A systems thinking approach of occupational safety and health applied in the micro-, meso- and macro-levels: A Finnish survey

Safety Science 82 (2016) 212–227

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A systems thinking approach of occupational safety and health applied in the micro-, meso- and macro-levels: A Finnish survey Toivo Niskanen a,⇑, Kyösti Louhelainen b, Maria L. Hirvonen b a b

Ministry of Social Affairs and Health, Department for Occupational Safety and Health, Legal Unit, P.O. Box 33, 00023 Government, Helsinki, Finland Finnish Institute of Occupational Health, P.O. Box 310, SF-70101 Kuopio, Finland

a r t i c l e

i n f o

Article history: Received 30 March 2015 Received in revised form 2 June 2015 Accepted 11 September 2015 Available online 30 September 2015 Keywords: Safety Health Socio-technical analysis Systems thinking Work environment

a b s t r a c t The aim of this study was also to explore the interconnected frameworks of the systems thinking approach between the micro-, meso- and macro-levels within occupational safety and health (OSH). The goal of this study was to analyze different entities through different types of aggregation. The aggregated variables of micro-, meso- and macro-levels of the systems thinking approach were found to exert an effect on each other in the regression analysis. For OSH managers and workers’ OSH representatives, the Hypotheses were supported with the aggregated variables as follows: Hypothesis 1 – OSH management & collaboration was positively related to training, measuring and monitoring, quality of OHC services and quality of OSH legislation; Hypothesis 2 – technical processes were positively related to training, measuring and monitoring, OSH management & collaboration and quality of OSH legislation; Hypothesis 3 – measuring and monitoring were positively related to training, OSH management & collaboration, quality of OHC services and quality of OSH legislation; Hypothesis 4 – instructions were positively related to training, measuring and monitoring, OSH management & collaboration and quality of OHC services; and Hypothesis 5 – use of personal protective equipment was positively related to training, measuring and monitoring, OSH management & collaboration, quality of OHC services and quality of OSH legislation. The functional specification related micro-, meso- and macro-levels revealed how one can apply a system approach model to demonstrate how the human, safety management, and organizational factors fit together in the broad safety context (e.g., OSH legislation). Ó 2015 Elsevier Ltd. All rights reserved.

1. Introduction The design of the work system consists of the technological and personnel subsystems and their joint optimization, as well as the organizational design and the physical environment (Murphy et al., 2014). In this context, the technological subsystem defines the tools and technologies of how work is performed (including the ‘‘non-human” structural aspects of work) and the personnel subsystem defines who performs the work. Furthermore, Murphy et al. (2014) showed that the work system is composed of people in the form of a personnel subsystem (also referred to as a social subsystem) and technology in the form of a technological subsystem (also referred to as a technical subsystem). Safety is an outcome of a work system with components that cooperate (i.e., joint optimization) so that one subsystem (e.g., personnel, technological) is not bearing all the responsibility of keeping the entire system and its workers safe (Murphy et al., 2014). ⇑ Corresponding author. Tel.: +358 50 594 2817; fax: +358 9 17073109. E-mail address: [email protected] (T. Niskanen). http://dx.doi.org/10.1016/j.ssci.2015.09.012 0925-7535/Ó 2015 Elsevier Ltd. All rights reserved.

The work system model can be uses as a guide when asking questions about the event (Carayon et al., 2014): (1) who was involved [Person]; (2) what were they doing [Tasks]; (3) what tools/technologies were they using [Tools/technologies]; (4) where did the event take place [Environment]; and (5) what organizational conditions contributed to the event [Organization]. A good system identification should have the following qualities (Jackson, 2003, p. 96): (1) be able to identify the purpose(s) to be pursued; (2) to determine the relevant system for achieving the purposes (technology and human factors as ‘system in focus’); (3) to specify the entity of which the system in focus is a part (wider systems, environments); and (4) specifies the viable parts of the system in focus (‘unfolding complexity’); these are the parts that ‘produce’ the system in focus. Lord and Din (2012, p. 55) found that the leadership is embedded within a larger social context and, as such, can be influenced by organizational forces of the micro-, meso- and macro-levels (e.g., legal, national policies), which influence an organization’s efficiency and ability to effect change. Attention to how OSH inputs

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are combined at one level to create OSH outputs at another level also needs to be integrated within the levels of analysis. Siemieniuch and Sinclair (2014) developed the concept ‘‘System of Systems” where a socio-technical perspective is adopted. It is the capacity of people to operate as thinking, decision-making, responsibility accepting, goal-seeking entities that is of fundamental importance to organizations, and the needs of people for a support network which significantly widens the boundaries to include many more systems (Siemieniuch and Sinclair, 2014). Wilson (2014) concluded that the systems thinking approach system must involve an understanding the interactions between people and all other elements, and use this as the basis for devising a system, which takes into account inter-related or coupled activities or entities (e.g., hardware, software, spaces, communities and people) with a joint purpose. Furthermore, a systems approach examines, accounts for and enhances the design of a system, and people’s interactions with it, rather than concentrating on an individual part of the entity (Wilson, 2014). This study applies the generic multi-level conceptual of the systems thinking approach presented by Karsh et al. (2014) to the ‘‘OSH” of the work system. The mesoergonomics model of Karsh et al. (2014) was chosen to be a reference frame in order to illustrate the application of the framework to ‘‘OSH conditions” across different system levels (multi-level and whole system), as well as the different types of purpose for using the framework in generating and testing hypotheses. The organizational conditions of the work system can be represented in the process through transitions between different individuals and their tasks, coordination and communication across the process, and other temporal aspects of the process (Carayon et al., 2014). In this context, the definition and conceptualization of entities need to be derived from the problems to be addressed. Edwards and Jensen (2014) found that technology, facilities, formal and informal organizations (structures, procedures and processes), workers (qualifications, competencies, attitudes and values), and (layers of) managers can be designated as examples of entities typically used in this kind of problem-solving process. Although research interest in the analysis related to mesoergonomics models (e.g. Karsh et al., 2014), work system models (e.g. Carayon et al., 2014), systems thinking approach (e.g. Wilson, 2014), ‘‘System of Systems” (e.g. Siemieniuch and Sinclair, 2014) has increased in recent years, no efforts in the statistical analysis have been made to determine the proactive dimensions of the OSH which underpin the concepts of socio-technical exchange and relationships. Therefore, the present study attempts to address this knowledge gap concentrating on socio-technical relationships. The focus of the present study is on the application of how the different levels of OSH measures can represent a basis for obtaining a better understanding that would allow the analysis of both top-down and bottom-up processes in the organizations. The present study focuses on a structured approach in order to operationalize hypotheses which involve the micro-, meso- and macro-levels of analysis within theoretical constructs of the systems thinking approach.

2. The aim of the study The aim of this study was also to explore the interconnected frameworks of systems thinking approach between the micro-, meso- and macro-levels within OSH. The aim was also to provide a rich and complex context to advance understanding of the interactional and relational contexts and processes, through which OSH practices operate in the workplace. The framework of this study developed different entities through which different types of aggregation were analyzed.

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3. Materials and methods 3.1. Data collection, methods and participants A cross-sectional analytical survey, based on an electronic questionnaire, was conducted for this study in September–October, 2011. The questionnaire form was filled in by OSH managers and by workers’ OSH representatives. In Finland, there are about 350 chemical industry workplaces. The researchers sent to OSH managers and workers’ OSH representatives an individually addressed e-mail, which contained information to allow him/herself to access a link to the electronic questionnaire. Key ethical issues in research include voluntary participation and maintenance of confidential data or anonymity. In relation to the present study, the purpose and content of the research was explained to potential participants in the e-mail introducing the questionnaire. Recipients of the questionnaire were strongly encouraged to complete the questionnaire although participation was voluntary. The register of OSH managers is kept by the Centre of Occupational Safety. A total of 258 usable e-mail addresses were obtained from the register of OSH managers. Their average age was 49 years. A total of 85 OSH managers chose to participate in the survey, resulting in a response rate of 33%. A total of 348 usable e-mail addresses were obtained from the registers kept by the Finnish Industrial Union TEAM (blue-collar workers; average age was 43 years) and by the Finnish Trade Union PRO (clerical workers; average age was 47 years). A total of 120 workers’ OSH representatives chose to participate in the survey, resulting in a response rate of 34%. Less than 30% of the respondents were female – 28% of the OSH managers 28% and 29% of the workers’ OSH representatives. The participants were informed about the purpose, aims, and methods of the study and assured that participation was voluntary. In addition, they were told that the anonymity of the participants would be assured during and after the study, and data security would be guaranteed. They were also given the opportunity to contact the researcher to obtain more information about the study. 3.2. Statistical analysis OSH managers and workers’ OSH representatives were asked to evaluate how well the different OSH measures were accomplished. The indicator values from the Likert scale were as follows: 1 = strongly disagree, 2 = somewhat disagree, 3 = somewhat agree and 4 = strongly agree. Statistical analysis was done with SAS software (2005). Hypotheses H1–H5 were tested using regression analysis. The coefficient of determination (R-squared) is calculated in regression analysis to indicate the percentage of the dependent variable that can be predicted by the independent variables; this level of accuracy in prediction of the dependent variable will change based on the independent variables being included in the model. There is a commonly accepted rule of thumb for describing in Tables B.1, C.1 and D.1 the internal consistency (George and Mallery, 2003): When Cronbach’s alpha (a) P 0.9, then the internal consistency is excellent; when 0.9 > a P 0.8, then the internal consistency is considered to be good; and when 0.8 > a P 0.7, then the internal consistency is acceptable. 3.3. Reference frame of the study In order to address these concerns, this study is based on the framework of the micro-, meso- and macro-levels presented Karsh et al. (2014) as applied to the OSH in the chemical industry’s workplaces. The applied framework developed by Karsh et al. (2014) consists of following steps: (1) establishing the purpose of the micro-, meso- and macro-levels within survey investigation

