Problematic internet use and safety behavior: The moderating role of safety climate

RESEARCH ARTICLE Problematic internet use and safety behavior: The moderating role of safety climate Fakhradin Ghasemi1, Hamed AghaeiID2*, Asghar NikraveshID3 1 Department of Occupational Health and Safety Engineering, Abadan University of Medical Sciences, Abadan, Iran, 2 Department of Occupational Health Engineering, School of Health, Arak University of Medical Sciences, Arak, Iran, 3 Department of HSE, Golgohar Mining and Industrial Company, Kerman, Iran * hamedaghaeih@gmail.com Abstract Problematic internet use (PIU) can cause mental and physical harm to individuals and may be an emerging factor contributing to unsafe work behavior. In this study, the relationship between PIU and safety behavior was investigated. Moreover, it was hypothesized that safety climate can moderate the relationship between PIU and safety behavior. Participants were employees from a mining industry in Iran. Three validated questionnaires were distrib- uted to gather the required data regarding PIU, safety climate, and safety behavior. Hierar- chical regression analysis was used to assess the moderating effect of safety climate on the relationship between PIU and safety behavior. The size of moderation effect was assessed using f2 index. Four hundred eighty-five employees participated in this study. The results demonstrated a negative relationship between PIU and safety behavior. The moderating effect of safety climate on the relationship between PIU and safety behavior was supported. The f2 index was 0.027 which demonstrates a small moderation effect. In conclusion, PIU

has a significant negative effect on the safety behavior of employees. Providing a strong safety climate can be a long-term solution for reducing the negative effect of PIU on safety behavior. Introduction Safety behavior refers to those behaviors which are in line with safety standards, rules, norms, and policies of the organization. Lack of safety behavior can make organizations prone to acci- dents [1, 2]. The contribution of unsafe work behaviors to most industrial accidents has been asserted by many studies [1, 3, 4]. There are many factors causing employees to engage in unsafe work behavior. These factors have been discussed by many studies and several remedies also have been developed. However, the ever-changing world can create new factors affecting the safety behavior of employees at workplaces. One of these factors is the omnipresence of the Internet and digital devices such as laptops, mobile phones, and tablets [5]. Nowadays, most people have continuous access to the internet and online information. The attracting nature of such information can lead to “problematic internet use (PIU)”. PIU is defined as “the intemperate utilize of the Internet that disrupts or harms the individ- ual” [5]. Organizations confront productivity losses as a result of workers’ association in PLOS ONE PLOS ONE | https://doi.org/10.1371/journal.pone.0279767 December 30, 2022 1 / 13 a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: Ghasemi F, Aghaei H, Nikravesh A (2022) Problematic internet use and safety behavior: The

moderating role of safety climate. PLoS ONE 17(12): e0279767. https://doi.org/10.1371/journal. pone.0279767 Editor: Hans-Uwe Dahms, Kaohsiung Medical University, TAIWAN Received: February 26, 2022 Accepted: December 14, 2022 Published: December 30, 2022 Copyright: © 2022 Ghasemi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: All relevant data are within the manuscript and its Supporting Information files. Funding: The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors have declared that no competing interests exist.

counterproductive behaviors, such as PIU. Some previous researches showed that more than half of Internet-enabled workers go online for individual purposes at an average rate of three hours per week [6] and some recent researches show that the number of hours has increased to almost 1h per day [7]. Other studies illustrated that workers go online for more than one hour for non-work-related activities at work per day [8]. Between 60% and 80% of workers use the internet for non-work-related activities, leading to around 30% to 40% decrease in produc- tivity [9]. It is estimated that Internet usage for non-work-related activities in the workplace in the United States costs approximately $85 billion yearly [10]. Internet usage for non-work- related activities in the workplace not only results in poor productivity but also slacks network security and makes the organization to cyber-attacks [11]. In addition to the aforementioned issues, PIU can lead to adverse physical and mental outcomes as well. Depressive symptoms [12], mental health problems [13], social fears [14], irritability and sleep problems [15] low physical [16], risk-taking behaviors [17], and poor academic performance [18] are some of these consequences reported by previous studies. PIU is associated with a decrease in a wide range of neuropsychological functions [19]. Studies demonstrated that some cognitive failures are related to PIU [20]. Hadlington [21] showed a connection between higher PIU use and self-reported cognitive failures in daily life.

