The impact of FOMO and Addiction on Individual Performance and Productivity

i

The impact of FOMO and Addiction on Individual Performance and Productivity

Sara Alves Ribeiro Pereira

Master thesis presented as partial requirement for obtaining

the Master’s degree in Information Management, with a

specialization in Information Systems and Technologies

Management.

i NOVA Information Management School

Instituto Superior de Estatística e Gestão de Informação Universidade Nova de Lisboa

THE IMPACT OF FOMO AND ADDICTION ON INDIVIDUAL PERFORMANCE AND PRODUCTIVITY

by

Sara Alves Ribeiro Pereira

Master thesis presented as partial requirement for obtaining the Master’s degree in Information Management/ Master’s degree in Information Management , with a specialization in Information Systems and Technologies Management.

Supervisor: Prof. Doutora Manuela Aparício

November 2022

ii

ACKNOWLEGDGEMENTS

I would like to start by thanking in particular professor Manuela Aparício for all the dedication, commitment and support to me and my master thesis. I would also like to show my gratitude for all the persons that voluntarily participated in my questionnaire and that make possible to proceed with this research. Finally, I would also like to thank my husband, parents, and friends for all the understanding and strength provided.

iii

ABSTRACT

The Digital Era has come to change the quotidian of organizations. The Information Systems area has developed optimized tools for collecting, processing, and dealing with data with the aim of increase companies’ value. The geographical barriers were eliminated and nowadays, every company is connected by the Internet. In a short term every consequence of the emergence of technologies has been seen as beneficial. Nevertheless, in a long term analysis, employees are starting to reveal levels of anxiety and stress related to the addiction developed by Information Systems Technologies. The numbers of reported cases with those symptoms are increasing and it is crucial to analyze and estimate the real consequences. Leadership is one of the core competencies in a management board, and the challenge is to understand how leaders and top managers should work together to thwart the actual tendencies with the aim of achieving commitment, motivation, happiness, and well-being of every collaborator. According to this, a theoretical model has been proposed and empirically tested. The results have been analyzed and the relationship between the diverse factors have been exposed.

KEYWORDS

Information Systems Technology; Decision Making; Technostress; FOMO; Always-On.

iv

INDEX

1. Introduction ... 1

1.1. Background Identification ... 1

1.2. Study Objectives ... 3

2. Theoretical Background ... 4

2.1. Information Systems Technologies ... 4

2.2. Technology Addiction ... 4

2.3. Technology Addiction Impact on Performance and Productivity ... 7

3. Research Model Proposal and Methodology ... 10

3.1. Theoretical Background ... 10

3.2. Operationalization ... 11

3.3. Methodology ... 13

4. Results and discussion ... 14

4.1. Sample description ... 14

4.2. Measurement model result ... 16

4.3. Structural model results ... 21

5. Discussion ... 24

6. Conclusions ... 26

6.1. Theoretical Implications ... 26

6.2. Practical Implications ... 26

6.3. Limitations and Future Work ... 28

7. References ... 29

v

LIST OF FIGURES

Figure I - Technology overload and the law of diminishing marginal returns ... 7

Figure II - Proposed Model ... 11

Figure III - Structural model results ... 22

vi

LIST OF TABLES

Table I - Constructs ... 9

Table II - Hypothesis Proposal ... 10

Table III - Measurement Model ... 12

Table IV - Sample Description ... 14

Table V - Construct Reliability and Validity ... 17

Table VI - Cross Loading ... 18

Table VII - Discriminant Validity ... 19

Table VIII - HTMT ... 19

Table IX - Inner VIF ... 20

Table X - Hypothesis Results ... 21

Table XI - Bootstrapping results vs hypotheses ... 23

vii

LIST OF ABREVIATIONS AND ACRONYMS

A Addiction

AVE Average Variance Extracted FOMO Fear of Missing Out

IS Information Systems

P Productivity

PI Past Increase

PLS-SEM Partial Least Squares Structural Equation Modelling

TCH Technostress

VIF Variance inflation factor WFC Work-Family Conflict WLB Work-Life Balance

WP Work Performance

1

1. INTRODUCTION

Information Systems Technologies can be defined as technologies that help managers and workers to analyze problems and create new solutions adding value to the company directly or indirectly by collecting, processing and distributing information to support decision making and control the organization processes (Laudon & Laudon, 2019).

Nowadays Information Systems Technologies has effective impact in humans’ routines. Everyday new tools and technologies appear in an incredible velocity and companies try to adapt them with the purpose of increasing the business value. The face to face meetings were substituted by video conferences and the mailrooms to email services. This change enabled companies to provide flexibility to their workers allowing them to work in cafés, at home or even on pool while employees are on vacations. The Internet revolution associated with the increase development of new Information Systems Technologies brings a lot of benefits for companies. Communication channels conduct to optimize reducing costs, reducing environmental damage, and increasing the productivity while searching for data (Joyce, Fisher, Guszcza, & Hogan, 2018).

Nevertheless, there are several negative impacts associated to the implementation of these type of digital technologies that have not been taken into consideration by most companies. While the organizations take benefits in the increase of employees’ productivity, in a short term, the balance between work and life demonstrates dark consequences related with addictions in long term. The value derived from the always-on employees is decreasing by the levels of productivity, performance, and well-being (Joyce, Fisher, Guszcza, & Hogan, 2018).

The aim of these study is to understand, based on a set of primary data collection and quantitative analysis, the impact of information systems technology on employees productivity and performance.

