Managers’ Interference with Employees’ Lifestyles While Working Remotely during COVID-19 Pandemic

Abstract

The purpose of this article is to identify what factors influence employees’ opinions on supervisors’ interference with their lifestyles in remote working conditions during COVID-19. Our sample included 1000 participants, both managers and non-managers. Outcomes variables included the following: managerial interference with the lifestyle of employees, acceptance of different lifestyles, and unequal treatment of employees in relation to their lifestyle. Explanatory variables related to employees’ characteristics included sex, age, education and seniority and the position in the company (managers/workers), size of the team of employees, size of the organization, characteristics of work (individual/teamwork), as well as the degree of formality in employees’ behaviors. Analysis was conducted using logistic regression in a multivariate analysis of individual (employee-side) and organizational determinants. Our research has shown that lifestyle acceptance, lifestyle interference when working remotely, and unequal treatment of employees based on lifestyle are determined as follows: the organization’s characteristics, the nature of the work, and the social characteristics of the employees. Our findings contribute to understanding how remote work is perceived in relation to employees’ lifestyles. These insights can help organizations develop effective policies and practices regarding, for example, health interventions to support and not unreasonably interfere with their privacy.

1. Introduction

During the COVID-19 pandemic, many people began working partially or entirely remotely for the first time. Often, employees were unprepared for the new form of work, faced a variety of problems, and experienced the negative consequences of working outside the traditional office.

Remote workers were confronted with the necessity to extend their working hours beyond the employee’s mandatory working hours, the expectation of excessive availability, regular contact or assignment of tasks by the supervisor outside of the designated working hours, extreme control (resulting from a lack of belief in the effectiveness of work) or lack thereof [1], and the creation of incomprehensible or illogical rules and norms by the employer [2].

The distancing of employees from each other limited their ability to support each other in various aspects of life, contributing to increased stress [3]. Employees were tired of the number of scheduled video calls and thought twice about starting another call to ask for social support [4]. The employees’ need for a social life was not being met [5]. A negative effect of remote work was a sense of isolation, loneliness, and alienation [6,7]. Social isolation, in turn, aggravates mental and physical illness [8].

Remote work caused difficulties separating work and home life [9,10]. While working from home, it was common for employees to mismanage their time and give up breaks of their own volition, which could lead to poorer mental and physical well-being [11].

Experience during the COVID-19 pandemic showed that remote work during the pandemic reduced the boundaries between work and private life [12,13,14,15,16]. An alternative work model was formed, disrupting workers’ social interactions and biological rhythms and causing changes in various behaviors such as eating, sleeping, and physical activity. Due to the intensity of work, remote workers’ lifestyles have changed [17,18,19,20,21,22].

Although used daily, the term lifestyle is ambiguous, heterogeneous, and difficult to interpret [23,24,25,26]. It is generally used to refer to how people function daily. At the individual level, a lifestyle is a set of daily behaviors characteristic of a particular person, distinguishing the individual from others; it is a set of habits oriented intentionally and regulated by social feedback [27]. It is an individual’s specific way of being, the foundation of which is formed by certain behavioral patterns resulting from the interaction of personal characteristics, social conditions, and the socioeconomic and environmental conditions of the individual’s life [28].

Lifestyle consists of people’s activities, interests, and opinions. Activities include work, hobbies, social events, vacations, entertainment, relationships, shopping, sports, etc. Interests are related to, for example, family, home, work, community, recreation, fashion, food, media, and achievements. Opinions are beliefs about self, social issues, politics, business, economics, education, products, future, culture, etc. [29,30]. Lifestyle refers not only to activities outside of work but also to the professional environment [31].

It happens that lifestyle is the basis of managers’ decision making. Employers make lifestyle interventions out of concern for employee health and safety [32,33,34].

Sometimes managers interfere in private life, such as family affairs, interests, appearance, and control behaviors resulting from a specific employee lifestyle. Sometimes lifestyle can be a reason for discrimination in the workplace. Controlling employees and interfering in their private affairs are generally debatable issues and can be controversial. It seems reasonable to ask to what extent employers should be allowed to intrude into employees’ privacy [35].

Many studies describe the impact of the COVID-19 pandemic on changing people’s lifestyles, on fatigue, job burnout and a decrease in job satisfaction and work engagement. Research also shows that the COVID-19 pandemic disrupted employees’ healthy lifestyles and negatively affected work–life balance and quality of life [7,8,12,13,14,20,22,25,26,36]. There is research on managerial interventions in employee lifestyles (safety, health promotion, and work–life balance) [32,33,34,35]. However, there is a lack of research on employees’ perceptions of managerial interventions. The literature review shows that the determinants of perceptions of managerial interference have not been studied. While it has been pointed out that lifestyle interference violates employees’ privacy and may be a reason for discrimination [35], it has not been investigated what factors increase employees’ negative feelings.

Our research adds to the knowledge of the determinants of perceived managerial interference. These determinants help understand that interference in employees’ lives can be evaluated differently. For managerial interventions to be effective, employers should understand the needs of employees and employees should not feel discriminated against.

In this article, we examine how employee characteristics, work characteristics, and organizational characteristics determine the acceptance of employees’ lifestyles while working remotely, interference with employees’ lifestyles while working remotely, and unequal treatment of employees based on their lifestyles while working remotely. To achieve this goal, Section 2 presents a discussion of lifestyle interference, Section 3 describes the materials and methods used in the study, Section 4 shows the results of the study, Section 5 discusses our findings in light of the literature and presents conclusions, theoretical and practical implications, and limitations of the study. Section 6 contains limitations of the study and directions for future research.