Safety climate is the shared perception of employees toward safety within the organization [27]. Safety climate is one of the most important factors affecting safety behavior at workplaces [1, 28–30]. A wide range of factors has the capability of affecting safety behavior, safety climate, as a strong moderator, can attenuate the negative effect of such factors on safety behavior. According to Hussain et al. [31], the effect of socio-cognitive factors on the risky driving behavior of truck drivers is moderated by safety climate. The negative effects of job insecurity on safety outcomes such as safety behavior and occupational injuries are alleviated by a strong positive safety climate [32]. Zhou and Jiang [33] demonstrated that the relationship between leader-member exchange is positive when there is a strong safety climate in the workplace, while the relationship is negative when safety climate is poor. In the same vein, safety climate moderates the effect of stress responses on safety behavior [34]. Moreover, providing a strong positive safety climate has some additional benefits. For example, it makes less severe the nega- tive effect of staffing inadequacy and unpleasant work conditions on occupational injuries among nurses [35]. PIU can be an emerging cause of unsafe behavior at workplaces. So far, to PLOS ONE Problematic internet use and safety behavior PLOS ONE | https://doi.org/10.1371/journal.pone.0279767 December 30, 2022 2 / 13

the best of our knowledge, no study investigates the role played by safety climate on this rela- tionship, so it can be hypothesized that safety climate can moderate the relationship between PIU and safety behavior. Accordingly, the present study was conducted to assess the effect of PIU on safety behavior. Moreover, the study aimed at assessing the moderating effect of safety climate on the relation- ship between PIU and safety behavior. Accordingly, the study had two hypotheses: Hypothesis 1: PIU is negatively associated with safety behavior, Hypothesis 2: the effect of PIU on safety behavior is moderated by safety climate. Demonstrated in Fig 1 is the hypothetical model of the present study. Material and methods Ethical statement The participation was voluntary and participants were assured that their information remain confidential. First, objectives and methodology of the study were verbally explained to partici- pants and they were verbally asked about their willingness to participate in the study, the ques- tionnaires were distributed among those who agreed to participate. Moreover, the first item of the questionnaires asked about this issue again; "would you like to participate in this study?" and participants that answered yes were included in the study. The study protocol was approved by the ethics committee of Abadan university of medical sciences (ethic code: IR. ABADANUMS.REC.1400.079). Participants There are many active underground and surface mines in Iran. Based on some statistics, Iran

is among 15 major mineral-rich countries. Iron ore, copper, zinc, gypsum, molybdenum, and antimony are some important minerals extracted in Iranian mines. Although the industry has not been fully developed, at least half-billion employees are working in this sector directly or indirectly. In this study, employees from an open-pit mining industry located in the east of Fig 1. The hypothetical model. https://doi.org/10.1371/journal.pone.0279767.g001 PLOS ONE Problematic internet use and safety behavior PLOS ONE | https://doi.org/10.1371/journal.pone.0279767 December 30, 2022 3 / 13

Iran were invited to participate in the study. The table from Krejcie and Morgan [36] was used to determine the required sample size. Given the total number of nine thousand employees in the company, the sample size was determined to be 368. However, the response rate is rarely 100% and we had to invite more participants to the study. Consequently and to guarantee the required sample size, the determined sample size was conservatively multiplied by 2, so 736 questionnaires were randomly distributed among employees. Data gathering tool Standard and validated questionnaires were used to gather the required data. A 6-item scale was developed by Siciliano et al. [37] to measure PIU. The scale was later modified by Abuba- kar and Al-Zyoud [5] to be usable in occupational settings. In this study, both tools were adopted to develop a Persian scale appropriate for the Iranian population. Content validity index (CVI) and content validity ratio (CVR) of all items were assessed before application [38, 39]. “I feel nervous and restless when I am offline and these feelings are disappeared when I go back online”, “I think that I use mobile phone and internet more than what I really need”, “sometimes I forget or neglect safety considerations because of being busy with mobile phone and internet”, and “I prefer to use mobile phone and internet instead of going out with my friends or exercising” are some items used to measure PIU. All items were responded using a 5-point Likert scale ranging from 1 (totally disagree) to 5 (totally agree).

