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Delivery truck drivers’ work outside the cab: psychosocial discomforts and risks based on participatory video analyses

  • Jose Manuel Ojel-Jaramillo Romero
  • Arto Reiman
  • Jose Juan Cañas Delgado
  • Seppo Väyrynen
  • Janne Pekkala
  • Mikael Forsman
Open Access
Original Paper
Part of the following topical collections:
  1. Topical collection on The Influence of Intelligent Transport Systems on Vulnerable Road User Accidents

Abstract

Purpose

Delivery truck drivers face various physical and psychosocial discomforts and risks in their work. Psychosocial perceptions are linked to physiological and psychological loads—strain and stress—affecting drivers throughout various mechanisms within activities and conditions. In this study, participatory video-assisted analyses were utilised for identifying psychosocially demanding work situations that delivery truck drivers encounter outside the cab.

Methods

Identifications were made by the drivers from previously recorded videos of their own work in their daily work environments. In addition, other stakeholders, such as managers and designers, also identified situations. The video identification data were further processed by the researchers, showing differences between the perceptions of the drivers and stakeholders on the causal conditions and intervening conditions behind the discomfort identifications.

Results

All together 99 identified situations—over half (53%) of which included a fear of causing different types of undesired events with risks of losses, such as human injuries or material damages. The results showed not only do risks and discomforts exist in demanding work situations, which seemed relevant, but they also indicated the importance of involving different stakeholders.

Conclusions

This study provides a unique methodological approach, as video observations and analyses and qualitative data analysis are combined to provide more in-depth data with visualizations into risk management processes.

Keywords

Delivery transportation Discomfort Psychosocial stress factors Risk management Stakeholder Truck drivers Video analysis 

1 Introduction

Delivery truck drivers face various kinds of physical and psychosocial stress factors in their work [1, 2]. Stress factors may arise from different origins. The work system theory provides a holistic approach on discussing humans and their work environments in which the work is performed and the technology and tools that are used to perform defined work tasks under certain organizational conditions [3, 4]. Work systems have been discussed in the delivery transportation context by e.g. Reiman et al. [1] and Murphy et al. [2].

Work systems are often discussed as sociotechnical systems where various individuals interact [5]. In ergonomics literature a categorization of personnel, technological, environmental, internal environmental and organizational subsystems is used when sociotechnical systems are discussed in macroergonomics, i.e. organizational and interorganisational contexts [6]. Changes in any of these subsystems may have direct or indirect impacts to occupational health and safety [7].

A transport system is comprised from elements such as artifacts and infrastructure, knowledge, regulatory aspects and different networks [8]. Similarly, compared to the work system theory, these elements may affect occupational health and safety. The quality of the infrastructure, e.g. trucks, roads, courtyards and premises may vary and change over time. Knowledge is related to organizational processes and personnel skills. For example, when new technology is applied, knowledge how to use it safely is needed. Thus, an interorganisational sociotechnical work system can be referred to a transport system. In this study, the focus is on identifying psychosocial discomfort factors in related to individual delivery driver’s work systems outside the cab. This human-centred approach is further expanded to organizational and interorganisational risk management processes.

1.1 Background

Delivery truck drivers work in several different work environments during their work shift [9, 10, 11, 12]. Besides routine driving tasks, delivery drivers’ work is performed at terminals; in common areas such as streets, roads, and pavements; and on customers’ premises as well as in buildings of wholesalers and retailers [13, 14]. In delivery transportations, drivers encounter demands for productivity and quality and the challenges of human well-being most often while working alone. Work phases—loading at the main terminal, driving, and unloading at customers’ premises—take up rather even shares of the total time of the driver’s work shift (Fig. 1) [11, 15]. Loading and unloading tasks entail predominantly manual work activities. Different kinds of aids and tools are used to ease the work. These include solutions that are embedded within the truck body structures, such as tailgate loaders and ladders. In addition, manual material handling tools, such as hand trucks, pallet trucks, and roll cages, are used in moving the cargo [9, 11, 16]. In addition to manual activities tasks such as the treatment of material flows and the differentiation of goods for the final customer may be included in delivery truck drivers’ work [17].
Fig. 1