1.1. B

I

Working long and with a lot of stress has been regarded for a lot of companies in the last years. Always- on employees and the addiction to work have been seen as an emblematic status in the organizations.

The evolution of digital technologies has introduced employees to a different workspace than the routine desk table in the open space but, it also has eliminated the routine breaks that were taken at the office. A recent study of American Psychological Association demonstrates that 53% of Americans work in weekends, 52% work after the pre-defined labor hours and 54% work when are sick (American Psychological Association, 2013). This study demonstrates that almost 54% of the American population work outside the assigned and expected hours.

The evolution of Information Systems was expected to contribute to freedom and is contributing to irrational behavioral addictions. According to Adam (Alter, 2017), nowadays the behavioral addiction expresses one of the following ingredients: the definition of attainable goals; recognition based on constant feedback; feeling of contributing to the constant progress; tasks become more slowly over time; not solved problems related to tensions and demand; and solid social contacts (Alter, 2017).

2 Currently, employee's daily life is marked by the reception of messages/emails and notifications. Part of them is relevant, however, another part is ultimately contributing to the employee's distraction from the tasks at hand and waste time reading information that does not contribute anything to the task. This eventually means that employees must work at a higher speed to bridge the interruptions resulting in greater individual stress for the employee, frustration, and time pressure. In this way, the contribution to the organization decreases, the performance reduces, and the appropriate and timely assertive decision-making also decreases. Access to conference meeting applications is presented by companies to reduce costs, time, and geographic locations (Parker, 2018). However, the ease with which employees are invited for meetings can eventually become a disadvantage. Since, the ease of adding a person without any additional costs, can result in an agenda always full of meetings where much is said, little is retained, and little is decided. This eventually contributes to employees with less time for their daily tasks that end up producing with a lower quality.

The excessive use of the information systems mentioned above enhances the development of unhealthy employees with associated mental and physical diseases. One of the most notorious health consequences is poor sleep. The always-on culture contributes to the decrease in sleep quality because employees tend to work longer hours beyond their working hours, reducing the hours of sleep to complete the tasks that were not done in the working hours (Centers for Disease Control and Prevention, 2016).

Physical disconnection is another consequence of the excessive utilization of Information Systems Technology. Employees are always available on their phones and computers and there is no need to see and meet new cultures. The family is leaved away and just met in the middle of the constant connection to the network information systems technology (Turkle, 2011).

Anxiety and Depression are two other consequences of the use of information overload. The continual exposition to a high amount of information is developing mental health diseases in employees. Time is limited and information in most of the companies is not filtered and limited which results in a phenomenon called Fear of Missing Out – FOMO. This phenomenon is very common when an employee needs to prioritize the meetings that will attend and is very difficult to discover which information is important and consequently, which meeting should attain (Heid, 2018).

The study of addiction to Information Systems Technologies consequences and the way those affect the employee’s productivity and performance is what is going to be discovered by this investigation.

Taking into consideration the actual working mode, including the pandemic inherent changes in work behavior, this questionnaire will take place and ask a population to participate in. The aim of the thesis is to measure the impact of technostress, Past Increase in Network, FOMO and technology addiction on the performance and productivity of workers. The key is to understand how a leader can take advantage with Information Systems Technologies without compromising the well-being of each employee.

3

1.2. S

O

The aim of these study is to answer to the following research question: What is the impact of FOMO, Past Increase in Network, Technostress and Addiction on individual performance and productivity?

Taking into consideration the previous research question, the following objectives were defined:

RO1: Identify the antecedents of Technology Addiction;

RO2: Understand and estimate the real impact of Information Systems Technologies in employee’s performance and productivity;

RO3: Propose a model that explains the impact of Technology addiction on individual performance and productivity;

R04: Empirically test the model.

4

2. THEORETICAL BACKGROUND

2.1. I

S

T

As referred in the introduction of the present dissertation, Information Systems Technologies aim is to help managers and workers to analyze problems and create new solutions adding value to the company directly or indirectly by collecting, processing and disturbing information to support decision making and control the organization processes (Laudon & Laudon, 2019). Nowadays, there is nothing to replace accurate information at the right time. Over the last decades, several information systems technologies were developed with the objective of providing the organization with access to accurate information, available for reading and easily accessible to all employees.

However, it is important to realize that information systems technologies are a means and not an end to the process. As has been scientifically proven, when properly applied and used, information systems technologies actively contribute to companies achieving their goals. When they are not well used, the benefits inherent to their use decrease for companies and employees, meaning spending time, money and improving dramatic mental health problems (Joyce, Fisher, Guszcza, & Hogan, 2018). Thus, it is urgent to identify how employees feel about the daily exposure to information systems technologies and how they contribute positively and negatively for daily tasks and life. Mental health is one of the major problems in companies nowadays and based on these, this study pretends to verify the main consequences and outline how the new technology Systems should be used and managed so that such consequences do not occur (Joyce, Fisher, Guszcza, & Hogan, 2018).

2.2. T

A

Technology addiction illustrates the intensity of psychological dependence a user can have on a technology device, such as ICTs (Turel, Serenko, & Bontis, 2011). According to many authors, addiction maybe be related to a lot of different variables such as Technostress, FOMO, Work-Life Balance, Work- Family Conflict, Past Increase in Network, Productivity, and Performance.