A 7-item scale from Ghasemi et al. [28] was used to assess perceived safety climate (accept- able content validity and Cronbach’s alpha coefficient = 0.870). “In my workplace, safety is as important as production” and “Managers actively participate in designing and implementing safety programs” are some items used for measuring safety climate [28]. All items were responded using a 5-point Likert scale ranging from 1 (totally disagree) to 5 (totally agree). A 6-item scale was used for measuring safety behavior. The items were adopted from Neal and Griffin [3] which were translated and validated into Persian by Mahdinia et al. [40]. The scale encompasses both safety participation and safety compliance aspects of safety behavior and demonstrates acceptable reliability and validity (Cronbach’s alpha coefficient = 0.8 and acceptable content validity). Data analysis First, the construct validity of the variables was assessed. To do so, a measurement model was built and the goodness of fit of the model was assessed using absolute and comparative indices such as RMSEA, chi-square/df, NFI, RFI, GFI, CFI, and so on. RMSEA lower than 0.08, chi- square/df lower than 3, and comparative indices higher than 0.9 were considered to be accept- able [28, 41, 42]. Convergent and discriminant validity are two main aspects to be investigated during construct validity assessment. The convergent validity of variables was investigated based on standardized factor loadings (SFLs) and average variance extracted (AVE). SFLs higher than 0.3 and AVE higher than 0.5

were considered to be acceptable, as recommended by Bagozzi and Yi [43]. The discriminant validity of variables was evaluated by comparing the square root of AVE with correlation coef- ficients among variables. The discriminant validity is confirmed if the AVE square root of a variable is higher than its correlations with other variables. Hierarchical regression analysis in accordance with the procedure explained by Helm and Mark [44] was conducted to assess the moderating effect of safety climate on the relationship between PIU and safety behavior. This approach is one of the most popular methodologies uti- lized for the assessment of moderation effect [44]. In this respect, two regression models were constructed and examined. The first model was the basic model in which PIU and safety PLOS ONE Problematic internet use and safety behavior PLOS ONE | https://doi.org/10.1371/journal.pone.0279767 December 30, 2022 4 / 13

climate were predictors and safety behavior was the dependent variable (Eq 1). The second model is called the interaction model in which PIU, safety climate, and the interaction term (i.e. PIU × safety climate) are predictors, and safety behavior is the dependent variable (Eq 2). The moderation effect is supported if the effect of the interaction term is significant [44]. Basic model : SB ¼ b0 þ SC:b1 þ PIU:b2 ð1Þ Interaction model : SB ¼ b0 þ SC:b1 þ PIU:b2 þ SC � PIU:b3 ð2Þ Where SB safety behavior, SC = safety climate, PIU = problematic internet use, and b1, b2, and b3 are regression weights. Visualization of the moderation effect can provide a deeper insight on how the moderation works. For this purpose, the procedure recommended by Hayes [45] was employed. In this regard, two new equations (simple slope lines) were generated from the interaction model for- mula by inserting certain values of the moderator into the equation, i.e. safety climate = mean + SD and safety climate = mean–SD. The effect of moderation variable would be visualized when these two slope lines are plotted for various values of PIU. Moreover, the size of moderation effect was calculated using Eq 3 [46]: f 2 ¼ R2 i R2 b 1 R2 i ð3Þ Where f2 = the size of moderation effect, R2 i = R2 of the interaction model, and R2 b = R2 of the basic model. The size of moderation effect is negligible when its value is lower than 0.002. Results A total number of 485 employees from a mining industry participated in this study (response