Simplified illustration of a delivery process based on Pekkala [11]. There are three main work phases in truck drivers’ routine, daily work: unloading and loading at the principal (main) terminal or warehouse, driving, and unloading and loading at different customers’ premises

Delivery truck drivers have only limited possibilities to control their work pace. A research report by the European Agency for Safety and Health at work [18] shows that roughly two-thirds of all employees in general in the European Union area reported that they were able to choose and change the order of their work tasks, whilst in the transportation industry only half of the respondents reported the same. In line with this finding, employees in the land transport branch also reported a lower-than-average ability to choose or change their methods of work—50% and 67%, respectively. Finally, in terms of choosing or changing one’s speed or rate of work, again, land transport workers rated lower than the average: 64% and 69%, respectively [18].

According to Croon et al. [19], the intensification of the work of truck drivers is related to an increased demand for time-sensitive deliveries accompanied by the emergence of the 24-h economy. In addition, various other external, organizational and regulatory forces can be associated influencing the drivers [20]. Further, as the drivers can be considered as lone workers, they often face ethical decision-making situations where they can choose whether to perform certain work tasks safely or unsafely to ease or fasten their work [20, 21]. According to the job demands and control models put forth by Karasek and Theorell [22] and Croon et al. [19], the psychological demands result in psychological strain and physical illness only when the level of decision latitude, later referred to as job control, is low [19]. Further, it is important to discriminate between psychosocial stress caused by underload and overload; the former leads to reduced alertness and lowered attention and the latter to distraction, diverted attention, and insufficient capacity and time for adequate information processing [23].

Truck drivers are key actors in the flow of goods throughout the supply chain. Still, too little attention has been paid to their well-being and work ability [24]. Anderson [21] urges to broaden in the focus from traditional occupational health and safety considerations to psychological and managerial aspects when studying truck drivers’ work. In this study, concrete sources of psychosocial strain and stress factors in delivery transportation work are identified. Specifically, the focus is on work performed outside of the cab. The case material is from the Nordic environment. Thus, it is assumed that there are certain tasks that contain characteristics (such as winter conditions) that are dependent on the geographical location and seasonal variation. This study aims to identify psychosocial discomforts in truck drivers’ work outside the cab by utilizing participatory video observations. These identifications are further analysed to create a risk management model for psychosocial risks at delivery transportations.

2 Methods and materials

2.1 General

This study is based on in-depth re-analyses of the video material on delivery truck drivers’ work that was reported by Reiman et al. [1]. In that study [1], delivery truck drivers’ own identifications of physical and psychosocial discomforts were analysed by identifying certain physical activities and deviations that had led to the discomfort. In the present study, the focus was strictly on the psychosocial discomfort identifications and their verbal descriptions.

2.2 Data collection

The data were collected with the Swedish participatory ergonomics video analysis method and tool VIDAR (a Swedish abbreviation for “Video- and computer based work analysis”; see [24]). VIDAR is a participatory ergonomics observational method for assessing workload through identifications of physical and psychosocial discomfort [24, 25].

The data collection for the video analyses was performed through separate filming occasions (N = 21) in midwinter 2008. During the data collection, the researchers followed delivery truck drivers on their daily driving routes and filmed all occasions when the driver was working outside the cab. The data were primarily collected between the early hours and late afternoon of the day. All work outside the cab during a delivery was videotaped except for a few occasions in which the customer companies refused to allow video-filming on their premises, for instance, due to security reasons. The video material was later edited (i.e., similar kinds of recurring work tasks were excluded) for psychosocial discomfort identification and analysis purposes. Video analysis sessions were conducted within a few days after the filming occasion.

The video analysis data were collected from the practices of three companies’ delivery transportation actions. The companies and the drivers were selected because of their willingness to improve the drivers’ work. The companies selected the drivers based on their voluntariness. However, schedules for the video filming occasions were decided by the researchers. The companies were considered typical in Finland.

