflict, powerlessness, and responsibility for persons did not affect job stress. In the case of the type of family, overall psychological risk factors, role ambiguity, role conflict, powerlessness, poor peer relations, strenuous working conditions, and responsibility for persons had an impact on the respondents’ stress. In the case of several children Role Overload, Role Ambiguity, Under-participation and Unprofitability were impacted. Various past studies believed that marital adjustment and family structure had an impact on the job stress of a person. This study contradicted with the earlier studies where employees with a nuclear family were experiencing more stress or risk [28, 42].

Under-participation, Powerlessness, Poor Peer Relations, Low Status, Intrinsic Impoverishment, and Responsibility for Persons had an impact on them based on their gender. As compared to female technocrats, male technocrats experienced a high level of stress. The results were in line with past studies [50] where male nurses experienced more stress compared to female counterparts. Bhagawan et al. [51] studied 53 male and 47 female teachers from 20 schools in Orissa. The results indicate that male teachers experienced more stress compared to female teachers. Parveen et al. [36] and Barkat et al. [44] studied the effect of gender on organizational role stress. Results indicated that females showed a lower degree of role stress than their male counterparts. Hence, in the past, the studies were conducted on different industries and to compare technocrats with various industry professionals and justify gender differences and their impact on psychological risk factors within the organization will be inappropriate (Fig. 1).

Fig. 1

Conceptual model from the Analysis. Source: Author has created from the data analysis

5.2 Practical implications

The study contributes to the literature on the psychological risk factors and its association with demographic variables, and specifically in Ahmedabad, Gujarat. The study provides empirical support for which psychological risk factors are crucial among technocrat millennials. The different psychological risk factors and their demographic associations are helpful to the organizations and especially to the HR managers in identifying a proper candidate for recruitment and selection. These factors will help the organizations for their internal assessment and retention strategy. With the help of this study, the organization’s can craft strategies for human resource planning due to its understanding of technocrats’ demographic profile, making it easier for needs assessment of the current employees as well as for the future. This will benefit both organizations as well as employees.

5.3 Study limitations and future directions

Despite the considerable theoretical and practical implications of the current study, several limitations are to be considered. First, the study has not examined all the generations but only millennial technocrats. It also addresses future scope that different generations can be included in the responses. Second, the study was only limited to Ahmedabad city as this city was considered a challenger for other IT hubs where more and more companies moved to this city (NASSCOM-AT Kearney study) [40]. In the future, the expanded geographical area can be taken to understand the broad horizon of the psychological risk factors within organizations. Third, the study has only considered technocrats, those who are working in mainstream IT and ITES. As information technology is a part of every industry, whether it is an academic institution or an advertising industry, IT is a crucial part of each sector. In future research, this limitation can be removed by considering IT employees from diverse industries. As every industry has its own culture, work setting, and psychological risk factors, which can give a broad overview and comparison between technocrats of different organizations from different sectors. Fourth, the study has only considered a quantitative approach. Further research is required to examine the present findings with the help of qualitative methodology. Fifth, after gauging the factors of psychological risk further research can be conducted by adding more variables like job satisfaction, emotional intelligence, work-life balance, turnover intention, health, and coping strategies, etc. which are also factors having an impact on psychological risk.

Conflict of interest

None to report.

Supplementary data

The supplementary files are available from https://dx.doi.org/10.3233/WOR-213531.

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