rate = 66%). All participants were male, most of them were married (84.1%), most of them were aged between 31 and 40 years old (61.4%), and most of them had 6–10 years of experi- ence (35.7%). Detailed demographic characteristics of the participants are demonstrated in Table 1. They were working in various units of the mining company, including extraction, pro- cessing mineral concentrates, and pelletizing units. Table 1. Demographic characteristics of participants (n = 485). Variable Number (%) Age (year) = < 30 115 (23.7%) 31–40 298 (61.4%) 41–50 63 (13.0%) > 50 9 (1.9%) Experience (years) <1 56 (11.5%) 1–5 159 (32.8%) 6–10 173 (35.7%) 11–15 70 (14.4%) >15 27 (5.6%) Marital status Single 77 (15.9%) Married 408 (84.1%) https://doi.org/10.1371/journal.pone.0279767.t001 PLOS ONE Problematic internet use and safety behavior PLOS ONE | https://doi.org/10.1371/journal.pone.0279767 December 30, 2022 5 / 13

Construct validity Confirmatory factor analysis was performed to assess the construct validity of variables. The CFA model is depicted in Fig 2 and it was satisfactory in terms of both absolute and compara- tive model fit indices: Chie-square/df = 2.47, RMSEA = 0.055, GFI = 0.939, NFI = 0.954, RFI = 0.942, IFI = 0.972, TLI = 0.964, and CFI = 0.972. SFL of each item on the related con- struct and AVE were two criteria based on which convergent validity was evaluated. Two items related to the PIU had an SFL lower than 0.3, so they were excluded. As demonstrated in Table 2, all SFLs are higher than 0.3 and all AVEs are higher than 0.5, so the convergent valid- ity of the constructs is satisfactory. Discriminant validity was assessed by comparing AVE root square of variables with their correlation coefficients. As the AVE root square of all variables was higher than correlation coefficients among them, the discriminant validity of variables was also supported. Bivariate analysis The results of bivariate analyses are presented in Table 3. Based on the type of variables, Pear- son or Spearman coefficients were calculated. As evident, personal characteristics such as age, experience, and marital status had no significant correlation with PIU, safety climate, and safety behavior. PIU was negatively related to both safety behavior and safety climate. As expected, a significant positive relationship was also observed between safety climate and safety behavior. Moderation analysis Hierarchical regression analysis was employed to assess the moderation effect of safety climate

on the relationship between PIU and safety behavior and the results are presented in Table 4. In the basic model, both PIU and safety climate had a significant effect on safety behavior. In the interaction model, the interaction term also had a significant effect on safety behavior. Accordingly, the main hypothesis of the study was supported so that safety climate moderated the effect of PIU on safety behavior. The f2 index was calculated to determine the size of moderation effect. The value of this index was 0.027 which demonstrates a small effect size. Fig 2. The measurement model. https://doi.org/10.1371/journal.pone.0279767.g002 PLOS ONE Problematic internet use and safety behavior PLOS ONE | https://doi.org/10.1371/journal.pone.0279767 December 30, 2022 6 / 13