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based on the systems thinking approach; (2) selecting dependent and independent aggregated variables; and (3) establishing relationships between the system levels. Jepperson and Meyer (2011) detailed the levels of analysis, the nature of the causal arguments at these levels, and of the relationships of structural (and institutional) levels to the individual level of analysis. Furthermore, ‘‘Levels of analysis” refer to sets of causal processes, each representing different degrees of organizational complexity and ‘‘levels of complexity” (Jepperson and Meyer, 2011). In the present study the definitions are as follows: (1) Micro-level of analysis includes the impacts on the individuals (e.g., managers and workers) within the organizations. (2) Meso-level of analysis refers to the impacts within OSH measurements that falls between the micro- and macrolevels, such as interactions within an organization. In addition, in the present study a meso-level may also refer to analyses that are specifically designed to reveal connections between micro- and macro-levels. (3) Macro-level of analysis involves the impacts of interactions that transfer impacts and interactions over organizations with impacts e.g. by OSH legislation and its applied rule practices. This study utilizes the reference frame devised by Karsh et al. (2014) applying this theory to structure a set of aggregated variables during Hypothesis testing. The framework is meant to be

used in conjunction with the sociotechnical model (Fig. 1) and it involves six steps (applied from Karsh et al., 2014). In the practical implications of the present results, we applied the approach presented by Wilson (2014). He showed that the systems thinking approach involves four phases: (1) identifying and assessing the links between the entities may be of state, form, function and causation; (2) conceptualizing any system of interest as existing within a boundary and thus a defined context, having inputs and outputs which may connect in many to many mappings; (3) treating the system as holistic with the whole usually greater (more useful, powerful, functional, etc.) than the sum of its parts; and (4) recognizing that the system changes and modifies its state and the interactions within it in the light of circumstances and events, thus revealing emergent properties. The system approach applied to the practical implications (Fig. 2) is based on the bridge levels of analysis – to move from accounts of the behavior of individuals (micro-level) to explanations of the properties of organizational systems (meso-level) and legislation-level (macro-level). 3.4. The hypotheses of this study 3.4.1. Background data for the aggregated variables of the Hypotheses 3.4.1.1. ‘‘Quality of Legislation” (macro-level) (see Appendix A.1: Question 1). Rasmussen (1997) indicated that the system of ‘‘control of activities and their safety by the classic prescriptive command-and-control approach deriving rules of conduct

PHASE 4

Macrolevel

PHASE 1

PHASE 2

PHASE 3

What is the purpose of the investigation

Select of OSH variables under investigation

Relationships in the systems thinking approach

This focus also aims to provide a rich and complex context for understanding to advance understanding of the interactional and relational contexts and processes, through which OSH practices operate in the workplaces

Macro-level: ”Quality of legislation” ”Quality of occupational health care services” Meso-level: ”OSH management & collaboration” ”Technical processes” ”Instructions” ”Measuring and monitoring” ”Training” Micro-level: ”Use of PPE”

Macro-, Mesoand Microlevel’s models applied to OSH

Mesolevel

Microlevel

Aggregated variables: ”Quality of legislation” ”Quality of occupational health care services” Aggregated variables: ”OSH management & collaboration” ”Technical processes” ”Instructions” ”Measuring & monitoring” ”Training”

PHASE 6 PHASE 5 Relationships (H1-H5) within OSH measurement with aggregated variables applying a regression analysis and with variables applying a correlation analysis

Practical improvements in technology

Practical improvements in organizational culture in OSH

Practical improvements in sociotechnical systems in OSH

Micro-level ”Use of PPE”

Fig. 1. The reference frame of Karsh et al. (2014) with the micro-, meso- and macro-levels approach which is being applied to aggregated variables in OSH.

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MACROLEVEL

”Quality of OSH legislation”, “Quality of OHC services”

Action formation

Situational mechanisms

MESOLEVEL

MICROLEVEL

”OSH management & collaboration”, ”Technical processes”, ”Instructions”, ”Measuring and monitoring”, ”Training” Situational mechanisms ”Use of PPE”

Practical implicationsin compliance with the legislation Transformation

Action formation

Practical implicationsin sociotechnical processes of companies

Transformation

Action formation

Practical implications related to empowerment of behavior of managers and workers

Fig. 2. A causal diagram relating the macro-, meso- and micro-levels in OSH (adapted from Jepperson and Meyer (2011) and Coleman (1986)).

top-down” is inadequate; a fundamentally different view of system modeling is demanded for today’s dynamics. Grote (2012) stated that the move toward goal-oriented regulation could also be understood in the context of a general trend toward acknowledging the need to cope with uncertainty instead of trying to manage it away. Hale and Borys (2014a) found that once we accept that rules cannot cover all eventualities and that all rules have exceptions, it becomes essential that operators, or others somewhere in the system but in close contact with those at the ‘sharp end’, apply their deep competence and tacit knowledge to exercise discretion in applying any rules so that they are operational. The rules have to be interpreted and implemented in the context of a particular company, taking into consideration the work processes and equipment applied (Hale and Borys, 2014a). The Finnish OSH Act (Finnish Legislation, 2002) implements the EU Directive 89/391/EC (EU Directive, 1989). The Finnish Government Decree on Chemical Agents at Work (Finnish Legislation, 2001a) implements the EU Directive 98/24/EC (EU Directive, 1998).

and therefore are not easily and readily adaptable to the natural and inevitable variations occurring in work being conducted and the hazards being encountered (Wachter and Yorio, 2014). Effective leaders make efforts to organize activities in such a way that they make better use of resources, information, and equipment; they plan activities, assign tasks, determine resource requirements, and coordinate interrelated activities (Dulac and Leveson, 2004). To achieve these goals, effective leaders need to apply a variety of different relation-oriented behaviors. As employees are given the opportunity to contribute to the development of safe work procedures they are more likely to identify with those safe work procedures and thus follow them and further encourage others to do so to (Wachter and Yorio, 2014). Effective communication channels are needed between the hierarchical levels of each control structure, both a downward reference channel providing the information necessary to impose constraints on the level below and a measuring channel to obtain feedback about how effectively the constraints have been enforced (Leveson, 2005).

3.4.1.2. ‘‘Quality of occupational health care (OHC) services” (macrolevel) (see Appendix A.1: Question 2). The Finnish Occupational Health Care Act (Finnish Legislation, 2001b) stipulates that the employer shall arrange occupational health care (OHC) at his/her own expense in order to prevent and control health risks and problems. The Government Decree (Finnish Legislation, 2013) on the principles of good OHC practice stipulates that the OHC provider shall investigate the OSH risks in a way that allows it to function as an expert to propose initiatives and suggestions to the employer. Hale and Borys (2014a) indicated that one fundamental aspect of any system of management is its need to view rule sets as dynamic in order to (1) cope with diversity and exceptions to whatever rule is formulated and (2) place the focus of the management on the processes around monitoring and change (flexibility). Effective control of workplace risks requires their systematic assessment carried out by OHC, the consequent identification of areas where risks need to be better controlled and followed by the adoption of appropriate OSH proactive measures. The relevant strategies need to be applied in implementing effectively the controls, and the adoption of mechanisms to monitor and review their adequacy and identify whether action is needed to improve them (Walters et al., 2011, p. 186).

3.4.1.4. ‘‘Technical processes” (meso-level) (see Appendix A.1: Question 4). Regulatory or control action involves imposing constraints upon the activity at one level of a hierarchy. Those constraints define the ‘laws of behavior’ at that level that yield activity meaningful at a higher level e.g. emergent behavior (Leveson, 2002). The control processes that enforce these constraints must limit system behavior to the safe changes and adaptations implied by the constraints (Dulac and Leveson, 2004). Dulac and Leveson (2004) showed that as the complexity of engineered systems increases, hazard analysis techniques have continued to lag behind the state-of-the-art engineering practice. The most important aspects of the technical system are the control measures themselves, the equipment and process controls which are the necessary measures of major accident prevention and the safe boundary of operation (Bellamy et al., 2008).