The term Technostress was defined by Brod in 1984 as a “modern disease of adaptation caused by an inability to cope with new computer world technologies in as unhealthy manner” (Brod, 1984). With the development of different studies, (Clark & Kalin, 1996) specified that "technostress is not a disease, and is a negative psychological, behavioral and psychological impact caused, either directly or indirectly, by technology". More recently, (Ragu-Nathan, Tarafdar, Ragu-Nathan, & Tu, 2008) defined technostress as a "problem of adaptation that individual experiences, when he or she is unable to cope with new technology". In this study, (Ragu-Nathan, Tarafdar, Ragu-Nathan, & Tu, 2008) proposed a

5 multi-dimensional scale composed by five components that indicates the origin of technostress (the techno-stressors): “techno-overload, techno-invasion, techno-complexity, techno-insecurity, and techno-uncertainty”. Meanwhile authors have found contradictory views regarding technostress impacts. (Schlachter, McDowall, Cropley, & Inceoglu, 2018) discovered that when workers use actively and intentionally ICTs, it empowers the possibility of performing job tasks remotely increasing employee’s Performance, Work-Life Balance and also job satisfaction. In 2019 Technostress has been proposed as “a double-edged sword” (Qi, 2019) meaning that it accomplishes negative and positive effects on individuals and impacting organizations. Also, in 2019 (Tarafdar, Cooper, & Stich, 2019) proposed a new framework that incorporates both positive and negative effects and the IS design and its impacts. Recently in 2021 (Upadhyaya & Vrinda, 2021) proposed to study the impact of technostress on the productivity of academic students. The study took place in a population of Indian private university students with ages between 18 and 28 years old. With this study, it was discovered that in academic environment, students experience moderate levels of technostress, nevertheless it also demonstrated that technostress negatively impacts their academic productivity. It was also concluded that companies should provide to employees ICT training with the objective of reducing Technostress since there is a constant pressure to upgrade all technical skills due to the constant appearance of new IS Technologies (Upadhyaya & Vrinda, 2021). According to (Samaha & Hawi, 2016), there is a correlation between Technostress, addiction and performance. This way, the following hypothesis presumes that:

Hypothesis 1: Technostress contributes to employees’ addiction in ICTs.

In 2014 Brough defined Work-Life Balance as an “individual’s subjective appraisal of the accord between his/her work and non-work activities and life more generally”. Brough discovered that Work- Life Balance changes according to job and resources demands (Brough, et al., 2014). In 2021, the impacts of Techno-stressors in Work-Life Balance have been studied (Ma, Ollier-Malaterre, & Lu, 2021). According to the major literature, the studies on technostress has been mostly focused on the work domain. Even though, in 2021 the technostress impact in Work-Life Balance has been studied with the objective of understanding the real impacts on employee’s life and health (Ma, Ollier- Malaterre, & Lu, 2021). According to the authors, the fast development of Information Systems Technologies is making even more difficult to define the barrier between work and life resulting in emotional exhaustion of the employees (Ma, Ollier-Malaterre, & Lu, 2021). Therefore, the next hypothesis presumes that:

Hypothesis 2: Technostress impacts Work-Life Balance.

6 Otherwise, taking into account Samaha and (Hawi & Samaha, 2016) study - technostress increases the employees’ addiction in ICTs - and (Ma, Ollier-Malaterre, & Lu, 2021) - technostress contributes to employees Work-Life Balance - it is clear that there is also a relationship between addiction and Work- Life Balance. According to this, the following hypothesis is identified:

Hypothesis 3: Addiction negatively impacts Work-Life Balance.

When talking about Work-Life Balance, another concept come to the table, the concept of Work- Family Conflict that was defined in 1985 as “a form of interrole conflict in which the role pressures from the work and family domains are mutually incompatible is some respect” (Greenhaus & Beutell, 1985).

In 2020, Carlson research found some of the consequences of this variables: “psychological distress, job satisfaction, organization commitment, turnover, and life satisfaction” (Carlson, Kacmar, &

Williams, 2000). Earlier, in 2011 “Work-Family Conflict” was defined as “an interrole conflict in which pressures from work and family are irreconcilable” (Turel, Serenko, & Bontis, 2011). This research suggested that this variable is fully increased by work overload, and this decreases the employee addiction for organizational commitment. It also refers that the older employees tend to have a more difficult balance between work and family life (Turel, Serenko, & Bontis, 2011). More recently, in 2019 Venkatesh considered in his study, three forms of “Work-Family Conflict” - “time-based conflict”;

“stain-based conflict” and “behavioral-based conflict”. “Time-based conflict” was defined as the difficult of managing the time used in one task that may lead to not have time to participate in another one. The concept of “stain-based conflict” suggested that it happens when the pressure lived in one task affects the other task. The concept of “behavioral-based conflict” may occur when there are some pre-defined behavioral requirements in one task that contradicts the ones that are required in another task (Venkatesh, Ann Sykes, Chan, Thong, & Hu, 2019). Otherwise, it is important the refer the linear relationship that Turel referred in his research in 2011. In this study it was referred that there is a direct relationship between the addiction to “organizational mobile emails” will result in consequences related to work and family environments (for example: work overload; organizational commitment;

and work-to-family conflict) (Turel, Serenko, & Bontis, 2011). According to this, it is possible to identify the following hypothesis:

Hypothesis 4: Addiction contributes to Work-Family conflict.

In 2015, Turel studied the addiction as a “Vicious cycle”. This concept was defined as “a pattern of dynamic influences of system use on addiction and vice versa, that makes it hard for users to quit” ( Turel, 2015). On this study the author explored how past increase in systems usage contributes to addiction behaviors. Turel concluded that the past increase in IS is an important factor that influences the technology-addiction symptoms. Nevertheless, according to this study variance, the past increase

7 is not enough to develop the addiction, it is also necessary to take into consideration other background influences such as “personality, mental states, family, demographic and social factors” ( Turel, 2015).