2.3 Data analysis

The identifications of psychosocial discomforts were analysed in the analysis sessions utilizing the internal psychosocial criteria included in the VIDAR method and tool (see Table 3 in Appendix 1). The internal criteria consist of nine alternatives for sources of psychosocial discomfort, including 1) time pressure, 2) obstruction/interruption/disturbance, 3) uncertainty, 4) poor control, 5) lack of response/feedback, 6) risks, 7) it is emotionally tough, 8) the task is boring or meaningless, and 9) other. The given alternatives are based on the action theory by Karasek and Theorell [22], in which the stressors are circumstances that disturb the goal-directed regulation of actions. The alternatives may contain sub-alternatives to define the discomfort more precisely. In addition, each identified discomfort was given a verbal description and special observations by the evaluator.

VIDAR analysis sessions were arranged for eight individual delivery truck drivers and for four interest groups (drivers [different from the drivers in the individual analyses], drivers’ immediate superiors, safety group members, and cargo space designers). The session classes were designated the “individual drivers group” and the “stakeholder group.” Individual drivers analysed edited video material from his/her own work, and in the group sessions edited video material representing each delivery truck driver’s work at their company was used.

A grounded theory approach was applied for further in-depth analyses. Grounded theory uses detailed procedures for analysis [26, 27]. In this study, three phases of coding [28]—open, axial, and selective—were performed for the identified psychosocial discomforts. In the open coding phase, researchers examined the identification database (including internal criteria selections and verbal descriptions and special observations for each identification) and text for salient categories of information. Using the constant comparative approach, the researcher attempted to “saturate the categories”. In the axial coding phase, researchers reviewed the data to provide specific coding categories that related to or explained the central phenomenon, causal conditions, and strategies for addressing the context and intervening conditions and the consequences. This analysis was carried out separately for two evaluator groups—drivers and other stakeholders. Finally, based on the coding phases a risk management model was derived from the data.

3 Results

3.1 VIDAR-identified psychosocial discomforts of delivery truck drivers’ work

In all, 99 identifications of different psychosocial discomforts were produced in the analysis sessions. These 99 identifications contained a total of 150 selections of alternatives or sub-alternatives (Table 1). Over a half (53%) of the identifications included a psychosocial discomfort of “fear of causing risks.” From those the sub-alternatives, “fear of causing own accident” (28%) and “fear of causing economic damage” (13%) were emphasized. The proportion of “fear of causing risks” was higher (63.1%) in the driver group than in the stakeholder group (43.9%). In the stakeholder group, identifications related to “obstruction, interruption, and disturbance” were also emphasized (33.8%).
Table 1

Identified psychosocial discomforts

 

Identified situations by the driver group (n)

Identified situations by the stakeholder group (n)

Total (n)

% (main criteria)

1. Time pressure

  

4

2.7

 1.1 Bad planning

2

1

  

 1.2 Too much to cope with at the same time

1

   

2. Obstruction/Interruption/Disturbance

  

40

26.2

 2.1 Tools, aids, non-working machines

7

11

  

 2.2 Difficulty reaching, difficult to get to

5

6

  

 2.3 Lack of specifications or knowledge

2

3

  

 2.4 Others have not done their job

3

1

  

 2.5 Noise, idle talk, or light reflection

 

2

  

3. Uncertainty

 

10

10

6.7

4. Poor control

  

1

0.7

 4.1 Too little influence over what I should do

 

1

  

5. Lack of response/feedback

  

1

0.7

 5.1 Need response from boss

 

1

  

6 Risks

  

79

53.0

 6.1 Risk of own accident/injury

26

16

  

 6.2 Risk of other/others being harmed

9

3

  

 6.3 Risk of causing economic damage

12

8

  

 6.4 Risk of criticism from fellow workers or boss

3

2

  

7. It is emotionally tough

3

1

4

2.7

8. The task is boring or meaningless

    

9. Other

  

11

7.4

 9.1 Limited spaces

8

   

 9.2 Long distances

1

   

 9.3 Incorrectly designed door

2

   

Total N

84

66

150

100

3.2 Analysis of the identified discomforts

From the 99 identified discomforts, a total of 60 discomforts included more in-depth verbal descriptions of the working situations by the subjects. Only these identifications were used in the further analyses, and the rest were excluded. Short verbal descriptions of the identifications are presented in Table 4 in Appendix 2.