3.4.1.3. ‘‘OSH management & collaboration” (meso-level) (see Appendix A.1: Question 3). Safety management systems tend to be institutionalized through policies, plans, procedures, and processes

3.4.1.5. ‘‘Instructions” (meso-level) (see Appendix A.1: Question 5). It is important to recognize that mastering any of the discipline requires efforts to understand the principles and to follow the best practices. Learning always involves new understandings and new behaviors, ‘‘thinking” and ‘‘doing” (Senge, 1994, p. 374). Traditional approaches to hazard analysis and safety-related risk management are based on an accident model that focuses on failure events in static engineering designs and linear notions of causality (Leveson, 2002). Therefore they are limited in their ability to include complex human decision-making, software errors, system

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accidents (versus component failure accidents), and organizational risk factors in the analysis (Leveson, 2002). When safety incidents (i.e. employee injuries and near misses) do occur, organizations can investigate those accidents with the ultimate goal of reducing the probability of the event occurring again (Wachter and Yorio, 2014). If they need to achieve a more comprehensive view of accidents, then risk management tools and models need to treat systems as dynamic processes that are continually adapting to achieve their ends and to react to changes in themselves and their environment (Leveson, 2002). 3.4.1.6. ‘‘Measuring and monitoring” (meso-level) (see Appendix A.1: Question 6). In each control loop, at each level of the sociotechnical control structure, hazardous behavior results from inadequate enforcement of constraints on the process controlled at the level below (Dulac and Leveson, 2004). Safe work procedures provide important and consistent information to workers of what is expected of them from a safety perspective (Wachter and Yorio, 2014). Worker adaptation can be fostered by a work system that allows workers to exercise control and be autonomous (Carayon et al., 2014). Depending on the complexity of the system and its social setting, the control structure may need to take into consideration the management and organizational components that affect the system’s development and operation (Dulac and Leveson, 2004). The most important aspect of the technical system is the control measures themselves, the equipment and process controls which are the necessary measures of major accident prevention and the safe boundary of operation (Bellamy et al., 2008). 3.4.1.7. ‘‘Training” (meso-level) (see Appendix A.1: Question 7). The training as a process of the model introduces guidelines about how to identify the OSH risks and implement the risk control measures. The on-the-job training is mainly aimed at increasing individual knowledge about risks and risk protection measures (Grote, 2012). Argyris (1999, p. 67) showed that learning is defined as occurring under two conditions. First, learning occurs when an organization achieves what it intended; that is, there is a match between its design for action and the actuality or outcome. The best human performance tools allow the workers to continually learn and adapt from their work situations in order to be more aware of and safely deal with deficiencies within or changes occurring in the workplace (Wachter and Yorio, 2014). An organizational climate will strengthen personal mastery in two ways (Senge, 1994, p. 172). First, it will continually reinforce the idea that personal growth is truly valued in the organization. Second, to the extent that individuals respond to what is offered, it will provide an ‘‘on-the-job training” that is vital to developing personal mastery. As with any discipline, developing personal mastery must be viewed as a continual development. The management system described by Bellamy et al. (2008) has two components: an output of information on hazards as criteria and inputs also to other processes such as training. Developing an organization’s capacity to work with the models of the continuous learning involves both learning new skills and implementing institutional innovations that help bring these skills into regular practice (Senge, 1994, p. 188). 3.4.1.8. ‘‘Use of personal protective equipment (PPE)” (micro-level) (see Appendix A.1: Question 8). Leaders can provide feedback about the use of PPE that facilitates learning thereby making more relevant the use of PPE in OSH orientation sessions. Worker engagement in safety may systematically act to reduce the probability of human errors from occurring by making workers more involved with and aware of their tasks/surroundings and associated risks, as well as error traps that could be present (Wachter and Yorio, 2014).

Managers who supervise the use of PPE need extensive knowledge about the best training methods to be used to perform the training process. One needs to have technical knowledge of both PPE and work processes before starting to plan and organize the use of PPE in work operations, to direct and train employees with specialized activities, and to monitor and evaluate their OSH performance. Technical expertise is needed to deal with disruptions in the work due to equipment breakdowns, quality defects, accidents, insufficient materials, and coordination problems. 3.4.2. The Hypotheses H1–H5 of this study Based on the above mentioned (Section 3.4.1) results, it was hypothesized that the following relationships could exist for OSH managers and workers’ OSH representatives: Hypothesis 1 – H1. The aggregated variable H1(a) ‘‘Training” (meso-level), H1(b) ‘‘Measuring and monitoring” (meso-level), H1 (c) ‘‘Quality of OHC services” (macro-level), H1(d) ‘‘Quality of OSH legislation” (macro-level) would be positively related to the aggregated variable of ‘‘OSH management & collaboration”.

Hypothesis 2 – H2. The aggregated variable H2(a) ‘‘Training” (meso-level), H2(b) ‘‘Measuring and Monitoring” (meso-level), H2 (c) ‘‘OSH management & collaboration” (meso-level), H2(d) ‘‘Quality of OHC services” (macro-level), H2(e) ‘‘Quality of OSH legislation” (macro-level) would be positively related to the aggregated variable of ‘‘Technical processes” (meso-level). Hypothesis 3 – H3. The aggregated variable H3(a) ‘‘Training” (meso-level), H3(b) ‘‘OSH management & collaboration” (mesolevel), H3(c) ‘‘Quality of OHC services” (macro-level), H3(d) ‘‘Quality of OSH legislation” (macro-level) would be positively related to the aggregated variable of ‘‘Measuring and monitoring” (mesolevel). Hypothesis 4 – H4. The aggregated variable H4(a) ‘‘Training” (micro-level), H4(b) ‘‘Measuring and Monitoring” (meso-level), H4(c) ‘‘OSH management & collaboration” (meso-level), H4(d) ‘‘Quality of OHC services” (meso-level), H4(e) ‘‘Quality of OSH legislation” would be positively related to the aggregated variable of ‘‘Instructions” (meso-level). Hypothesis 5 – H5. The aggregated variable H4(a) ‘‘Training” (meso-level), H4(b) ‘‘Measuring and Monitoring” (meso-level), H4 (c) ‘‘OSH management & collaboration” (meso-level), H4(d) ‘‘Quality of OHC services” (macro-level), H4(e) ‘‘Quality of OSH legislation” (macro-level) would be positively related to the aggregated variable of ‘‘Use of personal protective equipment” (micro-level).

4. Results of the questionnaire study 4.1. Hypotheses 1–5 for OHS managers and workers’ OSH representatives 4.1.1. The effects of the aggregated OSH variables with regression analysis For workers’ OSH representatives, the regression analysis indicated that the independent aggregated variables had a statistically highly significant effect on the aggregated dependent variable (‘‘OSH management & collaboration” – meso-level) within micro-, meso- and macro-levels (Table 1, see the aggregated variables in Appendix A.1).

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Table 1 For workers’ OSH representatives, R-squared and the parameter estimates in the regression analysis with respect to the relationships of the different variables: ‘‘Training” (mesolevel), ‘‘Measuring and monitoring” (meso-level), ‘‘Quality of OHC services” (macro-level), ‘‘Quality of OSH legislation” (macro-level) on the aggregated variable ‘‘OSH management & collaboration” (dependent). Dependent: aggregated variable of the ‘‘OSH management & collaboration (Q3)” (meso-level)

***

Independent aggregated variable

R2

Parameter estimate

Standard error

t-value

p-value

Standardized estimate

95% confidence limits

(1) (2) (3) (4)

0.46 0.38 0.42 0.12

1.10 1.03 0.96 0.36

0.11 0.12 0.11 0.09

10.03 8.49 9.04 3.99

0.0001*** 0.0001*** 0.0001*** 0.0001***

0.68 0.62 0.65 0.35

0.89 0.79 0.75 0.18

Q7: Q6: Q2: Q1:

‘‘Training” (meso-level) ‘‘Measuring and monitoring” (meso-level) ‘‘Quality of OHC services” (macro-level) ‘‘Quality of OSH legislation” (macro-level)

1.32 1.27 1.17 0.53

p < 0.001.