Therefore, the following hypothesis can be identified:

Hypothesis 5: Past Increase in Network contributes to Addiction.

Therefore, to understand users’ interaction with ICTs it is also important to recognize the concept of FOMO – Fear of missing out (Perrone, 2017). The first defined scale of fear of missing out was designed in 2013 and concluded that FOMO negatively impacts life satisfaction and positively contributes to addiction to social media (Przybylski, Murayama, DeHaan, & Gladwell, 2013). More recently in 2014 a different study investigated the impacts of this variable on human beings and concluded that in one hand the ICTs, internet connection and mobile availability can cause in the user a positive feeling of connection and a way to search and find anytime the answers needed. Nevertheless, in other hand it might increase the dependency, resulting in a pathology similar to addiction (Chayko M. , 2014) (Ahn

& Shin, 2013). According to this, another hypothesis is considered for the present research:

Hypothesis 6: FOMO contributes to Addiction.

2.3. T

A

I

P

P

In 1980s the “Productivity Paradox” has been studied and the MIS researchers didn’t find a linear relationship between the investment on IT and the increase in productivity (Dendrick, Gurbaxani, &

Kraemer, 2003) .

According to “the law of diminishing marginal returns”, it is possible to explain why the gains of adding technology are limited. This way, this law explains that there is an optimum where there is possible to maximum increase the productivity using the technology. Nevertheless, after attaining this point, the productivity starts to decrease and the demands on human resources start to increase (Pamela & Ying, 2010).

Figure I - Technology overload and the law of diminishing marginal returns

8 In 1999 Productivity has been defined as the measure in which an application improves employee’s output/production by unit time (Torkzadeh & Doll, 1999). In 2007, Productivity concept in association with technology notion has defined a new concept of productivity as “increased work efficiency and output during work hours through mobile technologies as perceived by staff members” (Tarafdar, Tu, Ragu-Nathan, & Ragu-Nathan, 2007). This study revealed that exist 5 technostress creators – “Techno- Overload”; “Techno-Invasion”; “Techno-Complexity”; “Techno-Insecurity”; “Techno-Uncertainty” - that negatively impact productivity at workplace. Later in 2011, (Hung, Chang, & Lin, 2011) studied the relation between productivity and the use of mobile phones and discovered that the use of mobile phones in the workplace environment negatively impacts the productivity of employees. In 2017, a study proposed to understand if there is a relationship between the use of smartphones and its associated applications (including ICTs) with workers productivity. On this study, the authors concluded that there is a moderate relationship between the variables which means that the addiction to smartphone use negatively impacts the work productivity and the participation of the worker in their daily life duties (Duke & Montag, 2017). Therefore, it is possible to identify the following hypothesis:

Hypothesis 7: Addiction negatively impacts Productivity.

In 2014 a new important theory was presented and studied, the JD-R theory– “Job Demands-Resources theory” (Bakker & Demerouti, 2014). This theory supports that working conditions can be divided into different categories - ”job demands” and “job resources” – that can affect employees working tasks and consequently, the company outcomes. This means that these two different categories could have positive or negative impacts on employee’s job performance (Bakker & Demerouti, 2018). Otherwise, in 2021 a new model studied the job demands association with ICTs anxiety and the relationship between job resources and addiction (Prodanova & Kocarev, 2021). According to JD-R principles, in 2021 it was possible to understand the job strain consequences on job performance and employee’s motivation (Prodanova & Kocarev, 2021). Otherwise, this study concluded that ICTs addiction can impact efficacy on job performance, nevertheless this research also refers that the studied population defends that besides the interruptions caused by working from home, ICTs problems and addiction issues it is possible to actively continue to be motivated and adequately perform job tasks without job operation (Prodanova & Kocarev, 2021). In 2020, a study with the purpose of understanding the impact of social media on Work Performance concluded that when communication overload occurs (example:

instant messages) the worker is bound to interrupt the current task and read the new information which leads to lose the focus and impact in the performance (Babu, VR, & Subramoniam, 2020).

According to the presented studies, it is necessary to considerer the following hypothesis:

9 Hypothesis 8: Addiction impacts Performance.

Summarizing all the constructs definitions, it is possible to produce the following table I.

Table I - Constructs

Construct Construct Definition Reference

Technostress Technostress is defined by stress pathology inherent to the difficulty that a person has to adapt to the use of new technologies.

(Brod, 1984) (Ragu-Nathan, Tarafdar, Ragu-Nathan, & Tu, 2008) (Upadhyaya

& Vrinda, 2021) (Samaha & Hawi, 2016)

Past Increase in Network Past Increase in Network can be defined as a model of impacts of system usage on addiction that make not easy for users to leave.

( Turel, 2015)

FOMO FOMO means “Fear of Missing Out”

and it is defined as the movement performed by users that tend to be always online to not lose any information regarding what is happening.

(Perrone, 2017)

Work-Life Balance Work-Life Balance is the level of prioritization that a person attributes to professional and personal life. The ratio of this prioritization is named by Work-Life Balance.

(Ma, Ollier-Malaterre, & Lu, 2021)

Work-Family Conflict Work-Family conflict is considered to be an existent conflict between the work and family duties. It is verified when there is not possible to attend the demands from work and family together.

(Venkatesh, Ann Sykes, Chan, Thong,

& Hu, 2019) (Turel, Serenko, & Bontis, 2011)

Addiction Addiction is defined as one of the consequences of the excessive use of information systems technologies.