2. Literature Review

2.1. Managerial Interference in Employees’ Lifestyles—Positive and Negative Examples

Managerial control practices are fundamental in organizations because they coordinate employees’ efforts, enable agreement between managers at different levels, serve as a source of motivation by establishing an incentive system, and act as triggers for necessary management interventions [37].

The problems of managerial interventions in the workplace are described in the literature in the context of, for example, safety, health promotion, and work–life balance support. Examples of managerial interventions may be those based on concern for the employees—aimed at improving their health or increasing their well-being in the workplace, for example, access to the gym and fitness in the workplace, courses in the field of stress prevention, initiatives that explain why smoking is harmful and encouragement to stop, programs leading to body mass reduction and teaching healthy nutrition stress management training, smoking awareness campaigns and encouraging smoking cessation, and weight reduction programs and teaching healthy eating [32,38]. Such interventions improve employee health, quality of life, and emotional well-being, reduce employee turnover, and increase productivity and engagement—they are cost-effective [34]. These actions are necessary because health problems related to lifestyle impact the organization’s economic situation and contribute to decreased productivity, increased absenteeism, presenteeism, and healthcare costs [33].

Given the economic impact, employers increasingly express their concerns related to health and employees’ lifestyle and pay attention to their behaviors and choices outside of working hours. By offering wellness programs and other benefits to the employees, at the same time, they grant themselves the right to infringe on their privacy. This is a manifestation of new corporate ethics that, in the name of health and well-being, violate the boundary between work and private life and extend the reach of the company’s interests onto the health habits and lifestyle of the employees without taking into account whether they take place at work or home, and if they realistically affect the efficiency of performed work in any direct way. The consequences of such ethics may prove more detrimental than the supporters of health in the workplace could ever imagine. There is a risk that, at a certain point, employers will turn health and lifestyle into a condition of employment or promotion [32].

Most interventions [39] that support employees’ physical and mental health through various programs apply to office work. Meanwhile, the COVID-19 pandemic has led to a sudden work shift from the office to the home. Changing to a hybrid model requires organizations to think differently about lifestyle interventions.

Organization-initiated interventions can facilitate remote work, improve physical activity and eating habits, counter loneliness and stress, and prevent addictive behaviors. Unfortunately, they can become more acute when employees work from home. Recent evidence suggests that alcohol and drug use and cigarette smoking have increased dramatically during the COVID-19 pandemic. Because these problems are too complex for organizations to handle, these areas require specialized medical monitoring and intervention by health professionals.

As office work shifts to a hybrid home-office model, organizations should examine ways to maintain and improve the health of their remote workers so that their behavioral interventions make home offices a viable and healthy alternative to traditional office work in the long run [39].

Another example of intervention is working time monitoring. Working time monitoring not only facilitates work planning and helps ensure work continuity but also helps eliminate situations in which a tired employee performs work, endangering himself and others. This kind of control is advisable as long as it is not repressive. Another example, which is not positive, is controlling email sent or received from company computers and controlling and restricting internet traffic-blocking social networks, instant messaging, and chat rooms [40].

Developments in communication technology have encouraged remote monitoring of employee performance through methods such as controlling keystrokes, computerized time keeping, global positioning system (GPS) observation, or monitoring phone calls. Due to the heavy reliance on technology in day-to-day work, organizations can continuously observe, record or analyze information about employee behavior [37].

Even if it is in the interest of the employers to control those aspects of employees’ lives that affect their performance in the workplace, this still does not justify controlling every aspect of employees’ lives [41].

Excessive interest in private matters of employees (including lifestyle, health-related circumstances, family, and interests) may cause a sense of discomfort in staff. Attempts to regulate the area of private life, including employees’ lifestyle, may be perceived by them as unwanted, which will trigger reluctance towards the employer and resistance to participating in actions initiated by them. The expectation that employees would declare their intention to conceive a baby one year in advance is a perfect example of such interference, even if the necessity to share this information is motivated by the need to ensure staff continuity. Showing excessive interest in employees’ health issues may cause discomfort, embarrassment, and a sense of pressure. Inquisitive questioning by the manager about subordinates’ interests and lifestyle or attempts to regulate the area of their private life can trigger distaste, and strong and direct resistance [40].

Some consider that not all interventions of a superior in employees’ private lives have to be perceived badly. In a situation of long-term cooperation and familiarity leading to a good and reciprocal acquaintance, interference with an employee’s private life (family or home) can manifest concern for the employee, being friendly, interested, and willing to help [40]. In some cases, managers’ attempts to influence how their employees act outside the workplace may be justified, especially when their behaviors risk the company’s reputation. D. Sugarman [35], for example, describes situations where an employer’s decision related to candidates for employment took into account their behavior outside the workplace.

Studies conducted by us [42,43] show that there is social acceptance for interference with family life activity and with matters related to health, such as smoking or being a non-smoker. Our research shows that it is acceptable to influence appearance (clothing, hairstyle, make-up, tattoos, and jewelry).

Some companies implement a set of rules that regulate the way of dressing (including make-up, use of accessories, etc.), the so-called dress code. Such regulations apply only to the place of employment (clothing and hairstyle). Still, they do not interfere with privacy unless they extend outside of the work context (length of hair and nails, banning mustache or beard), in which case interference exists [40]. Studies mentioned by the author describe cases in which requirements related to appearance were rigorous and limiting for the freedom of expression; such situations can potentially provoke tensions and stress and, consequently, affect employees’ work–life balance. Subjects who participated in the study drew attention to comments made by superiors concerning an employee’s appearance, which could be perceived as exerting pressure, attacking and mocking.

