Examining the Relationship Between Stress Levels and Cybersecurity Practices Among Hospital Employees in Three Countries: Ghana, Norway, and Indonesia

 Muhammad Ali Fauzi Department of Information Security and Communication Technology Norwegian University of Science and Technology (NTNU) Gjøvik, Norway muhammad.a.fauzi@ntnu.no

Prosper Yeng Department of Information Security and Communication Technology Norwegian University of Science and Technology (NTNU) Gjøvik, Norway prosper.yeng@ntnu.no

Bian Yang Department of Information Security and Communication Technology Norwegian University of Science and Technology (NTNU) Gjøvik, Norway bian.yang@ntnu.no

Dita Rachmayani Department of Psychology Universitas Brawijaya Malang, Indoneisa dh33ta@ub.ac.id

Peter Nimbe Department of Computer Science and Informatics University of Energy and Natural Resources Sunyani, Ghana peter.nimbe@uenr.edu.gh

Abstract

This study aims to investigate the relationship between stress levels among hospital staff and their risky cybersecurity practices. A web-based survey was conducted with a sample of 353 hospital staff from Ghana, Norway, and Indonesia. The results indicate a statistically significant positive correlation between the stress levels of hospital staff and their engagement in unsafe cybersecurity practices (r = 0.201, p < 0.01). Specifically, the study finds that staff members’ inclination to click on links from unknown sources is the cybersecurity practice most strongly influenced by stress levels. The study did not observe any significant differences in cybersecurity practices based on gender, age, job, position level, or work experience. However, it does highlight notable differences in cybersecurity practices across countries, with Norwegian hospital staff exhibiting better cybersecurity practices than their counterparts from Ghana and Indonesia. Index Terms—Stress, Cybersecurity Practice, Demographic, Healthcare, Hospital, Norway, Ghana, Indonesia, Correlation.

Introduction

Electronic health records (EHRs), telemedicine, and remote patient monitoring systems have all been adopted in recent years, undergoing a considerable digital transition in the healthcare sector. Even though these technological developments have improved patient outcomes and the quality of treatment, they have also presented new cybersecurity threats to hospitals and their workers. Due to the enormous volumes of sensitive patient data that are electronically kept and exchanged, the healthcare sector is especially susceptible to cyber-attacks [1]. Data breaches, financial losses, reputational harm, and, most crucially, jeopardized patient care are all possible outcomes of these attacks [2]

It is generally known that human factors are one of the major causes of cybersecurity breaches. Human error may compromise even the most sophisticated technological security measures [3], [4]. For instance, according to a recent Verizon research, humans were involved in 82% of all data leaks [1]. Therefore, many prior studies focused on understanding how the human factor can affect cybersecurity practices and identify the factors that affect cybersecurity practices [5]–[9]. Stress is one of the human factors that can affect cybersecurity practices. Stress can lead to lapses in judgment, increased impulsivity, and a reduced ability to make rational decisions [10]–[12]. In the context of cybersecurity, stress may lead to unsafe cybersecurity practices, such as clicking on suspicious links or responding to phishing emails. Hence, it is essential to understand the impact of stress on cybersecurity practices. However, only a few studies focused on this topic, especially in the healthcare setting. Moreover, most of the studies were conducted in developed countries. McCormac et al. [13] analyzed the effect of job stress on information security awareness among company workers in Australia while Fordyce et al. [14] investigated the effect of stress on password choice among students in United Kindom. There is no study on this topic conducted in developing countries. This study aims to investigate the relationship between hospital staff stress levels and cybersecurity practices in Norway and two developing countries, Ghana and Indonesia. This study follows the hypothesis that hospital workers with higher stress levels engage in riskier security practices. Additionally, we will also compare the cybersecurity practices between these three countries and examine the relationship between demographic variables and cybersecurity practices. By examining this relationship, this study can contribute to the existing literature on cybersecurity in the healthcare industry and provide insights for hospitals to improve their cybersecurity practices.