With respect to ‘‘OSH management & collaboration (Q3)” (Table 1) the statistical significance exists within the following categories (1)–(4) in the following sub-categories:

With respect to ‘‘Technical processes (Q4)” (Table 2) the statistical significance exists within the following categories (1)–(5) in the following sub-categories:

(1) Q7: ‘‘Training” – Q7 – (2) ‘‘Workers’ training” (t = 2.30, p = 0.0237⁄); – Q7 – (4) ‘‘Risks and safe working techniques have been taught” (t = .3.21, p = 0.0018⁄⁄) (2) Q6: ‘‘Measuring and monitoring” – Q6 – (3) ‘‘OEL values are not being exceeded” (t = 4.58, p = 0.0001⁄⁄⁄); – Q6 – (4) ‘‘Plans and guidance work are being implemented in practice” (t = 2.36, p = 0.0202⁄) (3) Q2: ‘‘Quality of OHC services” – Q2 – (1) ‘‘Workplace surveys which are utilized” (t = 2.43, p = 0.0172⁄); – Q2 – (5) ‘‘Health examinations are organized” (t = 3.08, p = 0.0028⁄⁄) (4) Q1: ‘‘Quality of OSH legislation” – Q1 – (5) ‘‘OSH regulations increase employer’s interest and motivation” (t = 4.82, p = 0.0001⁄⁄⁄)

(1) Q7: ‘‘Training” – Q7 – (2) ‘‘Workers’ training” (t = 2.68, p = 0.0086⁄⁄); – Q7 – (4) ‘‘Safety is a significant part of training” (t = .2.08, p = 0.0396⁄) (2) Q6: ‘‘Measuring and monitoring” – Q6 – (3) ‘‘OEL values are not exceeded” (t = 5.11, p = 0.0001⁄⁄⁄) (3) Q3: ‘‘OSH management & collaboration” – Q3 – (2) ‘‘HES management promotes OSH” (t = 2.20, p = 0.0316⁄); – Q3 – (7) ‘‘We resolve OSH problems together” (t = 3.91, p = 0.0002⁄⁄⁄) (4) Q2: ‘‘Quality of OHC services” – no statistical effect of sub-categories (5) Q1: ‘‘Quality of OSH legislation” – Q1 – (5) ‘‘OSH regulations increase employer’s interest and motivation” (t = 3.06, p = 0.0030⁄⁄)

For OSH managers, with respect to the corresponding categories in Table 1, the following aggregated variables of (1) Q7: ‘‘Training” (meso-level, p = 0.0001⁄⁄⁄), (2) Q6: ‘‘Measuring and monitoring” (meso-level, p = 0.0001⁄⁄⁄) and (3) Q2: ‘‘Quality of OHC services” (macro-level, p = 0.0006⁄⁄⁄) displayed a statistically highly significant effect on the aggregated variable of ‘‘OSH management & collaboration (Q3)” (meso-level). The aggregated variable of (4) Q1: ‘‘Quality of OSH legislation” (macro-level, p = 0.0097⁄⁄) exerted a statistically significant effect. For workers’ OSH representatives, the regression analysis indicated that the independent aggregated variables had a statistically highly significant effect on the aggregated dependent variable (‘‘Technical processes” – meso-level) within micro-, meso- and macro-levels (Table 2, see the aggregated variables in Appendix A.1).

For OSH managers, with respect to the corresponding categories in Table 2, the following aggregated variables of (1) Q7: ‘‘Training” (meso-level, p = 0.0008⁄⁄⁄), (2) Q6: ‘‘Measuring and monitoring” (meso-level, p = 0.0001⁄⁄⁄), (3) Q3: ‘‘OSH management & collaboration” (meso-level, p = 0.0004⁄⁄⁄) and (5) Q1: ‘‘Quality of OSH legislation” (macro-level, p = 0.0006⁄⁄⁄) had a statistically highly significant effect on the aggregated variable of ‘‘Technical processes (Q4)” (meso-level). No dependence was found with respect to the aggregated variable of (4) Q2: ‘‘Quality of OHC services” (macro-level). For workers’ OSH representatives, the regression analysis indicated that the independent aggregated variables had a statistically highly significant effect on the aggregated dependent variable (‘‘Measuring and monitoring” – meso-level) within micro-, meso-

Table 2 For workers’ OSH representatives, R-squared and the parameter estimates in the regression analysis with respect to the relationships of the different variables: ‘‘Training” (mesolevel), ‘‘Measuring and Monitoring” (meso-level), ‘‘OSH management & collaboration” (meso-level) ‘‘Quality of OHC services” (macro-level), ‘‘Quality of OSH legislation” (macrolevel) on the aggregated variable ‘‘Technical processes” (dependent). Dependent: aggregated variable of the ‘‘Technical processes (Q4)” (meso-level)

***

Independent aggregated variable

R2

Parameter estimate

Standard error

t-value

p-value

Standardized estimate

95% confidence limits

(1) (2) (3) (4) (5)

0.33 0.38 0.30 0.26 0.10

0.94 1.03 0.55 0.76 0.33

0.12 0.12 0.08 0.12 0.09

7.51 8.45 7.06 6.23 3.46

0.0001*** 0.0001*** 0.0001*** 0.0001*** 0.0008***

0.57 0.62 0.55 0.51 0.31

0.69 0.79 0.40 0.52 0.14

Q7: Q6: Q3: Q2: Q1:

‘‘Training” (meso-level) ‘‘Measuring and Monitoring” (meso-level) ‘‘OSH management & collaboration” (meso-level) ‘‘Quality of OHC services” (macro-level) ‘‘Quality of OSH legislation” (macro-level)

p < 0.001.

1.18 1.28 0.71 1.00 0.50

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Table 3 For workers’ OSH representatives, R-squared and the parameter estimates in the regression analysis with respect to the relationships of the different variables: ‘‘Training” (mesolevel), ‘‘OSH management & collaboration” (meso-level), ‘‘Quality of OHC services” (macro-level), ‘‘Quality of OSH legislation” (macro-level) on the aggregated variable ‘‘Measuring and monitoring” (dependent). Dependent: aggregated variable of the ‘‘Measuring and monitoring (Q6)” (meso-level)

*** **

Independent aggregated variable

R2

Parameter estimate

Standard error

t-value

p-value

Standardized estimate

95% confidence limits

(1) (2) (3) (4)

0.33 0.38 0.33 0.08

0.55 0.37 0.52 0.18

0.07 0.04 0.07 0.06

7.47 8.49 7.51 3.21

0.0001*** 0.0001*** 0.0001*** 0.0017**

0.57 0.62 0.58 0.29

0.41 0.28 0.38 0.07

Q7: Q3: Q2: Q1:

‘‘Training” (meso-level) ‘‘OSH management & collaboration” (meso-level) ‘‘Quality of OHC services” (macro-level) ‘‘Quality of OSH legislation” (macro-level)

0.70 0.46 0.66 0.29

p < 0.001. p < 0.01.

and macro-levels (Table 3, see the aggregated variables in Appendix A.1). With respect to ‘‘Measuring and monitoring (Q6)” (Table 3) the statistical significance exists within the following categories (1)–(4) in the following sub-categories: (1) Q7: ‘‘Training” – Q7 – (4) ‘‘Risks and the safe working techniques have been taught” (t = 2.18, p = 0.0318⁄) (2) Q3: ‘‘OSH management & collaboration” – Q3 – (5) ‘‘OSH representative receives necessary information” (t = 2.12, p = 0.0379⁄); – Q3 – (7) ‘‘We resolve OSH problems together” (t = 2.27, p = 0.0266⁄) (3) Q2: ‘‘Quality of OHC services” – Q2 – (1) ‘‘OHC service provider is being informed of safetyrelated changes” (t = 2.35, p = 0.0212⁄) (4) Q1: ‘‘Quality of OSH legislation” – Q1 – (5) ‘‘OSH regulations increase the employer’s interest and motivation” (t = 4.82, p = 0.0040⁄) For OSH managers, with respect to the corresponding categories in Table 3, the following aggregated variables of (1) Q7: ‘‘Training” (meso-level, p = 0.0001⁄⁄⁄) and (2) Q3: ‘‘OSH management & collaboration” (meso-level, p = 0.0001⁄⁄⁄) had a statistically highly significant effect on the aggregated variable of ‘‘Technical processes (Q6)” (meso-level). The aggregated variable of (4) Q1: ‘‘Quality of OHC services” (macro-level, p = 0.0270⁄) had an almost statistically significant effect whereas (3) Q2: ‘‘Quality of OSH legislation” (macro-level, p = 0.0086⁄⁄) had a statistically significant effect. For workers’ OSH representatives, the regression analysis indicated that the independent aggregated variables had a