Place of employment, as a physical and social environment, has an enormous potential to facilitate more positive lifestyle choices. Moreover, employers should feel responsible for their employees and undertake initiatives that promote healthy lifestyles in the workplace [34]. However, some supervisors’ actions far exceed work and private life boundaries. Some of the interventions and control actions may be perceived by employees as interfering with their privacy and lifestyle, resulting in discomfort and dissatisfaction. Such situations can lead to misunderstandings and “sharp clashes between employers and employees” [35].

2.2. Determinants of Employees’ Perceptions of Managerial Behaviour as Lifestyle Interference

Employees’ perceptions of managers’ behavior as lifestyle interference may be determined by a variety of factors. Social research often analyses people’s views and behavior according to gender, age, education, service length, and position. Some researchers argue for the influence of the variables mentioned above on the behavior of individuals, while others say that convincing evidence for this is lacking. For example, some researchers believe intergenerational differences manifest in work–life values, leadership behavior, personal characteristics, intentions to leave, and organizational commitment [44,45]. Others point to few generational differences or believe little evidence suggests intergenerational differences in traits, attitudes, and behaviors [46]. As reported in “The Voice of the European Workforce” [47], there are better guides to guessing what employees think than traditional demographics such as age. It is, therefore, useful to consider their other characteristics and needs. Other studies [48] have shown that depending on their age, participants behaved differently because they attributed different importance to tasks and environmental factors determining the decision-making process in other circumstances.

People’s characteristics and work-related characteristics (organizational variables) are considered in a study of various processes, for example, analyzing managers’ decision motives [49]. Many researchers see the former as a critical factor for effective decision making. These include personality traits (individual preferences, attitudes, needs, values, and knowledge) and demographic characteristics, i.e., age, gender, education level, length of service, or place of residence of the manager [50]. The same author gives an example of differences in decision making between older and younger managers. An analysis of the literature also provides evidence of differences in the modes of behavior practiced by female and male managers [51,52,53]. There are also differences in the views of employees and managers. Managers represent more of the employers’ interest and seek to present the best possible image of their company, which is interpreted in organizational theory as seeking to create an organizational facade. “Employee experience surveys offset this limitation and provide a closer-to-reality reflection of employers’ actual actions” [54].

Studies consider organizational variables, the organization’s size, teams, or job characteristics. The influence of work-related factors on the performance of teams differentiated by different criteria of the individuals comprising them is indicated, for example, by Milliken and Martins [55]. Their meta-analysis referred to differing experiences on functional and professional background, the moment of ‘entry’ into the organization, and seniority. In contrast, Sujin K. Horwitz S.K., and Horwitz I. B. [56] cited the views of other authors, which show that the effect of diversity on team performance depends on certain characteristics of the work itself (complexity and interdependence of the task but also on the characteristics of the team (its size).

3. Materials and Methods

Research described in this article was conducted as part of a project entitled “Diversity versus remote work—problems and challenges”. It aimed at investigating how remote workers perceived the change in working conditions, ways of performing work and relationships between colleagues. The study was based on a survey. The technique of data collection consisted in an online CAWI questionnaire. It was conducted in the 2nd quarter of 2022 by the company BIOSTAT Sp. z o.o. based in Rybnik, Poland. Sample selection was based on the availability of subjects to the contractor (BIOSTAT Sp. z o.o.). The study involved people who are listed in the BIOSTAT database. The questionnaire contained 84 closed questions (subjects had to choose from a provided set of answers) and respondent’s particulars. The research involved 1000 participants. The questionnaire was addressed only to people who had worked remotely (from home) or in a mixed model (partly from home or partly on company premises) over the two years before.

In this article, we are presenting results of our study on how employees’ lifestyle is perceived in the context of remote work during the COVID-19 pandemic.

After having analyzed the literature, we decided to adopt, for the sake of the present study, a “simplified” definition of lifestyle, according to which lifestyle is a configuration of elements, specific for a given individual, which can be “visible” for others (colleagues) during a certain period of time due the ritual character of their implementation. Such elements include interests not related to the professional life (hobbies), practicing sports, participation in socio-political life (for example charity, politics, ecological, nationalistic or religious movements), activity in the area of social life (i.e., involvement in “partying” and various forms of social entertainment), activity in the area of family life (especially involvement in the care for children, for the elderly or sick family members), taking care of one’s appearance (way of dressing, hairstyle, make-up, tattoos, jewelry, etc.), smoking/being a non-smoker, nutrition (diets, dietary habits).

In the conducted research, outcome variables included interference of the manager with the lifestyle of subordinates, acceptance of different lifestyles of the employees within the team, and unequal treatment of employees in relation to their lifestyle. Explanatory variables related to employees’ characteristics included sex, age, education, and seniority, while explanatory variables related to the place of employment included time of remote work, position in the company (managers/workers), size of the team of employees, size of the organization, characteristics of work (individual/teamwork), as well as the degree of formality in employees’ behaviors.

Participants had to fill in the questionnaire and assess proposed statements using a 5-point Linkert scale. They could choose from the following answers: definitely yes (5), rather yes (4), difficult to say (3), rather not (2), and definitely not (1). Initially, the analysis of the obtained results and assessment of the answers’ variability in relation to the selected explanatory variables were conducted using statistical analysis tools specific for ordinal scales: contingency tables and Pearson’s chi-squared test of independence. It was assumed that a correlation between variables existed if it was verified with a chi-squared test at the relevance level of 0.05.