Materials and methods

Research approach

This study’s primary objective was to examine the relationship between stress levels and cybersecurity practices among hospital staff. The research approach is outlined in Figure 1. To achieve this objective, an online survey developed using Nettskjema was utilized to collect data on healthcare staff’s demographic details, stress levels, and cybersecurity practices within the past month. Nettskjema is an online survey platform that places a high priority on data privacy and security run by the University of Oslo. The survey was composed in English for participants from Ghana, Norwegian for participants from Norway, and Indonesian for participants from Indonesia. The participants’ stress levels and risky cybersecurity practices were assessed using the Perceived Stress Scale (PSS) and Hospital Staff’s Risky Cybersecurity Practices Scale (HSRCPS), respectively. Additionally, the questionnaire included an attention-checking question to guarantee the response’s quality. Hospital employees from three hospitals in Ghana, a hospital in Norway, and a hospital from Indonesia were invited to participate in the study. All participants provided their written consent electronically, and the surveys’ completion and analysis were completely anonymous.

Perceived Stress Scale (PSS)

The Perceived Stress Scale (PSS) is a self-report survey used to measure individuals’ perceptions of stress. It uses a 5-point Likert scale to assess the frequency of thoughts and feelings connected to stress during the previous month. PSS assesses the subjective experience of stress rather than specific stressors. PSS has three variations: PSS-14, PSS-10, and PSS4, with PSS-10 having superior psychometric properties than the others [15]. PSS has been translated and validated in many languages, indicating its cross-cultural applicability. This study will use the Norwegian language version of PSS. In this study, we used the original English version of PSS-10 for participants from Ghana. Meanwhile, the Indonesian version by Pin [16] was used for Indonesian participants and the Norwegian version translated by CheckWare AS [17] was employed for Norwegian participants.

Hospital Staff ’s Risky Cybersecurity Practices Scale (HSRCPS)

The Hospital Staff’s Risky Cybersecurity Practices Scale (HS-RCPS) was developed to evaluate hospital staff’s cybersecurity practices based on the Human Aspects of Information Security Questionnaire (HAIS-Q) and the Security Behavior Intentions Scale (SeBIS). The scale was tailored specifically to measure the cybersecurity practices of healthcare workers based on feedback from interviews with 36 healthcare employees and cybersecurity professionals from various universities and hospitals in Ghana, Indonesia, and Norway. The scale consists of 12 items with a possible total score ranging from 0 to 48. Higher scores indicate riskier cybersecurity practices over the past month. Participants were asked to rate their engagement in specific cybersecurity practices using a scale of 0 to 4 (”disagree” to ”agree”). This scale is available in English, Norwegian, and Indonesian versions.

TABLE I: Items for the HS-RCPS

1. In the last month, I usually write my username and password on a piece of paper and stick the paper onto my computer for easy access
2. In the last month, I sometimes visit at least one of the following websites using the hospital’s computer: social media; Dropbox and other public file storage systems; online music or videos sites; online newspapers and magazines; personal e-mail accounts; games; instant messaging services, etc
3. In the last month, I did not often read the alert messages/emails concerning security
4. In the last month, I sometimes click on a link in an email from an unknown sender
5. In the last month, I usually postpone software updating activities (restarting, clicking to run an update, accepting to update, or following the update schedule) of my computers at my workplace
6. In the last month, I usually postpone backup activities when I am prompted
7. In the last month, I usually do not prevent my colleagues from seeing patients’ records for a non-therapeutic purpose when I am working on patient information on my laptop
8. In the last month, I did not post patient information on social media
9. In the last month, I sometimes share my passwords with my colleagues in the hospital
10. In the last month, I usually do not take any action when I notice my colleague ignoring information security rules
11. In the last month, I used a combination of letters, numbers, and symbols in my work passwords
12. In the last month, I have changed my passwords

Data Analysis 

The present study employed SPSS software to analyze the collected data. The reliability of the PSS and HS-RCPS was measured using Cronbach’s alpha. Furthermore, Pearson’s correlation coefficient was utilized to evaluate the relationship between the PSS and HS-RCPS scales. The mean differences among various demographic groups, such as age, position, position level, and work experience, were assessed using ANOVA. A t-test was utilized to evaluate the mean difference in HS-RCPS scores between males and females. Additionally, a Kruskal-Wallis test with Bonferroni-Dunn posthoc analysis was conducted to examine the variance in HS-RCPS scores among staff groups based on their country