statistically highly significant effect on the aggregated dependent variable (‘‘Instructions” – meso-level) within micro-, meso- and macro-levels (Table 4, see the aggregated variables in Appendix A.1). The dependence was not found with respect to the aggregated variable of ‘‘OSH legislation” (macro-level). With respect to ‘‘Instructions (Q5)” (Table 4) the statistical significance exists within the following categories (1)–(5) in the following sub-categories: (1) Q7: ‘‘Training” – Q7 – (4) ‘‘Safety is a significant part of training” (t = 2.21, p = 0.0294⁄) (2) Q6: ‘‘Measuring and monitoring” – Q6 – (3) ‘‘OEL values are not exceeded” (t = 2.68, p = 0.0001⁄⁄⁄); (3) Q3: ‘‘OSH management & collaboration” – no statistical effect of the sub-categories (4) Q2: ‘‘Quality of OHC services” – Q2 – (4) ‘‘OHC service provider can be requested for information” (t = 3.44, p = 0.0009⁄⁄⁄); – Q2 – (5) ‘‘Health examinations are organized” (t = 3.08, p = 0.0089⁄⁄) (5) Q1: ‘‘Quality of OSH legislation” – Q1 – (5) ‘‘OSH regulations increase employer’s interest and motivation” (t = 4.39, p = 0.0001⁄⁄⁄) For OSH managers, with respect to the corresponding categories in Table 4, the following aggregated variables of (1) Q7: ‘‘Training” (meso-level, p = 0.0001⁄⁄⁄) and (3) Q3: ‘‘OSH management & collaboration” (meso-level, p = 0.0002⁄⁄⁄) displayed a statistically highly significant effect on the aggregated variable of ‘‘Instructions (Q5)” (meso-level). The aggregated variables of (2) Q6: ‘‘Measuring and Monitoring” (meso-level, p = 0.0020⁄⁄) and (4) Q2: ‘‘Quality of

Table 4 For workers’ OSH representatives, R-squared and the parameter estimates in the regression analysis with respect to the relationships of the different variables: ‘‘Training” (mesolevel), ‘‘Measuring and Monitoring” (meso-level), ‘‘OSH management & collaboration” (meso-level), ‘‘Quality of OHC services” (macro-level) and ‘‘Quality of OSH legislation” (macro-level) on the aggregated variable ‘‘Instructions” (dependent). Dependent: aggregated variable of the ‘‘Instructions (Q5)” (meso-level)

*** **

Independent aggregated variable

R2

Parameter estimate

Standard error

t-value

p-value

Standardized estimate

95% Confidence Limits

(1) Q7: ‘‘Training” (meso-level) (2) Q6: ‘‘Measuring and Monitoring” (meso-level) (3) Q3: ‘‘OSH management & collaboration” (mesolevel) (4) Q2: ‘‘Quality of OHC services” (macro-level) (5) Q1: ‘‘Quality of OSH legislation” (macro-level)

0.34 0.25 0.29

0.40 0.35 0.23

0.05 0.06 0.03

7.75 6.19 6.82

0.0001*** 0.0001*** 0.0001***

0.58 0.50 0.53

0.30 0.24 0.16

0.50 0.46 0.29

0.28 0.09

0.33 0.13

0.05 0.04

6.59 3.30

0.0001*** 0.0013**

0.53 0.30

0.23 0.05

0.44 0.20

p < 0.001. p < 0.01.

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Table 5 For workers’ OSH representatives, R-squared and the parameter estimates in the regression analysis with respect to the relationships of the different variables: ‘‘Training” (mesolevel), ‘‘Measuring and Monitoring” (meso-level), ‘‘OSH management & collaboration” (meso-level), ‘‘Quality of OHC services” (macro-level), ‘‘Quality of OSH legislation” (macrolevel) on Use of personal protective equipment’ (dependent). Dependent: aggregated variable of the ‘‘Use of PPE (Q8)” (micro-level)

*** **

Independent aggregated variable

R2

Parameter estimate

Standard error

t-value

p-value

Standardized estimate

95% confidence limits

(1) Q7: ‘‘Training” (meso-level) (2) Q6: ‘‘Measuring and Monitoring” (meso-level) (3) Q3: ‘‘OSH management & collaboration” (mesolevel) (4) Q2: ‘‘Quality of OHC services” (macro-level) (5) Q1: ‘‘Quality of OSH legislation” (macro-level)

0.21 0.31 0.25

0.53 0.66 0.36

0.10 0.09 0.06

5.54 7.17 6.21

0.0001*** 0.0001*** 0.0001***

0.46 0.55 0.50

0.34 0.48 0.24

0.72 0.85 0.47

0.18 0.08

0.46 0.20

0.09 0.07

5.02 3.07

0.0001*** 0.0027**

0.43 0.28

0.28 0.07

0.64 0.33

p < 0.001. p < 0.01.

OHC services” (macro-level, p = 0.0092⁄⁄) has a statistically significant effect. No dependence was found with respect to the aggregated variable of (5) Q1: ‘‘Quality of OSH legislation” (macro-level). For workers’ OSH representatives, the regression analysis indicated that the independent aggregated variables had a statistically highly significant effect on the aggregated dependent variable (‘‘Use of personal protective equipment” – micro-level) within micro-, meso- and macro-levels (Table 5, see the aggregated variables in Appendix A.1). With respect to ‘‘Use of PPE (Q8)” (Table 5) the statistical significance exists within the following categories (1)–(5) in the following sub-categories: (1) Q7: ‘‘Training” – Q7 – (4) ‘‘Safety is a significant part of training” (t = 2.61, p = 0.0103⁄) (2) Q6: ‘‘Measuring and monitoring” – Q6 – (3) ‘‘OEL values are not exceeded” (t = 4.18, p = 0.0001⁄⁄⁄); – Q6 – (4) ‘‘Plans and guidance work in practice” (t = 3.22, p = 0.0018⁄⁄) (3) Q3: ‘‘OSH management & collaboration” – Q3 – (3) ‘‘OSH representative receives training” (t = 2.29, p = 0.0253⁄); – Q3 – (5) ‘‘OSH representative receives necessary information” (t = 2.12, p = 0.0383⁄); – Q3 – (7) ‘‘We resolve OSH problems together” (t = 2.79, p = 0.0070⁄⁄) (4) Q2: ‘‘Quality of OHC services” – no statistical effect of sub-categories (5) Q1: ‘‘Quality of OSH legislation” – Q1 – (5) ‘‘OSH regulations increase the employer’s interest and motivation” (t = 2.47, p = 0.0154⁄) For OSH managers, with respect to the corresponding categories in Table 5, the following aggregated variables of (1) Q7: ‘‘Training” (meso-level, p = 0.0001⁄⁄⁄), (2) Q6: ‘‘Measuring and monitoring” (meso-level, p = 0.0001⁄⁄⁄), (3) Q3: ‘‘OSH management & collaboration” (meso-level, p = 0.0009⁄⁄⁄) and (5) Q1: ‘‘Quality of OSH legislation” (macro-level, p = 0.0001⁄⁄⁄) had a statistically highly significant effect on the aggregated variable of ‘‘Use of PPE (Q8)” (micro-level). The aggregated variables of (4) Q2: ‘‘Quality of OHC services” (macro-level, p = 0.0059⁄⁄) had a statistically significant effect. 4.1.2. Results of the testing of Hypotheses 1–5 For workers’ OSH representatives, the results verified the five hypotheses H1–H5. For OSH managers, most of the hypotheses in H1–H5 were supported although not H2(d) and H4(e).

H1. With respect to the aggregated variable of ‘‘OSH management & collaboration” the relationships were as follows: aggregated variable of H1(a) ‘‘Training” (for Workers (W) and Managers (M) p < .0.001), H1(b) ‘‘Measuring and monitoring” (for W and M p < .0.001), H1(c) ‘‘Quality of OHC services” (for W and M p < .0.01) and H1(d) ‘‘Quality of OSH legislation” (for W p < .0.001; for M p < .0.01).

H2. With respect to the aggregated variable of ‘‘Technical processes” the relationships were as follows: aggregated variables of H2(a) ‘‘Training” (for W and M p < .0.001), H2(b) ‘‘Measuring and monitoring” (for W and M p < .0.001), H2(c) ‘‘OSH management & collaboration” (for W and M p < .0.001), H2(d) ‘‘Quality of OHC services” (for W p < .0.001; for M no effect) and H2(e) ‘‘Quality of OSH legislation” (for W and M p < .0.001). H3. With respect to the aggregated variable of ‘‘Measuring and monitoring” the relationships were as follows: aggregated variables of H3(a) ‘‘Training” (for W and M p < .0.001), H3(b) ‘‘OSH management & collaboration” (for W and M p < .0.001), H3(c) ‘‘Quality of OHC services” (for W p < .0.001; for M p < 0.05) and H3(d) ‘‘Quality of OSH legislation” (for W and M p < .0.01). H4. With respect to the aggregated variable of ‘‘Instructions” the relationships were as follows: aggregated variables of H4(a) ‘‘Training” (for W and M p < .0.001), H4(b) ‘‘Measuring and Monitoring” (for W p < .0.001, for M p < 0.01), H4(c) ‘‘OSH management & collaboration” (for W and M p < .0.001), H4(d) ‘‘Quality of OHC services” (for W p < .0.001, for M p < 0.01) and H4(e) ‘‘Quality of OSH legislation” (for W p < .0.01; for M no effect). H5. With respect to the aggregated variable of ‘‘Use of PPE” (micro-level) the relationships were as follows: aggregated variables of H5(a) ‘‘Training” (for W and M p < .0.001), H5(b) ‘‘Measuring and monitoring” (for W and M p < .0.001), H5(c) ‘‘OSH management & collaboration” (for W and M p < .0.001), H5(d) ‘‘Quality of OHC services” (for W p < .0.001, for M p < 0.01) and H5(e) ‘‘Quality of OSH legislation” (for W and M p < .0.01). 4.2. Correlations between micro-, meso- and macro-levels’ variables in OSH 4.2.1. Correlations between macro- and meso-level variables in OSH The correlations detected between the two variables i.e. ‘‘Quality of OSH legislation (Q1)” (macro-level) and ‘‘Technical processes