Among the provided answers, which served as a basis for operationalizing outcome variables, there was a high percentage of “difficult to say” (

Table 1. Questions to operationalize outcome variables.

Statement:	Definitely Not	Rather Not	Difficult to Say	Rather Yes	Definitely Yes
Q51: Remote work is, to a greater extent, associated with the manager’s interference in my lifestyle. variable: interference with lifestyle	103	241	259	259	138
	344		259	397
Q52: Remote work makes the team more accepting of employees’ different lifestyles. variable: acceptance of different lifestyles	25	72	314	415	174
	97		314	589
Q55: I have experienced unequal treatment of some employees because of their lifestyle. variable: unequal treatment because of lifestyle	155	234	273	415	174
	331		273	338

). Over one-fourth of the respondents had difficulty providing a straightforward answer. Therefore, we started by analyzing which explanatory variables differentiated the difficulty level in providing straightforward answers to the statements and checking whether these differences would prove important for our research. With this objective, dependent variables were recoded to values 0–1, where 0 was assigned to one of the answers (definitely not and rather not, definitely yes and rather yes), and 1 when the provided answer was “difficult to say”.

For the interference with the lifestyle question (Q51) analysis of answers “difficult to say” revealed that statistically relevant differences could be detected only for the size of the organizations employing our respondents (p = 0.00). The choice of the answer “difficult to say” (whether remote work leads to a higher degree of manager’s interference with employees’ lifestyle) was statistically more frequent in participants who reported working in smaller organizations. Participants who, at a statistically relevant level, had more difficulty in determining whether remote work contributed to greater acceptance of different lifestyles (Q52) were those who reported a lower degree of teamwork and a lower degree of formality in employees’ behaviors within the team (p = 0.00 in both cases). Difficulties in specifying whether the respondent encountered unequal treatment due to lifestyle (Q55) were more frequent in people employed in organizations of less than 50 staff (p = 0.03). The answer “difficult to say” was excluded from further analysis, and initial values for dependent variables were recoded as values 0–1, where 0 was assigned to a negative (answers definitely not and rather not) and 1 to a positive (answers definitely yes and rather yes) answer.

In the next step, logistic regression was applied in a multivariate analysis of individual (employee-side) and organizational determinants. The outcome variables were interference (Q51), acceptance (Q52), and unequal treatment (Q55), respectively, with 1 representing positive answers (yes or definitely yes) and 0 representing negative answers (no or definitely not). The “difficult to say” answers were excluded from the analysis. The explanatory variables in each model were as follows:

• Individual characteristics of the participant: sex, age, seniority, position held, level of education, position in the company (manager/no-manager), time of remote work (all variables were treated as qualitative and transformed into zero-one);
• Characteristics of the team in which the participant is employed: characteristics of work (individual/teamwork) and degree of formalization in employees’ behaviors (both variables were treated as quasi-quantitative, with values ranging from 1 to 5, the higher the value of the variable, the more collaborative/formalized the teamwork is), and the size of the team (variable recoded into 0–1);
• Organizational characteristics: number of employees.

A factor was found to be statistically significant if, using the Wald test, p < 0.10. The OR allows an assessment of how a factor affects the outcome variables (OR > 1 means that it increases it, while 0 < OR < 1 means that it decreases it, ceteris paribus). Models were estimated using the stepwise method, i.e., variables not significantly related to the estimated probability were eliminated from the model. The statistical quality of the model is confirmed by the results of the omnibus test (p < alpha is expected). The pseudo-determination ratio, i.e., the Nagelkerke ratio, can take values in the range [0; 1], with higher values meaning the better the adopted set of variables explains the relations.

4. Results

4.1. Participants

The present study included 1000 participants who declared having worked remotely in the two years preceding this research (

Table 2. Respondents’ remote work time in the last two years.

Remote Work Time in the Previous Two Years	Percentage %
100% remote work	14.2
75–99% remote work	17.1
50–74% remote work	21.4
49–25% remote work	23.0
Below 25% remote work	24.3

).

Women represented 65.5% of the totality of participants. Respondents varied in age, seniority, and education (

Table 3. Respondents’ features.

Age:	Percentage %	Seniority:	Percentage %	Education:	Percentage %
Less than 30	30.8	Less than 1 year	2.2	Primary	0.3
31–40	38.2	1–5 years	25.1	Lower secondary	0.7
41–50	20.4	6–10 years	26.0	Basic	4.8
51–60	7.7	11–15 years	17.0	Secondary	34.3
60 and above	2.9	16–20 years	12.4	Higher	59.9
		21 years and above	17.3

). The biggest groups in terms of the mentioned variables were participants aged 31–40 years (38.2%), having already worked for 6 to 10 years (26%), and having higher education (59.9%).

Respondents’ professional situation was also varied (

Table 4. Workplace features.

Team Size	Percentage %	Organization Size (Number of Employees)	Percentage %
Less than 5 people	26.8	Less than 9 employees	17.3
6–10 people	28.2	10–49 employees	23.7
11–20 people	15.5	50–249 employees	31.1
21–30 people	11.4	250 employees and above	27.9
31 people and above	18.1

). The majority (73.5%) were non-managers. Over half of the participants (55%) worked in teams that did not exceed 10 people. The biggest group worked for organizations employing 50–249 people.

Explanatory variables also included declared characteristics of work performed by the team of the respondent (assessed in a scale from 1 to 5, where 1 meant that the work was performed individually by each team member and 5 that it was teamwork) and the degree of formality in the behaviors within the respondent’s team (assessed in a scale from 1 to 5, where 1 meant that there was no regulation whatsoever and employees enjoyed total freedom of behavior while 5 meant that behaviors were formalized and specific conventions were applied with rules related to, for example, clothing, way of communication, etc.).