Table 2. Construct validity of problematic internet use (PIU), safety climate, and safety behavior. Variable Item Standardized factor loading (SFL) AVE ( ffiffiffiffiffiffiffiffiffiffi AVE p ) Cronbach’s α Problematic internet use P1 - 0.53 (0.73) 0.81 P2 - P3 0.56 P4 0.68 P5 0.78 P6 0.85 Safety Climate SC1 0.72 0.60 (0.77) 0.91 SC2 0.84 SC3 0.85 SC4 0.91 SC5 0.83 SC6 0.63 SC7 0.57 Safety Behavior SB1 0.75 0.67 (0.82) 0.92 SB2 0.83 SB3 0.87 SB4 0.94 SB5 0.75 SB6 0.74 https://doi.org/10.1371/journal.pone.0279767.t002 Table 3. Correlations among variables investigated in this study. PIU SC SB Age Experience PIU 1 SC -0.246�� 1 SB -0.230�� 0.547�� 1 Age 0.050 -0.008 0.003 1 Experience -0.100� 0.047 0.004 0.412�� 1 Marriage 0.002 0.014 -0.013 0.329�� 0.293�� Note: PIU: Problematic internet use; SC: Safety climate; SB: Safety behavior ��p < 0.01 �p < 0.05. https://doi.org/10.1371/journal.pone.0279767.t003 Table 4. Basic and interaction models for assessing the moderation effect of safety climate on the relationship between problematic internet use and safety behavior. Model predictor B SE Beta t p R R2 f2 Basic PIU -0.136 0.052 -0.102 -2.607 <0.01 0.556 0.309 0.027 SC 0.375 0.028 0.522 13.372 <0.01 Interaction PIU -0.766 0.182 -0.575 -4.201 <0.01 0.572 0.327 SC 0.146 0.069 0.203 2.095 0.037 PIU×SC 0.031 0.008 0.520 3.603 <0.01 Note: PIU: Problematic internet use; SC: Safety climate https://doi.org/10.1371/journal.pone.0279767.t004 PLOS ONE Problematic internet use and safety behavior

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For a deeper insight into the moderation effect of safety climate on the relationship between PIU and safety behavior, two simple slope lines were generated from the interaction model for- mula as follows: SB ¼ 19:031 þ ð 0:766 � PIUÞ þ ð0:146 � SCÞ þ ð0:031 � PIU � SCÞ ð4Þ For SC ¼ �x SD; SB ¼ 21:350 þ ð 0:274 � PIUÞ ð5Þ For SC ¼ �x þ SD; SB ¼ 22:759 þ ð0:026 � PIUÞ ð6Þ These lines are depicted in Fig 3 based various values of PIU. When safety climate was high (i.e. Mean + SD), the slope of regression line was very close to zero, indicating that PIU lost its effect on safety behavior in such situations. In contrast, when safety climate was low (i.e. Mean–SD), safety behavior was drastically decreased with the increase of PIU, suggesting that the lack of a positive safety climate made the negative effect of PIU on safety behavior stronger. Discussion Nowadays, as using the internet is inevitable for carrying out many occupational and non- occupational activities, PIU can be an emerging cause of unsafe work behavior. The addictive potential of the internet is progressively increased by reinforcing attention-capturing charac- teristics of websites and apps without sufficient consideration of its probable effects on human health, safety, and well-being [22, 47]. Although the relationship between PIU and accidents, particularly car and truck collisions, has been the subject of several studies [48, 49], studies investigating the relationship between PIU and safety behavior are rare. Meanwhile, the effect

of PIU and cyber-loafing on job performance has been shown to be inconsistent across Fig 3. The slope analysis of the moderation effect. https://doi.org/10.1371/journal.pone.0279767.g003 PLOS ONE Problematic internet use and safety behavior PLOS ONE | https://doi.org/10.1371/journal.pone.0279767 December 30, 2022 8 / 13

literature [50]. Accordingly, in this study, the effect of PIU on safety behavior and also the moderating effect of safety climate on this relationship were investigated. A negative significant relationship between PIU and safety behavior was identified in this study. Similarly, Abubakar and Al-Zyoud [5] investigated the relationship between PIU and safety behavior in the marine sector and found a significant relationship between these two variables. Furthermore, Oktan [51] reported a positive significant relationship between PIU and risk taking behavior among high school students and Guo et al. [52] found that PIU can increase the risk of suicidal behavior. As mentioned before, the effect of PIU on safety behavior can be mediated by cognitive failures. Moreover, PIU is associated with mental problems such as depressive symptoms, stress, and burnout [53]. As some of these problems are associated with poor safety behavior [54], they might be able to mediate the relationship between PIU and safety behavior at workplaces. However, there is a need for more researches in this area. This study recommended safety climate as a way through which the negative effect of PIU on safety behavior can be alleviated. The analyses demonstrated the moderation effect of safety climate on this relationship so that safety climate can relieve the negative effect of PIU on safety behavior. This finding emphasizes that providing a positive safety climate is effective in reducing the effect of newly-emerged issues such as PIU on safety behavior. The finding is in