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(Q4)” (meso-level) for OSH managers and workers’ OSH representatives are presented in Table B.1 (Appendix B). These results reveal that within sociotechnical systems, the upper technical systems and human factors of organizations exert influences on their sub-systems. With respect to regulations, their ‘‘clarity (Q1–1)”, ‘‘understandability (Q1–2)” and ‘‘coverage (Q1–4)” are significantly correlated. The present results show that when ‘‘Production methods (Q4–2)” are correct, this influences the probability that ‘‘General air conditioning system (Q4–4)” and ‘‘Local exhaust ventilation (Q4–5)” will be functioning well and will be used. In this context, work systems of production methods need to be jointly optimized if they are to attain the maximum efficiency in achieving the system’s primary OSH task. With respect to the OHS managers, the value of the correlation coefficient between the following two variables (a) ‘‘Employer has chosen such production methods that cause the least OSH harm (Q4–2)” and (b) ‘‘Regulations are clear (Q1–1)” was statistically significant (r = 0.38, p < 0.01); the correlation coefficient between a(Q4–2) and (c) ‘‘Regulations are easy to understand (Q1–3)” was statistically significant (r = 0.36, p < 0.01). Similarly, for workers’ OSH representatives, the values of the correlation coefficient between (a) and (b) were significant (r = 0.28, p < 0.01), and between a (Q4–2) and (c) significant (r = 0.43, p < 0.001). In addition, for workers’ OSH representatives, the corresponding values between a(Q4–2) and ‘‘Regulations cover very well the various aspects of using chemicals (Q1–4)” were statistically significant (r = 0.40, p < 0.01) as were those between a(Q4–2) and ‘‘Economic benefits from the regulations are larger (Q1–5)” (r = 0.33, p < 0.01). In addition, the results show that managers have to continue to ensure that organizational processes are efficient and that they are taking advantage of the latest developments concerning the safe use of chemicals and in production technology. In this context, the correlation coefficients between a(Q4–2) ‘‘Employer has chosen such production methods that cause the least OSH harm (Q4–2)” and ‘‘Employer has chosen such chemicals for use that cause the least OSH harm (Q4–1)” were significant for OHS managers (r = 0.71, p < 0.001) and workers’ OSH representatives (r = 0.49, p < 0.001). 4.2.2. Correlations among meso-level variables in OSH The correlations between the variables of ‘‘OSH management & collaboration (Q3)” for OSH managers and workers’ OSH representatives are presented in Table C.1 (Appendix C). The results show that within sociotechnical systems ‘‘Solving and taking care together (Q3–7)” in safety management tend to be institutionalized through the activities conducted by OSH manager and HES managers. In addition, OSH promotion is being encountered in the real work situation concerned with OSH training and giving OSH information to all members of the organizations. The results indicate that the optimal system balance is achieved when the overall combination of positive elements achieves maximum benefits for the whole organization. For OHS managers, a statistically significant correlation coefficient was detected between the following variables (a) ‘‘We solve and take care of OSH issues together (Q3–7)” and (b) ‘‘OSH manager actively promotes (Q3–1)” statistically significant (r = 0.53, p < 0.001). The correlation coefficient was significant between a(Q3–7) and (c) ‘‘HES management actively promotes (Q3–2)” (r = 0.32, p < 0.05) as was the correlation coefficient between a(Q3–7) and the following statements: (A) ‘‘OSH representative receives OSH training (Q3–3)” (r = 0.39, p < 0.01); (B) ‘‘OSH training has been utilized (Q3–4)” (r = 0.42, p < 0.001); (C) ‘‘OSH representative receives all information (Q3–5)” significant (r = 0.30, p < 0.05); and (D) ‘‘The personnel is being informed (Q3–6)” significant (r = 0.32, p < 0.01). Similarly for the workers’ OSH representatives, the

corresponding figures were in general even more statistically significant in categories (A)–(D). 4.2.3. Correlation between meso- and micro-level variables in OSH The correlations between the variables of ‘‘Training (Q7)” (meso-level) and of ‘‘Use of personal protective equipment (Q8)” (micro-level) for OSH managers and workers’ OSH representatives are presented in Table D.1 (Appendix D). The results show that many of the OSH practices most conducive to developing the organization’s adherence to good safety procedures – e.g., developing a more systemic OSH training, learning how to reflect on ‘‘Use of personal protective equipment” are all embedded into the disciplines for building a learning organization. For the workers’ OSH representatives, the following correlation coefficients between these variables were statistically significant (a) ‘‘Safe ways to work (Q7–4)” in relation to training of ‘‘Line management (Q7–1)” (r = 0.56, p < 0.001), to training of ‘‘Workers” (Q7–2) (r = 0.68, p < 0.001), and ‘‘Safety a significant part of worker’s on-the-job training (Q7–3)” (r = 0.73, p < 0.001). The effective control of ‘‘Use of personal protective equipment” requires a systematic assessment of their necessity, the consequent identification of areas where risks need to be better supervised and then this must be followed by the adoption of PPE’s as well as ensuring that they are being used appropriately. Thus the relevant strategies need to be applied in implementing the effective use of PPE and the adoption of mechanisms to monitor and review their adequacy and to identify whether action is needed to improve their use. For both OHS managers and workers’ OSH representatives, highly significant correlation coefficients were detected between all variables of the safety training (Q7) and use of PPE (Q8). The results show with respect to the appropriate use of PPE, that a good management system should possess the following qualities: (1) be able to identify the end result being pursued by the PPE; (2) to determine the relevant training system which will ensure the appropriate use of the PPE; (3) to examine the entire range of the process (e.g. the need for PPE, workers’ participation, supervision) as a proactive component for managing and controlling the work environment; and (4) to make sure that the effective use of PPE is considered as a natural part of the organization’s safetyconsciousness. 5. Discussion 5.1. Theoretical implications For OSH managers and workers’ OSH representatives, the Hypotheses 1–5 were supported with the aggregated variables as follows: Hypothesis 1 – OSH management & collaboration was positively related to training, measuring and monitoring, quality of OHC services and quality of OSH legislation; Hypothesis 2 – technical processes were positively related to training, measuring and monitoring, OSH management & collaboration and quality of OSH legislation; Hypothesis 3 – measuring and monitoring were positively related to training, OSH management & collaboration, quality of OHC services and quality of OSH legislation; Hypothesis 4 – instructions were positively related to training, measuring and monitoring, OSH management & collaboration and quality of OHC services; and Hypothesis 5 – use of personal protective equipment was positively related to training, measuring and monitoring, OSH management & collaboration, quality of OHC services and quality of OSH legislation. This study supports the results of Murphy et al. (2014) that the purpose of foundational theoretical framework of sociotechnical systems theory is to establish a ‘‘harmonized” work system that improves numerous aspects of organizational performance and