Results

General characteristics of participants

In total, 389 hospital employees participated but 36 of them failed to answer the attention-checking question correctly. As seen in Table II, 353 qualified participants were finally included in the study, with 212 participants from Ghana, 42 from Norway, and 99 from Indonesia. Based on gender, 209 (59.20%) of them are females and 143 of them are males (40.50%). One participant (0.30%) preferred not to disclose their gender. The age range of the participants varied, with 117 (33.10%) falling in the 21-31 years category, 168 (47.60%) falling in the 31-40 years category, 47 (13.30%) falling in the 41-50 years category, and 21 (5.90%) falling in the over 50 years category. Regarding participants’ positions, 15 (4.20%) were in top-level management, 34 (9.60%) were doctors, 180 (51.00%) were nurses, 22 (6.20%) were lab staff, 28 (7.90%) were pharmacy staff, 14 (4.00%) were IT staff, 3 (0.80%) were researchers, 3 (0.80%) were nutritionists, and 54 (15.30%) reported other positions. The participants’ position level was categorized as executives (1.40%), managers and supervisors (16.70%), and operational staff (81.90%). Regarding work experience, 131 (37.10%) participants had less than six years of experience, 166 (47.00%) had 6-15 years of experience, 46 (13.00%) had 16-25 years of experience, and 10 (2.80%) had more than 25 years of experience.

TABLE II: Participant Characteristics

Variable Category n %

• Country:
• Ghana 212 60.10 %
• Norway 42 11.90 %
• Indonesia 99 28.00 %
• Gender
• Female 209 59.20 %
• Male 143 40.50 %
• Prefer not to say 1 0.30 %
• Age
• 21-31 117 33.10 %
• 31-40 168 47.60 %
• 41-50 47 13.30 %
• Over 50 21 5.90 %
• Position
• Top Level Management 15 4.20 %
• Doctor 34 9.60 %
• Nurse 180 51.00 %
• Lab staff 22 6.20 %
• Pharmacy staff 28 7.90 %
• IT staff 14 4.00 %
• Researcher 3 0.80 %
• Nutritionist 3 0.80 %
• Other 54 15.30 %
• Position level Executive 5 1.40 %
• Managers and supervisors 59 16.70 %
• Operational staff 289 81.90 %
• Work experience
• <6 Year 131 37.10 %
• 6-15 Years 166 47.00 %
• 16-25 Years 46 13.00 %
• >25 Years 10 2.80 %

PSS Score

Figure 2 displays the distribution of Perceived Stress Scale (PSS) scores among the study participants. The PSS is a selfreported scale that measures the degree to which individuals perceive their lives as stressful. The scores range from 0 to 40, with higher scores indicating higher levels of perceived stress during the past month. The statistic of the PSS score from the three countries is depicted in table III. Ghana had the highest PSS score average, followed by Norway, and Indonesia became the last. From Ghana, the PSS scores reported ranged from 1 to 27, with an average score of 16.12 and a standard deviation of 5.23. The lowest PSS score was obtained by one participant with 1, while the highest score was also reported by one participant with 27. From Norway, the PSS scores reported ranged from 1 to 29, with an average score of 14.05 and a standard deviation of 6.4. The lowest PSS score was obtained by one staff member, while the highest score was also reported by one staff member. From Indonesia, the PSS scores reported ranged from 3 to 21, with an average score of 13.89 and a standard deviation of 4.41. The lowest PSS score was obtained by one participant, while the highest score was reported by four participants. Combining all of the results from these three countries, the mean PSS score was 15.25 with a standard deviation of 5.28. Finally, we assessed the reliability of the PSS. According to the survey results, PSS in English, Norwegian, and Indonesian versions had Cronbach’s α of 0.750, 0.844, and 0.733, respectively. It indicates that the items in all three PSS versions had a good level of internal consistency [18], [19].

Figure 2: Frequency distribution of the PSS  score:

TABLE III: Descriptive statistic of PSS score in Ghana, Norway, and Indonesia

TABLE IV: Descriptive statistic of HS-RCPS score in Ghana, Norway, and Indonesia.

TABLE V: Descriptive statistic of risky cybersecurity practices score based on gender

TABLE VI: Descriptive statistic of risky cybersecurity practices score based on age.