As examples related to cargo, insufficient knowledge on the placement of the goods inside the terminals and deficiencies in locating certain packages in the cargo space were identified as discomfort factors slowing down the work. In addition, uncertainty of the proper ways to handle different sized cargo was identified causing psychosocial discomfort. Inadequate tools and technologies, such as the ones related to cargo spaces (e.g. floor hooks, cargo space doors) and to the equipment used (e.g. cages, tailgate loader) were identified as discomfort factors affecting efficiency but also as potential risks for accidents. Different work environments (such as cargo spaces) and other environments (public roads, customer’s environments) were identified as sources of discomforts. For instance, constant accident risks for falling or slipping from the tailgate loader or the cargo space or at unsafe customers’ premises and courtyards were identified causing psychosocial discomfort. Drivers own decisions to act unsafely were often identified as possible discomfort causes in the stakeholder group identifications. Often that was recognised to relate to poor training or instructions. However, drivers’ own decisions to act in an unsafe manner were also identified. Table 2 contains the results of the axial coding for the groups.
Table 2

A summary of the axial coding phase

 

Driver group

Stakeholder group

Phenomenon

Labour risk

Labour risk

Identified causal conditions

Stress, poor accessibility, darkness, inadequate tools and technologies, too many tasks, bottlenecks, too many interruptions

Poor communication practices, unclear processes

Identified context-related issues

Deficiencies in the work environments, loading and unloading tasks, wintertime

Deficiencies in the work environments, loading and unloading tasks

Identified intervening conditions

Lack of resources, poor work environments, poor assistance from others

Poor assistance from others

A labour risk can be provoked by poorly planned tasks and inadequate guidance, such as the stress related to performing tasks in time, improper working manners and the amount of tasks and interruptions while performing the work. These tasks are performed in different work environments, in which the risk can be evoked by unsafe physical work environment issues, such as bad lighting, darkness and wintertime conditions. Further, work environments where unloading tasks are performed may be used for other purposes, such as a temporary store room, or they have not been designed for unloading purposes at all. The work environments also include concrete risks for accidents. Organizational issues evoking risks arise from poor company communication, lack of resources, and unclear and undefined processes. In addition, work equipment and technology may evoke risks in relation to, for example, poor usability and improper tools. These risks may lead to injuries and accidents. In addition to occupational accidents, property and economic damage may also occur. In addition to injuries and accidents, criticism from others (co-workers, management, customers, other stakeholders) may also transpire from the identified risks and discomforts. Thus, a broad risk management process taking into account all the above-mentioned aspects is required to avoid accidents and injuries. A holistic work system approach allows categorization for these risk management aspects. This process is presented in a form of a risk management model in Fig. 2.
Fig. 2

The risk management model for psychosocial discomforts

4 Discussion

Various physical and psychosocial discomfort factors and their combinations can be associated to delivery drivers’ work [29]. In addition to enabling adverse health effects to humans, psychological discomfort factors have been associated with low performance at work; as such, psychosocial work characteristics have a strong relationship with productivity loss [30]. The mitigation (or elimination) of risks and discomforts leads to more optimal work systems, with higher expectations in terms of productivity, quality, and conformity [3]. Before risks can be mitigated or eliminated, they must be identified. The aim of this study was to provide a participatory analysis to identify psychosocial discomforts in delivery truck drivers’ work outside the cab and to deepen that knowledge by further analyses. As such, this study presents a process in which psychosocial discomforts and risks can be identified and analysed by different stakeholders inside the transportation company and within the value chain. Further, the results can be used to facilitate strategic risk management and value chain management processes.

Value chains consist of series of activities that create and build value [31]. Transportation work can be considered a part of a value chain. A transportation company, like any company [1, 2], can be thought of and analyzed as a constellation of individual work systems interacting with each other in the value chain. Based on this perspective, the truck and the driver are naturally in the centre of such work system, a very special one of varying character. Carayon and Smith [3] purport aiming at balanced work systems at an individual level and further at organization level. They define a balanced organization as one that takes into account business goals and human outcomes, that examines the positive and negative aspects of work system design, and that minimizes negative outcomes like all non-conformities, such as errors, disturbances, accidents, and incidents (related to personnel, goods, devices, and/or the work environment).