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effects. In addition, this study supports the results of Murphy et al. (2014) that there is a need to emphasize a multilevel-analysis that simultaneously examines the impact of the work system design interfaces in order to integrate a sociotechnical system model. This study supports the findings of Karsh et al. (2014) that research about micro-, meso- and macro-levels are important in helping to determine the extent and the design of workplace-related improvements and their reasoning. The present study supports the theoretical framework presented by other investigators (Marras and Hancock, 2014; Norros, 2014; Hollnagel, 2014). Marras and Hancock (2014) showed that there are significant interactions between the various components of the physical and cognitive subsystems when integrating work environment dimensions and the socio-technical interactions in the overall-system performance, e.g. with respect to risk and safety. Furthermore, Marras and Hancock (2014) claimed that it is important to identify all the potentially significant parts and understand how the each interacts with each other in mediating the system performance. The approach of Norros (2014) provided a reference to which the actual observed course of events can be compared in order to understand a sequence of correct actions; the modeling technique reveals the connection of the actual constraints and possibilities in the situation with the upper level control functions and objectives of the activity. Hollnagel (2014) showed that the systems thinking approach can be involved in all stages of planning, design, implementation, evaluation, maintenance, redesign and continuous improvement of systems. The present study supports the theoretical framework presented by other investigators (e.g. Wilson, 2014; Carayon et al., 2014; Siemieniuch and Sinclair, 2014). Wilson (2014) indicated that that study design and data analysis techniques are being useful since they allow interpretation of the entire system across a number of levels of analysis. Wilson (2014) adopted a holistic approach to determine that the solutions involved in the cognitive, physical and social sub-system must be combined to an extent which is appropriate to the system. The impact of the work system can be evaluated by examining the effect on processes and outcomes (Carayon et al., 2014). Siemieniuch and Sinclair (2014) reviewed ‘‘optimisation and operation” that in the socio-technical issues for Systems of Systems the individual systems are components of the systems and each of them exists in a relationships with the others in a holistic system. 5.2. Practical implications The present study found that micro-, meso- and macro-levels approach in OSH matter can be applied in systems defining: (1) system boundary, (2) inputs and outputs, (3) components, (4) structure, (5) relevant interactions and the integrity within the behavior of the components and their effect on the overall system state. The efficiency of OSH measures can be increased by finding more rational ways to organize and perform the work, and by deciding how to make the best use of available technology, resources, and personnel. The functional specification related micro-, meso- and macro-levels revealed how one can apply a system approach model to demonstrate how the human factors (e.g. ‘‘Training”), safety management (e.g. ‘‘OSH management & collaboration”), and organizational factors (e.g. ‘‘Measuring and monitoring”) fit together in the broad safety context (e.g. OSH legislation) and which can be considered as an integrated part of normal operational decision making. The present results revealed that within sociotechnical systems, the upper technical systems and human factors of organizations exert influences on their sub-systems. In this context, work systems of production methods need to be jointly optimized if they

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are to attain the maximum efficiency in achieving the system’s primary OSH task. In addition, OSH promotion is being encountered in the real work situation concerned with OSH training and giving OSH information to all members of the organizations. The results indicated that the optimal system balance is achieved when the overall combination of positive elements achieves maximum benefits for the whole organization. The results showed that many of the OSH practices most conducive to developing the organization’s adherence to good safety procedures are all embedded into the disciplines for building a learning organization. This study showed that an effective control of workplace risks requires the systematic assessment of the OSH impacts, the consequent identification of areas where risks need to be better controlled and the joint optimization of technology, organization and human factors. The results have a number of practical implications with respect to macro-level (impacts of legislation) and of the systems thinking approach as follows: First, this study suggests that complying with OSH (in this study ‘‘Quality of OSH legislation”) and OHC (‘‘Quality of OHC services”) legislations is a necessary foundation for achieving a safe working environment, but it cannot guarantee it. In this context, the requirements, practices and processes (e.g. ‘‘OSH management & collaboration”) implemented through OSH and OHC legislations provide the necessary phases in safety behavior. These legislations cannot anticipate and control all possible work situations and the management systems tend to be slow to adapt to changing situations or uncertainty because of their rigid, controlled and complicated structures. This study supports the results of Walters et al. (2011, p. 6) that the distinguishing feature of the development of regulation on OSH has been its shift from a prescriptive approach to a process-based approach in which improved OSH management is sought. Second, the present results support the findings of Wachter and Yorio (2014) who revealed that the practices and processes implemented through safety management system standards (e.g. ‘‘OSH management & collaboration”) provide the necessary ‘‘first steps” in arriving at safety excellence. According to one report (Grote and Künzler, 2000), there are two core assumptions in the sociotechnical systems approach to safety culture: (1) that the technical and social subsystems of a work system need to be jointly optimized to achieve maximum efficiency in the accomplishment of the system’s primary task and (2) that a crucial criterion for joint optimization is the system’s ability to control variances at their source. Walters et al. (2011) showed that efforts to prevent illness and injury at work have come from a combination of political activities, regulatory intervention, professional and managerial initiative and expertise as well as from the impact of technological innovation and economic restructuring. The results have a number of practical implications with respect to meso-level (organizations) of the systems thinking approach as follows: First, this study supports the results of Obadia et al. (2007) who concluded that for the hazardous technology a functional developed management system must include a day-to-day based integrated adaptive framework. Thus, one key requirement is the development of means to safety as a strategic dimension of the organization performance, thus strengthening the organizational commitment to safety. Human-related activities (e.g. ‘‘Training”) surrounding safety are those that support the performances of individuals (e.g. ‘‘Use of Personal Protective Equipment”) in the unit process model as well as in all other organizational practices (e.g. ‘‘Technical processes”). The contributions of both of these systems are needed to effectively manage safety performance in organizations. An argument can be made that the two types of systems are complementary and that their respective strengths can be merged into devising a more balanced and comprehensive system to managing safety (Wachter and Yorio, 2014).

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Second, this study supports the results of Bellamy et al. (2008) who revealed that within the loop of the safety management system one can distinguish several distinct layers in the prevention measures. In this context, the level of worker engagement (e.g. ‘‘Training”) associated within a system of safety management practices and processes (e.g. ‘‘OSH management & collaboration”) may be very important to their safety performance success (e.g. ‘‘Technical processes”). Furthermore, most managerial activity can be subdivided into four general components (Yukl, 2002, p. 40): (1) developing and maintaining relationships, (2) acquiring and distributing information, (3) making decisions, and (4) influencing people. Third, the present results support the results of Mohaghegh and Mosleh (2009) who showed that in complex socio-technical enterprises, the organizational safety practices should include those activities that support the ‘‘resources”, ‘‘procedures”, and ‘‘human actions” in the unit process model that are ultimately linked to the safety critical performances. Furthermore, a good organizational safety causal model should integrate the relevant aspects of organization practices (Mohaghegh and Mosleh, 2009). In addition, this study supports the findings of Bass (1997) that when applying the principles of the transformational leadership in OSH, the OSH managers should promote collaboration. According to Bass et al. (2003), transformational leaders provide constructive feedback to their followers and encourage followers to think creatively about complex problems. In addition, the ‘trust’ theme has also emerged in transformational theories (Bass, 1997) and in leader-member exchange theory (Schriesheim et al., 1999). Fourth, the present results support the findings of Siemieniuch and Sinclair (2014) who found that the implementing of ‘‘best practices” (e.g. ‘‘Technical processes”) and improvements to the organization’s processes (e.g. ‘‘Training”) are of high importance in the different engineering process in order to ensure that key decision-making roles (e.g. ‘‘OSH management & collaboration”) are clearly identified. The management of an uncertainty framework may help to further encourage the utilization of human factors/ergonomics knowledge in the domain of risk management (Grote, 2014).In addition, the engineering processes should ensure that the OSH competencies of the staff is optimal, and practice promoting the competent staff to OSH roles in the most effective and flexible way (Siemieniuch and Sinclair, 2014). By allowing employees voice in organizational decisions, using rewards to encourage ethical behavior, and injecting ethical values in regular business activity, ethical leaders enrich the autonomy and significance of work (Piccolo et al., 2010). The transformational patterns have direct implications for levels of core engagement characteristics (Piccolo and Colquitt, 2006). Fifth, this study supports the findings of Wachter and Yorio (2014) that the system of worker engagement should be strongly embedded into the implementation of the safety management system and its individual components. Cooperation between managers and employees is more likely when the OSH improvements are important to the organization and the top-management perceives that their coordination will actually exert a major influence by initiating OSH proactive measures. In a turbulent environment in which organizations must continually adapt, innovate, and reinvent themselves, leaders must be flexible enough to learn from mistakes, change their assumptions and beliefs, and refine their mental models (Yukl, 2002, p. 198). Sixth, the present results support the results of Argyris (1999, p. 157) that on-the-job training shall be integrated into an organizational learning system. In addition, when leaders have a practical influence on work decisions and articulate a compelling vision of the future, followers may view their jobs as more significant (Piccolo and Colquitt, 2006). While traditional organizations require management systems that control people’s behavior,