This is considered to be a pathology.

(Ghislieri, et al., 2022)

Productivity Productivity is considered a measure calculated taking into consideration the number of units produced by the number of labour hours.

(Tarafdar, Tu, Ragu-Nathan, & Ragu- Nathan, 2007) (Karr-Wisniewski & Lu, 2010)

Performance Performance is considered an

evaluation of how employees execute their job due to normal roles, tasks, and responsibilities (this evaluation is based on criterions).

(Cohen, 1980) (Karr-Wisniewski & Lu, 2010) (Babu, Vr, & Subramoniam, 2020)

10

3. RESEARCH MODEL PROPOSAL AND METHODOLOGY

3.1. T

B

According to the references identified on the previous chapter, the hypothesis were justified and identified as it is possible to confirm in table II.

Table II - Hypothesis Proposal

Independent Variable Dependent Variable Hypothesis Reference

Technostress Addiction H1: Technostress

contributes to

employees’ addiction in ICTs.

(Samaha & Hawi, 2016)

Technostress Work-Life Balance H2: Technostress

impacts Work-Life Balance.

(Ma, Ollier-Malaterre, & Lu, 2021)

Addiction Work-Life Balance H3: Addiction

negatively impacts Work-Life Balance.

(Ma, Ollier-Malaterre, & Lu, 2021)

Addiction Work-Family Conflict H4: Addiction

contributes to Work- Family Conflict.

(Turel, Serenko, & Bontis, 2011)

Past Increase in Network Addiction H5: Past Increase in Network contributes to Addiction.

( Turel, 2015)

FOMO Addiction H6: FOMO contributes

to Addiction.

(Chayko M. , 2014) (Ahn & Shin, 2013)

Addiction Productivity H7: Addiction

negatively impacts Productivity.

(Duke & Montag, 2017)

Addiction Performance H8: Addiction impacts

Performance.

(Prodanova & Kocarev, 2021)

Therefore, based on the previous justified hypothesis, the next research model is proposed:

11 Figure II - Proposed Model

3.2. O

This study intends to understand the impact that information systems addiction have on employee’s productivity and performance which is a topic that so far has not met the full consensus. After completing the literature review it was possible to identify the hypothesis and its correspondent constructs. The scales were carefully chosen and adapted. The pre-test questionnaire was performed with a sample of 12 Portuguese workers to guarantee that the questions were understandable and applicable for all the respondents. After receiving some feedback, it was necessary to proceed with some adjustments, and the final questionnaire was distributed to empirically test the model and its hypothesis relationships. This questionnaire was distributed in a lot of different platforms such as LinkedIn where a small introduction about ICTs was given.

12 Table III - Measurement Model

Construct Code Items Reference

WFC

WFC1 “My work keeps me from my family activities more than I would like.”

(Venkatesh, Ann Sykes, Chan, Thong, & Hu, 2019) WFC2 “The time I must devote to my job keeps me from participating equally in

household responsibilities and activities.”

WFC3 “I have to miss family activities due to the amount of time I must spend on work responsibilities.”

TCH

TCH1 “ICT helps to improve the quality of my work.”

(Upadhyaya & Vrinda, 2021) TCH2 “ICT helps to improve my productivity.”

TCH3 “ICT helps me to accomplish more work than would otherwise be possible.”

TCH4 “ICT helps me to perform my work better.”

WP

WP1 “I learned managerial techniques effectively from my colleagues in ICT.”

(Babu, VR, & Subramoniam, 2020) WP2 “I always perform better than an acceptable level.”

WP3 “I often perform better than what can be expected from me.”

WP4 “I often put in extra effort in my work.”

WP5 “I intentionally expend a great deal of effort in carrying out my job.”

WP6 “I try to work as hard as possible.”

WP7 “The quality of my work is top-notch.”

PI

P1 “This technology helps to improve the quality of my work.”

(Upadhyaya & Vrinda, 2021) P2 “This technology helps to improve my work productivity.”

P3 “This technology helps me to accomplish more work than would otherwise be possible.”

P4 “This technology helps me to perform my work better.”

WLB

WLB1 “I currently have a good balance between the time I spend at work and the time I have available for non-work activities.”

(Ma, Ollier-Malaterre, & Lu, 2021) – fo

WLB2 “I have difficulty balancing my work and non-work activities.”

WLB3 “I feel that the balance between my work and non-work activities is currently about right.”

WLB4 “Overall, I believe that my work and non-work life are balanced.”

A

A1 “I sometimes neglect important things because of my interest in ICT.”

( Turel, 2015) A2 “My social life has sometimes suffered because of me interacting with ICT.”

A3 “Using ICT sometimes interfered with other activities.”

A4 “When I am not using ICT I often feel agitated.”

A5 “I have made unsuccessful attempts to reduce the time I interact with ICT.”

A6 “I am sometimes late for engagements because I interact with ICT.”

A7 “Arguments have sometimes arisen because of the time I spend on this ICT.”

A8 “I think that I am addicted to ICTs.”

A9 “I often fail to get enough rest because I interact with ICTs.”

PI

PI1 “Increase in times per day I use ICTs.”

( Turel, 2015) PI2 “Increase in the duration of use of each session in ICTs.”

PI3 “Increase in the time per day I spend on ICTs.”

F

F1 “When I go on vacation, I continue to keep tabs on what my team is doing.”

(Perrone, 2017) F2 “I get anxious when I don’t know what my team is up to.”