4.2. Interference in Lifestyle in the Remote Work

Over half of the respondents (54% after exclusion of responses “difficult to say”) confirmed that remote work leads to a bigger interference from the manager with their lifestyle (Q51). Statistically relevant factors increasing the percentage of participants agreeing with this opinion were education (p = 0.01), size of the organization (p = 0.01), characteristics of work (p = 0.00) and the degree of formalization (p = 0.00). We identified statistically relevant differences in the answers between managers and non-managers. Managers more often (61.7%) than workers (50.7%) pointed a higher interference of the manager in their lifestyle.

Therefore, to identify differentiating factors for the variable’s interference with lifestyle, answers provided by workers and managers were analyzed separately (

Table 5. Variables differentiating the perceived level of manager’s interference with lifestyle.

Respondents’ Features	Workplace Features
Managers
No statistically significant variables	No statistically significant variables
Non-managers
sex (p = 0.02)	organization size (p = 0.00)
education (p = 0.00)	characteristic of work (p = 0.00)
	formalization of behavior (p = 0.00)

).

For managers, there was no explanatory variable that would differentiate answers to this question in a statistically relevant manner.

For non-managers, variables related to employees’ parameters that were statistically relevant for the differences in provided answers included the following: sex (p = 0.02) and education (p = 0.00). Men (57.6%) in the roles of workers considered that remote work leads to an increased interference with their lifestyle significantly more often than women (47.4%). In addition, the higher the education, the less frequently respondents claimed that remote work was related to a high interference of the manager with their lifestyle (vocational education—73.3%; secondary education—57.8%; and higher education—43.6%). Factors related to the place of employment that were relevantly meaningful for answers related to the interference with lifestyle included the following: the size of the organization (p = 0.00; the smaller the organization, the more often the respondents reported an increase in manager’s interference with their lifestyle in the remote model of work; less frequently, such an answer was provided by employees of organizations employing more than 250 people—37.9%); characteristics of work (p = 0.00; the higher the degree of teamwork, the more frequently the respondents confirmed that remote work was related to an increase in manager’s interference with their lifestyle); and level of formality of behaviors within the team (p = 0.00; the higher the degree of formality of behaviors within the team, the higher the percentage of employees reporting that, in the context of remote work, interference with their lifestyle increased).

In the model describing the probability of positive answers in question Q51, the ceteris paribus list of factors that are significant over other potential determinants of managers’ interference with subordinates’ lifestyles in remote working includes such organizational and team characteristics as the size of the organization (p = 0.001), the number of people in the team (p = 0.041), the characteristic of teamwork (p < 0.001), and the degree of formalization (p < 0.001). Among individual factors, the level of education (p = 0.069) and age (p = 0.068) play a significant role at a slightly higher level of significance (

Table 6. Determinants of the outcome variables—logistic regression results.

	Model 1 (y = Q51)		Model 2 (y = Q52)		Model 3 (y = Q55)
Specification	OR	p	OR	p	OR	p
Team size a		0.041 **		0.003 ***		0.001 ***
6–10 people	1.213	0.390	0.902	0.737	1.210	0.392
11–20 people	1.370	0.238	2.233	0.041	1.849	0.017
21–30 people	1.356	0.290	2.682	0.041	1.467	0.182
31 people and above	2.238	0.002	2.640	0.013	2.858	0.000
Organization size b		0.001 ***		0.010 **		0.025 **
10–49 employees	0.921	0.769	0.620	0.272	0.606	0.061
50–249 employees	0.819	0.455	0.496	0.091	0.648	0.086
250 employees and above	0.433	0.002	0.282	0.003	0.456	0.003
Characteristics of work (individual/teamwork) 1	1.449	<0.001 ***			1.235	0.004 ***
Degree of formalization in employees’ behaviors 1	1.303	<0.001 ***			1.254	0.002 ***
Education c		0.069 *		0.042 **
lower secondary	1.983	0.063	0.527	0.136
secondary	1.348	0.086	1.579	0.093
Age d		0.068 *		0.010 **		0.005 ***
31–40	0.714	0.078	0.505	0.026	0.894	0.549
41–50	0.556	0.010	0.331	0.001	0.479	0.001
51 and above	0.795	0.424	0.568	0.203	0.597	0.074
Sex e			1.573	0.053 *
Constant	0.205	<0.001	11.155	<0.001	0.320	0.001
Omnibus test of model coefficients	χ2 (14) = 95.9; p < 0.001 ***		χ2 (13) = 515.0; p < 0.001 ***		χ2 (12) = 64.0; p < 0.001 ***
Nagelkerke R2	0.162		0.112		0.113
Classification quality for y = 1	73.0		65.7		51.5
Count R2	65.5		65.2		62.0
N	741		686		727

Reference groups: ^a less than 5 people, ^b less than 9 employees, ^c higher, ^d less than 30, ^e men, ¹ quasi-continuous variable (values from 1 to 5, where w is the lowest, 5 is the highest). OR—odds ratio, p—probability in the Wald test/omnibus test of model coefficient, *** p < 0.01, ** p < 0.05, * p < 0.10.