learning organizations invest in improving the quality of thinking, the capacity for reflection and team learning, and the ability to develop shared visions and shared understandings of complex business issues. It has been claimed that it is these capabilities that allow learning organizations to be both more locally controlled and well-coordinated than their hierarchical predecessors (Senge, 1994, p. 289). The top-managers are likely to engage in task behaviors such as setting challenging but realistic OSH goals and deadlines, developing specific action plans of proactive OSH, determining ways to overcome obstacles, organizing the work efficiently, and emphasizing OSH performance (applied from Yukl (2002, p. 83). The results have a number of practical implications with respect to micro-level (individuals) of the systems thinking approach as follows: First, with respect to the empowerment of the managers and workers this study supports the results of Yukl (2002, p. 106). In the workplaces top-management should be empowered to initiate and guide OSH improvements and to encourage OSH managers and to support ‘‘bottom-up” changes (e.g. ‘‘Use of Personal Protective Equipment”). The forms of participation are likely to be effective when the managers have sufficient skills in managing OSH matters, facilitating proactive OSH problem solving, and dealing with common OSH process problems that occur in organizations. It is essential for each employee to understand what duties, functions, and activities are required in the job and what OSH results are expected. The decision process used by the organization will determine if managers and employees are able to reach OSH goals, and it will determine the extent to which any decision incorporates their expertise and knowledge (applied from Yukl, 2002, p. 83). In this context, the participants of the collaborating teams who are involved in all aspects of the OSH decision process have more performance ability than an individual who merely contributes to one aspect. The safety performance is dependent on the company’s organizational culture, safety management and their human factors in terms of ‘‘the whole technical system and human elements being integrated in the right way” (Bellamy et al., 2008). 5.3. Limitations Some limitations should be considered, but notwithstanding, this study provides a new kind of the perspective micro-, mesoand macro-levels’ analysis of OSH that are linked to the distinctive contexts of work places. A cross-sectional analytical survey, based on an electronic questionnaire, was adopted as the research method for this study. However, despite the widely adopted utilisation of the cross-sectional questionnaire, there are limitations associated with such an approach. Socially desirable responding is most likely to occur when responding to socially sensitive questions (King and Bruner, 2000). Health and safety related research often covers sensitive topics for an organization, therefore researchers must ‘‘identify situations in which data may be systematically biased toward respondents’ perceptions of what is socially acceptable, to determine the extent to which this represents contamination of the data, and to implement the most appropriate methods of control” (e.g. King and Bruner, 2000). Chung and Monroe (2003) define social desirability as ‘‘the tendency of individuals to deny socially undesirable actions and behaviors and to admit to socially desirable ones”. In the present context of responses to questionnaires provided by the OSH managers and workers’ OSH representatives, this concept of organizational desirability will affect their responses; it is likely that the respondents will report higher frequencies of actions which are considered positive according to the organization’s standards and report lower frequencies of activities consid-

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Table B.1 Correlations between the variables (no 1, 2, 4 and 5) of ‘‘Quality of OSH legislation (Q1)” (macro-level) and the variables (no 1, 2, 4 and 5) of ‘‘Technical processes (Q4)” (mesolevel) for OSH managers (M) and workers’ OSH representatives (W). Cronbach alpha values are in parentheses.

⁄⁄⁄

p < 0.001. p < 0.01. ⁄ p < 0.05. ⁄⁄

ered undesirable by the standards of their reference groups. One is more likely to encounter a social desirability bias when individuals provide answers they believe to be more socially desirable, rather than revealing their true attitudes, preferences, or beliefs. Arnold and Feldman (1981) found that subjective weight methodologies were more likely to result in the appearance of a social desirability bias in questions related to job and organizational characteristics. Social desirability bias is a social science research term that describes the tendency of survey respondents to answer questions in a manner that will be viewed favorably by others (King and Bruner, 2000). With respect to the OSH managers, it is possible that their responses take the form of over-reporting ‘‘good behavior” or under-reporting ‘‘bad”, or undesirable behavior. With respect workers’ OSH representatives, there may be a tendency to focus on aspects for which they have critical opinions about the organization’s practices and leadership’s behavior; these are problems that are encountered when conducting research with selfreporting, especially when respondents answer questionnaires. Theoretically, a possible explanation of safety-related differences between OSH managers and workers’ OSH representatives may be derived also from the differences in their psychological contracts with the organizations (e.g. Robinson and Rousseau, 1994) or in the rewards inherent in the social exchange relationships (Blau, 1964, p. 55). The psychological contract stands in contrast

to other types of contracts (i.e., legal, social, normative, implied) in that it is based on an individual-level perception, focuses on mutual obligations, and offers an explicit description of the exchange relationship between the employee and the employer (Ferris et al., 2009). The psychological contract stands in contrast to other types of contracts (i.e., legal, social, normative, implied) in that it is based on an individual-level perception, focuses on mutual obligations, and offers an explicit description of the exchange relationship between the employee and the employer (Ferris et al., 2009). The present questionnaires were available as an attachment to an informative e-mail which described the research and provided details about how to complete and submit the questionnaire. The letter emphasized that there would be complete confidentiality. Responding to this questionnaire was also voluntary. The workers’ OSH representatives responded to the questionnaire independently. The independent role of the workers’ OSH representatives is enshrined within the Finnish OSH legislation; this individual is selected through a democratic ballot organized by the employees themselves (Finnish Legislation, 2001b). The OSH representatives transmitted their responses in the questionnaire forms through the internet platform directly to the researchers. It seems likely that the fact that confidentiality was guaranteed by anonymity would ensure more neutral, less confrontational responses and this was further reinforced since the respondents were assured that

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Table C.1 Correlations between the variables of ‘‘OSH management & collaboration (Q3)” for OSH managers (M) and workers’ OSH representatives (W). Cronbach alpha values are in parentheses.

⁄⁄⁄

p < 0.001. p < 0.01. ⁄ p < 0.05. ⁄⁄

names of the subjects who filled in the questionnaires could never be linked to their responses. Acknowledgements The authors are grateful for completing the questionnaire to Pirjo Korhonen, Niina Kallio, Milja Koponen and Eija-Riitta Hyytinen from the Finnish Institute of Occupational Health. The authors thank the two anonymous reviewers for the important comments they provided and which improved the quality of the paper. Appendix A A.1. Categories of the online questionnaire survey to OHS managers (N = 85) and workers’ OSH representatives (N = 120) in the chemical industry The following statements were provided and the respondents answered by rating with a Likert scale: 1 = strongly disagree, 2 = somewhat disagree, 3 = somewhat agree, 4 = strongly agree. A.1.1. Macro-level Q1 – Question 1: ‘‘Quality of OSH legislation”. The following statements were included in this section: (1) The OSH regulations are clear. (2) The OSH regulations are easy to understand. (3) The OSH regulations are easy to follow.

(4) The OSH regulations cover very well the various aspects of using chemicals. (5) The OSH regulations increase the employer’s interest in the safety and health of personnel. (6) The OSH regulations are useful for improving employees” own motivation. (7) The OSH regulations are good but they should be more detailed. (8) The economic benefits from the OSH regulations are larger than the costs they incur. Q2 – Question 2: ‘‘Quality of occupational health care (OHC) services”. The following statements were included in this section: (1) The OHC service provider has carried out workplace surveys which are utilized when improving the safety. (2) The OHC service provider is being informed of safety-related changes in the workplace. (3) Health examinations of employees in the beginning of their employment relationship are carried out, as well as periodical health examinations. (4) The OHC service provider can be requested for information on health effects of chemicals, when necessary. (5) Health examinations are organized for employees carrying out work that may cause particular risk of illness. A.1.2. Meso-level Q3 – Question 3: ‘‘OSH management & collaboration”.

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Table D.1 Correlations between the variables (no 1–4) of ‘‘Training (Q7)” (meso-level) and the variables (no 1–4) of ‘‘Use of personal protective equipment (Q8)” (micro-level) for OSH managers (M) and workers’ OSH representatives (W). Cronbach alphas are in parentheses.

⁄⁄⁄

p < 0.001. p < 0.01. ⁄ p < 0.05. ⁄⁄

The following statements were included in this section: (1) The OSH manager actively promotes the OSH activities at our workplace. (2) HES (health, environment and safety) management actively promotes the OSH activities at our workplace. (3) The OSH representative receives appropriate OSH training. (4) OSH training has been utilized in the OSH activities at my workplace. (5) The OSH representative receives all of the necessary information on OSH matters. (6) The personnel is informed about matters relating to OSH. (7) We solve and take care of OSH issues together. (8) Only a few persons take care of OSH matters in my workplace. (9) The OHC care service provider is active in OSH matters in my workplace. Q4 – Question 4: ‘‘Technical processes”. The following statements were included in this section: (1) The employer has chosen such chemicals for use that cause the least OSH harm. (2) The employer has chosen such production methods that cause least OSH harm.

(3) My workplace is actively searching for safer alternatives to replace dangerous chemicals. (4) There is a general air conditioning system that functions well, and the amount of both exhausted air and replacement air are large enough. (5) Local exhaust ventilation is used in work stations when necessary. (6) The effectiveness of ventilation is followed-up and the ventilation equipment is serviced regularly. Q5 – Question 5: ‘‘Instructions”. The following statements were included in this section: (1) Actual deviations from safety are investigated, for example near accidents, accidents, etc. (2) The safety aspects have been taken into account in sufficient detail in the instructions for work in order to make sure that work is safe. (3) The safety practices in my workplace are also applied to subcontractors working in the enterprise. Q6 – Question 6: ‘‘Measuring and monitoring”. The following statements were included in this section: (1) Work air impurities are followed-up through regular measurements of occupational exposure limit (OEL) values.

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