Risks and discomforts are signs of possible imbalances in the work systems. The video analysis tool utilized in this study can be used to identifying risks and discomfort factors, but also other development needs for delivery drivers work. Thus, not only improvements but innovations as well could be approached through this participatory process. For instance, Goffin and Mitchell [32] present many definitions of innovation. The characteristic feature of most definitions is “introducing something new” as far as technology (i.e., products) is concerned. However, innovations comprise (business) processes and services, as well as products [32].

This study shows the labour risk from the perspectives of the two groups (drivers and stakeholders), firstly, that there was no agreement between the groups regarding causal conditions. The drivers’ group reported more concrete issues, including poor accessibility, darkness, difficult tools, too many tasks, bottlenecks, and too many interruptions; while the stakeholders reported organizational issues, including poor company communication and unsuitable processes. In terms of intervening conditions, there was agreement between the groups on the importance of the availability of resources; however, only the drivers also reported the lack of facilities aspect. Based on this, we emphasize that labour risk must be analysed from different perspectives of the value chain. If not all stakeholders are included, long-lasting and effective solutions are hard to achieve.

One possible next step towards an innovative design of holistic work systems in delivery transportation could be to aim at producing delivery truck drivers’ work system(s) scenarios with potential risk descriptions to facilitate discussion on discomfort mitigation or elimination solutions. These solutions could be evaluated by relevant groups of drivers and stakeholders with a participatory approach, as Rajala and Väyrynen [33] did in their trials at metal industry.

4.1 Contributions to risk management

Risk management is a complex process that must be led by top management. Risk management aims at finding solutions to mitigate and/or remove risks. A key question is to find models and tools for risk identification purposes. The psychosocial discomforts identified in this study were most often related to different types of risks and the fear of causing such risks. Drivers work in various environments, in which other people, such as other workers, pedestrians, and cyclists are present. This applies both at driving and loading and unloading work phases (refer to [34]). The behavior of such people can be unpredictable. These people cannot be systematically removed and/or restrained from drivers’ work environments. Thus, other risk mitigation choices must be made. Drivers must be aware of the possible risks during the loading and unloading phases, and they must know how to deal with the risks concerning other people in their work environments. We identify this as a future research challenge 1 (RC1). Informing and training are two risk mitigation measures that can be used for such purposes. In addition, managerial decisions on, for example, route and task planning can be made for risk mitigation purposes; often this also requires discussion with clients. The need for improving stakeholder discussion processes is highlighted as the second research challenge (RC2).

In addition to be used in improving delivery truck drivers’ skills and awareness, the work system model enables systematic intra- and interorganisational development approaches. Risks and discomforts can be categorized by the work system elements. The categorization helps in prioritizing risk management actions. In this study, a large proportion of the risks identified was related to vehicles and their structures, such as cargo spaces and tailgate loaders. Drivers might fear causing damage, and a lack of adequate tools might cause psychosocial discomfort. Thus, a more in-depth discussion is needed between drivers and transportation companies and with the stakeholders that design and manufacture technologies and work equipment. Developing collaboration processes between designers and end-users is emphasised as the third research challenge (RC3). Technological development and possibilities risen by the digitalization may bring out possibilities for various development paths. A participatory video-assisted analysis process is an approach that produces in-depth knowledge with visual material that can be used in design processes to facilitate better collaboration processes between the technology designers and drivers as end-users.

In addition to technologies and equipment, the different types of work environments on the customers’ premises or in the common areas where the driver performs manual delivery work also possess risks. Limited spaces, long distances, and bad planning are some of the related psychosocial risk factors causing mainly psychosocial overload by e.g. distraction, rush and malfunctioning technology. Such psychosocial discomfort factors reduce the possibilities for the driver to adjust their working manners and pace during the work day. Work environment related development needs are highlighted as the fourth future research challenge (RC4).