, Model 1). The larger the organization, the lower the probability of interference with the employee’s lifestyle—compared to micro-enterprises in large organizations, this probability is significant, lower by more than half than in micro entities. Conversely, the larger the team, the higher this probability is—compared to the smallest teams, in teams of more than 30 people, this probability is significantly more than double. We also note that the less individual work (the stronger the team nature of the work), and the more formalized the employee behavior is, the higher the probability of interference with the employee’s lifestyle. Compared to those with higher education, employees with an elementary education are nearly twice as likely to be interfered with, and those with secondary education are about 1.4 times more likely to be interfered with. Older workers have a significantly lower probability compared to workers aged up to 30 years, with the most significant differences for those aged 41–50. In contrast, a non-significant relation to other factors is observed in sex, seniority, and position in the organization.

Although position (manager/non-manager) is not a factor that significantly determines the probability of lifestyle interference, as the analyses using the chi-square independence test showed, the relations are different for managers and non-managers. This is also confirmed by the results of multivariate analysis using logistic regression (

Table 7. Determinants of the outcome variables for non-managers and managers.

	Model 4 (y = Q51)				Model 5 (y = Q52)				Model 6 (y = Q55)
	Non-Managers		Managers		Non-Managers		Managers		Non-Managers		Managers
Specification	OR	p	OR	p	OR	p	OR	p	OR	p	OR	p
Team size a				0.052 *		0.077 *				0.002 ***
6–10 people			2.246	0.097	0.872	0.709			1.151	0.594
11–20 people			1.898	0.234	2.110	0.114			2.112	0.016
21–30 people			2.152	0.220	2.085	0.172			1.390	0.326
31 people and above			6.071	0.003	2.173	0.091			2.902	0.000
Organization size b		0.029 **		0.045 **		0.033 **				0.024 **
10–49 employees	0.891	0.705	1.762	0.322	0.738	0.561			0.530	0.047
50–249 employees	0.881	0.659	0.981	0.971	0.590	0.295			0.623	0.123
250 employees and above	0.495	0.018	0.428	0.138	0.307	0.018			0.399	0.003
Characteristics of work (individual/teamwork) 1	1.494	<0.001 ***							1.317	0.002 ***
Degree of formalization in Employees’ behaviors 1	1.293	0.003 ***	1.459	0.013 **					1.336	0.001 ***
Education c		0.033 **				0.083 *
Lower secondary	2.297	0.042			0.593	0.273
Secondary	1.496	0.040			1.675	0.106
Age d				0.055 *		0.009 ***						0.097 *
31–40			0.310	0.009	0.392	0.009					0.465	0.055
41–50			0.338	0.024	0.280	0.001					0.344	0.014
51 and above			0.364	0.071	0.721	0.596					0.515	0.191
Seniority e										0.023 **
6–10 years									1.513	0.094
11–15 years									1.258	0.413
16–20 years									0.720	0.313
21 years and above									0.579	0.090
Sex f					1.778	0.043 **					1.674	0.084 *
Constant	0.205	<0.001	0.577	0.430	10.850	<0.001	6.074	<0.001	0.170	<0.001	1.633	0.190
Omnibus test of model coefficients	χ2 (8) = 70.2; p < 0.001 ***		χ2 (11) = 26.4; p < 0.001 ***		χ2 (13) = 37.5; p < 0.001 ***		χ2 (4) = 5.1; p = 0.274		χ2 (13) = 65.4; p < 0.001 ***		χ2 (4) = 10.8; p < 0.028 **
Nagelkerke R2	0.160		0.128		0.131		x		0.155		0.072
Classification quality for y = 1	65.8		86.6		80.5		x		50.4		63.5
Count R2	66.2		69.4		75.8		x		63.8		62.2
N	548		193		495		191		531		196

Reference groups: ^a less than 5 people, ^b less than 9 employees, ^c higher, ^d less than 30, ^e less than 6, ^f men ¹ quasi-continuous variable (values from 1 to 5, where w is the lowest, 5 is the highest). OR—odds ratio, p—probability in the Wald test/omnibus test of model coefficient, *** p < 0.01, ** p < 0.05, * p < 0.10.

, Model 4). For non-managers, the list of factors that are significant over other potential determinants of higher lifestyle interference in remote working includes such organizational and team characteristics as the size of the organization (p = 0.029), the nature of teamwork (p < 0.001), and the degree of formalization of employee behavior (p = 0.003). Among individual factors, the level of education is significant (p = 0.033). For managers, the factors are slightly different. Apart from the size of the organization (p = 0.045) and the degree of formalization of behavior (p = 0.013), the size of the team is significant (p = 0.052), but the nature of the work is not a determinant of the occurrence of lifestyle interference for managers.

4.3. Acceptance of Different Lifestyles in Remote Work

Most participants (85.9% after excluding “difficult to say” responses) considered that remote work contributed to a greater acceptance of the different lifestyles of the employees (Q52). Statistically relevant factors increasing the percentage of participants agreeing with the opinion that in a situation of remote work, acceptance for different lifestyles increased include the following: age up to 30 years (91.6%; p = 0.01), secondary education (90.7%; p = 0.04) and the size of the team being more than 10 people (90%; p = 0.04).

In multivariate logistic regression analysis, workplace characteristics significantly increasing the probability of accepting different employee lifestyles in a team were as follows: organization size (p = 0.010) and number of people in the team (p = 0.003). Among individual factors, education level (p = 0.042), age (p = 0.010) and sex (p = 0.053) were found to be significant in this area (Table 6, Model 2). On average, the larger the organization, the lower the probability of employees’ acceptance of different lifestyles—this probability is significant, lower by more than half in medium and large enterprises than in micro ones. In turn, the larger the team, the higher this probability is—compared to the smallest teams, it is already significantly more than twice as high in teams of more than 10 people. Compared to those with higher education, employees with an elementary education have half the probability, while those with a secondary education have about 1.6 times the probability. In contrast, older people have a significantly lower probability than workers up to 30, with the most significant differences for those aged 41–50. Women are more than 1.5 times more likely than men to indicate that working remotely increases the acceptance of different employee lifestyles within the team. The other factors analyzed related to workplace characteristics were found to be insignificant.