HS-RCPS Score

The distribution and statistics of the HS-RCPS scores are shown in Figure 3 and Table IV. HS-RCPS is a scale of 0 to 48, with 0 denoting the lowest risky cybersecurity practice and 48 denoting the highest. Overall, the results showed that the mean HS-RCPS score for all three countries was 14.94, with a standard deviation of 6.64. From Ghana, the results indicate that the minimum HS-RCPS score among the participants was 0, while the maximum was 36. The mean score was 15.95 with a standard deviation of 6.64. Meanwhile, the minimum and maximum scores in Norway were 2 and 26, respectively, with a mean of 10.88 and a standard deviation of 4.90. Finally, in Indonesia, the minimum and maximum scores were 0 and 27, respectively, with a mean of 14.49 and a standard deviation of 6.64. The findings suggest that risky cybersecurity practice is relatively low among individuals in the three countries. Comparatively, Ghana had the highest mean score while Norway had the lowest. Furthermore, the reliability of the HS-RCPS was assessed through survey results obtained from Ghana, Norway, and Indonesia. The scale’s internal consistency was evaluated using Cronbach’s α coefficient. The survey results from Ghana, Norway, and Indonesia indicated that the scale had a Cronbach’s α of 0.595, 0.502, and 0.697, respectively. Overall, the HSRCPS demonstrated acceptable internal consistency across the surveyed populations [18]–[20].

Demographic and Risky Cybersecurity Practices

The descriptive statistic of risky cybersecurity practices score based on gender, age, position, position level, and work experience are displayed in Table V, VI, VII, VIII, and IX respectively. The statistical analysis revealed no significant differences in the levels of risky cybersecurity practices between male and female participants. Technically, the t-test results indicated that the mean scores for females (M = 14.65, SD = 6.47) and males (M = 15.32, SD = 6.90) were not significantly different, t(350) = 0.980, p = 0.323. In addition, the ANOVA results indicated that there were no significant differences in mean scores of risky cybersecurity practices among various groups, including age (F(3, 349) = 0.347, p = 0.791), position (F(8, 344) = 1.774, p = 0.081), position level (F(2, 350) = 0.144, p = 0.866), and work experience (F(3, 349) = 1.369, p = 0.252). On the other hand, the Kruskal-Wallis test showed that the scores for risky cybersecurity practices varied significantly across various staff groups based on country (χ2(2) = 23.124, p < 0.001). Specifically, the scores for hospital staff from Norway were significantly lower than those from Ghana and Indonesia (p = 0.000 and p=0.04, respectively), suggesting that hospital staff from Norway have better cybersecurity practices.

 Correlation Between Stress Level and Cybersecurity Practices

Table X presents the correlation between the perceived stress levels of hospital staff and their risky cybersecurity practices. The results reveal that there was a statistically significant positive correlation between staff’s stress levels and their cybersecurity practices, as indicated by a Pearson’s correlation coefficient of r = 0.201 (p < 0.01). This finding suggests that employees who reported higher levels of stress, as measured by the Perceived Stress Scale (PSS), were also more likely to engage in riskier cybersecurity practices, as assessed by the Hospital Staff Risky Cybersecurity Practices Scale (HS-RCPS). Specifically, item 4 of the HS-RCPS, which measures staff’s tendency to click on links from unknown sources, had the highest positive correlation with stress levels, indicating that this is the riskiest cybersecurity behavior that is most influenced by stress levels among hospital staff. In addition, this significant correlation also appears when we analyze only the data from Ghana or only the data from Indonesia with r = 0.138 (p < 0.05) and r = 0.311 (p < 0.01), respectively. However, a significant correlation between stress and risky cybersecurity practices was not found in Norway (r = 0.101).

TABLE VII: Descriptive statistic of risky cybersecurity practices score based on position.

TABLE VIII: Descriptive statistic of risky cybersecurity practices score based on position level.

TABLE IX: Descriptive statistic of risky cybersecurity practices score based on years of work experience

TABLE X: PSS score correlation to cybersecurity practices score.