It is surprising, and against our original assumption, that wintertime conditions were included in only a few identifications (6 out of the 60 analysed identifications) as a risk or discomfort factor. A commonality among all these discomforts is that they are more or less related to bad planning, especially in terms of premises, logistics, and tools. These discomforts and risks can usually be easily removed, or at least mitigated, with better planning and cooperation among all the interested groups in the value chain. Winter conditions is identified as a special research challenge for certain areas (RC5).

4.2 Limitations

Some limitations were identified concerning this study. Firstly, the identifications are analysed utilizing internal criteria. It is possible that a possibility to open answers could have provided more ample descriptions. However, it should be noted that the possibility for verbal descriptions was offered to complement inner criteria. Secondly, the amount of data is rather restricted, as only three transportation companies were included due to budget restrictions. Thus, it is questionable how generalizable the results are. Nonetheless, the companies included represented typical delivery transportation companies in Finland. Thirdly, the video material was collected in winter and spring conditions, thus possible problems related to weather condition factors may be over-emphasized. Nonetheless, our analysis shows that only 10% of the identifications contain characteristics that can be associated with Nordic winter conditions. Fourthly, the data was collected in 2008. Thus, there is a possibility that due to technological development not all of the identifications are valid anymore. However, the delivery drivers’ work is still based on physical work activities and very little development has been made to the assisting work equipment. Neither the varying work environments have changed. Deliveries are still made to various locations from which the quality of the work environment varies (refer to [20, 21]). Fifthly, the video material was edited by the researchers (JP and AR) to facilitate efficient video analysis sessions. Thus, there is a possibility that the researchers own perceptions while editing the data might have affected the analysis data. Based on researcher group’s experiences, video analyses take around three to four times the duration of the video material (i.e. one hour of video material is analyzed in 3 to 4 h). Video editing was done for practical reasons, as it was not possible to recruit the participants for longer analysis sessions. Sixthly, video cameras have a limited field of vision (refer to [35]). Thus, there is a possibility that not all affecting elements were captured. Lastly, the analyses were based on subjective assessments. Nonetheless, various evaluators participated and material was discussed as an entity. This lowers the possibilities for misjudgement. In addition, the amount of evaluators in this study is equal or higher when referred to similar kinds of study settings [15, 24].

5 Conclusions

The results showed not only do risks and discomforts exist in demanding work situations, which seemed relevant, but they also indicated the importance of involving different stakeholders. Further, this study provides a unique methodological approach, as video observations and qualitative analysis are combined to provide more in-depth data with visualizations.

Regarding the risk management model, the main causal conditions of the psychosocial labour risks are related to the nature of the task and communication problems within the company. Thus, it is important for companies to establish different strategies to mitigate such risks, as causal conditions can affect the productivity, conformity, and quality of the work. An interesting area for future research might be the study of psychosocial discomfort identifications and their relationship to non-productivity and non-conformity regarding quality criteria.

Notes

Acknowledgements

The research was funded by the Finnish Work Environment Fund, the Finnish Funding Agency for Technology and Innovation, the Ministry of Social Affairs and Health, and the participating companies.

Authors’ contributions

JMO-JR carried out the data analyses and drafted the manuscript. AR and JP collected the empirical data, participated in the data analyses and manuscript preparation. JJCD, SV and MF participated in the design of the study, data analyses and manuscript preparation. All authors read and approved the final manuscript.

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Copyright information

© The Author(s). 2018

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors and Affiliations

  • Jose Manuel Ojel-Jaramillo Romero
    • 1
  • Arto Reiman
    • 2
  • Jose Juan Cañas Delgado
    • 3
  • Seppo Väyrynen
    • 2
  • Janne Pekkala
    • 2
  • Mikael Forsman
    • 4
  1. 1.Unit4 Business SoftwareGranadaSpain
  2. 2.Industrial Engineering and ManagementUniversity of OuluOuluFinland
  3. 3.University of GranadaGranadaSpain
  4. 4.Institute of Environmental MedicineKarolinska InstitutetStockholmSweden

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