F3 “Sometimes, I wonder if I spend too much time keeping up with what is going on.“

F4 “It bothers me when I miss an opportunity to meet up with the team.“

13

3.3. M

The Partial Least Squares Structural Equation Modelling (PLS-SEM) was applied to test the research hypothesis of this study as long as this methodology is the one that best suits the proposed model characteristics. To conceptualize and measure the constructs, the following studies were conferred and the scales were adapted and slightly modified: (Venkatesh, Ann Sykes, Chan, Thong, & Hu, 2019) – for Work-Family Conflict, (Upadhyaya & Vrinda, 2021) – for Technostress and Productivity, (Babu, VR, & Subramoniam, Impact of Social Media on Work Performance at a Technopark in India, 2020) – for Performance, (Ma, Ollier-Malaterre, & Lu, 2021) – for Work-Life Balance, ( Turel, 2015) – for Addiction and Past Increase and (Perrone, 2017) – for FOMO. Every item of the questionnaire has been measured using seven-point Likert scale from 1 (Strongly Disagree) to 7 (Strongly Agree).

Demographic metrics were also used to characterize the sample by gender, age, country, level of education and job sector. The following table collects all the measurement items used on the present study.

14

4. RESULTS AND DISCUSSION

4.1. S

The participants of the study were workers that volunteered to participate in this research without any obligation to do it. Three hundred and seven volunteers have shown interest in the topic nevertheless just one hundred and forty seven completed all the responses.

The socio-demographic analysis of the participants show that women’s were the most responsive with 59% of the responses followed by men’s (41%) and one of the participants prefer not to say it (1%).

The greatest representation of responses is from Portuguese workers (94%) however there are responses from other parts of the world such as United Kingdom (2%), Brazil (1%), France (1%), Georgia (1%), Indonesia (1%) and Spain (1%). Regarding the age of the sample, the most common age is between 25 years and 27 years (37%). In terms of highest level of education, 41% of the volunteers have a master’s degree, 33% have a bachelor’s degree, 14% are postgraduates, 8% have the high school and 3% have a Ph.D. The volunteers that were considered to the study are currently working in the following areas: Information Technology (20%); Transports and Logistics (14%); Engineering and manufacturing (8%), Business (7%), Healthcare (7%), Law (8%), Marketing (6%), Hospitality and events management (3%), Teaching and training education (3%), Recruitment and HR (2%), Accountancy (1%), Creative (1%), Pharmaceuticals (1%) and others (17%).

Table IV - Sample Description

Gender

Women 59%

Men 41%

Prefer Not to Say 1%

Country

Portugal 94%

United Kingdom 2%

Brazil 1%

France 1%

Georgia 1%

Indonesia 1%

15

Spain 1%

Most common Age 25 - 27 years 37%

Level of Education

Master’s degree 41%

Bachelor’s degree 33%

Postgraduates 14%

High School 8%

Ph.D 3%

Areas of Working

Information Technology 20%

Transports and Logistics 14%

Engineering and manufacturing 8%

Business 7%

Healthcare 7%

Law 8%

Marketing 6%

Hospitality and events management 3%

Teaching and training education 3%

Recruitment and HR 2%

Accountancy 1%

Creative 1%

Pharmaceuticals 1%

others 17%

16

4.2. M

Considering PLS-SEM results, the first step is evaluating the measurement model by analyzing the relationships between constructs and associated indicators. This means that it is necessary to start by studying the reflective measurement model and the formative measurement model. The reflective measurement model is evaluated based on three fundamentals´ criteria: construct reliability, convergent validity, and discriminant validity (Hair, Hult, Ringle, & Sarstedt, 2017).

To verify construct’s reliability, it is necessary to analyze Cronbach’s alpha and Composite Reliability criteria. Cronbach’s alpha is an internal consistency reliability conservative measure that supposes equal indicator loadings and that estimates the reliability built on the intercorrelations of the examined variables (Hair, Hult, Ringle, & Sarstedt, 2017). The Composite Reliability criteria is an internal consistency reliability measure that assumes that the indicator may have different loadings. According to the previous literature, for both criteria’s the values should be amongst 0.6 and 0.7 or above (Hair, Howard, & Nitzl, 2020). On the below table V, it is possible to verify that all constructs have Cronbach’s alpha and composite reliability values above 0.7.

Convergent Validity is the limit in which a measure can positively correlates with other measures of the same construct. This fundamental criterion is measured by Average Variance Extracted (AVE). This AVE is achieved by the indicator’s reliabilities average (Hair, Howard, & Nitzl, 2020). The reference value that is considered on this criterion is above 0.5 and, as it is possible to confirm on table V, that all the constructs have an AVE value higher than 0.5. which means that they are convergent validated (Hair, Hult, Ringle, & Sarstedt, 2017).

On the table V it is possible to consult Cronbach’s Alpha, rho_A, Composite Reliability and AVE for all measurement items.

17 Table V - Construct Reliability and Validity

Construct Cronbach's Alpha rho_A Composite Reliability Average Variance Extracted (AVE)

Addiction 0.928 0.93 0.94 0.633

FOMO 0.827 0.829 0.886 0.66

PastIncrease_in_Network_ 0.959 0.96 0.974 0.925

Productivity 0.942 1.057 0.953 0.835

Technostress 0.912 0.927 0.939 0.793

WorkFamilyConflict 0.825 0.851 0.894 0.738

WorkLifeBalance 0.891 0.924 0.931 0.819

WorkPerformance 1 1 1 1

Discriminant Validity criteria analysis the limit to which a construct is distinct from another. This means that this criterion allows to understand how much the constructs correlates to each other and what are the indicators that just represent one of the constructs. To validate these criteria, it is necessary to first analyze the cross-loadings and after apply the Fornell-Larcker criterion (Hamid, Sami, & Sidek, 2017).