As in the case of interference (Q51), although the position is not a significant determinant of acceptance of different lifestyles (Q52), the analyzed relationship is nevertheless different for managers than non-managers. This is confirmed by the results of multivariate analysis using logistic regression (Table 7, Model 5)—in the case of managers, none of the factors analyzed are statistically significant.

4.4. Unequal Treatment Because of Lifestyle in the Remote Work

Despite the dominating opinion that the context of remote work contributed to an increase in acceptance for employees’ diverse lifestyles, almost half of the participants (46.5% after excluding answers “difficult to say”) confirmed having encountered unequal treatment of some employees on account of their lifestyle (Q55). Interestingly, the problem was pointed out more frequently by the managers (53.1%) than by the non-managers (44.1%). Factors meaningful for the variability of answers in these two groups also differed (

Table 8. Variables differentiating the perceived level of unequal treatment because of lifestyle.

Respondents’ Features	Workplace Features
Managers
sex (p = 0.02)	No statistically significant variables
Non-Managers
age (p = 0.01)	team size (p = 0.00)
	the character of work (p = 0.00)
	formalization of behaviors (p = 0.00)

).

The only statistically relevant factor for the managers was sex (p = 0.04). Female managers (59.5%) reported having encountered unequal treatment of employees because of their lifestyle more often than male managers (44.7%).

In the group of employees, sex was not statistically important in terms of the differentiation of answers. Factors correlated with an increased percentage of subjects confirming encountered cases of unequal treatment included the following: age—the younger the employees, the more frequently they reported having encountered such treatment; number of people in the team—participants working in small teams of less than 10 people declared having encountered unequal treatment less often; characteristics of work—participants reporting higher degrees of teamwork encountered unequal treatment more frequently. Additionally, the higher the level of formality of behaviors within the team, the higher the percentage of respondents declaring having encountered unequal treatment.

In the model describing the probability of positive answers in question Q55 ceteris paribus, the list of significant factors compared to the other potential determinants of unequal treatment because of employee lifestyle includes the following (Table 6, Model 3):

(1)

The size of the organization (p = 0.025)—the larger the organization, on average, the lower the probability of unequal treatment because of employee lifestyle; compared to micro companies, statistically significant differences in this respect are found in large companies (p = 0.003), where averaged out, the probability is more than half that of micro companies;

(2)

The size of the team (p = 0.001), compared to the smallest teams, up to five people, significant differences are observed for the largest teams, of more than 30 people (p = 0.000)—it is almost three times higher; nevertheless, in general, the larger the team, the higher this probability is (odds ratios are greater than 1 and generally increase with the size of the team);

(3)

Characteristic of teamwork (p = 0.004)—the less individual work (the stronger the team nature of the work), the higher the probability of lifestyle inequality on average;

(4)

Degree of formalization of employee behavior (p = 0.002)—the more formalized the employee behavior, the higher the probability of unequal treatment because of employee lifestyle on average;

(5)

Age (p = 0.068)—compared to employees under 30, older people have a significantly lower probability of unequal treatment because of employee lifestyle.

In contrast, non-significance across the other factors is observed for gender, seniority, education, and position held. Therefore, the probability of unequal treatment because of employee lifestyle is, ceteris paribus, similar for women and men, managers and non-managers, and employees with different education and seniority.

As for the previous two variables (Q51 and Q52), also in the case of unequal treatment (Q55), position is not a significant factor; nevertheless, the relationship is different for managers than for non-managers. This is confirmed by the results of multivariate analysis using logistic regression (Table 7, Model 6)—in the case of managers, age (p = 0.097) and gender of respondents (p = 0.084) are statistically significant, and in the case of non-managers, the size of the organization (p = 0.024), the number of people in the team (p = 0.002), the nature of the work (p = 0.002), the degree of formalization of employee behavior (p = 0.001) and seniority (p = 0.023) are statistically significant.

5. Discussion and Conclusions

Literature shows that lifestyle is a broad and equivocal category, difficult to interpret, and therefore a rather “complicated” object of analysis [23,24,27,28,29,30,31]. This can explain the high percentage of answers “difficult to say”. The proposed understanding of lifestyle in our research does not reflect the complexity and multidimensionality of the concept and the whole range of today’s interpretations of the lifestyle category. In the conclusion, we included that future research could ask about employees’ attitudes towards positive interventions. In the future, the surveys can be repeated on a representative sample, and more in-depth analyses can be carried out to give their conclusions greater scientific and practical value. It is worth asking respondents about the areas of interference (lifestyle components), the manifestations of interference, the potential benefits of superiors’ influence on lifestyles for the organization and, for the employees themselves, in what specific situations is interference in the lifestyles of subordinates acceptable, and why.

Our current study revealed, however, that remote work contributed to a bigger sense of acceptance for lifestyle than in the traditional model of work in the office (58.9% and 85.9%, if we exclude answers “difficult to say”).

During remote work, it is more difficult to recognize features such as race, sex, age, physical appearance, clothing, and others; behaviors resulting from the adopted lifestyle are also more difficult to identify. Thus, the virtual environment becomes free from stigmatization [57].