line with numerous previous studies emphasizing the buffering capacity of a positive safety cli- mate in workplaces [28, 33]. Safety climate is the shared perception and attitude of employees toward safety. There are several factors constituting safety climate including management commitment to safety, supportive environment for safety, safety training, safety procedures, and so on [55, 56]. Improving these dimensions, particularly management commitment to safety, is necessary for creating a safety climate at workplaces. Once such a positive environ- ment is created, the negative impact of PIU on safety behavior can be expected to decrease. In the same vain, Abubakar and Al-Zyoud [5] recommended time autonomy as a factor moderat- ing the effect of PIU on safety behavior. PIU is a huge problem in both personal life and workplaces because it can result in many undesirable outcomes [5, 57]. Moreover, all problems created by PIU have yet to be identified mainly because the pace of technological progress is much faster than researches conducted in this field [57]. PIU should be managed properly nonetheless. Some organizations inhibit the use of mobile and the internet for personal purposes in workplaces, however, organizations have no control over the off-the-job activities of their employees so they may still be affected by the bad consequences of PIU. Cognitive-behavioral therapy has been recommended for treating PIU [57] while increasing the knowledge of individuals regarding the adverse side

effects of PIU may be a preventive strategy. Moreover, Davis et al. [58] developed a pre- employment tool for singling out people who are prone to abuse the internet in the work- places. This tool may be used for selecting and employing individuals with less proneness toward PIU. Training regarding the adverse effects of Internet addiction is another counter- measure in this respect [59]. Overall, studies investigating the effect of PIU on safety outcomes in workplaces are rare and more studies are needed in this respect. There are several factors that are able to mediate the effect of PIU on safety behavior, including poor sleep quality and mental disorders stem from PIU. Moreover, the effect of PIU on injuries and occupational accidents needs more researches. Conclusion PIU can negatively affect safety behavior so employers should pay special attention to this sub- ject. Safety climate can attenuate the negative effect of PIU on safety behavior. Strategies for PLOS ONE Problematic internet use and safety behavior PLOS ONE | https://doi.org/10.1371/journal.pone.0279767 December 30, 2022 9 / 13

reducing PIU and enhancing safety climate are required to improve the safety behavior of employees. Supporting information S1 File. Data used in the study. (DOCX) Author Contributions Conceptualization: Fakhradin Ghasemi, Hamed Aghaei. Data curation: Hamed Aghaei, Asghar Nikravesh. Formal analysis: Fakhradin Ghasemi, Hamed Aghaei. Funding acquisition: Fakhradin Ghasemi, Asghar Nikravesh. Methodology: Fakhradin Ghasemi. Project administration: Fakhradin Ghasemi. Validation: Asghar Nikravesh. Visualization: Fakhradin Ghasemi, Hamed Aghaei. Writing – original draft: Fakhradin Ghasemi, Hamed Aghaei. References 1. Ghasemi F, Aghaei H, Askaripoor T, Ghamari F. Analysis of occupational accidents among nurses working in hospitals based on safety climate and safety performance: A Bayesian Network analysis. Int J Occup Saf Ergon. 2022; 28: 440–446. https://doi.org/10.1080/10803548.2020.1768759 PMID: 32508274 2. Mahdinia M, Mohammadfam I, Soltanzadeh A, Aliabadi MM, Aghaei H. A fuzzy Bayesian network DEMATEL model for predicting safety behavior. Int J Occup Saf Ergon. 2021; 1–8. https://doi.org/10. 1080/10803548.2021.2015741 PMID: 34898390 3. Neal A, Griffin MA. A study of the lagged relationships among safety climate, safety motivation, safety behavior, and accidents at the individual and group levels. J Appl Psychol. 2006; 91: 946–953. https:// doi.org/10.1037/0021-9010.91.4.946 PMID: 16834517 4. Aghaei H, Mirzaei M, Id A, Mollabahrami F, Najafi K. Human reliability analysis in de-energization of

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