The Cross Loadings analysis is the indicator´s correlation between constructs which means that it is used to validate if the indicators outer loadings on the associated constructs are bigger than every cross loadings on further constructs. The outer loadings define if an item contributes to its assigned construct (Hamid, Sami, & Sidek, 2017). According to this previous literature review, it was necessary to disregard some of the items which value was lower than 0.7: WP2, WP3, WP4, WP5, WP6 and WP7.

In table VI it is possible to confirm all the cross loading values.

18 Table VI - Cross Loading

A F PI P TCH WFC WLB WP

A1 0.757 0.415 0.574 0.061 0.028 0.278 -0.145 0.273 A2 0.825 0.512 0.611 -0.146 -0.149 0.33 -0.242 0.214 A3 0.799 0.489 0.584 -0.09 -0.026 0.272 -0.105 0.185 A4 0.787 0.554 0.603 -0.164 -0.067 0.21 -0.042 0.405 A5 0.803 0.433 0.509 -0.16 -0.076 0.206 -0.185 0.113 A6 0.821 0.481 0.505 -0.25 -0.171 0.264 -0.135 0.225 A7 0.785 0.436 0.461 -0.171 -0.185 0.21 -0.15 0.141 A8 0.814 0.454 0.533 -0.182 -0.125 0.216 -0.139 0.077 A9 0.77 0.494 0.604 -0.091 -0.01 0.206 -0.091 0.222 F1 0.452 0.787 0.414 -0.118 -0.011 0.275 -0.104 0.142 F2 0.503 0.884 0.455 -0.113 -0.098 0.268 -0.237 0.22 F3 0.481 0.799 0.487 -0.106 -0.065 0.346 -0.318 0.194 F4 0.507 0.775 0.464 -0.048 0.03 0.308 -0.063 0.238 P1 -0.082 -0.052 0.134 0.91 0.616 -0.047 0.201 0.323 P2 -0.169 -0.113 0.026 0.939 0.573 -0.051 0.223 0.243 P3 -0.021 -0.003 0.124 0.857 0.551 -0.041 0.214 0.228 P4 -0.191 -0.143 0.039 0.946 0.611 -0.121 0.267 0.306 PI1 0.653 0.541 0.945 0.089 0.07 0.326 -0.092 0.338 PI2 0.684 0.542 0.969 0.035 0.047 0.34 -0.106 0.368 PI3 0.682 0.537 0.97 0.047 0.071 0.327 -0.15 0.341 TCH1 -0.051 0.009 0.057 0.465 0.804 -0.03 0.225 0.308 TCH2 -0.145 -0.083 0.011 0.621 0.94 0.039 0.28 0.34 TCH3 -0.038 -0.011 0.128 0.55 0.881 0.006 0.286 0.276 TCH4 -0.139 -0.058 0.044 0.618 0.93 0.026 0.277 0.323 WFC1 0.209 0.279 0.282 -0.02 0.133 0.83 -0.403 0.098 WFC2 0.261 0.297 0.296 -0.081 -0.001 0.861 -0.322 0.051 WFC3 0.309 0.362 0.308 -0.1 -0.056 0.886 -0.487 0.016 WLB1 -0.264 -0.242 -0.193 0.252 0.28 -0.485 0.906 0.157 WLB3 -0.101 -0.19 -0.06 0.21 0.293 -0.358 0.897 0.129 WLB4 -0.057 -0.152 -0.044 0.221 0.234 -0.434 0.911 0.163

WP1 0.266 0.246 0.363 0.303 0.35 0.058 0.165 1

The Fornell-Larcker is a measure that compares the square root of each construct’s AVE values with the correlations value of the other constructs of the model. This means that the square root value of each construct AVE should be greater than the best correlation with the other constructs (Fornell &

Larcker. 1981). On table VII it is possible to verify the Discriminant Validity.

19 Table VII - Discriminant Validity

Addictio

n FOMO

Past Increase

in Networ

k

Producti vity

Technostres s

Work Family Conflict

Work Life Balance

Work Performanc

e

Addiction 0.796

FOMO 0.599 0.812

Past Increase in

Network

0.7 0.561 0.962

Productivity -0.167 -0.117 0.059 0.914

Technostres

s -0.109 -0.044 0.065 0.638 0.891

Work Family

Conflict 0.309 0.369 0.344 -0.084 0.015 0.859

Work Life

Balance -0.172 -0.222 -0.121 0.254 0.301 -0.474 0.905

Work Performanc

e

0.266 0.246 0.363 0.303 0.35 0.058 0.165 1

Fornell-Larcker criterion is one of the most applied to analyze the discriminant validity, nevertheless there is another correlation method that could be also used – Heterotrait-Monotrait (HTMT) (Hamid, Sami, & Mohmad Sidek, 2017). Table VIII shows that all the values are below 0.9 which means that there are no multicollinearity problems. These values demonstrate that the volunteers did not get too confused when answering to the questionnaire.