To determine whether remote work leads to a greater acceptance of various lifestyles caused more difficulty to respondents who reported a lower degree of teamwork and a lower degree of formality of employees’ behaviors within the team (in both cases p = 0.00). It can be explained by the fact that, in the case of employees working independently, dependency on the work of others and the need for coordination of many people’s work are not that important, neither in stationary work mode nor in remote work. The lower degree of formality of behaviors within the team, both in remote and in-office work, allows for some freedom of behavior, which makes it difficult to assess managers’ attitudes toward employees’ lifestyles.

Almost 40% of the totality of respondents, and over half of the subjects (54%), if we exclude answers “difficult to say”, confirmed that remote work resulted in a more significant interference of the manager with their lifestyle than it was the case with traditional work in the office. The study revealed that variables related to the place of employment relevantly differentiated subjects’ answers on interference with lifestyle. These variables included the following: the size of the organization—the smaller the organization, the more often the respondents reported an increase in manager’s interference with their lifestyle in the remote model of work; teamwork—the higher the degree of cooperation, the more frequently the respondents confirmed that remote model of work was related to an increase in manager’s interference with their lifestyle; and the level of formality of behaviors—the higher the degree of formality of behaviors within the team, the higher the percentage of employees reporting that in the context of remote work interference with their lifestyle increased. It was confirmed that the work experience of teams that are diverse in terms of different criteria of their members may depend on factors related to the place of employment, for example, the complexity and dependency of the task, as well team size.

The research suggests that the more collaborative the work, the more often respondents indicate that remote working increases the manager’s interference in their lifestyle. As suggested by the research of others, the level of task interdependence (the extent to which task performance requires team member interaction) determines team processes. In tasks requiring low interdependence, team members tend to act more independently, thus reducing the need for coordination and cooperation between members. A greater team size generates more intra-team relationships, complicating these relationships and making the coordination process more difficult [56].

Despite the dominating opinion that remote work contributes to the acceptance of employees’ diverse lifestyles, almost half of the subjects (46.5% if we exclude answers “difficult to say”) confirmed having encountered unequal treatment of some employees on account of their lifestyle; in addition, respondents who reported a higher degree of teamwork encountered unequal treatment more often.

The relatively low value of the Nagelkerke coefficient (0.162) indicates that, in addition to the analyzed factors characterizing the organization and the employee within the organization, more significant interference by the manager in the employee’s lifestyle when working remotely is also determined by other factors. Such a factor could be, for example, management style. Among the problems associated with remote working in the pandemic, management style was indicated, which, according to 33%, was either negative or very negative [10].

Nevertheless, the relatively high percentage of correct classifications for y = 1 indicates that the factors already indicated make it possible to accurately predict the occurrence of lifestyle interference in remote working situations.

On the one hand, intervening in employees’ private, non-work affairs is objectionable; on the other hand, in certain circumstances, employers may have legitimate grounds for interfering in what employees do outside the workplace, mainly when they act against the company’s interests [35]. Managers’ influence on lifestyle elements in some cases may be justified by concerns for employee safety, work requirements (e.g., related to appearance), concern for desired work outcomes and for the company’s image, and the need to counteract rule breaking, as evidenced by the observations of other authors presented at the beginning of this article [32,33,34,35,38,39].

The awareness that influencing lifestyles can be repressive and dysfunctional should accompany managers if they want to shape positive relationships with subordinates to make decisions that will not be judged as unjustified interference in employees’ private affairs. This will happen if managers create the right conditions for employees: setting new norms, being transparent in decision making, and allowing employees to participate in decision-making processes [7].

Suppose decisions have to be made on the basis of lifestyle. In that case, it is essential to examine the needs of employees and the appropriate behavior of managers towards their subordinates, how expectations are formulated, and how feedback is given. The attitude of managers towards their employees is also important, resulting, for example, in an understanding of employees’ problems and a willingness to show support.

However, since remote working is set to become more widespread, it is worth analyzing the first experiences in this area to create conditions for employees and employers to collaborate satisfactorily, achieve benefits and realize goals. Psychological safety is important [36]. It is therefore necessary to eliminate the causes of discrimination.

6. Limitation and Future Research

Our study has some limitations. First, the participants were not representative, so the results cannot be applied to the entire population of people working remotely. In addition, the majority of the sample consisted of executive employees. On the other hand employee experience surveys provide a closer-to-reality reflection of employers’ actual actions [54]. The CAWI technique made it impossible to control who answered the questions in the questionnaires. Moreover, understanding lifestyle in the context of research does not reflect the complexity of the concept and all its contemporary interpretations. A limitation is the small number of surveys dedicated to the problem addressed in the article, which prevented a more expansive interpretation and discussion of our results. In the future, the surveys can be repeated on a representative sample, and more in-depth analyses can be carried out to give their conclusions greater scientific and practical value. It is worth asking respondents about the areas of interference (lifestyle components), the manifestations of interference, the potential benefits of superiors’ influence on lifestyles for the organization and for the employees themselves, in what specific situations interference in the lifestyles of subordinates is acceptable, and why. Using qualitative methods can help understand the lifestyles of individual groups and individuals.

Beech S. et al. [58] point out that in order to understand the lifestyle of the respective groups of people and individuals, it might be helpful to implement qualitative methods, such as open interviews and diary techniques. For these reasons, in the future, it would be advisable to conduct qualitative research with the use of the mentioned techniques.

The issues we are interested in are rarely discussed in the literature, but they impact the process of managing people. Researchers need to analyze unusual factors, e.g., combining cognitive abilities, personality, and effectiveness [59]. It is worth exploring various characteristics of individuals to understand the whole range of challenges in the workplace and shape people’s behavior that is desirable from the organization’s point of view [60].