Discussion

The results of this study have important implications for organizations concerned with cybersecurity and employee well-being. The positive correlation between stress levels and risky cybersecurity practices supports the notion that stress can impair cognitive functioning and increase the likelihood of individuals engaging in risky behavior, including online behavior. This is consistent with the broader literature on the negative effects of stress on decision-making [11], [12]. This result was also supported by Fauzi et al. [21] and McCormac et al. [13] who found that workers with greater levels of stress engaged in riskier cybersecurity practices or had worse information security awareness (ISA). From a practical perspective, these findings highlight the importance of addressing stress and well-being in the context of cybersecurity training and awareness programs. Specifically, organizations should consider incorporating stress management techniques and well-being training into their cybersecurity training programs to help employees manage stress and reduce their engagement in risky cybersecurity practices. Having an understanding of how stress can influence an individual’s cybersecurity practices, one can take measures to regulate their stress levels and maintain a heightened awareness of their cybersecurity practices. These measures could comprise tactics such as taking breaks to alleviate stress, exercising increased mindfulness with regard to cybersecurity practices while experiencing stress, and seeking assistance as necessary. Furthermore, the finding that clicking on links from unknown sources was the riskiest cybersecurity behavior most influenced by stress levels is also reasonable since stress can harm an individual’s cognitive functioning, impairing their ability to make rational decisions and increasing the likelihood of impulsive behavior [10]. In addition, stress can lead to feelings of anxiety or overwhelm, causing individuals to rush through tasks or pay less attention to details, making them more likely to overlook the signs of a phishing email [22].

In addition, the research results also revealed a significant difference in cybersecurity practices between healthcare professionals in Norway, a developed country, and those in Ghana and Indonesia, two developing countries. Developing nations have historically slowly adopted and utilized computer and internet technologies. As identified by Ben-David et al.’s research [23], developing nations’ security landscape is affected by five fundamental factors: inadequate ”security hygiene,” unique resource constraints (such as one computer for multiple users), novice internet users, use of pirated software, and limited comprehension of cybersecurity adversaries. These factors could explain why people in developing countries generally exhibit poorer cybersecurity practices than their counterparts in developed nations. Insufficient IT education and a lack of computer and internet manuals in local languages have also contributed to unsafe cybersecurity practices [24]. Moreover, Norway’s healthcare systems and infrastructure are comparatively advanced and better equipped to implement and enforce cybersecurity protocols than Indonesia and Ghana. Future research can investigate cultural factors and explore how they may be leveraged to improve cybersecurity practices in different regions.

Limitations

 There are several limitations of this study that need to be acknowledged. First, the study used a self-report survey to collect data, which may result in social desirability bias, meaning that participants may have needed to be more honest in their responses. Second, memory bias could also occur when participants have trouble remembering details correctly, particularly if the details relate to previous events or behaviors. Finally, the study’s cross-sectional design precludes the establishment of causality. Using this study design, it is difficult to determine if high-stress levels cause risky cybersecurity practices or if it is the other way around.

Conclussions

In conclusion, this study explored the relationship between stress levels and risky cybersecurity practices among hospital staff in three countries. The results showed a statistically significant positive correlation between staff stress levels and their engagement in riskier cybersecurity practices. Specifically, the staff’s tendency to click on links from unknown sources was found to be the risky cybersecurity practice most heavily associated with higher stress levels. Interestingly, no significant differences were found in the levels of risky cybersecurity practices between male and female participants or among different age groups, positions, position levels, and work experience. However, a significant difference was observed in risky cybersecurity practices scores across staff groups based on the country of origin, with hospital staff from Norway showing significantly lower scores than those from Ghana and Indonesia, suggesting Norwegian healthcare staff had safer cybersecurity practices. There are several directions that future studies can take based on the findings of this study. Firstly, further research can explore the causal relationship between stress levels and cybersecurity practices. Second, future studies can explore other factors influencing risky cybersecurity practices among hospital staff, such as personality traits, motivation, or job satisfaction. By gaining a more comprehensive understanding of the various factors that influence cybersecurity practices, interventions can be developed that target these factors to promote safer cybersecurity behaviors among employees. Finally, future studies can examine the effectiveness of various interventions aimed at promoting safer cybersecurity practices among hospital staff. Such interventions may include training programs, awareness campaigns, or technological solutions such as secure communication platforms.

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