Table VIII - HTMT

Addiction FOMO

Past Increase

in Network

Productivity Technostress

Work Family Conflict

Work Life Balance

Work Performance

Addiction

FOMO 0.679

Past Increase in

Network

0.738 0.63

Productivity 0.169 0.108 0.093

Technostress 0.141 0.081 0.072 0.683

20

Addiction FOMO

Past Increase

in Network

Productivity Technostress

Work Family Conflict

Work Life Balance

Work Performance

Work Family

Conflict 0.342 0.44 0.386 0.088 0.094

Work Life

Balance 0.179 0.252 0.118 0.265 0.327 0.542

Work

Performance 0.269 0.269 0.371 0.307 0.367 0.071 0.175

Variance Inflation Factor (VIF) is a tolerance indicator that should be taken into consideration in the formative measurement model and that allows to check the collinearity of the indicators. When the inner VIF values are lower than 5 there are no multicollinearity problems (Hair J. , Hult, Ringle, &

Sarstedt, 2014). In table IX it is possible to confirm that there are no multicollinearity issues on this data.

Table IX - Inner VIF

Addiction FOMO

Past Increase

in Network

Productivity Technostress

Work Family Conflict

Work Life Balance

Work Performance

Addiction 1 1 1.012 1

FOMO 1.474

Past Increase

in Network 1.477

Productivity

Technostress 1.014 1.012

Work Family

Conflict

Work Life

Balance

Work

Performance

21

4.3. S

Measurement model proved that there is reliability and validity. Thus, it is feasible to explore the structural model. The Structural model (also known as Inner model on PLS-SEM) is a component of PLS model which includes all the relationship existent between constructs (Hair J. , Hult, Ringle, & Sarstedt, 2014). PLS-SEM presumes that data are not normally distributed, it depends on the Bootstramp procedure to generate a larger number of subsamples (5000 bootstrap samples). These subsamples are created based on the original sample (Hair J. , Hult, Ringle, & Sarstedt, 2014). According to this, the study of the relationship between hypothesis and constructs was performed by bootstrapping. Once operating bootstrap, it is possible to analyze the T Statistics and the P values which can establish the significance of the hypothesis. To consider that a hypothesis is significant, the T Statistics values should be over 1.96 and the P Values should be lower than 0.05 (Hair J. , Hult, Ringle, & Sarstedt, 2014). On table X it is possible to consult the bootstrapping results, demonstrating the non-significant hypothesis - Addiction negatively impacts Productivity; and Addiction negatively impacts Work-Life Balance – and confirming that the following hypothesis has also a very low level of significance - Technostress contributes to employees’ addiction in ICTs.

Table X - Hypothesis Results

Original Sample

(O)

Sample Mean (M)

Standard Deviation (STDEV)

T Statistics

(|O/STDEV|) P Values Significance Addiction ->

Productivity -0.167 -0.175 0.122 1.371 0.171 NS

Addiction -> Work

Family Conflict 0.309 0.317 0.079 3.926 0 ***

Addiction -> Work Life

Balance -0.141 -0.141 0.096 1.462 0.144 NS

Addiction -> Work

Performance 0.266 0.266 0.076 3.51 0 ***

FOMO -> Addiction 0.286 0.293 0.065 4.431 0 ***

Past Increase in

Network -> Addiction 0.548 0.544 0.068 8.096 0 ***

Technostress ->

Addiction -0.132 -0.131 0.057 2.338 0.019 **

Technostress -> Work

Life Balance 0.286 0.293 0.071 3.996 0 ***

To evaluate the Structural Model it is necessary to take into consideration the Coefficient of Determination (R² value). This coefficient denotes the variance on endogenous latent variables

22 supported by the exogenous constructs related to it. According to some literature, R² value varies between 0 and 1 in which the closer values to one mean that there are elevated levels of predictive accuracy (Hair J. , Hult, Ringle, & Sarstedt, 2014). As it is shown on the next path model, this model has the following R² values: 0.095. 0.110. 0.569. 0.071 and 0.028. The higher R² is justified because of the number of paths pointing Addiction construct (Hair J. , Hult, Ringle, & Sarstedt, 2014). According to PLS-SEM model, R² equal to 0.75 is considered substantial, 0.50 moderate and 0.25 weak ( Hair, Ringle,

& Sarstedt, 2011). So, following this logic, Addiction R² can be considered moderate.

Considering the hypothesis . T statistics and P values, it is possible to determine that: H1 Technostress has a negative impact on addiction in ICTs; H2 Technostress impacts Work-Life Balance; H4 Addiction contributes to Work-Family conflict; H5 Past Increase in Network contributes to Addiction; H6 FOMO contributes to Addiction; H8 Addiction has an impact on Performance. Nevertheless, hypothesis H3 Addiction negatively impacts Work-Life Balance is not supported which means that addiction doesn’t impact negatively work-life balance and H7 Addiction negatively impacts Productivity is not supported either meaning that productivity is not negatively impacted by addiction. Analyzing the path coefficients, it is clear to say that the strongest connection of the model is between Past Increase in Network and Addiction followed by the connection between Addiction and Work-Family conflict.

Figure III - Structural model results

23 Table XI - Bootstrapping results vs hypotheses

T Statistics

(|O/STDEV|) P Values Path

Coefficient Hypothesis Result

Technostress -> Addiction 2.338 0.019 -0.132 H1 Supported **

Technostress -> Work Life

Balance 3.996 0 0.286 H2 Supported

Addiction -> Work-Life

Balance 1.462 0.144 -0.141 H3 Not Supported

Addiction -> Work-Family

Conflict 3.926 0 0.309 H4 Supported

Past Increase in Network ->

Addiction 8.096 0 0.548 H5 Supported

FOMO -> Addiction 4.431 0 0.286 H6 Supported

Addiction -> Productivity 1.371 0.171 -0.167 H7 Not Supported

Addiction -> Work

Performance 3.51 0 0.266 H8 Supported